Special Issue Reprint

### Challenges and Research Trends of Energy Business and Management

Edited by Bernard Ziębicki and Edyta Bielińska-Dusza

mdpi.com/journal/energies

#### **Challenges and Research Trends of Energy Business and Management**

#### **Challenges and Research Trends of Energy Business and Management**

Editors

**Bernard Zie˛bicki Edyta Bieli ´nska-Dusza**

Basel • Beijing • Wuhan • Barcelona • Belgrade • Novi Sad • Cluj • Manchester

*Editors* Bernard Zie˛bicki Department of Management and Organization Methods Cracow University of Economics Krakow Poland Edyta Bielinska-Dusza ´ Department of Strategic Analysis Cracow University of Economics Krakow Poland

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *Energies* (ISSN 1996-1073) (available at: https://www.mdpi.com/journal/energies/special issues/Energy Business and Management).

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

Lastname, A.A.; Lastname, B.B. Article Title. *Journal Name* **Year**, *Volume Number*, Page Range.

**ISBN 978-3-0365-9604-4 (Hbk) ISBN 978-3-0365-9605-1 (PDF) doi.org/10.3390/books978-3-0365-9605-1**

© 2023 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license. The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs (CC BY-NC-ND) license.

#### **Contents**


Reprinted from: *Energies* **2023**, *16*, 1955, doi:10.3390/en16041955 . . . . . . . . . . . . . . . . . . . **227**


#### **About the Editors**

#### **Bernard Zie˛bicki**

Dr. and Associate Professor at the Krakow University of Economics, Dean of the College of Management and Quality Sciences, head of the Department of Organization and Management Methods, member of the Committee on Organization and Management Sciences of the Polish Academy of Sciences, editor of the scientific quarterly *Organization and Management*, editor of the scientific bimonthly *Cracow Review Economics and Management*, and author and co-author of over 200 scientific publications in the form of monographs, articles, and conference materials on the following issues: organization and management methods, new management concepts, organizational effectiveness, and information management.

#### **Edyta Bieli ´nska-Dusza**

Ph.D. in economics in the field of management science. Assistant Professor in the Dept. of Strategic Analysis of the Cracow University of Economics. Director of postgraduate studies in the "MBA in Community Management" program at the Cracow Business School of the Cracow University of Economics. Her research revolves around strategy, technology, artificial intelligence, innovation, digitalization, internal audit, and inter-organizational phenomena. She leads the project "The transformative impact of artificial intelligence on replacing people in innovation processes in the research and development activities of Life Science companies," which has financial support from the National Science Centre. Her lectures are dedicated to strategy management, technology audit, and technology design and forecasting, including management methods and tools. She has been a co-organizer of scientific conferences. She has served as an expert in various projects ("Revolution 4.0. Regional Initiative of Excellence", "Innovation Brokers as a tool for effective development", "First Business - Support to start", and "The Rapid Response Instrument"). She has also participated in study visits and scientific workshops (Stanford University and the University of Berkeley) and has various certificates (Ecole Polytechnique F ´ ed´ erale de Lausanne, Switzerland; ISIS Innovation Ltd, ´ Oxford; Strategic Business Insights, Stanford; PRINCE 1&2).

#### *Article* **Barriers and Drivers for Changes in Circular Business Models in a Textile Recycling Sector: Results of Qualitative Empirical Research**

**Anna Wójcik-Karpacz <sup>1</sup> , Jarosław Karpacz <sup>1</sup> , Piotr Brzezi ´nski <sup>1</sup> , Anna Pietruszka-Ortyl <sup>2</sup> and Bernard Zi˛ebicki 3,\***


**Abstract:** The growing environmental problems associated with the dumping of large amounts of textile waste and the demand for circular products are prompting textile waste recycling enterprises to develop circular business models (CBMs). This implies a radical change in the way some enterprises operate to obtain growth. Considering the importance of the drivers of and barriers for the adoption and implementation of CBMs in the textile recycling sector, it is claimed that the comprehension of these factors to CBMs is limited and deserves more attention in empirical research. Therefore, our research investigates the antecedents of circular business models in the textile recycling sector by highlighting influencing factors. The aim of the article is to explore the main enhancing and inhibiting factors in the development of circular business models on the example of a large enterprise operating for 30 years in the textile recycling sector. In this study, a case study design of mixed methods, including semi-structured interviews with a business practitioner and the data presented on the websites of the surveyed enterprise, is used. The results suggest that main enhancing factors are relevant regulations at the European level, appropriate technologies and digitisation, and increasing social and environmental awareness of consumers and managerial capabilities. However, inhibiting factors are supply chain complexity and supply chain collaboration in connection with a large scale of business in crisis situations, a large scope and range of geographic diversification of outlets in the perspective of the consequences of the information gap, and readiness to take the so-called "being the first in the market" risk. In practice, this means that general drivers of the CBMs may facilitate the reuse of second-hand clothing and recycling of textiles for other new products as the primary CE action. On the other hand, enterprises have to overcome a number of technological barriers, and in the case of the textile recycling sector, it is necessary to understand which barriers they face to take appropriate actions. Research findings indicate factors that may be the subject of intervention or support of managers or policymakers. This study has practical implications and suggests future study paths.

**Keywords:** circular business model; circular economy; environmental awareness; supply chain; development; waste

#### **1. Introduction**

The textiles and apparel manufacturing industry in the upstream fashion supply chain generates substantial materials waste which requires urgent efforts to manage effectively, reduce environmental impact, and foster sustainable practices [1,2]. Textile waste consists of industrial /pre-consumer waste and post-consumer waste. Industrial waste is generated by a production process and post-consumer waste, i.e., textiles thrown away (i.e., used clothes)—after the end of their service life [3,4]. For the specificity of the sector studied (i.e., textile recycling sector), an important area that should be taken into account is textile

**Citation:** Wójcik-Karpacz, A.; Karpacz, J.; Brzezi ´nski, P.; Pietruszka-Ortyl, A.; Zi˛ebicki, B. Barriers and Drivers for Changes in Circular Business Models in a Textile Recycling Sector: Results of Qualitative Empirical Research. *Energies* **2023**, *16*, 490. https:// doi.org/10.3390/en16010490

Academic Editor: Krushna Mahapatra

Received: 7 December 2022 Revised: 23 December 2022 Accepted: 30 December 2022 Published: 2 January 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

waste management, as today's clothes are designed for shorter use [5] and then utilised. It was estimated in various studies that only 20% of textile waste is recycled or reused, and the remaining 80% is landfilled or incinerated, which causes large losses of raw materials, energy, and has a negative impact on the environment [6–8].

Such a low level of recycling of post-consumer textile waste results from the conclusion that recycling and (more broadly) CBMs are strongly related to the existence of efficient and economically justified technologies. Recycling of multi-material products [2] is much more complicated (and generates significantly higher investment and operating costs of such technology) than the recycling of products made from a single material. This results from both the level of complication and the possibility of ensuring sufficiently large volumes of waste material which meets material specification. For example, the recycling of packaging bags made mostly of homogeneous polyolefins such as PE (PE HD, etc.) meets both conditions of a relatively cheap technology (mono-material with a significant amount of a given waste material). For the post-consumer textile sector being under consideration, the morphology of second-hand clothing is an important issue. Different types of clothes are recycled. Extremely different morphology of materials is used in their production (from a huge variety of synthetic and natural fibre, using various types of braids and fibre mixes, to a wide range of chemical additives and dyes). Clothing manufacturers, by introducing new clothes every new season (or for fast-fashion, even several times during one season), make sure that they differ visually from the ones already existing on the market, contribute unknowingly to further complicate the situation with regard to generated waste recycling.

That is why, recycling of post-consumer textile waste requires much more advanced (and most desirable in the CE model) technologies with a higher level of technological risk approach than the classic one based on separation and recovery of raw materials in order to create new things. This implies changes in existing business models toward higher levels of circularity.

Nevertheless, the barriers which enterprises face before implementing CBM [9] may delay the transition to a sustainable future [10], it is necessary to identify what prevents or delays the implementation of circular activities in organisations operating in the textile recycling sector. It is equally important to identify factors which may influence the successful development of CBMs. Especially since CBM has yet been widely implemented not only in the textile recycling sector, but also in the fashion sector [11].

That is why, the following research questions (RQ) were posed:

RQ1: What are the main drivers for the development of circular business models in the textile recycling sector?

RQ2: What are the main barriers to the development of circular business models in the textile recycling sector?

These two research questions guided our empirical research. At the same time, it was found important to understand these factors from the perspective of business practitioner being an important stakeholder in the transformation towards CE [12].

The aim of the article is to explore the main enhancing and inhibiting factors in the development of circular business models on the example of a large enterprise operating for 30 years in the textile recycling sector. This indicates that the article refers to specific challenges faced by enterprises belonging to the textile recycling sector by answering the above-mentioned research question.

The paper is structured as follows. The following section provides an overview of CBMs conceptualisation which also allows different types of CBMs to be identified. In addition, it sheds light on the issue of the legitimacy of continuing research about factors enhancing and inhibiting changes in circular business models in the textile recycling sector. Next, the case study with sections on research methodology, data collection from the VTR's websites and the primary data from semi-structured interviews with an enterprise representative are presented. In Section 4, the results of the analysis of primary and secondary data are described, while Section 5 focuses on the discussion of drivers and barriers to CBMs. Section 6 contains conclusions and implications for practice based on

our research findings. The article ends with an indication of research limitations and suggestions for future research.

#### **2. Literature Review**

#### *2.1. What Is the Circular Business Models?*

Circular business models are essential elements of a circular economy framework to enable economically viable recapturing of value [13]. Circular business models (CBMs) describe the ways in which an organisation creates, delivers and captures values, while keeping resources at the highest level and for as long as possible [14,15]. In addition, this indicates that enterprises should think about what to do to keep their values (resources) as long as possible. This problem applies not only to textile waste management, but to production management from the very beginning: the first stage of production, predicting what would happen with our product at the end of its life. According to Wrålsen et al. [13], CBMs intend to maintain the maximum value of resources by eliminating or reducing its leakage through closing, slowing, or narrowing their flows. Moreover, Bocken et al. [16] explains that CBMs includes and aligns an enterprise value proposition with the creation, delivery, and capturing of value. Hultberg and Pal [11], in turn, conceptualise CBMs in another way. He argues that circular business model (CBMs) are terms used to describe business models based on circular economy practices. The literature studies also confirm the usefulness of this conceptualisation of the CBM construct. In the study by Lüdeke-Freund et al. [17], six different types of CBMs have been identified which support resource flows (referred to as "major CBM patterns"); these are: repair and maintenance; reuse and redistribution; refurbishment and remanufacturing; recycling; cascading and repurposing; and organic feedstock. A review of current circular practices in the vehicle industry in the EU reveals that several manufacturers implement CE strategies, focusing on CBMs for LIBs (lithium-ion batteries), and these are: intensify use; repair; refurbish; remanufacture; repurpose; and recycling [18]. However, in the studies by Lieder and Rashid [19], Akter et al. [1], Rahman et al. [2] and Mostaghela and Chirumallab [5], three different types of CBMs were identified, distinguished as: 3Rs: Reduce, Reuse and Recycle.

The results of the studies presented above provide a picture of the current circular practices and indicate the possibility of different operationalisations of CBMs in business practice, but also some similarities between them. There is no doubt that each industry has its own specificity, requiring adjustment of activities to products or services offered by an enterprise, and this generates various circular business models. Because of the fact that there is no waste in a purely circular economy, everything is looped back in different resource flows [20]. Within these loops, a large variety of CBMs emerge (i.e., distinguished above as "3R" and "6R"); while some cover the entire resource flow, others only focus on a specific activity and need to connect to other CBMs to create a complete loop [20].

Therefore, circular economy business models (circular business models) are a real alternative to the current linear systems of production and consumption [21]. "R-activities" are typical activities for circular business models, but do not appear in traditional linear models. CBMs help to reconcile resource efficiency with the creation of commercial value, using both environmental and economic values embedded in products [21]. The business advantage in the circular model is that enterprises use the idea of a circular economy to create values. In addition to resource efficiency, enterprises may also create economically significant products rather than waste. They may create resources for other market players or design a closed resource flow for their own business. This business model is in line with the principle of manufacturer's extended responsibility. The manufacturer's responsibility for a product is extended to the end of its life cycle [21]. Thus, these types of business models may significantly reduce the negative impact on the environment [22] if actively designed for this purpose [23,24]. However, the shift from a linear to a circular business model is an ambitious undertaking which requires a re-evaluation of how a given organisation creates, acquires and delivers values. Enterprise's values (goals), strategy and business opportunities already motivate enterprises to explore CE-based value proposition and

develop circular business models (CBMs) [16]. Thanks to circular business models, enterprises gain a competitive advantage, increase customer loyalty to the brand, meet waste requirements more easily, and improve production towards zero waste [25]. Therefore, these BMs are facilitators and provide a framework for enterprises to create and capture values [26,27].

#### *2.2. Circular Business Models in a Textile Recycling Sector: Influencing Factors*

Textile recycling industry is one of those industries which may benefit economically from efforts for CE. Enterprises belonging to this sector, by thinking more specifically about promoting sustainable development, may want to get involved in CBMs. These organisations would strive to minimise the negative impact on the environment, striving to extend the life cycle of "industry/pre-consumer waste" and/or "post-consumer waste" from the phase of obtaining this waste to its disposal. In addition, especially since the global amount of textile waste will increase by 60% each year from 2022 to 2030, generating an additional 57 million tons of waste per year and reaching a total of 148 million tons per year (see more: [1,8,28,29]). Moreover, textile waste causes a potentially huge loss of value and business opportunities in a textile and clothing production chain [1].

That is why, Payne [30] and Jamshaid et al. [4] argue, among others, that solid waste should be recycled or reused to strengthen the concept of a circular economy. There is no doubt that wearing clothes for longer periods of time and efficiently using textile waste may significantly reduce the need for end products and fibres [4].

The aforementioned potentially huge increase in textile waste and the demand for circular products in the near future would make the circular economy (CE) more and more visible in business organisations [22,31]. Thus, in the textile recycling industry is an important category to discuss CE and CBMs. However, despite the general interest of a private sector in CE, implementation of CBMs is in fact still low in this sector [6–8].

In the context of the possibility of managing second-hand clothing, circular reuse business models (CBMs) have emerged in recent years, also in Poland (e.g., [32]), aimed at slowing down or closing resource cycles [33]. This shows that the transition to CE today requires radically new ways to design and implement business models, including the textile recycling industry. Most enterprises are concerned with transforming existing innovative linear economy processes, characterised by significant experimentation towards an ambitious circular value creation goal [33]. Nevertheless, there are some controversies over what may and what may not be considered as recycling. This includes (non-) treating energy recovery or production of fuels from textile waste as recycling. There are different interpretations and procedures on this issue, which raises a lot of controversy and discrepancies even in the recycling statistics. It is worth adding that European [34] and national [35] regulations directly exclude energy recovery and production of fuels from textile waste from the definition of recycling. It is worth emphasising that in Poland it is VTR that is very much involved in striving to improve the existing CBMs towards more sustainable CBMs (e.g., [36]) and this does not mean that there are no other enterprises which have already undertaken some steps in this direction; but we do not know much about them at the moment.

The literature research indicates that proper business models (BMs) are essential for enterprises to make the reuse of second-hand clothing and textile waste recycling economically feasible and require constant improvement and adaptation [37]. Thus, in order to improve BMs, the drivers for CBMs need to be strengthened to reach higher levels of circularity (i.e., [38–40]). Apart from that, in order to continue the process of change towards a more circular economy, the current barriers to the development of CBMs need to be addressed (i.e., [10,20,41–43]). Despite these valuable contributions to the scientific literature on drivers and barriers to implementing different circular business models, there are also many limitations to the research findings. Drivers and barriers differ depending on the sector and type of business model [10,43–45]. Therefore, there is shortage of knowledge about factors enhancing and inhibiting changes in circular business models in the textile recycling sector. Moreover, Ferasso et al. [22] emphasises that there is currently no unified understanding of the current state of knowledge about circular business models, as many studies have been published in a short time, their structures and discourses are not well established and interconnected. It is not clear which lines of research into circular business models are well developed and potentially saturated, and which deserve more attention in future studies.

#### **3. Research Methodology**

#### *3.1. Research Design*

Case study is an effective type of studies for gaining an in-depth understanding of the factors influencing changes of business models in the enterprise mentioned below [18]. Moreover, it represents a unique, credible, and valuable justification for the formulation of guidance for managers. It was decided to include one case because single case studies i.e., [46–48] are well established in the area of BMs [44]. The case was selected on the basis of a deliberate sampling [18].

The enterprise was chosen because it has more than one circular business model which it is actively trying to rethink. It is a business organisation which implements CE strategies, focusing mainly on the resale, re-purpose and recycling of second-hand clothing into textile composites. At the same time, VTR is one of the world's largest producers of second-hand clothing. In the enterprise's on-line store, one may order wholesale, directly from the manufacturer, i.e., VTR, sorted and top-quality second-hand clothing from the Western Europe. For 30 years, VTR has been developing its competences in this area, among other things. Currently, every day, it segregates textile waste in the amount of hundreds of tons, using the most modern in Poland and fully computerised lines for sorting clothes, enabling the processing of 500 tons of raw material per day while maintaining the highest quality standards, namely ISO 9001 and 14001. Every day, the VTR's employees sort and pack finished products from 700 different assortment groups. Moreover, the geographic diversification of sales markets is very large. Products (second-hand clothing) go to over 70 countries around the world and to a chain of several dozen VIVE Profit stores all over Poland, owned by VIVE Textile Recycling (e.g., [36]). Moreover, closed processes of recycling textile into innovative textile composites have long reached the commercial stage. The raw material which does not comply with the VTR's quality requirements is, in turn, processed into industrial cleaning products. VTR also produces industrial cleaning products used by enterprises from many industries (e.g., [32]). All of this makes VTR an interesting case to study.

The study used a mixed-method case study design [33,49], including semi-structured interviews with an enterprise representative and data presented on the VIVE Textile Recycling's (VTR) websites. Thus, complementary methods were used. The basic method was a semi-structured interview, and the subordinate method was an analysis of the data posted on the above-mentioned websites. Both methods were implemented several times to triangulate the data (e.g., due to outdated reports, official documents, end-user reviews, or respondent availability—in the case of semi-structured interviews). The triangulation of the methods in this study was parallel (complementary). Qualitative data were collected simultaneously and analysed to answer research questions.

Firstly, a literature review was conducted, interview guides were developed, and semistructured interviews (see: Appendix A, Table A1) were carried out with a professional implementing CBMs at a textile recycling enterprise.

The purpose of the analysis of the empirical material was to search for answers to identify the main enhancing and inhibiting factors for business experiments towards a more circular economy, as well as actions which are aligned with circular economy.

The process of data analysis obtained in the interviews was carried out in three stages: (1) coding, (2) searching for similarities and differences, (3) interpretation. However, the preliminary analytical stage was the transcription of audio recordings (see: part 3.2. Data collection). Data in a written form were encoded, and the individual fragments were

1

given appropriate labels for the categories under consideration (a priori, deductive coding). Collections of data with identical labels were subjected to a comparative analysis in order to capture similarities and possible repeating regularities. In the analytical process, the semantics of statements in the context of the studied categories were verified. The last stage of data analysis was the interpretation of the content of the interviews. Descriptions of individual aspects were presented in the form of narratives with references to directly quoted fragments of the respondent's statements collected during the interviews (abbreviated respondent's statements are presented in Part 4. Results and analysis; while selected full statements made by the respondent are in Appendix A, Table A2). As a result of the semi-structured interview, a summary of the main influencing factors for changes in CBMs in VTR was developed and "R-activities" were identified. The interview guides mentioned above, in turn, result from the literature review.

Secondly, data presented on enterprise's websites (e.g., reports, official documents, interviews with various enterprise's representatives) or other data (e.g., press releases, end-user reviews) were reviewed in parallel to better understand R-activities as well as external and internal forces which may accelerate or delay the achievement of higher levels of circularity by VTR. The result of this research method is the exemplification of VTR's circular activities, business achievements and relevant regulations. A flowchart summarises the research methods used and the outputs of each one of them, as shown in Figure 1.

**Figure 1.** Research methodology review. Source: own elaboration.

#### *3.2. Data Collection*

Only one-to-one interviews were conducted [33]. In total, 7 semi-structured interviews were conducted with a business practitioner. The respondent was a manager employed in a position included in the highest management level (top management teams, TMT) in VIVE Textile Recycling.

Over the course of the interviews, open-ended questions were asked (see: Appendix A, Table A1), which allowed the respondent to explain complex issues. As a rule, the interviews were aimed at getting to know views on the factors inhibiting and enhancing changes in business models in the surveyed enterprise in which the respondent has been employed for several years. Basing on the interviewee's consent, the interviews were recorded and then all transcribed for further analysis. Data collection was taking place from November 2021 to June 2022. The data were then triangulated with publicly available information from the enterprise's website. In preparing for the interviews, information was gathered about the enterprise and its CBMs through a review of official documents, reports, interviews, press releases, and end-users' reviews. Making consultations with the respondent (VTR's internal stakeholder) was favouring the triangulation of the literature results and provided more details on the factors influencing changes in the business models already existing in VTR.

#### **4. Results and Analysis**

#### *4.1. Drivers: External Environment*

Changing business models is seen as the key to organisational success, especially in times of increased competition, advancing globalisation, and the advent of new technologies [50]. Following this trend, VTR is actively trying to redesign its business models.

The analysis of the content of the interviews allowed for identifying what and how drives the development of business models in an enterprise which has been successfully operating in the textile recycling sector for 30 years. The VTR's representative believes that there are three main factors driving VTR to change its business models towards higher levels of circularity: *"Here we have the three most important factors (* . . . *), but we are talking about the three main ones, absolutely the most important in my opinion"*.

From the perspective of the respondent's business practice, it follows that one of the most important driving forces behind the development of CBMs in an enterprise are the relevant regulations required at the international level: *"Very important factor is legislation at the European level, through which we move in the world of waste, which should be very precisely accounted for and landfilled. Legislation has a huge impact on this"*. As a result of changes in international regulations and policies, changes are made to national regulations specifying the conditions for running a business in the textile recycling sector, which may close the current directions of textile waste imports and open up new ones as well as (do not) support the transition to higher levels in the textile waste management hierarchy. External pressure (such as regulatory and policy pressures) means that through changes to regulations, governments and institutions may encourage or discourage businesses and consumers to or from adapting to CBMs. Therefore, it is important that the legislation on recycling and life-cycle extension issues should focus on both environmental performance and economic incentives.

At the same time, the respondent pointed out of regulatory tools is their change from time to time: *"Waste storage and processing issues. The legislator defines them very precisely and, what is more, wants to change them from time to time"*. For enterprises, this implies a change in the conditions of market competition, changes in customers' needs, and may reveal discrepancies between existing VTR's operating routines and competition requirements. This is because turbulent environment reduces the potential value of existing products due to changes in market needs, technologies and competing products. Therefore, adaptation to the new legal regulations is necessary for VTR to conduct this type of economic activity in accordance with the applicable regulations.

Gaps between the current configuration of operational routines in VTR and the requirements of the environment, if they have existed so far, they were only temporary (see further: lockdown in the event of the unprecedented COVID-19 pandemic, introduction of an ad hoc public holiday connected with National Independence Day in Poland on 12 November) [51] as VTR's capabilities were and are still adapted to the changing requirements of the environment. The achievements of this enterprise (e.g., [52]) show that it may use dynamic capabilities (e.g., [53]) to adapt to the type of changes in the environment in which it operates, knowing that a highly dynamic environment offers new opportunities which at the same time offer more options for improving existing operational routines.

In this interview, the respondent also highlighted the importance of the growing number of socially and environmentally conscious consumers looking for cleaner and more sustainable ways to express their attitudes through fashion. In the era of shifting away from the linear economy model and transition to the circular economy model, re-using and the best possible using of textile waste (i.e., second-hand clothes) become a necessity. Re-use is part of a circular economy which seeks for greater use of material goods. The main change noted only in this respect by VTR is the development of the market of *second-hand stores* (e.g., [54]). The respondent explained this in these words: *"Social awareness, which is key in this second-hand market, circular economy, CE. (* . . . *) Today is a complete shift in awareness. This is the glory of modern times. The whole peer 2 peer movement, reselling or donating clothes; second, third, fourth, fifth life. This is exactly the case with our stores as well. It is all the result*

*of our social awareness which we create, but also which creates us. This social awareness is a huge factor of change. Because we have many such detailed factors, and I could list them for a long time"*.

Growing social and environmental awareness of the importance of re-using and recycling of old clothes drives the development of CBMs. Enterprises have already recognised the fact that consumers are increasingly seeing that their choices may have an impact on the environment. Therefore, enterprises begin to look at what customers buy, and VTR does the same or even more than other enterprises, thanks to a professional marketing team (e.g., [55,56]). Customers are aware that second-hand shops exist and have now overcome changes in the above-mentioned behaviour. Of course, second-hand clothing resale practices have always existed. People have long been selling second-hand clothes at flea markets or in second-hand stores. However, this "R-activity" offered by VTR is booming today. More and more buyers, as the awareness of the negative impact of disposable culture grows, begin to understand that fast fashion has little to do with sustainable development. They start to make choices guided not only by their own style, but also by environmental and social values. Therefore, slow fashion business models, such as a re-sale model (longer use of clothes) used by VTR, reflect a fundamental shift in customers' thinking and subsequent purchasing decisions. Consumers buying recycled clothes create a trend against fast fashion, which may limit the fast fashion industry, or at least its impact on the environment. Thus, social expectations are another factor driving the development of CBMs. However, according to Masi et al. [20] business practices related to green purchasing and customer cooperation are still not very widespread. Eco-design and in-house environmental management practices have a medium level of implementation.

#### *4.2. Drivers: Organisational Environment*

According to business arguments, apart from external pressure (legislative changes, customers' environmental and social awareness), changes in the internal environment of the enterprise, including those in the technological area, motivate VTR to re-design business models as well.

The respondent further argued that changes in VTR's business models are currently driven by the creation of modern business and technological solutions, which at the same time are an example of VTR's conscious operation taking corporate social responsibility and a circular economy into account: *"Technological changes related to digitisation, automation, but also other alternative methods of using waste, which directly translate into business models, because they create new business opportunities. This is a space for research and development, for new technologies. R&D is here"*. While discussing the future, the interviewee emphasises the role of new recycling technologies (e.g., [57]) along with sorting technologies (e.g., [53]) and the construction of installations for the pyrolysis of the discards from sorting processes, which are to respond to market challenges. Respondent explained this challenge: *"It takes energy to turn it [textile waste—authors' explanation] into a composite. In this approach, I will have my own power station in the form of pyrolysis. I will generate energy that will cover my composite needs. (* . . . *) But there are a lot of technical problems to be solved, but this is what we are here for to solve. (* . . . *) It is a project that is thoroughly researched. (* . . . *) This is the concept and it shows how it changes the structure of the enterprise. Well, circular economy generates R&D costs, it is absolutely a cost item"*. It is also worth mentioning that the technological transformation, which generates new business opportunities, is also emphasised by the European Commission in the study entitled "Circular Economy Perspectives in the EU Textile Sector" [58]. "R-activities" undertaken by VTR fit into this perspective.

In sum, the regulations at the European level and, by analogy, national regulations, social and environmental awareness of consumers towards sustainable development, "consumers' social and environmental awareness and attitudes towards sustainability", as well as product (i.e., innovative textile composites made of recycled textiles) and process (development and implementation of more sustainable methods of using textile waste) innovations based on, for example, digital technologies or materials engineering create a set of main drivers of circular practices in this enterprise, which push it to change its

business models. Teece [59] also draws attention to the relationship between technological innovations and a business model, and stated that product or process innovation based on new technologies is often not effective without the appropriate adjustment of the business model. Therefore, VTR's activities in this area confirm the findings of Teece [59], because the implementation of new methods and technologies for the management of textile waste less harmful to the environment and most desirable by VTR is associated with the change of existing business models.

The respondent's above statements also reveal the very high cognitive ability of top managers, which contributes to understanding the value of the potential of new business models. This observation is very important. The mere fact that an enterprise has dynamic capabilities does not guarantee its success. The use of dynamic abilities is intentional, just like in the case of VTR. Although dynamic capabilities are embedded in VTR, the ability to assess and determine changes in resource configuration is already on the shoulders of top managers, including our interviewee. Dynamic capabilities are therefore a tool that top managers may use to manipulate the enterprise's existing resources and operational capabilities, and re-group them in order to create new configurations in response to the challenges of a changing market. As such, other very important enhancing factors for changes in circular models of second-hand clothing reuse and textile recycling are managerial capabilities.

#### *4.3. Barriers: External Environment*

This study also provides an overview of the factors which were preventing the top management from changing business models. The three most important of them have been described in detail. The first barrier mentioned by the respondent is the large scale of business which in crisis situations requires top management to solve many times more and more complex decision-making problems in various dimensions, such as suppliers, customers, employees, revenues and costs, than required by a small scale of business. One of the crisis situations took place in Poland on 13 March 2020 when, by means of the Regulation of the Minister of Health, an epidemic threat had been introduced in the territory of the Republic of Poland, covering the period from 14 March 2020 until the state was recalled [60], which caused serious disruptions in the activities of the surveyed enterprise. The scale of the VTR's business, previously well-established in terms of infrastructure, people and processes, required at that time a thorough and at the same time quick analysis of its various aspects, and the creation of an action plan aimed at making the enterprise's operation more flexible in the conditions of turbulent changes in the environment. Of course, the introduced changes are covered not only by VTR, but also by the ecosystem in which VTR is embedded. The respondent expressed it in these words: *"The first factor is the scale of an enterprise. (* . . . *) When we run over 40 stores directly across Poland, medium-sized and large-area stores, it is not simple anymore [managing such a business—author's explanations]. For example, such a problem as changing the size of store is of great importance (* . . . *) At over 40—it is* [i.e., changing the size of stores, termination of contracts, changes in the work schedule—author's explanations] *"n" times more difficult. It is not 10 times more difficult, but many times more difficult, that is the scale of the enterprise is a limiting factor. Just like in every area of activity"*. According to the respondent, a serious obstacle to changes towards more circular activities is the complexity of the chain of cooperating business partners in connection with the large scale of business.

Problems in managing a large retail chain appeared in a crisis situation, because the introduced business bans in connection with the SARS-CoV-2 coronavirus pandemic also affected the VIVE Profit chain of stores. They lost their capacity to function from day to day. In the short term, this was associated with a huge drop in their revenues and thus difficulties in meeting their liabilities (e.g., [61]). As the epidemic progressed, the number of bans and restrictions on their activities grew (i.e., limited number of customers served in a store) (see: [60]). The introduced restrictions caused by the COVID-19 pandemic situation also delayed the implementation of planned projects to change the business

models of the VIVE Profit chain of stores throughout Poland. The respondent explained this situation in these words: *"The events were crazy. After total closure, the shipment was suddenly unblocked and left overnight. Stores could trade and then suddenly could not trade. The first lockdowns completely ploughed Poland (* . . . *). And all this resulted in: stores opening, stores closing, limiting the number of people in the store, then closing stores again, opening stores again. All this had a significant impact on the current work, but also on the operating costs. This also destroyed the stability, and thus threatened the implementation of the change projects being under implementation, because then all the efforts and resources went to support the enterprise and make it more flexible in this respect, which nobody planned to make more flexible"*. As may be seen, unprecedented events influenced the need to re-think the enterprise's supply chain in terms of resistance to various types of variability, not only those in the past, but also these which would be in the future. Therefore, the supply chain would be created with flexibility in mind, as emphasised by the respondent, so that it may guide VTR through the following bigger/smaller disruptions in the supply chain. This would be in line with the suggestion Sarkis [62] that a crisis is a difficult and at the same time inspiring challenge for managers, because there are opportunities to improve the enterprise, for which there would be no will of stakeholders under normal conditions, including sufficient mobilisation of managers' attention for such changes.

During the COVID-19 pandemic, the enterprise was also unable to continue production activities in the short term. The production lines were closed until 10 April 2020. Workers' health and safety were a major concern. When VTR was able to continue its activity, limiting the possible transmission of coronavirus among workers in the workplace became a key challenge. VTR has implemented safety measures to protect its employees from infections or to prevent the spread of coronavirus by limiting physical interactions during work and introducing enhanced sanitation measures, among others.

As mentioned above, the disruptions caused by the COVID-19 pandemic had negative effects in the area of manufacturing activities, such as that when the enterprise was suffering from severe obstacles to its operations, including supply chain disruptions caused by problems with smooth border crossing (see: Appendix A, Table A2, ID.1).

It may be said that during the unprecedented COVID-19 pandemic, the conditions of the market game changed significantly and abruptly. As a result, the main source of costs in the enterprise, in accordance with the above findings, was the maintenance of downtime in production. Maintaining a reserve of resources (personnel, machinery, equipment, buildings) related to adapting to changes in the environment caused by the introduction of the above-mentioned sanitary cordons at the border generated costs for the recipient, i.e., VTR in this case.

These recent developments have shown that management may begin to consider over-reliance on Just-in-Time (JIT). The weaknesses of the JIT supply chain model which even led to a global logistics bottleneck were revealed: *"The pandemic has also made our logistics systems very difficult for us (* . . . *). Shipments and cost of shipment, that is unintentional situations, but such on a macro scale. This pandemic really hit us, but not only us* . . . *"* As may be seen, the COVID-19 pandemic has changed the business world in an unprecedented way. Enterprises applying Just-in-Time strategies, just like VTR, were drastically affected by forces beyond their control. These enterprises had an increased susceptibility to external disruptions. For this reason, management had to develop strategies to deal with these short-term discontinuities and considerable uncertainty in order to survive.

Although the effects of the global COVID-19 pandemic are fully noticeable in the minds of the top management team, this is not the first major event to cause significant disruptions to the VTR's supply chain. A public holiday on the occasion of the 100th anniversary of regaining independence by the Republic of Poland on 12 November 2018 [51] was another event that also posed a logistical challenge (see: Appendix A, Table A2, ID.2).

This shows that disruptions have always (i.e., before, during the pandemic and thereafter) been part of supply chain management. However, the last crisis situation was even more unpredictable than the previous ones. As a result, the Just-in-Time supply chain

models became a burden, not only for VTR, as the effects crossed the organisational boundaries of VTR and affected other stakeholders (i.e., suppliers, customers) of the circular ecosystem [5]. Thus, the dynamic and unpredictable business environment and global exposure of VTR show that there is an urgent need to re-think supply chain models to better reflect today's realities.

#### *4.4. Barriers: Organisational Environment*

What is more, the respondent indicated that another factor which slows down changes in business models is significant geographic diversification of sales markets: *"A very large variety of markets we work with. If my target market is one market and I know it perfectly well, then I adapt to it, predict it, react to it. And if my target markets are many different markets such as the Western Europe, Poland as well as Russia, Kazakhstan, or even further markets such as Pakistan, South America, Africa, then we come to a situation in which I have many markets and there is no economic justification for knowing them so deeply* [markets—authors' explanation]. *I am not able to know all of these markets so thoroughly and simultaneously. It is very hard to have several irons in the fire, that is, to have one level of quality for Western markets, because there is higher quality required, another level of quality for Polish stores and another level of quality for Kazakhstan, and yet another level of quality for export to India. It is simply so difficult. Although having the knowledge of this weakness and despite this awareness, it is difficult to find a solution to this problem, so that it would be obviously economical and would also involve access to such staff that would be able to crack it on. Theoretically, it is all possible to be cracked on, but it is not simple"*. The respondent's statement indicates that export is the form of foreign expansion which allows VTR to use the potential of foreign markets. However, these markets are very different from each other (e.g., [32]). Sale of products for the above-mentioned geographically distant markets causes VTR to experience problems due to uncertainty, which in this case is equated with an information gap resulting from, among other things, large geographical distance between contractors, fluctuations in the economic situation, different needs of customers. Therefore, a big challenge for an enterprise which offers products practically all over the world is to match the offer to local needs.

The third important inhibiting factor of change in BMs in an enterprise which constantly improves its product offer and discovers completely new paths of business development (VTR often operates outside the beaten path and behavioural patterns) is the readiness to take the so-called "being the first on the market" risk. In the case of such innovative products as textile composites (e.g., [63,64]), new recycling technologies along with sorting technologies i.e., [65], or the construction of a textile waste pyrolysis installation (a case of large-scale production volume in the textile recycling sector), there are no well-trodden paths for the enterprise's development. VTR has to work out all this from scratch and each investment involves some risks. They are examples of courage in action which allow for undertaking and consistent implementation of entrepreneurial ventures despite the natural risk in such situations.

This study documents that risk is always present in business and includes both negative and positive effects of events. For example, "being the first on the market" gives the chance to reach for the "Schumpeter's pension", i.e., the "priority rent in the market", the "candy" as called by the respondent. However, it is burdened at the same time with "entry costs" (i.e., technology and R&D expenditures) which do not have to be borne by subsequent players entering the market. As a consequence, it may mean that the competitor's product or technology would be much cheaper. Because the competitor, by analysing an already functioning market, is able to better develop a product or technology. The respondent told about it in these words: *"Well, that is the problem, but we also think it has some advantages. So, we are looking at the market where we are the pioneer in most cases. Especially in Poland. And generally, in this part of Europe. And this is, on the one hand, the <<first advantage>>, and it is OK, but on the other hand, we are the <<first in costs>>". Well, the enterprise which is the first, then it goes through this minefield; when it crosses this minefield, it is the first for the <<candy>> which is there. (* . . . *) but it also bears all these risks and costs. And it is*

*not just about the risks associated with a sudden entry with a new technology or a new model. It is not just that this particular process or technology will not work. In developing new activities, the path towards them itself is equally important. And being the first, I risk that this path will also fail, apart from the technology itself"*.

These studies and the existing literature [27,37,44,66] clearly show that innovations may be commercialised in different ways, which means that an identical innovation commercialised in different ways is likely to bring two different results. The commercialisation of technology includes not only the physical implementation of technological changes, changes in hardware resources, and finally changes in production processes. It also includes a number of additional factors—sometimes equally costly—related to communication with the market, marketing message, or even educating the market itself. A number of commercialisations do not bring the intended business results, not because they did not have the potential, but they were burdened with enormous costs of education, transforming the target market, etc. Costs that imitators bear only a fraction of the original outlay. The commercialisation paths chosen by enterprises may be highly diversified, despite the fact that they are based on a similar technological potential or a similar optimisation of processes. The absorbed financial resources may or may not be returned. It means that the BMs do not represent a single objective value. Innovative BMs rather develop from commercialisation possibilities which are realised by a unique setup [67]. Our findings confirm the views of Chesbrough [37] and Breier et al. [44] who claim that BMs are necessary for enterprises and require constant improvements and adjustments. Therefore, it would be desirable for top managers to see the potential for improvements and adjustments [66,68].

Based on the results of this study, it may be concluded that experimenting and trying out different ways of implementing BMs to achieve enterprise's goals requires the effort and attention of different members of this organisation [66], including additional interaction and collaboration between managers and individuals at different levels and from different units in the enterprise [22,45], who may distract managers from matters important to the enterprise. Because some issues, tasks or domains (creating value or delivering value or capturing value) attract more attention or priority than others [66,69,70]. The respondent highlighted these issues as follows: *"And if I am doing this, that is implementing an innovation, as the first one on the market, I have a lot of additional communication with the staff and people. Lots of extra thinking, many such side roads where you can go astray and they can turn out to be dead ends. A lot of the enterprise's energy can be absorbed in this way, which means that the focus of the enterprise starts to fade a little. (* . . . *) When, just like in our case, we are the first. Everybody says that being the first is so great but forgets about the high costs of not only the potential risk, but also organisational distraction carried by "being the first". How easy it is to get lost, how easy it is to confuse goals. And to get bogged down in some minor topics*" (see more: Appendix A, Table A2, ID.3).

The study also found that "being the first" involves making improvements to an enterprise, which may reduce its operational efficiency before improving it in the initial period. As Obłój [71] (pp. 103–104) claims, there is no simple solution to this problem, which often appears in management practice as an "either-or" dilemma, while it must be perceived as an "and-and" necessity. The enterprise's successes cannot be postponed until an undefined future. The enterprise must be successful both now and in the future. Therefore, one of the key issues is the dilemma of how to build the enterprise's future without sacrificing the enterprise' current performance.

In sum, large-scale business in crisis situations, a large scope and range of geographic diversification of outlets in the perspective of the consequences of the information gap, and the readiness to take the "being the first in the market" risk (lower level of this readiness in relation to that required by a given situation) may be those factors which inhibit the enterprise from implementing changes in business models.

#### **5. Discussion**

#### *5.1. Drivers to CBMs*

This study is part of the ongoing broad discussion on the antecedents of business models by highlighting enhancing and inhibiting factors. Business models are not static; they are dynamic [66,72] and their role is strategically important [44]. The literature studies indicate that the business model takes shape through experimentation, which may vary depending on organisations and competitive environments [68,73]. Therefore, the development of a business model requires constant changes, adaptation, experimentation, and thus the constant attention of managers [74]. In this context, the enhancing factors create the need to change and further support the development of BMs, which may result in new prospects and profit potential for enterprises which seize the opportunity for change.

However, it should be borne in mind that changing existing business models may put the enterprise's actual business model at risk, and enterprises may hesitate to change and thus leave too many activities unchanged. Changing value creation, value proposition and value capture may not be radical enough. As a consequence, future development and changes to the business model may be too limited [66]. In order to increase the chances of success of the new, alternative business model, it should be adequately supported by managers [68,75] who saw the potential for improvement.

The main factors enhancing and inhibiting changes in the existing CBMs in VTR were identified through interviews. The set of main driving factors for the development of CBMs in VTR includes relevant regulations at the European level, appropriate technologies and digitisation, and increasing social and environmental awareness of consumers and managerial capabilities. Table 1 shows the categories of drivers explored in this study.


**Table 1.** Drivers and barriers to CBMs.

Source: own study.

These factors may inspire or underpin new and developed business models in enterprises in the textile recycling sector. They may influence enterprise's decisions, among other things, to move up the tiers of the "textile waste management hierarchy" [76], which have long-term implications and require changes to existing business models. For example, changes made to whether the alternative fuel is a fully functional product or a waste see: [34,76]. In this context, the dynamically changing societal temporal construction of

the concept of "waste" will strongly influence the perception and management of material resources in the design of CBMs [25].

Lewandowski [77] in his research argues that implementing the principles of circular economy often requires new visions and strategies as well as a fundamental redesign of product concepts, service offers and channels towards long-life solutions. It is worth emphasising that technological and business ideas have economic value only when they are commercialised through the enterprise's business model. In this respect, technology and innovation alone have no measurable economic value [75]. Chesbrough [78] even claims that "a better BM often will beat a better idea or technology" [75].

Thus, the ways in which enterprises successfully implement new technologies or other innovations are largely related to its CBMs.

In addition, it should be added that enterprises should pay great attention to digitisation, taking advantage of opportunities in technology, processes and markets. Digital technologies may currently take various forms, including e-platforms (i.e., B2B online store, B2B wholesale platform) see more: [50], or the development of systems automating processes based on the so-called artificial intelligence and deep machine learning algorithms. It was indicated that digital technologies are the basis of digitisation in an enterprise. Based on the suggestions by Bouncken et al. [66], the advances in digital technology would require enterprises to develop and implement a wide range of digital activities (i.e., improved or new internal processes and within their supply chains and environment) in business models. Enteprises should therefore consider appropriate and perhaps develop new business models in digitisation. However, as suggested by Reim et al. [79] it is important not to choose an overly ambitious business model where the risk of failure is high. Rather, the enterprise should strive for the gradual development of a given business model.

Further research provides evidence that the growing segment of ecologically and socially minded individual and institutional customers in recent years is also putting pressure on the introduction of business models to support them. For this reason, enterprises verify their business models from time to time. The example of the VIVE Profit retail chain shows that after the COVID-19 pandemic, the second-hand market is undergoing a revolution. Small and local second-hand shops are disappearing, and large-scale networks and e-sales platforms with second-hand clothes are growing.

These research findings are in line with recent evidence provided by Mostaghela and Chirumallab [5] who claim that the retail sector is evolving not only as a result of technological advances, but also due to crisis situations as well as governments and customers' new requirements for ethical and sustainable products.

Currently, without focusing on an organisation's customers, any strategy will eventually fail in a competitive environment [80]. According to Jansson et al. [80], customer focus is the degree to which an organisation views its purpose as creating satisfied customers and the extent to which the organisation puts a customer first. Thus, the enterprise's main goal must be the satisfaction of its current and future customers. In this case, the enterprise guided by social responsibility strives to achieve environmental and social goals which go beyond the legal requirements, and thus commits itself to achieving better environmental performance. Environmental impacts need to be integrated into the global process of enhancing productivity and competitiveness [81].

#### *5.2. Barriers to CBMs*

Our research also broadens the existing knowledge about the factors inhibiting enterprises from introducing changes in business models. These factors include: supply chain complexity and supply chain collaboration in connection with a large scale of business in crisis situations, a large scope and range of geographic diversification of outlets in the perspective of the consequences of the information gap, and the readiness to take the "being the first in the market" risk. Table 1 shows the categories of barriers explored in this study.

This study revealed that the enterprise has faced, for the first time since its thirtyyear international operation, a serious threat to its supply, failure to meet its delivery

times and operational efficiency in an uncertain environment. Recent disruptions have demonstrated the dangers of the supply chain built around JIT strategies, which have become serious problems, not only from an organisational, but also inter-organisational points of view. VTR and other enterprises from this sector suddenly needed appropriate mechanisms to fix themselves, i.e., ones which would correspond to the challenges of the modern world. This would be in line with the findings by Ritter and Pedersen [82] who emphasise that the COVID-19 crisis is going to affect established business models (BMs). These insights therefore contribute to the debate on the pros and cons of highly coordinated global manufacturing supply chains.

During this serious crisis, other important weaknesses in the enterprise's operations were also revealed, such as preparation for disasters, or the organisation and conditions of working in executive positions, taking the new ad hoc rules and regulations on hygiene as well as social distancing into account. This required the implementation of new ways of arranging the enterprise's operations.

Building on the results of this study, lockdown has contributed to changing the way business in the textile recycling sector may be conducted in the future. Crises have one important feature, namely, they provide an excellent "cover-up" and are an excellent motivating factor, an ideal element of permission for change. If there were no crises, organisations would be less active. Crises serve to renew organisations and accelerate changes. This is perfectly illustrated by the metaphor—*"Sequoias like fires because then their bark bursts and they can grow larger"* [83] (p. 190).

Moreover, Meyer et al. [84,85] (p. 93) described the pandemic environment in which the VTR was operating: *"From time to time, organizational environments undergo cataclysmic upheavals—changes so sudden and extensive that they alter the trajectories of entire industries, overwhelm the adaptive capacities of resilient organizations, and surpass the comprehension of seasoned managers"*.

However, according to Obłój [71] (p. 93), managers consistently make mistakes and may do so. Therefore, it is obvious that organisations would fall into periodic crises because they are too complex systems to be fully steered and controlled, no matter what illusion of control supervisory boards and managers want to maintain [71]. At the same time, the way in which leaders intend to balance solutions for creativity and product innovation with administrative solutions, such as risk management and management control, is important [86].

The respondent expressed a similar opinion in the above quotations, pointing not only to the very issues of the risk of these mistakes and difficulties in management, but also to the risk of losing the enterprise's focus on achieving goals and paying too much attention to solve smaller problems.

Ko´zmi´nski [83] (pp. 197–198), in turn, argues that cognitive limitations concern the recognition of operating conditions by managers and are obviously related to a lack of competences. He emphasises that it is a sin not to recognise crisis symptoms, conflicts and an excess of polemics and debates. Moreover, he claims that they should give top management food for thought, and are often ignored or misinterpreted, too hastily and superficially interpreted. In his opinion: *"(* . . . *) it is a mistake to try to implement difficult, ambitious plans too early or too late, in unfavourable conditions"*.

Concluding that the ability to properly read and understand the situation of an organisation is extremely important. Thus, this managerial skill is very important in making decisions. At the same time, according to Obłój [71] (pp. 96–97), every more important decision is made in the conditions of incomplete information; otherwise, there is no problem of choice and decision at all. In his opinion, the key mental problem of managers is the acceptance of uncertainty.

This quantum discontinuous change described by Meyer et al. [84], now caused by the unprecedented COVID-19 pandemic, also required a response from VTR. Top management at VTR has developed strategies to deal with short-term discontinuities and significant uncertainty by making significant changes to components and/or their configurations in the existing CBMs. These solutions enabled the enterprise to overcome the crisis.

However, bearing in mind the earlier suggestions of North [87] concerning a new equilibrium, it continues to change after such a serious disturbance. If so, according to Hitt et al. [85], even after the COVID-19 pandemic, long-term strategic changes may be needed to ensure that enterprises in many industrial activities, and VTR is among them, may operate in the newly created competitive environment resulting from technological, social, political, and institutional changes [88] which resemble the causes of environmental shocks explained by Meyer et al. [84].

These factors influence an enterprise's decisions when it comes to creating new ideas [27]. It has been emphasised in the management literature that enterprises need to identify or get ideas for new BMs even outside their boundaries (e.g., [44,89,90]), which are burdened with a high degree of risk [27]. Simply, enterprises have to take risks to increase the likelihood not so much of survival as of growth and success [71]. However, this risk must be handled by the enterprise's ability to take it.

In sum, VTR's product life extension and recycling models faced supply chain and market challenges. The findings of Wrålsen et al. [13] are identical as he argued that circular supply business models are mainly threatened by the supply chain and market barriers. Identification of these challenges is an indication for decision-makers and politicians in the search for solutions in the area of regulation and appropriate support for entrepreneurs in the face of crisis situations or the requirement of effectiveness of a specific "R-activity".

The above considerations are summarised in Table 1. Enhancing and inhibiting factors for the development of circular models of second-hand clothing reuse and textile recycling in VTR are grouped according to two categories as to their origins: internal and external to an organisation. Previously, similar categories were proposed by Galvão et al. [10] for the grouping barriers to CMBs. This division was also used for drivers to CBMs.

Our research findings indicate that implementing of circular actions in an organisation is largely dependent on regulations, especially at the European level, and increasing social and environmental awareness of consumers, i.e., on factors included in the external environment in which the enterprise operates. This suggests that the previously described wholesale and retail of second-hand clothing is evolving not only as a result of the digitisation of commerce (i.e., e-platforms: B2B online store, B2B wholesale platform) (see more: [50]), but also due to new government and customers' requirements for ethical and sustainable products. Social and environmental awareness of consumers and proper legal regulations are crucial for the successful implementation of circular business models. However, few studies focus on the role of customers in enabling circular business models [5].

Organisational factors such as the capabilities of the management team responsible for developing and implementing CBMs are also of great importance. It is believed that these capabilities help VTR to overcome obstacles on its way to change. In addition, the developed and used technologies for recycling textile waste, better in terms of the environment, which allow to obtain textile composites (i.e., composite board) with better properties and their recycling uses less and less energy, and in the near future the discards from sorting textile waste processes (the so-called waste which does not meet the prescribed recycling requirements) are an equally important factor facilitating the sustainable development of CBMs. Other recent studies have also recognised the role of technology in business models based on the reuse and recycling of waste materials [91]. This indicates that enhancing factors create a need to change and further support a CBMs (Figure 2).

models [5].

(Figure 2).

**Figure 2.** Crisis—New CBM relationship model in the context of influencing factors. Source: own elaboration. **Figure 2.** Crisis—New CBM relationship model in the context of influencing factors. Source: own elaboration.

environment in which the enterprise operates. This suggests that the previously described wholesale and retail of second-hand clothing is evolving not only as a result of the digitisation of commerce (i.e., e-platforms: B2B online store, B2B wholesale platform) (see more: [50]), but also due to new government and customers' requirements for ethical and sustainable products. Social and environmental awareness of consumers and proper legal regulations are crucial for the successful implementation of circular business models. However, few studies focus on the role of customers in enabling circular business

Organisational factors such as the capabilities of the management team responsible for developing and implementing CBMs are also of great importance. It is believed that these capabilities help VTR to overcome obstacles on its way to change. In addition, the developed and used technologies for recycling textile waste, better in terms of the environment, which allow to obtain textile composites (i.e., composite board) with better properties and their recycling uses less and less energy, and in the near future the discards from sorting textile waste processes (the so-called waste which does not meet the prescribed recycling requirements) are an equally important factor facilitating the sustainable development of CBMs. Other recent studies have also recognised the role of technology in business models based on the reuse and recycling of waste materials [91]. This indicates that enhancing factors create a need to change and further support a CBMs

On the other hand, the reported barriers seem to originate from the internal limitations experienced by an organisation, such as the readiness to take the "being the first in the market" risk, a large scope and range of geographic diversification of outlets in the perspective of the consequences of the information gap; on issues external to organization, such as supply chain complexity and supply chain collaboration in connection with a large scale of business in crisis situations. According to the respondent's statements, VTR faces these challenges as temporary problems to be overcome. Nevertheless, these barriers may delay the achievement of higher levels circularity (Figure 2). On the other hand, the reported barriers seem to originate from the internal limitations experienced by an organisation, such as the readiness to take the "being the first in the market" risk, a large scope and range of geographic diversification of outlets in the perspective of the consequences of the information gap; on issues external to organization, such as supply chain complexity and supply chain collaboration in connection with a large scale of business in crisis situations. According to the respondent's statements, VTR faces these challenges as temporary problems to be overcome. Nevertheless, these barriers may delay the achievement of higher levels circularity (Figure 2).

The so-called drivers and barriers to CBMs discovered by us will complement or exemplify those previously identified by other researchers (e.g., [5,9,10,43,91]). Therefore, our findings contribute to the ongoing discussion on circular business models by ex-The so-called drivers and barriers to CBMs discovered by us will complement or exemplify those previously identified by other researchers (e.g., [5,9,10,43,91]). Therefore, our findings contribute to the ongoing discussion on circular business models by expanding the existing knowledge base in this field. Our theoretical research contribution is to deepen our understanding of drivers and barriers and their links to the development of CBMs.

However, it is worth emphasizing that CBMs might not be more environmentally sustainable. Perceived savings from circularity can sometimes lead to rebound effects by increasing consumption of other resources. Business experiments conducted by companies that may have started out as "circular" or sustainable, could either create new "circular" business opportunities, or move them towards linearity through unintended "rebound effects" [12]. The above discussion is summarised by the conceptual model presented in Figure 1.

On the basis of presented findings, the crisis—New CBM relationship model for the textile recycling sector, is proposed (Figure 2). The model comprises the results and shows that a crisis can be a trigger event [44,82,83] to start changes in established circular models of second-hand clothing reuse and textile recycling, through business experiments towards a more circular economy, which can help firms that are shut down to create new CBMs and open up again. This overall finding is consistent with recent evidence provided by Kraus et al. [27] in a cross-industry context from various European countries, that the role of new BMs may be even more strategically important in the context of the crisis. While individual enterprises adjust BMs only temporarily to maintain liquidity, it turns out that a new business model innovation (BMI)—initiated in response to the crisis—may also have long-term consequences. In other words, the crisis may result in new prospects and profit potential for enterprises which seize the opportunity for change.

#### **6. Conclusions and Practical Implications**

The textile recycling industry is one of those industries which may benefit economically from efforts for CE, that is they aim to address the challenges not only of resource scarcity but also of waste disposal in a win–win approach with an economic and value-added perspective. This means that enterprises in this sector, by thinking more specifically about promoting sustainable development, may want to engage in CBMs.

The carried out analysis of data collected directly from the top manager during interviews in conjunction with secondary data provides a unique, credible and valuable justification for the formulated practical implications, which may be of a great interest to a broad interdisciplinary audience. Therefore, major factors discovered which enhance and inhibit the development of circular models of second-hand clothing reuse and textile recycling may be the subject of intervention or support of business managers, practitioners, consultants and policy makers, as well as academics by minimising the negative impact of post-consumer textile waste on the environment.

For instance, top management of enterprises with Just-in-Time (JIT) supply chain models should be aware of the potential long-term implications for supply chains in the post-COVID-19 world. Given the uncertainty surrounding the consequences of the SARS-CoV-2 coronavirus, it is theorised that it is worth re-thinking supply chains for resilience to various types of variability in the competitive landscape. To the best of our knowledge, this would build greater long-term resilience of enterprises to the crisis and increase the chances of success. If top management develops a new standard, it should be associated with a better understanding of risk so that risk management stops the destruction of supply chains caused by crisis situations. Thus, insisting on JIT supply chain models may not be the best way to go. It is worth considering that several stakeholders need to work together to strengthen drivers and overcome barriers to retrieve values from used clothing.

In addition, thinking about the circular business model is ecosystem-oriented, not a central enterprise, should be taken into account. CBM in fact functions at the level of other ecosystem participants, which can be both B2B enterprises (i.e., suppliers, wholesalers, retailers) and private individuals who are end users in the B2C market. This means that changes in the circular business model may require changes at the system level, i.e., they should take cooperation with external partners into account. After all, a business organisation is part of a larger ecosystem. Therefore, the above suggestions for solutions to the problems of strategic cooperation with partners in the supply chain built around JITs in crisis situations provide, at the same time, new information to the literature.

Moreover, the closure of VIVE Profit physical stores proved that running an on-line business has become not only a good practice, but a necessity to maintain operational efficiency. Thanks to increasing numbers of buyers in the on-line space, these stores may be better at reaching their target groups, and thus the risk of failure would be relatively lower in the event of a pandemic that may be still observed. Therefore, today's best practice in the trading industry is the e-commerce model, or hybrid business models.

Based on the respondent's statements, the view that new technologies open the door to many business models used in the recycling industry is highlighted. Osterwalder et al. [92] also state in their study that managers analyse the adequacy of the current business model to environmental pressures and design a new business model.

Due to the emergence of these new technologies and the invention of new products (i.e., textile composites), BMs in enterprises in the textile recycling sector may have to be improved or changed to new ones. According to Osterwalder et al. [92], new business models may become the goal to be achieved and may guide planning, change, and reaction. In this context, understanding the enterprise's business model may facilitate and rationalise the choice of infrastructure. This suggestion also applies to the integration of digital technologies and their use in new business models. This may require managers to engage in digital transformation and the digitisation of business models. Bouncken et al. [66] have recently found that enterprises may already apply digital technologies to improved or innovative internal and external processes and integrate them into new business models. At the same time, it is important to foresee the potential environmental impact of new business models at an early stage in order to maximise their impact reduction potential. To do this, the organization must give a senior manager the resources and authority to define and launch business-model experiments [78].

From a practical point of view, this article also aims to deepen understanding of how policy makers can facilitate the development of CBMs in the textile recycling sector. Our findings are consistent with the evidence of Evans et al. [45], Ranta et al. [38] and Galvão et al. [10] who recognise that policy may have an impact at the individual enterprise level as well as at the broader level of the industrial system by appropriately modifying stakeholder's behaviour through appropriate policy interventions such as: regulation, legislation, taxation, education, and incentives. For instance, Galvão et al. [10] suggest that tax incentives are needed to help enterprises to invest more in circularity.

Thus, the contribution of this study to the theory of crisis management, supply chain management, to the emerging CBM literature as well as the implications for practice indicate that the studies on the development of CBMs in the textile recycling industry are empirically important in this industry.

#### **7. Limitations and Future Studies**

This study has some limitations that should be taken into account when interpreting its results. Firstly, a case where the enterprise is actively working on the development of its CBMs offer was chosen, and insights from its 30 years of experience were obtained. These observations, however, are limited to a large Polish enterprise dealing in second-hand clothing reuse and textile recycling into innovative textile composites used in industry (i.e., construction industry) on a global scale. Thus, adopting a broader case selection would provide scope for better cross-case analysis.

Secondly, research results based on the subjective assessment of the respondent should be treated with caution. Case study does not allow for an empirical generalisation in probabilistic or deterministic terms [44]. Therefore, presented results should be treated as ideas that provide reasonable expectations for similar results in other cases of enterprises dealing with reusing and/or textile recycling and which may be confirmed or falsified by future quantitative research.

Therefore, future research could conduct further empirical studies to validate or extend the present study findings through quantitative analysis. Thus, this article may be seen as the basis for further research.

**Author Contributions:** Conceptualization, A.W.-K., J.K., B.Z., P.B. and A.P.-O.; methodology, A.W.-K., J.K., B.Z. and A.P.-O.; investigation, A.W.-K., J.K., B.Z. and A.P.-O.; writing—original draft, A.W.-K.; formal analysis, A.W.-K., J.K., P.B., B.Z. and A.P.-O.; writing—review and editing, A.W.-K., J.K., P.B., B.Z. and A.P.-O.; resources, A.W.-K., B.Z. and A.P.-O.; supervision, A.W.-K.; final editing and revision, A.W.-K. and A.P.-O. All authors have read and agreed to the published version of the manuscript.

**Funding:** The publication was co-financed under the subsidy granted to the Jan Kochanowski University of Kielce (Project no. SUPS.RN.22.009) and the subsidy granted to the Cracow University of Economics (Project no. 014/ZZO/2022/DOS).

**Data Availability Statement:** The other data are not publicly available due to their containing information that could compromise the privacy of research participants.

**Acknowledgments:** The authors would like to thank the anonymous peer reviewers.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Appendix A**

**Table A1.** Interview guides.


**Table A2.** List of selected full responses of the Respondent.


**Table A2.** *Cont.*


Source: own elaboration.

#### **References**


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#### *Article* **Strategies of European Energy Producers: Directions of Evolution**

**Jerzy Niemczyk <sup>1</sup> , Aleksandra Sus <sup>2</sup> , Edyta Bieli ´nska-Dusza 3,\*, Rafał Trzaska <sup>1</sup> and Michał Organa 1,\***

	- <sup>2</sup> Department of Management, General Tadeusz Ko´sciuszko Military University of Land Forces, Piotra Czajkowskiego 109, 51-147 Wrocław, Poland; aleksandra.sus@awl.edu.pl
	- <sup>3</sup> Department of Strategic Analysis, Cracow University of Economics, 31-570 Cracow, Poland

**Abstract:** The article presents an innovative method of analyzing energy companies' strategies, which aims to identify the strategic orientation of the entities subject to the research and, thus, to initially define the directions of strategic changes in the analyzed sector. The aim of the research, the results of which were used in this publication, was to identify the features of energy sector companies' strategies in the European Union in the period of sector transformation caused by the new climate policy. The analysis area is the energy sector, i.e., the sector whose fundamental strategic goal is energy production. The research used a critical analysis of the subject literature and desk research method with the use of the researchers' own analytical equipment, developed for the needs of this analysis. It was assumed in the conducted research that the primary source of information in the empirical study, the information subject to subsequent analysis, was the analysis of official documents (strategies, financial reports, etc.) posted on the websites of the surveyed corporations. The research results indicate the dominance of the resource-based approach in implementing strategic postulates of the surveyed companies. Nevertheless, the operational activity focuses on the implementation of innovative solutions towards decarbonization and climate neutrality.

**Keywords:** management; schools of management; planning approach; positional school; resourcebased approach to strategy; the innovative and entrepreneurial approach; network organization; energetics; regulated sector; strategy; energy producers

**1. Introduction**

Changes in the energy sector across the world are happening due, inter alia, to political and legal conditions and regulations [1], which have transformed this strategic sector and affected the efficiency of operations that guarantee the proper functioning of the economies of states, countries, and regions. The energy policy of the European Union, its goals, and measures have been subject to profound evolution due to the global energy crises [2], environmental threats [3,4], an increase in the prices of energy resources and electricity prices [5], as well as the economic integration in the European Union [4].

These factors have resulted in the formation of three main groups of energy policy objectives: increasing the economy's competitiveness, maintaining the energy security of EU countries, and the protection of the natural environment against the harmful effects of energy production and supply [2,6]. Importantly, their implementation should include sets of activities at the macro-, meso- and micro-levels.

The economic practice of many highly developed countries shows that social and economic development results from the impact and cooperation of many interdependent

**Citation:** Niemczyk, J.; Sus, A.; Bieli ´nska-Dusza, E.; Trzaska, R.; Organa, M. Strategies of European Energy Producers: Directions of Evolution. *Energies* **2022**, *15*, 609. https://doi.org/10.3390/en15020609

Academic Editor: Behnam Zakeri

Received: 9 December 2021 Accepted: 8 January 2022 Published: 15 January 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

groups and the actions of authorities and economic leaders. Moreover, the potential development opportunities of regions increase by conducting an active regional development policy, in line with the adopted strategy of the energy sector [7]. This strategy should take into account the differences of interests, the autonomy of participants, cooperation between them, unforeseen events, and mutual learning resulting from them. On the other hand, its effectiveness will be greater if the aspects of networking, innovation, creativity, and intellectual values are taken into account.

Considering the problem of strategy in the analyzed energy sector, attention should be paid to the importance of the goals hierarchy that determines various types of strategies. The primary strategic documents on which the conducted energy policy is based are EU guidelines [8–17] as well as the long-term and medium-term development strategies of European countries [18], containing significant indications from the perspective of defining development activities. It should be emphasized that the energy policy of the EU countries determines the strategies of the Member States [5]. These strategies, in turn, provide the basis for guiding policy documents at lower levels. However, all EU countries should be precisely considered and analyze their statutory obligation to prepare a document equivalent to the European Union's legal system [3]. The coherence of the goals' hierarchy [19] and the direction of interventions in the field of energy policy is a condition for the effectiveness of these activities. This causes the necessity to redefine and adjust the strategy and actions of all participants [20].

B. Fattouh et al. [21] note that the global energy industry is approaching another energy transformation. The 21st century will be characterized by an increasing share of cost-competitive renewable technologies and a shift away from high-carbon fuels. Critical uncertainty is related to the pace of transformation and its impact on the business strategy of companies and countries.

Therefore, it is essential that the EU tackle the significant energy challenges, setting out an ambitious energy policy that encompasses the full range of energy sources, allowing the transformation of economies to low energy while ensuring greater security, competitiveness, and sustainable energy consumption [5]. However, this means that policy change is transforming the sector [22] and its companies [23–26], who are seeking to align their strategy with top-down guidelines [27], as well as become change agents.

It should be emphasized that one cannot talk about a long-term operation in the energy sector without a clearly defined strategy, which, apart from the tasks mentioned above, also allows the creation of a desired vision of the future. The issue of strategy is critical in the energy sector, as the reliable functioning of this part of the economy is a crucial element of the entire national economy's stability [28].

The area of research on identifying strategies and types of enterprises developed by the energy sector, including those as a result of the adopted EU strategies, environmental conditions, digitization, or climate transformation, is still evolving in the scientific literature [20,24,26–36].

It can be noticed that the strategies of these enterprises are constantly changing, being influenced by global trends and adapting to the turbulent market environment, technology development, and the continually growing social awareness of the energy impact on health and the environment. On the one hand, this requires a new approach to linking the issues of long-term development planning with building the company's strategy. On the other hand, companies must respond to new development directions of the entire sector. To be able to function effectively, these companies must have flexible [21], carefully prepared and implemented development strategies, systematically adapted to the new operating conditions [28] It is also crucial to review strategic priorities more frequently [37].

Despite the great interest in energy transformation and the strategy of companies from the energy sector, we have noticed a research gap in identifying the changes within strategies of these companies concerning the main strategic trends. The insufficient analysis and description of the presented thematic area has also been highlighted by J. Brzóska [31], who believes that in the aspect of the changes taking place in the EU energy sector, energy companies determine their cognitive and utilitarian values in a specific way.

Therefore, the purpose of this publication is to identify the features of the main strategies of the energy sector companies in Western Europe in the period of sector transformation caused by the new climate policy.

This paper is part of a current and relevant scientific discussion. The article's structure consists of the theoretical part in which the evolution of energy policy, the conceptual assumptions of the research approach, and the characteristics of the distinguishing features in selected approaches to the strategy in sectors of energy enterprises are discussed.

The second empirical part presents unique research results along with the application of a critical analysis of the subject literature and desk research with the use of the researchers own analytical equipment, developed for the needs of this analysis. The research part is based on the results of a study focused on 40 corporations. The article ends with conclusions, research limitations, and further research directions.

#### **2. Evolution of Energy Policy**

The year 1973 was a turning point for the whole world. The oil crisis, known as the first energy crisis, and the associated increase in oil prices, triggered by the Israeli–Arab war changed the approach to energy resources worldwide. The drastic increase in the price of a crude oil barrel from USD 3 to USD 25 was the beginning of an extensive search for alternative energy sources that could become a guarantee of quasi energy stability. The aforementioned experiences of the first crisis resulted in a focus on the search for new hydrocarbon deposits, the exploitation of known but so far unprofitable oil deposits, and a reduction in liquid fuels' consumption in the power and heating sectors [38]. This trend also accelerated the implementation of nuclear programs and intensified cooperation between highly developed countries, forming, among others, The International Energy Agency (IEA, 1974), the main goal of which was to optimize and increase the energy security of the member states. The second oil crisis (1979–1980) and the Gulf War (1990–1991) were not so dramatic in their consequences thanks to the mechanisms developed, inter alia, by the IEA, supported by the activities of OPEC (Organization of the Petroleum Exporting Countries) [2]. Nevertheless, they made it clear that the possibilities of oil supply were limited and that the time of cheap oil had passed irretrievably, thus triggering the need for economical fuel and energy management [39] and the search for other sources of energy. The oil crises also changed the area of strategic management theory. It was then that M. Porter's concepts appeared, aimed at actions in a situation of market constraints and boiling down to companies creating a strategy mainly as a competitive struggle strategy [40].

Crude oil, including liquefied gas from oil deposits, is one of the primary energy sources. Other non-renewable energy sources are: coal and lignite, bituminous shale and natural bitumens, natural gas, and uranium fuel, which are classified as resources, based on proven recoverable reserves and estimated additional amounts in place. In addition to non-renewable sources, the reserves of which are presented in various studies [41], renewable energy sources began to play a larger role in the global energy economy, namely: hydropower, biomass, wind energy, and geothermal energy, as well as the energy of the sun and sea waves and the thermal energy of the oceans. The depletion of fossil fuel resources, the potential instability of fuel supplies resulting from historical data, as well as the formation of a significant amount of various pollutants during their combustion, which negatively affect the natural environment, has shifted the attention of global economies towards renewable energy sources.

Renewable energy can be defined as "derived from natural repeating processes, obtained from renewable non-fossil energy sources". These energies are an alternative to traditional, non-renewable fossil fuels, and their resources complement each other in natural processes, creating not new but the most current energy from the perspective of contemporary needs and sources of "green" energy [42]. These values are steadily growing, and in the entire European Union, the share of energy from renewable sources increased to

18.9% in 2018 and 19.7% in 2019 (EU-27) [43], and the five EU countries with the highest share of renewable energy in energy consumption are Sweden, Finland, Latvia, Denmark, and Austria.

The latest report on renewable energy appeared in Nature Communications. It presents research on energy consumption in the 42 most important countries in the period from 1980 to 2018. It turns out that wind and solar energy can meet more than 80% of energy needs without storing too much. The study shows that even without energy storage, the countries covered by the analysis would have their energy needs met 72–91% of the time. With proper energy storage, rates would increase to 83–94% [44]. These coefficients differ for the different latitudes of the countries they apply to. In the case of Germany, energy storage is suggested, as well as the combined use of its many sources. Denmark is the undisputed leader in energy from wind farms and Spain from the sun. This is in line with WWF's (World Wildlife Fund) vision, assuming that by 2050 the world will be powered by 100% renewable energy [45].

Such a breakthrough moment for the renewable energy sector's development was the 2015 conference in Paris (COP21), the effect of which was the signing of the Paris Agreement and the commencement of cooperation between countries from all over the world. The agreement's long-term goal is to keep the global average temperature below 2 ◦C compared to pre-industrial times and take measures to ensure that this index does not exceed 1.5 ◦C. The agreement came into force on 4 November 2016 upon the ratification of the agreement by at least 55 countries responsible for at least 55% of global greenhouse gas emissions, and it was ratified by all European Union countries [14].

Europe's greatest challenge is to become the world's first climate-neutral continent by 2050. For this purpose, on 11 December 2019, the European Commission presented the European Green Deal, i.e., a set of mechanisms and related activities aimed at the green transformation. Moreover, the document provides for integrating the UN Sustainable Development Goals and defines energy strategies for a safe, sustainable, and low-carbon economy [10]. The European Green Deal is also expected to help end the COVID-19 pandemic. The European Green Deal will be financed by one-third of the EUR 1.8 trillion invested in the NextGenerationEU recovery plan and by the EU's seven-year budget [15].

The year 2020 was a continuation of the world's plans to decarbonize (i.e., reduce the carbon intensity) the world. The number of renewable energy deals has increased as both companies and utility companies prepare to meet climate goals. The consequence of such assumptions will be consolidations throughout the value chain, the improved economic competitiveness of various entities, and an increase in transactional activity in the energy sector: 144 out of 174 mergers and acquisitions since December 2020 concerned assets or companies from the renewable energy sector [46].

#### **3. Conceptual Distinguishing Features of the Research Approach**

Strategy is one of the most important management and organizational documents of any organization. It is also, and above all, a way of conduct for the organization's management that is acceptable to the organization's stakeholders. In common understanding, it is synonymous with what is most important in the activities of an organization. At the same time, in formal terms, it is only a selected method of achieving the strategic goal of each organization. In the face of the enormous diversity of conceptual approaches to both the definition of the strategy and the methods of its determination (strategic management schools [28,47–53], it was decided to use the classification presented by J. Niemczyk [40]. The author indicates concepts that allow determining significant boundaries between their key distinguishing features, thus providing a clear distinction of their specific types [40]:


The precursor of the analytical approach to strategy was H.I. Ansoff [54], who indicated that strategy consists of synergistic components [55]:


Together, they create the market path for the organization in its environment. The first describes the scope of the search, the second, their directions, and the third, barriers to entering the market. According to the author, there is a synergy between them, which is the company's measurable ability to generate profits from new product-market orientations. Strategy in the planning approach, along with a detailed specification of organizational goals that H.I. Ansoff was the first to classify into three main groups, distinguishing strategic, administrative, and operational purposes, focuses on issues related to the environment rather than the inside of the organization [55] and is a disciplined and rational process [56]. The distinguishing features of this approach are a strategy adjusted to the environment and oriented at maximizing profits resulting from their proper distribution [40,57]. The strategic plan, which was the basic tool [28], was a consequence of the belief in the predictability and stability of the environment, as well as the accuracy of forecasts and rationality of decisions made.

The development of the positional school in theoretical terms, management science owes to M.E. Porter. However, the foundations of this school can be found in the works of E.H. Chamberlin, who was the first to emphasize monopolistic competition [58]. The essence of the strategy in the positional approach is to achieve a competitive advantage [59], while its basic distinguishing features are the following assumptions: (a) the company's environment is the starting point for building and analyzing the strategy, (b) strategy formulation always takes place in terms of competition, (c) minimization of costs and differentiation determine strategic success, and (d) the recommended size of the organization is small or large [57].

Therefore, the development of the strategy is accompanied by diagnostic tests in the field of the strategic and competitive position of the organization, which results in the organization's transformation, consisting of the optimization of production decisions, inventory management, or coordination of activities [57]. Paradoxically, operational efficiency becomes the strategic goal in the positional trend, and the return on invested capital and income per employee are its basic assumptions [60]. The organization's attention focuses on the following changes [61]:


In the positional approach, the organization's activities are operationalized, and concepts such as Total Quality Management, benchmarking, time-based competition, and outsourcing are created, which, however, does not lead to a permanent increase in profitability [62]. The most frequently implemented strategies in this trend are the so-called generic strategies: overall cost leadership, differentiation, and focus [63].

The resource-based approach to strategy diminishes the role of the environment in the process of shaping the strategy, and the focus is more on the inside of the organization and its resources and skills. Scientists permanently connected to this trend are E. Penrose, K.

Prahalad, G. Hamel, J.B. Barney, G. Stalk, P. Evans, L.E. Shulman, and J. Kay. However, the origins of this strategic idea should be sought earlier, in the works of I. Ansoff [55], A.D. Chandler Jr. [64], B. Wernerfelt [65], and P. Selznick [66], who introduced the category of "distinctive competence" [67] of the organization into management, contrasting it with the category of "ineptitude".

The leading theories of competitive advantage sources in terms of resources are (a) the core competencies theory by G. Hamel and C.K. Prahalad, (b) the concept of competing based on the company's ability of G. Stalk, P. Evans, and L.E. Shulman, and (c) J. Kay's theory of distinctive abilities. These theories define what types of resources are critical in the process of generating competitive advantage, ways of shaping these resources, and their effective use in a strategic dimension.

Therefore, the company's strategy is focused on building such a bundle of competencies, according to G. Hamel and C.K. Prahalad, which will guarantee the organization's dynamic development using knowledge and learning processes, regardless of organizational boundaries (according to the authors). They pay particular attention to the coordination of employees' skills and the integration of various sources of knowledge (including technology flows), for which the key success factors are communication, commitment, and involvement in organizational processes [68]. In a polemic with G. Hamel and C.K. G. Stalk, P. Evans and L.E. Shulman in the "Harvard Business Review" [69] argued that key competencies in the context of creating a competitive advantage constitute a complex category of resources and skills, and therefore should instead be called "skills" of the company. These, in turn, can be defined as "a set of strategically understandable business processes" [70], which the authors prove by analyzing global organizations (Honda, Wal-Mart, Canon).

R. Amit and P.J. Schoemaker also placed the critical importance of processes, describing "skills" as (iterative) processes and product innovation, production flexibility, responsibility for shaping market trends, and short product development cycles [71]. This approach was developed later by D.J. Teece, G. Pisano, and A. Shuen, giving these skills a dynamic character and defining them as those that enable the company to adapt, integrate, and reconfigure internal and external organizational skills, resources, and competencies [72]. Therefore, dynamic capabilities are the ability to integrate, build, and reconfigure internal and external competencies aimed at the suddenly changing environmental conditions in which these organizations function, the primary sources of which should be sought in microfoundations, individual activities (microactions), and microaspects [72].

In turn, relations and the connected relational capital become determinants of the organization's success, thus permanently occupying an unquestionable place in the theory and practice of strategic management, including the concept on which J. Kay based his conclusions. The author identified three basic distinguishing capabilities of the company, namely the architecture of the organization, the company's reputation, and innovation, making market efficiency dependent on these elements [73]. At the same time, he emphasized that they do not have to co-occur to allow the company to be able to achieve a competitive advantage, and they may not exist at all. In this case, access to strategic resources becomes crucial, replacing the need to have distinctive capabilities (e.g., a license to use a natural resource or having an exclusive right to provide a specific good) [73].

The foundation of the strategy in the innovative and entrepreneurial approach are activities that highlight building a competitive advantage based on the skillful use of fleeting opportunities [57]. They result from the disruptive technologies theory, according to which organizations investing significant financial resources in developing a product based on new technologies cannot maintain a competitive advantage in the long term. The authors of this concept argue that these types of enterprises are doomed to failure for one prosaic reason, namely, they stay too close to their customers. As a result of performance trajectories, progressive product and service cannibalization generates results in the form of customer satisfaction with solutions already known to them [74]. In the case of the energy sector, already in 2013, McKinsey Global Institute emphasized that the renewable energy sector will be such disruptive technologies, and with it the generation of electricity from renewable sources with reduced harmful climate impact, and also advanced oil and gas exploration and recovery, which will make the extraction of unconventional oil and gas economical [75]. This fact was additionally emphasized by the findings at the Paris conference in 2015 and by other studies on the disruptive technologies' impact on the energy sector [76].

In this way, a new business model is implied, the foundation of which is innovation and the implementation of a self-regenerating mechanism for the emergence of market opportunities, which implies the need to apply an innovative strategy. Open innovations taking the form of outside-in (e.g., crowdsourcing) and inside-out (e.g., knowledge sharing in the form of coopetition) strategies are such a tool of the entrepreneurial and innovative trend [77]. Therefore, the changing model of organization development based on the global network caused a change in the value chain of many organizations and resulted in the expansion of the channels of knowledge inflows and outflows [78–80]. Coopetition in the energy sector may seem particularly interesting, mainly due to the fact that it is a sector: (a) regulated and (b) composed of a relatively limited number of market players in individual countries, which is confirmed in the following parts of this study. Nevertheless, the growing importance of coopetition alliances underlines the need for research in the field of value creation achieved by competitive behavior [45], especially when proposed research confirms such systems' creation in Western Europe, regardless of the issue dimensions (locally, in the form of international cooperation, or network cooperation [46], and also in the context of pandemic threats [81].

In addition, due to the growing importance of innovation, an approach that strongly emphasizes the role of innovative solutions in the area of strategy is value innovation. This model aims to minimize costs without having to offer less value to its customers due to giving up competition in existing markets. Value innovation is a strategy covering all areas of the organization, consisting of integrating utility, price, and costs with market pioneering [82].

The newest way to think about strategy is the concept of network organization. The strategy of such an organization will focus on achieving a common goal—not an individual entity goal anymore but a network of individuals connected by common bonds [83], engaged in a long-term relationship [84]. Interactions between organizations are long term, involving exchange, commitment, and reciprocity [85], in terms of resources, actors, and activities, without clearly delineating boundaries and structure [86]. The strength of an entity in the network does not depend on its specific position in imperfect markets but on the links with customers, suppliers, sellers, and competitors, and the very adaptability and the ability to exchange information are critical features of the network according to much of the research [87].

Networks are nowadays slowly becoming the dominant form of economic activity. This is mainly because most of the contemporary organizations notice the undeniable benefits from participation in this type of system. Such a tendency is also driven by a good diagnosis of inter-organizational network functioning mechanisms and increasing awareness of these interaction systems' diversity [88–90].

The analyzed schools and their distinguishing features are shown in Table 1.


**Table 1.** Differentiators of strategies in selected schools of strategic thinking.

Source: [91] (based on a research proposal by [92]).

#### **4. Research Procedure and the Research Sample Description**

The aim of the research, the results of which were used in this publication, was to identify the features of energy sector companies' strategies in the European Union in the period of sector transformation caused by the new climate policy. The analysis area is the energy sector, i.e., the sector whose fundamental strategic goal is energy production. In turn, the strategy was defined as the manner of achieving this strategic goal declared in the company's strategic documents. The strategies of energy producers in the European Union were analyzed, which was a deliberate choice. Here, all producers should already be in the area of the adopted climate policy defined in Paris. Therefore, the article poses the following hypothesis:

#### **Hypothesis 1 (H1).** *The strategies of energy producers are evolving towards innovation strategies.*

To confirm this hypothesis, the 1st iteration of the research was conducted using the adopted methodology, the results of which are presented in this article. The subsequent iterations of the study with the proposed method will be carried out in 3 years (2nd iteration) and 6 years (3rd iteration). Thanks to this approach, comparative statements will be obtained, from which it will be possible to finally verify the presented hypothesis. The graphical flowchart for the crucial work steps within the adopted method has been prepared (Figure 1).

The research used a critical analysis of the subject literature and desk research method with the use of researchers own analytical equipment, developed for the needs of this analysis. It was assumed in the conducted research that the primary source of information in the empirical study, information subject to subsequent analysis, would be the analysis

of official documents posted on the websites of the surveyed corporations. The first step was to select the research sample. Energy corporations located in the European Union were chosen for the research. During the preliminary procedure, 41 corporations meeting the following criteria were selected:


One organization located in the United Kingdom was selected, despite the fact that in 2020 it left the European Union. Most of the analyzed data concern the period up to 2019, and the influence of British organizations on the EU market is evident. Since, in the case of one of the pre-selected organizations from Romania (Transgex), it was impossible to obtain appropriate data allowing for a proper assessment of the dominant strategic approach, this unit was excluded from the research sample. As a result, a list of 40 corporations (Table 2) was obtained, which were subject to detailed analysis.


**Table 2.** Research sample \*.

\* Countries by population, companies by Market Capitalization (highest first)**.** Source: own research based: Bloomberg industry Classification Standard [93].

The next step was to verify the availability and timeliness of the information on the strategy on the websites of individual corporations. The research assumption was the analysis of the facts as of 1 October 2021. The next stages of the research were carried out in accordance with the descriptions contained in Figure 1. The research procedure was based on the methodological guidelines included in the study by J. Niemczyk and J. Jurczyk [94].

In line with the previously adopted assumption, this study uses selected approaches to the strategy, and their summary is presented in Section 3 of this study. The analytical approach used in the paper assumes conducting a study of the energy corporations' strategies based on the adjustment of the expressions used for the purposes of strategy formulation and the epistemology of description to a specific strategic approach implemented in economic practice.

**Figure 1.** Planned work steps of the conducted study.

#### **5. Results**

The interpretation of the research results after the desk research procedure, with the use of the previously described, in-house analytical apparatus, allows for the unequivocally negative verification of the hypothesis adopted in this article: "the strategies of energy producers are evolving towards innovation strategies". According to the summary presented in Figure 2, the dominant strategic approaches in the activity of the 40 surveyed companies of the European energy sector, declared in the formal strategic documents, as well as in the vision, mission, and organizational values, and in the implemented system of strategic goals, are primarily the resource-based approach (exactly 50% of the analyzed entities) and the innovative and entrepreneurial approach (32.5%). The dominance of the positional approach to strategy was found in only five organizations (12.5%) and the network approach to strategy in only two of them (5%). It is also worth noting that with the applied analytical apparatus, no dominance of the planning approach to strategy was found in any of the studied entities.

**Figure 2.** Prevalence of dominant strategic approach types among the researched European corporations from the energy sector (source: own elaboration).

It is worthwhile analyzing the observed phenomenon in a little more detail. For this reason, the considered distribution of the dominant strategic approaches in relation to the individual countries in which the analyzed organizations from the energy sector operate is presented in Table 3. The analysis of the indicated table enables further conclusions to be reached, based on several criteria.


**Table 3.** Prevalence of dominant strategic approach types among the researched European corporations from the energy sector concerning individual countries (source: own elaboration).

One of the criteria used to analyze the achieved results may be the broadly understood level of development of individual countries. Chronologically, the most recent approach to strategy, the network approach, is represented to a small extent by the energy sector organizations under study. This may result from a lack of knowledge or awareness of operating in network structures and conditions by individual entities of modern business. Additionally, the aversion to declaring potentially unrecognized concepts as key values for the strategy of a particular organization may in this case constitute a real barrier to the popularization of this approach. However, it is represented by two of the analyzed companies, which conduct their activities in advanced economies (Germany and Spain).

In addition, the innovative and entrepreneurial approach to strategy is mostly represented by such economically advanced countries (e.g., Germany, France, Spain, Belgium, The Netherlands). It is worth using specific rankings to organize considerations. An example of such a ranking may be the World Competitiveness Ranking prepared by the International Institute for Management Development (IMD), which is primarily based on the categories of innovation, digitalization, welfare benefits, and social cohesion ranks in particular countries, comparing their overall competencies in achieving long-term value creation [95]. Indications for particular countries, according to the mentioned World Competitiveness Ranking, are presented in Table 4.

**Table 4.** Position of considered countries in the World Competitiveness Ranking.


Source: [95].

The resource-based approach to strategy, due to its highest frequency of occurrence, is represented by European countries that are diverse in terms of development. It is worth noting, however, that most of the indications in this area are given by countries that occupy lower positions in the aforementioned International Institute for Management Development (IMD) ranking in Europe. This is the case for Italy, Greece, and Poland, for which a total of 12 out of 20 indications were noted in the strategic documents of individual companies under consideration. It is also worth emphasizing that the high total number of references to resource issues may be related to a good, firmly rooted recognition of these categories, both in the scientific area and in business practice.

The positional approach, in turn, in several cases was found to be dominant both among companies from countries considered to be among the most developed (Germany), but also among entities operating in countries not belonging to the European economical top rank (Romania). This may prove the universality of strategy consideration in the already very well-established concept of the systematic building of position and competitive advantage in the environment of the energy sector organization.

What is slightly surprising is the lack of identification of expressions of the planning approach in the strategic materials of the surveyed organizations. Some justification for this may be the complete absorption of this approach by other approaches as an integral set of distinctions and guidelines. In such a situation, the concept of strategy as a long-term plan of action, of course, does not have to be negated, and in principle is recognized as a natural and basic feature of all other approaches.

The indicated results of the conducted desk research analysis, despite the observation of significant and clear conclusions, may seem somewhat blurred and ambiguous, which the authors of this study are aware of. Increasing the precision of indications would require extending the research scope, both in terms of geographical range (subsequent countries included in the analysis), as well as increasing the research sample. However, there are clear barriers in this case, primarily in the form of a limited number of corporations from the sector under consideration, which is related to the specificity of the activity and significant initial requirements for its initiation.

The relatively popular recent criterion of the use of fossil fuels compared to renewable energy sources also does not provide a clear dividing key in the case under consideration between the strategy approaches used by the organizations under study. Data on this issue for 2019, presented in Table 5, confirm that the current use of fossil fuels (as a share of total energy demand and gross available energy) in almost all countries considered clearly exceeds 72–73%. A significantly lower indicator is shown in this case by France (level of only 49.63%). However, for this country, no exceptionally different approach to the strategy was identified. On the basis of the analysis carried out, it was concluded that the French energy companies under consideration show quite diverse inclinations in this respect, presenting elements of positional, resource, or innovation–entrepreneurial approaches in their strategy documents. At the other end of the scale is The Netherlands (with a level of use of fossil fuels currently at 92.4%). One company reviewed for this country was found to be dominated by an innovative and entrepreneurial approach to strategy. The Netherlands itself is seen as one of the most developed countries in the world (according to the previously presented World Competitiveness Ranking by IMD, with the fourth position in the global scale, which should be considered as really outstanding).

In addition, it is worth analyzing the scale of total energy supply in each of the countries analyzed, as the share of a particular type of energy in total consumption is a relative value and may be unreliable. The analysis of Table 6 shows that for the countries with the largest total energy supplies (in 2019): Germany (almost 3.5 million Gigawatt hours), France (2.85 million GWh), United Kingdom (1.98 million GWh), Poland (1.19 million GWh), or Italy (1.76 million GWh), there is a whole spectrum of approaches to strategies in line with the adopted differentiators. Similarly, for the countries with low energy supply (2019): Greece (only 260.76 thous. GWh), Romania (384.4 thous. GWh), or Czech Republic (495.17 thous. GWh), there is a wide variation in the adopted research sample.


**Table 5.** Share (%) of fossil fuels in gross available energy.

Source: [96].

**Table 6.** Total energy supply (Gigawatt hours, GWh) in analyzed EU countries.


Source: [97].

The criterion of alignment with specific strategic approaches can also be adopted as the degree of internal national regulations supporting or hindering the efficient development of the energy sector. In Figure 3, some comparisons between analysed countries were presented (the higher the score, the more favorable the regulations), on the basis of which certain discrepancies can be observed already in the internal perspective (scale of the domestic market). Based on this criterion, however, it is difficult to identify a reproducible key for matching the strategies of companies from individual countries with selected strategy approaches.

**Figure 3.** Comparing "GCI 4.0: Energy efficiency regulation" indicators with "GCI 4.0: Renewable energy regulation" indicators in analyzed EU countries in 2019: (**a**) GCI 4.0: Energy efficiency regulation indicators—assessments for chosen countries' policies and regulations for energy efficiency energy promotion (0—means "not conducive: 100—means "very conducive"); (**b**) GCI 4.0: Renewable energy regulation indicators—assessments for chosen countries' policies and regulations for renewable energies promotion (0—means "not conducive: 100—means "very conducive"). Source: [98,99].

The last of the criteria selected by the authors of this article for matching the strategies of the surveyed companies to specific strategic approaches is differentiation in terms of the "Energy Transition Index" (ETI) indicator, prepared by the World Economic Forum. This factor is based on previous analyses from the "Global Energy Architecture Performance Index" series, but, in this case (focusing on comparisons of 115 countries), the perspective aspect of the real readiness of a particular country to implement the energy transition was added, taking into account the actual level of the energy systems' performance, and the current readiness of the macro environment for the transition to a stabilized, secure, sustainable, and affordable energy system of the future [100]. This indicator is presented in Table 7. The higher the value of the indicator, the better prepared the energy sector in a given country is for the challenges of the future. The ETI indicator provides a good basis for comparison in the context of ongoing changes in the Industry 4.0 perspective. The 11 countries selected for the study by the authors of this article were arranged in two categories, Emerging and Developing Europe (with only Poland and Romania indicated here) and Advanced Economies (the rest of analyzed countries).


**Table 7.** Position of considered countries in the perspective of Energy Transition Index, ETI.

Source: [101].

The analysis of the results generated in the course of the desk research procedure described above clearly indicates the difficulty of unambiguously attributing the strategies of the surveyed companies to specific strategic approaches (adopted on the basis of the characteristics indicated in Table 1 in Section 3 of this paper. This is because it is extremely difficult to determine authoritative criteria (from among the following considered: general level of competitiveness of a given economy, share of fossil fuel use in total energy use, size of energy supply, energy efficiency regulation levels vs. renewable energy regulation levels, Energy Transition Index, ETI).

#### **6. Discussion**

Enterprises are an integral part of the external and internal conditions that determine their development, which in turn depends on introducing positive strategic changes allowing for adaptation to the changes taking place. Strategic management is also subject to transformations resulting from different views on its essence, e.g., methodological, axiological, or epistemological. The result is the emergence of new approaches, schools of strategy, and an attempt to organize and classify them, confirming the complexity and multidimensionality of this problem. As noted [102], having a wide range of different approaches to strategy, the company's management can choose the one that best suits the company's specificity and its environment. The desire to maintain the development potential and competitiveness in the long term forces the perception of the strategy as a dynamic process of overcoming difficulties and/or taking advantage of opportunities. Thus, the energy transformation, conditioned by several factors, may affect the change of companies' strategy from the energy sector and, consequently, the choice of methods of operation assigned to the main strategic trends.

The analytical approach used in the paper has allowed for conducting a study of the strategies in the European Union's energy corporations' during the period of sector transformations, aiming to identify the strategic features of these companies. Empirical research also allows for the unambiguously negative verification of the article's hypothesis "the strategies of energy producers are evolving towards innovation strategies". The results of this study have several potentially significant observations.

Firstly, the European energy sector companies declare in the formal strategic documents and the implemented system of strategic goals, that the companies' approaches are primarily determined by the resource-based (50%), as well as the innovative and entrepreneurial (32.5%) schools, meaning that these schools are well-known and wellestablished, in theory and in practice. This implies that managers of this sector should pay particular attention to the activities in these areas.

Secondly, the positional approach to strategy was found in 12.5% of organizations and the network approach to strategy in only 5% of them. Our research shows that the presence of the positional approach proves its importance in building a strategy that takes into account the achievement of competitive advantage in the sector.

However, the network approach, which is the most recent approach to strategy, has been represented to a small extent by the energy sector organizations under this study. In light of the obtained results and theoretical indications, the low popularity of the network approach to strategy may be related to it being a relatively new one. However, the importance of this approach is indicated by studies [103,104]. Nevertheless, [105] notes that management strategies with respect to a networked organization are chosen based on an assessment of access to critical resources and competencies, and in the context of internal and external relationships with other network participants and requires activation of all network participants, exploiting the combined potential and enhancing the cohesion.

The last significant observation concerns the lack of the planning approach to strategy, as no implementation of it was found in studied entities. It may be that this approach was absorbed by others as an integral set of its distinctions and guidelines, as well as the diminishing role of planning, the lack of unambiguous references in the analyzed documents, or a shift to resource-based/innovative strategic approaches, which would be

a manifestation of a new way of thinking and a response to changes. However, there is research emphasizing the importance of planning as a strategic approach in the energy sector [28]. Overall, we believe that the strategies of the surveyed companies will continue to develop, and that the innovative and entrepreneurial strategies and inter-organizational relations, especially in network systems, will become particularly important. Hence, the indications are to continue research taking into account these essential areas.

The conducted analytical procedure was based mostly on the appropriate understanding of the statements included in the documents and descriptions of a strategic nature presented by the researched organizations, and assigning them to particular approaches to strategy on the basis of the adopted set of distinctions presented in Table 1. The subjectivity of the realised assessment clearly affected the meaning of the quality of semantic analysis, also significantly conditioning the obtained results. In order to increase the objectivity of the analytical apparatus used, in the future it would be advisable to consider further formalising the assessment procedure and perhaps introducing the principle of parallel assessment of strategic materials by the research team in relation to individual organizations, with an assumed discussion until a consensus is reached. The obvious disadvantage of such an assumption is that it significantly increases the time-consumption and complexity of the planned procedure.

#### **7. Conclusions**

The conducted research proves that the dominant strategy of energy companies located in the European Union (40 examined entities) is the resource-based approach. This conclusion allows the rejection of the hypothesis formulated in the article that the strategies of energy producers are evolving towards innovation. This is all the more surprising as, in the face of changes caused by the evolution of energy policy, they should focus on applying innovative solutions and not building the organization's value in terms of economic value. This situation most likely results from the implementation of operational goals, which are focused on implementing innovative solutions, but a consistent description of the strategy in the language of resources.

Changes in energy companies are tangible and visible; after all, they include product, technological, and process solutions that are highly innovative. Such solutions include economic energy installations, the construction of wind and photovoltaic farms, energy storage, and others, with the general purpose of decarbonizing the natural environment. Their implementation is possible due to energy companies' resource concentration in the European Union. This means that focusing on the value generated by human, financial, material, and information resources is still a guarantee of stable growth and strategic development. However, this assumes potential differences in operational goals of various energy companies, considering changing nature of these resources in particular entities.

Based on the analytical procedure described in this paper, frequently occurring words and terms were selected from the strategic materials developed by selected companies in the European energy sector. This set of key words is presented in Figure 4 and can serve as a prelude to further, extended semantic analysis including extended sets of key words to precisely match specific strategic documents, missions, visions, values, and goal systems with specific strategic approaches.

**Figure 4.** Description: (**a**) Word clouds of words and expressions words that appeared at least 5 times; (**b**) Word clouds of words and expressions words that appeared at least 10 times (source: own elaboration with the use of "WordItOut" [106].

**Author Contributions:** Conceptualization, J.N., A.S., E.B.-D., R.T. and M.O.; methodology, J.N., A.S., E.B.-D., R.T. and M.O.; software, J.N., A.S., E.B.-D., R.T. and M.O.; validation, J.N., A.S., E.B.-D., R.T. and M.O.; formal analysis J.N., A.S., E.B.-D., R.T. and M.O.; investigation, J.N., A.S., E.B.-D., R.T. and M.O.; resources, J.N., A.S., E.B.-D., R.T. and M.O.; data curation, J.N., A.S., E.B.-D., R.T. and M.O.; writing—original draft preparation, J.N., A.S., E.B.-D., R.T. and M.O.; writing—review and editing, J.N., A.S., E.B.-D., R.T. and M.O.; visualization, J.N., A.S., E.B.-D., R.T. and M.O.; supervision, J.N., A.S., E.B.-D., R.T. and M.O.; project administration, J.N., A.S., E.B.-D., R.T. and M.O. All authors have read and agreed to the published version of the manuscript.

**Funding:** The research has been carried out as part of a research initiative financed by the Ministry of Science and Higher Education within "Regional Initiative of Excellence" Program for 2019–2022. Project no.: 021/RID/2018/19. Total financing: 11,897,131.40 PLN.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Acknowledgments:** The research team would like to thank Jerzy Niemczyk, long-standing Head of the Department of Strategy and Management Methods at the Wrocław University of Economics, for his conceptual, substantive, and formal support, both in the development of this article and other works written jointly.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


#### *Article* **From Words to Deeds: The Impact of Pro-Environmental Self-Identity on Green Energy Purchase Intention**

**Magdalena Gr˛ebosz-Krawczyk <sup>1</sup> , Agnieszka Zakrzewska-Bielawska 2,\* and Sylwia Flaszewska <sup>2</sup>**


**Abstract:** This study examines the mechanism by which pro-environmental self-identity (PESI) affects green energy purchase intention (GEPI) through different dimensions of consumption values. The concept of pro-environmental self-identity is rarely discussed in the context of green energy purchase intention. Additionally, the amount of research concerning consumers' attitudes and behaviours towards photovoltaic panels is limited. We fill this cognitive gap by testing a relation between pro-environmental self-identity and green energy purchase intention. The data collection was carried out based on an indirect method of gathering information—using an online survey. Research was conducted among 250 Polish customers. The partial least squares structural equation modelling technique was applied. The research results show that the relations between PESI and GEPI is mediated totally by social and partially by emotional values. The mediating impact of functional values was not confirmed. The results of this study illustrate the importance of intangible social and emotional—values and its impact on the consumer behaviour toward green energy. This study can help marketers more efficiently promote the installation of photovoltaic panels in European countries.

**Keywords:** pro-environmental self-identity; green energy purchase intention; photovoltaic panels; social value; emotional value

#### **1. Introduction**

The development of the modern world, which has been driven mainly by the desire to improve people's quality of life, has been accompanied by an important increase in energy demand. In the second half of the XXth century, the international community started thinking about the increase in global energy consumption and its long-term consequences such as pollution of the atmosphere, soil and water. Nowadays, the eyes of the whole world are turned toward climate change. On the one hand, we need to increase energy production, but at the same time, it is necessary to protect the natural environment. Most countries in the world allocate important resources to encourage the use of renewable energy sources to protect natural resources and reduce pollution.

In 2020, the renewable energy use in the world increased 3% while the demand for all other fuels decreased. It was caused by an almost 7% growth in electricity generation from renewable sources. Consequently, the share of renewables in worldwide electricity generation increased to 29% in 2020 (in comparison with 27% in 2019) [1]. Thanks to policy support, the market of photovoltaic panels has developed in China, the United States, India, Brazil and Vietnam. In total, electricity generated from photovoltaic panels is estimated to grow by 145 TWh (18%), and will achieve about 1000 TWh by 2021 [1].

Previous research concerning green energy has concentrated especially on consumers' environmental concerns, attitudes, awareness, knowledge or responsibility [2–19], as well as internal and external factors influencing customers' intentions to adopt green energy [20–24].

**Citation:** Gr˛ebosz-Krawczyk, M.; Zakrzewska-Bielawska, A.; Flaszewska, S. From Words to Deeds: The Impact of Pro-Environmental Self-Identity on Green Energy Purchase Intention. *Energies* **2021**, *14*, 5732. https://doi.org/10.3390/ en14185732

Academic Editors: Bernard Zi˛ebicki and Edyta Bieli ´nska-Dusza

Received: 18 August 2021 Accepted: 8 September 2021 Published: 11 September 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

The area of research concerning factors influencing consumer decisions in case of photovoltaic panels is still evolving in the scientific literature. We consequently observe important progress in the practical and theoretical aspects of this research [7,18,25–32]. Nonetheless, the international literature lacks a defined picture of the issues of pro-environmental self-identity that consumers of green energy are experiencing. Such information would provide both theoretical and managerial implications. Pro-environmental self-identity (PESI) has not been considered alongside consumption values and green energy purchase intention (GEPI) in the research regarding photovoltaic panel adoption. Consequently, we identified a research gap and confirmed the novelty of the problem. Previous papers concerning green energy adoption in Poland concentrated on the factors determining adoption by testing the willingness of customers to pay for green energy, to convert to green energy tariffs, and to install small-scale generators [29,33]. Therefore, the interconnection between the issue of pro-environmental self-identity and consumers' green energy purchase intention seems to be an innovation that this study brings compared with others that approached the same topic of study [7,18,25–32].

The objective of this paper is to evaluate the impact of pro-environmental self-identity on consumers' green energy purchase intention through different dimensions of chosen consumption values. The scientific problem was expressed through the following research question: How do functional, social and emotional values affect the relations between pro-environmental self-identity and consumers' green energy purchase intention?

This article can contribute to a better understanding of the importance of various factors influencing green energy purchase intention. The topic is important, taking into account the increasing use of photovoltaic panels and growing ecological attitudes of modern consumers. In their studies, Van Der Werff et al. [34], Barbarossa and de Pelsmacker [35] and Mutum et al. [36] highlighted the impact of self-identity on purchase intention of green products, but these issues were not discussed in the context of green energy adoption. Our contribution lies in using pro-environmental self-identity to explain sustainable energy adoption. We would like to strengthen this contribution by appraising the mediating effect of chosen consumption values between pro-environmental self-identity and green energy purchase intention.

The paper consists of five sections. The first is an introduction. The theoretical framework with characteristics of the European photovoltaic sector, the literature review concerning pro-environmental self-identity and its relation with consumption values and green energy purchase intention, and the hypotheses of the study are presented in Section 2. In Section 3, the research methodology is described. The results are presented in Section 4. To finish, in Section 5, the discussion and conclusions with managerial and theoretical contributions, as well as the limitations of this study, are formulated.

#### **2. Theoretical Framework**

#### *2.1. The Characteristics of the European Photovoltaic Sector*

In the past ten years, the EU has continued a proactive climate policy and incorporated a considerable amount of renewables into the energy system [37]. Therefore, renewable energy is one of the important areas of EU policy [38]. Renewable energy is a source of economic growth and jobs for Europeans. The larger amounts of renewable energy are a significant factor behind the decline in wholesale energy prices in previous years. It can in turn decrease energy costs for industry and possibly improve industrial competitiveness. Finally, the dropping costs of the technology, combined with digitalisation, is making renewables an important driving force for permitting consumers to play a central role in the energy transition [39].

EU countries, every second year, inform on their progress towards the EU's 2020 renewable energy goals. Based on the national statements, the European Commission creates an EU-wide report that gives an overview of renewable energy policy developments in EU countries. Available data show that the EU is on the right track for reaching its renewable energy goals for 2020. In 2018, twelve Member States already achieved a

renewable energy share above their respective 2020 targets. Eleven other Member States met or exceeded their RED I average indicative trajectory for 2017–2018. Five Member States (France, Ireland, the Netherlands, Poland and Slovenia) failed to do so [39]. Ambitions of renewable energy targets are consistently raised in many countries [40].

One of the fastest growing renewable sources of electricity is solar energy. One of the most promising technologies all over the world is solar photovoltaic panels [41]. In 2020, 134 TWh of solar energy was produced in the EU-28 countries, mostly in Germany (49 TWh), Spain (15 TWh) and France (13 TWh). Poland's result was at the level of 1.9 TWh. This accounts for around 1.5% of the total electricity produced in Poland in 2020, which is seven times more than in 2018. This is mainly the result of the popularization of photovoltaic panels installed by prosumers and government forms of support, such as My Electricity program implemented from September 2019 to December 2020. It is estimated that in 2021 Poland will obtain as much as 3.5% of its electricity from solar energy [42,43].

Referring to the increase in installed power in photovoltaics, it was about 153 GW in the European Union countries. Germany did best with an increase of 4.74 GW, followed by the Netherlands (3 GW) and Spain (2.8 GW). Poland, with a result of 2.4 GW, was placed fourth. Detailed data on Poland prove that 2020 was the best year in the history of photovoltaic development. The installed power in photovoltaics was 3.936 MW at the end of 2020, which means growth of 2.463 MW year on year, translating to a 200% annual rise. Individual prosumers made the largest input to the increase in new power [43]. Importantly, despite the difficult period caused by the global pandemic, domestic photovoltaic made a significant contribution to the maintenance of investment processes to the tune of PLN 9.5 billion and provided Poland with 35 thousand jobs [43]. The growing ecological awareness of Polish people and the financial benefits associated with investing in a home photovoltaic installation translate into a large increase in interest in such a solution. Photovoltaic panels have proven themselves both as a technology that allows for a significant reduction in electricity bills and as an environmentally friendly solution [42,43].

The dynamics of the development of the photovoltaic market remains high in the EU-28 countries. Poland is the leader of Europe under the growth rate of photovoltaic power, calculated based on the compound annual growth rate (CAGR). Sweden, Hungary, Ukraine, the Netherlands and Spain are behind Poland [43].

#### *2.2. Consumers' Pro-Environmental Self-Identity (PESI) and Its Relation with Consumption Values and Green Energy Purchase Intention*

Pro-environmental self-identity (PESI)—also called green self-identity—is described as the consumers' self-obligation to protect the environment through their everyday behaviour [34,44]. Self-identity is a main predictor of consumption choice-making [45]. Consequently, it is significant also in explaining pro-environmental behaviour. In the opinion of Sparks and Shepherd [46], self-identity influence is stronger than attitudes and values. According to Dermody et al. [47] pro-environmental self-identity is an "environmentally friendly self-concept that is symbolically expressive and shaped by mainstream socio-cultural forces". Accordingly, pro-environmental self-identity is situationally cued. In consequence, these cues direct consumers' ecological behaviours [47–51].

Consumers with PESI tend to consider green behaviour an obligation that has a significant influence on the purchase intention of environmentally friendly products [52,53]. According to Van De Werff et al. [34], pro-environmental self-identity is related to consumers' obligation-based intrinsic motivation to behave pro-environmentally, and consequently it affects pro-ecological actions.

By practising green behaviours, consumers can feel better and perceive that their activities affect positively on environmental protection [36]. Bei and Simpson [54] stated that respondents perceived purchasing recycled products as an act of ecological protection and Wüstenhagen and Bilharz [16] found that the motivation for buying green electricity at a premium is to feel better about themselves.

Dermody et al. [47] confirmed that PESI has a significant influence on Polish and Chinese consumers' buying and curtailment behaviours. Thorbjørnsen et al. [55], Whitmarsh and O'Neill [50] and Mutum et al. [36] found that green consumption behaviours are correlated with PESI.

According to the theory of reasoned action (TRA) model, developed by Ajzen and Fishbein [56] and the theory of planned behaviour model proposed by Ajzen [57], an individual's performance of a specific behaviour is determined by his/her behavioural intention to perform the behaviour. In the case of studies concerning environmentally friendly consumer behaviours, the purchase intention of green products is understood as a customer's intention to buy a product that is less dangerous for both the environment and society [58]. Oliver and Lee [59] defined the purchase intention of the green product as a customer's real purchase of an environmentally friendly product once the customer is conscious of its ecological features. In our research, green energy purchase intention (GEPI) is related to the consumer's actual purchase or the consumer's intention to buy an environmentally friendly installation of photovoltaic panels.

In this context, a question arises whether the relation between PESI and green energy purchase intention is determined by any other factors. Seeking the answer, we used the consumption values theory. This theory provides fundamental and comprehensive constructs representing different values including functional, social, emotional, epistemic, and conditional values that are independent [60,61]. The consumption values theory was analysed in the context of green consumer behaviours by several researchers [61,62]. From different consumption values, three were supposed to be crucial for mediating the relation between PSEI and green energy purchase intention, which are: functional, social, and emotional values.

Functional value can be described as the perceived utility through the possession of the main functional, physical, or utilitarian attributes [60,63–66]. The positive impact of product utilitarian and physical attributes on the intention to purchase green products has been confirmed in different studies [32–38,41]. Wang et al. [67] and Yao et al. [68] also underlined the role of price–quality relation in the product evaluation. Consumers often complain about the high prices of green products, and they perceive ecological products as expensive in comparison to conventional ones. Consequently, a high price may influence purchase decisions, especially when consumers are price sensitive [69]. Previous research has confirmed the relation between price and green purchase intention [69–72]. In the case of green energy, purchases can be driven by functional value that determines the installation choice [9,10]. On the other hand PESI can determine the functional value because consumers are able to accept the green offer if its features and functions bring a potential positive effect to the environment. According to Chen and Chang [73], a consumer's opinion concerning the benefit of a product is based on the consumers' green needs, desires or sustainable expectations. Additionally, the study of Confente et al. [44] showed that the perceived functional value of green product is driven by consumers' pro-environmental self-identity. Therefore, we came up with following hypothesis:

**Hypothesis 1 (H1).** *Functional value mediates the relation between pro-environmental selfidentity and green energy purchase intention*.

Social value can be described as the perceived utility by the connotation with positively or negatively stereotyped demographic, cultural, social, economic, and ethnic groups [60,74]. According to the social identity theory (SIT), social membership gives an individual a sense of belonging [75]. The needs of belonging and acceptance by the group influences the choices of a consumer. Consequently, an individual defines himself through the prism of his group membership; that is, his place in the societal system. In this regard, social identity predefines an individual's attributes as a member of that group; that is, self-perception and conduct.

The adoption of solar PVs can be affected by social-interaction effects because social norms can encourage consumers to invest in pro-environmental activities [14,32,76–79]. Caird et al. [8], Kaenzig and Wüstenhagen [9], Salazar et al. [80], Lee [81] and Klepacka [11] confirmed a positive relation between social values and consumers' purchase behaviour

toward environmentally friendly products. PESI is related with the way an individual sees himself and how he wants to follow the behaviours and values of the groups to which he wants to belong or belongs [36,47,50]. The conclusions from previously mentioned research suggest that the value system of a consumer can affect the value of green products and the degree to which the consumer identifies himself as a member of the green community. Therefore, we proposed a second hypothesis:

**Hypothesis 2 (H2).** *Social value mediates the relation between pro-environmental self-identity and green energy purchase intention*.

Emotional value can be defined as perceived utility related with a new product satisfying the consumer's sentimental needs and delivering novelty through the creation or perpetuation of feelings or affective states [60,63,64].

Yoo et al. [82], Lin and Huang [64], Rex and Baumann [83] and Hartmann et al. [84] found a positive relationship between consumers' purchasing decisions and emotional value. On the other hand, there is also a positive relationship between pro-environmental self-identity and emotional value, which was proved among others by Confente et al. [44] and Mutum et al. [36]. Consumers can accept photovoltaic panels if the fit between the consumers' personal values and PV's modern traits is also underlined. The emotional value of green products can be influenced by consumers' pro-environmental self-identity, particularly if the consumer can see a strong similarity between himself and the product. Therefore, we assumed another hypothesis:

**Hypothesis 3 (H3).** *Emotional value mediates the relation between pro-environmental self-identity and green energy purchase intention*.

#### **3. Materials and Methods**

*3.1. Sample*

To achieve the objective of this research and to verify the hypotheses, the survey was conducted in January 2021. The sampling frame consisted of 250 Polish consumers who were house owners or co-owners. We used a self-administered questionnaire and random selection [85]. These consumers were selected from a database prepared by Norstat, which has a lot of experience in market research and offers online panels of 650,000 consumers from eighteen European countries who are highly motivated, pre-profiled, and open to various digital research methods. For Poland, the active panel counts 41,752 consumers over 18 years old. The population of Poland in 2020 was about 38.6 million people [86] and Internet penetration was 78% [87].

In the first stage, a pilot test was conducted with ten randomly selected house owners. During the process, respondents checked the content and relevance of each item to make sure every question is adequate and accurately understood. Based on the feedback from pre-testing, minor modifications were made to the questionnaire.

In the second stage, the sample of 1000 consumers was randomly selected from a Norstat database of 12,329 consumers who are owners and co-owners of houses. Of this sample, 250 consumers (25%) responded to the questionnaire. Such a response rate is acceptable for this type of survey [88]. Of these respondents, 43.6% were female and 56.4% were male (Table 1). Almost half of the respondents were people with higher education aged between 35 and 54 years old with houses that are more than 30 years old, mainly in the countryside. However, the women were younger than the men, and the men were better educated.


**Table 1.** Demographic description of respondents.

To reduce biases, the respondents were asked to fill in the questionnaire on different days and time slots [89]. We checked the response bias issue based on the procedure suggested by Armstrong and Overton [90]. We compared early and late respondents and key demographic variables, such as gender, age, education, and place of residence using t-tests. All t-statistics were insignificant, suggesting that response bias was unlikely to affect our findings [91].

#### *3.2. Measures, Validation and Reliability Analysis*

The questionnaire consisted of 22 items taken from the literature measuring basic constructs, such as pro-environmental self-identity, green energy purchase intention, functional, social, and emotional value, and the demographic characteristics of respondents (indicated in a Table 1), which played the role of control variables. This study used existing scales from the literature focusing on consumption value theory as well as green consumers' attitudes and behaviours. We used multi-item Likert scales (from 1—strongly disagree to 5—strongly agree) that are used in the literature for the purposes of constructing operationalization and allowing questioning without systematic errors [92]. A list of the scales used with associated items is presented in Appendix A.

*Pro-environmental self-identity (PESI)* was measured by means of four items adapted from Barbarossa and de Pelsmacker [35], Dermody et al. [48], and Whitmarsh and O'Neill [50] through which the respondents were asked to indicate their level of agreement or disagreement with presented statements concerning their attitudes and behaviours to protect the environment. An example of the items we used is "I am willing to commit myself to environmental protection" or "I am convinced that my personal responsibility for the problems of the environment is important".

*Green energy purchase intention (GEPI)* was also measured by four items adapted from Yoo and Donthu [93] and Chan [94]. These items, e.g., "I have installed/would install photovoltaic panels instead of using conventional energy sources due to worsening environmental conditions" or "I would install/have installed photovoltaic panels for ecological reasons", focus on the intention of the consumer to buy a product that is less dangerous for both the environment and society (in this case photovoltaic panels).

Consumption values, such as functional, social, and emotional value measures were served as mediators in the relation between PESI and GEPI.

*Functional value (FV)*. Six items were used to measure functional value adopted from Sangroya and Nayak [4], Zailani et al. [65], and Sweeney and Soutar [62]. Some items described functional value in terms of economic utility and rationalism, such as "The photovoltaic panels are reasonably priced", and some consider product quality and attributes, such as "The photovoltaic panels available on the market are of good quality".

*Social value (SV)*. This value is measured by five items proposed by Sweeney and Soutar [62], Zailani et al. [65], and Yoo et al. [82], who described it as the gain acquired from acceptability in different social groups [95]. As example item is "Photovoltaic panels installation improves the image of its owner" or "Photovoltaic panel installation gives its owner social approval".

*Emotional value (EV).* This study defined emotional value as the perceived utility acquired from a customer's feelings. It used research from Yoo et al. [82], Arvola et al. [96], and Khan and Mohsin [97] to measure emotional value with three items, such as "Installing photovoltaic panels as an alternative to conventional energy sources would make me feel like a better person".

We followed a procedure proposed by Gerbing and Anderson [98] and Hair et al. [99] to evaluate the unidimensionality, validity, and reliability of the constructs. Because all our items are adopted from literature, we used a confirmatory factor analysis (CFA) with maximum likelihood estimation [100] to assess the reliability and validity of the multi-item scales. Table 2 presents the results of CFA.

All factor loadings were higher than 0.7 and all t-tests of the observed variables were significant at the 0.001 level with *t*-values greater than 2. In addition, the average variance extracted (AVE) values were higher than 0.50. Consequently, we confirmed the convergent validity of the scales [101].

Discriminant validity helps to check the extent to which the constructs are statistically different from each other [101], suggesting that the square root of AVE of any construct should be higher than the inter-construct correlation. Table 3 shows that the square root of the AVE of the constructs exceeded the correlations of the constructs. There is one exception relating to the correlation between SV and EV. In this case, the correlation is higher than the square root of the AVE, which means that these variables should be considered in separate models instead of in one. Except for this limitation, discriminant validity was established.


**Table 2.** The confirmatory factor analysis results.

**Table 3.** Descriptive statistics, discriminant validity and correlations between variables.


Note. N = 250; s.d.—standard deviation; the diagonal values (in bold) present the square roots of AVE; correlation is statistically significant for *p* < 0.01 (\*\*\*).

The reliability of scales was conducted by calculating composite reliability (CR) and Cronbach's α. Cronbach's alpha values for all the scales exceeded 0.7 and passed the recommended threshold level [99]. Similarly, the CR of all the constructs were above the threshold value of 0.7, suggesting internal consistency and reliability [101].

To verify the extent to which our data are likely to suffer from common method bias, we guaranteed the anonymity of respondents and confidentiality of the study, affirmed that there are no correct or incorrect responses, and that they should respond honestly [102]. We also took care with particular items to ensure that they were not ambiguous, vague, or unfamiliar, and we formulated the questionnaire as concisely as possible, pre-testing it in a pilot study.

In addition, we performed Harman's single factor test and we followed the approach of Podsakoff et al. [103] for controlling for an unmeasured latent factor. Common method variance is possible if items load on multiple factors and one factor does not account for most of the covariance. Our analysis, i.e., an unrotated principal component factor analysis, principal axis analysis with varimax rotation, and principal component analysis with varimax rotation revealed the presence offive5 distinct factors with an eigenvalue greater than 1.0, rather than a single factor. The cumulative variance was 72.4%. The first factor explains less than half of the overall variance (20.3%), implying that single-source bias is not a significant concern.

Summing up, the CFA supported our measurement model and showed that proenvironmental self-identity, green energy purchase intention, functional value, social value, and emotional value are five distinct constructs.

For our analysis, we employed two statistical packages, Statistica and Amos, which enabled us to enter data in numerical form and then carry out statistical analyses, facilitating and accelerating the process of developing research results. In addition, Amos was useful for the visualization of the structural equations.

#### **4. Results**

To test our hypotheses, first we verified that there was no basis to reject the Gauss– Markov assumptions following a procedure proposed by Hair et al. [99], then we performed structural equation modelling (SEM) with maximum likelihood (ML) estimation and covariance matrix as data inputs. This method is frequently selected to test and develop a theory [104]. Due to the mentioned limitation of discriminant validity (the correlation between SV and EV is higher than the square root of the AVE), we were not able to build one SEM model. Therefore, for our analyses, we built three separate SEM models to test the mediating impact of FV, SV, and EV on the relationship between PESI (independent variable) and GEPI (dependent variable). Firstly, we checked a direct effect, i.e., we tested whether the pro-environmental self-identity (PESI) affected green energy purchase intention (GEPI). Next, we examined the mediation (indirect) effect between independent (PESI) and dependent (GEPI) variables using particular mediators, such as FV, SV, and EV. For each model, we checked the following statistics: goodness of fit index (GFI), comparative fit index (CFI), root mean square error of approximation (RMSEA), Tucker– Lewis index (TLI), and adjusted goodness of fit index (AGFI). The results are presented in Figure 1 and Table 4.


**Table 4.** Results of structural equation modelling.

Note. N = 250; significance level for *p* < 0.01 (\*\*\*); significance level for *p* < 0.05 (\*); SE—standard error; *B*—unstandardized path coefficient.


1 and Table 4.

**4. Results** 


Mean 4.09 3.81 3.54 3.61 3.81 s.d. 0.75 0.82 0.68 0.78 0.84

Summing up, the CFA supported our measurement model and showed that pro-environmental self-identity, green energy purchase intention, functional value, social value,

For our analysis, we employed two statistical packages, Statistica and Amos, which enabled us to enter data in numerical form and then carry out statistical analyses, facilitating and accelerating the process of developing research results. In addition, Amos was

To test our hypotheses, first we verified that there was no basis to reject the Gauss– Markov assumptions following a procedure proposed by Hair et al. [99], then we performed structural equation modelling (SEM) with maximum likelihood (ML) estimation and covariance matrix as data inputs. This method is frequently selected to test and develop a theory [104]. Due to the mentioned limitation of discriminant validity (the correlation between SV and EV is higher than the square root of the AVE), we were not able to build one SEM model. Therefore, for our analyses, we built three separate SEM models to test the mediating impact of FV, SV, and EV on the relationship between PESI (independent variable) and GEPI (dependent variable). Firstly, we checked a direct effect, i.e., we tested whether the pro-environmental self-identity (PESI) affected green energy purchase intention (GEPI). Next, we examined the mediation (indirect) effect between independent (PESI) and dependent (GEPI) variables using particular mediators, such as FV, SV, and EV. For each model, we checked the following statistics: goodness of fit index (GFI), comparative fit index (CFI), root mean square error of approximation (RMSEA), Tucker–Lewis index (TLI), and adjusted goodness of fit index (AGFI). The results are presented in Figure

Note. N = 250; s.d.—standard deviation; the diagonal values (in bold) present the square roots of

AVE; correlation is statistically significant for *p* < 0.01 (\*\*\*).

useful for the visualization of the structural equations.

and emotional value are five distinct constructs.

#### (a) Functional value as mediator between PESI and GEPI

(b) Social value as mediator between PESI and GEPI


#### (c) Emotional value as mediator between PESI and GEPI

**Figure 1.** SEM models of direct and indirect (mediation) effect; Note. N = 250; significance level for *p* < 0.01 (\*\*\*); significance level for *p* < 0.05 (\*). **Figure 1.** SEM models of direct and indirect (mediation) effect; Note. N = 250; significance level for *p* < 0.01 (\*\*\*); significance level for *p* < 0.05 (\*).

**Table 4.** Results of structural equation modelling. **Unstandar. Standar.**  *t p*  Hypothesis H1, considering the mediation effect of the functional value (FV) on the relation between the pro-environmental self-identity (PESI) and green energy purchase intention (GEPI) must be rejected. There is no significant impact of FV on GEPI (β = 0.012; *p* > 0.05), therefore no mediation effect exists.

**Path Analysis Coefficients Coefficients β Value Value Results**  *B* **SE** Model 1 PESI GEPI 1.152 0.121 0.911 9.553 0.000 \*\*\* H1 is not confirmed PESI FV 0.646 0.069 0.657 9.349 0.000 \*\*\* Social value (SV) is significantly associated with PESI (β = 0.927; *p* < 0.01) and with GEPI (β = 0.644; *p* < 0.01), which supports Hypothesis H2. There is no significant impact of PESI on GEPI, which confirms total mediation effect. It means that the pro-environmental self-identity impacts consumers' green energy purchase intention but only through the social value, i.e., the perceived utility through the connotation with one or more social, economic, cultural, or demographic groups.

FV GEPI 0.016 0.096 0.012 0.165 0.869 Model 2 PESI GEPI 0.400 0.248 0.321 1.611 0.107 H2 is confirmed PESI SV 0.860 0.084 0.927 10.265 0.000 \*\*\* SV GEPI 0.868 0.273 0.644 3.179 0.001 \*\*\* Emotional value (EV) partially mediates the relationship between PESI and GEPI. PESI is significantly associated with GEPI (β = 0.408; *p* < 0.05) but also with EV (β = 0.903; *p* < 0.01), and EV is significantly associated with GEPI (β = 0.564; *p* < 0.01). It means that PESI affects GEPI directly and indirectly through EV which expresses a partial mediation effect and confirms Hypothesis H3.

PESI GEPI 0.516 0.222 0.408 2.325 0.020 \* H3

Note. N = 250; significance level for *p* < 0.01 (\*\*\*); significance level for *p* < 0.05 (\*); SE—standard

Hypothesis H1, considering the mediation effect of the functional value (FV) on the relation between the pro-environmental self-identity (PESI) and green energy purchase intention (GEPI) must be rejected. There is no significant impact of FV on GEPI (β = 0.012;

Social value (SV) is significantly associated with PESI (β = 0.927; *p* < 0.01) and with GEPI (β = 0.644; *p* < 0.01), which supports Hypothesis H2. There is no significant impact of PESI on GEPI, which confirms total mediation effect. It means that the pro-environmental self-identity impacts consumers' green energy purchase intention but only through the social value, i.e., the perceived utility through the connotation with one or more social,

Emotional value (EV) partially mediates the relationship between PESI and GEPI. PESI is significantly associated with GEPI (β = 0.408; *p* < 0.05) but also with EV (β = 0.903; *p* < 0.01), and EV is significantly associated with GEPI (β = 0.564; *p* < 0.01). It means that PESI affects GEPI directly and indirectly through EV which expresses a partial mediation

Considering the control variables (respondents' gender, level of education, place of residence, and age of the house), some differences appeared. We grouped our respondents into two bigger subgroups (with minimum *n* = 60) within a single control variable as follows: males (*n* = 141) and females (*n* = 109); younger people (18–44 years old; *n* = 128) and older people (over 44 years old; *n* = 122); people with higher education (*n* = 118) and people

error; *B*—unstandardized path coefficient.

*p* > 0.05), therefore no mediation effect exists.

economic, cultural, or demographic groups.

effect and confirms Hypothesis H3.

Model 3

Considering the control variables (respondents' gender, level of education, place of residence, and age of the house), some differences appeared. We grouped our respondents into two bigger subgroups (with minimum *n* = 60) within a single control variable as follows: males (*n* = 141) and females (*n* = 109); younger people (18–44 years old; *n* = 128) and older people (over 44 years old; *n* = 122); people with higher education (*n* = 118) and people with primary or secondary education (*n* = 132); people living in the countryside (*n* = 114) and people living in cities (*n* = 136); people who are owners of a house built less than 30 years ago (*n* = 134) and those who own a house built over 30 years ago (*n* = 116). The mediation effect of social value on the relation between pro-environmental self-identity and consumers' green energy purchase intention is:


In the case of the mediation effect of emotional value on the relation between proenvironmental self-identity and consumers' purchase intention of photovoltaic panel installations, the following differences were identified:


In both cases of mediation effects, i.e., impact of social and emotional values on the relation between PESI and GEPI, there were no statistically significant differences in terms of gender.

#### **5. Discussion and Conclusions**

The research results affirm the results of previous studies by Van Der Werff et al. [34], Barbarossa and de Pelsmacker [35] and Mutum et al. [36], who highlighted self-identity as an antecedent of purchase intention for green products. Our study focuses on consumers' intentional decision-making regarding photovoltaic panels in the context of proenvironmental self-identity and theory of consumption values, thus developing the discussion on crucial factors influencing green energy purchase intention, which contributes to sustainable development and it thus desirable in today's world.

Research results showed that the relationship between pro-environmental self-identity (PESI) and green energy purchase intention (GEPI) is mediated by social, and partially emotional, values. Other studies also confirmed that PESI mediates the relationship between values and consumer behaviour [34,50]. As Confente et al. [44] mentioned, pro-environmental self-identity is an important component of an individual's value-construction process.

The importance of social value was confirmed by Mutum et al. [36], who stated that social value mediated the relationship between PESI and green purchase behaviour (GPB). The research results also confirmed those of previous studies suggesting that social influence and environment respect affect consumers' green purchase behaviours [54,60,105]. PESI supports the positive associations of photovoltaic panels with eco-friendly communities and consequently in this context supports consumers' purchase behaviours. Consumers' personal self-obligation to protect the environment is reinforced by a need for social

acceptance, which affects purchase intention. The relation between personal moral norms reinforced by public approval and intention of green purchase was also underlined by Chowdhury et al. [52] and Attaran and Celik [53]. External, expert opinions are important, especially for older people without experience with modern energy solutions who feel responsible for the environment.

Our research results also show that emotional value partially mediates the relation between pro-environmental self-identity and green energy purchase intention. This was consistent with the findings of Yoo et al. [82], Lin and Huang [64], Rex and Baumann [83] and Hartmann et al. [84] concerning a positive relationship between emotional value and purchasing decisions, as well as with the studies of Confente et al. [44] and Mutum et al. [36] regarding the mediating effect of values. The importance of the emotional value of green products is observed especially in the case of the most engaged consumers that expect to change something like in the case of older people having a house for a long time. Eco-friendly behaviours related to the installation of photovoltaic panels are driven by consumers' feelings of being green energy creators. The same was proved by Sparks and Shepherd [46] for consumers' intention to buy eco-friendly products, which were affected by individuals' perceptions of being eco, as well as by Mannetti et al. [106], who found that recycling behaviours are driven by consumers' feelings of being recyclers.

The research results did not find support for the mediation effect of functional value. Similar conclusions were formulated by Mutum et al. [36], who studied the relationship between PESI and green purchase behaviour (GPB). This was consistent also with the results of Ecker at al. [107] who stated that consumers chose green energy source especially for independence, autonomy, self-sufficiency, supply security, and control, and not functional value. However, the opposite conclusions were formulated by Confente et al. [44] for bioplastic products.

From a theoretical standpoint, the research results contribute to the pro-environmental self-identity concept and its relation with consumption value theory as well as providing a better understanding of consumers' purchase intentions towards photovoltaic panel installation and consequently the development possibilities in the green energy sector in Europe, a sector that is under-researched. This paper can be a starting point for a scientific discussion, but also can be used by managers working in the sector of green energy.

Considering managerial implications, this study's results can be useful for marketers, ecological organisations, policymakers, and public institutions in developing their policies, strategies, and marketing communication campaigns to encourage photovoltaic panel installation. The approach to understand consumers' green behaviours based on emotional and symbolic values has significant implications. Representatives of public institutions should understand that for citizens, social values are more important than functional benefits, and thus offer legal solutions that will allow the development of green local communities. Such communities can jointly support the development of green energy. The results of this study can have consequences for companies from the green energy sector. They can be used during the creation of marketing communication strategies, especially during the selection of communication tools and the formulation of advertising messages, taking into account the differences between groups of consumers. The most relevant social and emotional values should be communicated by different communication channels used by photovoltaic panel producers. The results prove that social value importantly mediates the relationship between PESI and GEPI. Consequently, marketers should present the opinions of experts and influencers to affect consumer behaviour by increasing trust in green energy and minimizing the perceived risks of photovoltaic panel installation. They can also refer to consumers' sense of social responsibility. Taking into account that emotional value was also found to partially mediate the relationship between PESI and GEPI, marketing communication campaigns of public institutions and ecological organisations should include emotional appeals to encourage consumers to adopt green energy and appealing to their sense of responsibility for the environment by showing the negative effects of the long-term use of conventional energy.

These conclusions were formulated taking into account the sample's limitations. The study was limited to Polish consumers, the sample was relatively small, and we examined consumers' green energy purchase behaviour limited only to one source of green energy, i.e., photovoltaic panels. Therefore, this research can encourage further reflection on the impact of PESI and consumption values on green energy purchase intention. It seems interesting to undertake international studies enabling comparison between the attitudes of Polish consumers with consumers from other countries or expanding them to other sources of green energy and environmental knowledge as a mediator representing other consumer characteristics.

**Author Contributions:** General concept, M.G.-K. and A.Z.-B.; theory, M.G.-K. and S.F.; methodology, M.G.-K. and A.Z.-B.; validation and formal analysis, A.Z.-B.; investigation, M.G.-K.; data curation, A.Z.-B.; preparation of the draft version, M.G.-K., A.Z.-B. and S.F.; preparation of the draft version, M.G.-K., A.Z.-B. and S.F. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Appendix A. Key Constructs and Items**

*Pro-Environmental Self-Identity (PESI)* [35,48,50]:


*Functional Value (FV)* [4,63,66]:


*Social Value (SV)* [63,66,83]:


*Emotional Value (EV)* [83,97,98]:


#### **References**


#### *Article* **Restructuring of the Coal Mining Industry and the Challenges of Energy Transition in Poland (1990–2020)**

**Jarosław Kaczmarek \*, Konrad Kolegowicz and Wojciech Szymla**

Department of Economics and Organization of Enterprises, Cracow University of Economics, Rakowicka St. 27, 31-510 Cracow, Poland; kolegowk@uek.krakow.pl (K.K.); szymlaw@uek.krakow.pl (W.S.) **\*** Correspondence: kaczmarj@uek.krakow.pl

**Abstract:** The European Union's climate policy and the energy transition associated with it force individual countries, their economies and their industrial sectors to carry out thorough changes, often of a deep, high-cost and restructuring nature. The aim of the article is to provide a multidimensional assessment of the forms and effects of the restructuring of coal mining companies in Poland in light of the current energy transition process. The research problem is encapsulated within the following two interdependent questions: Has the restructuring process allowed the coal mining industry to achieve sufficient efficiency to sustainably compete in the open market, and to what extent, if at all, have the objectives of restructuring been achieved from the perspective of changes in the energy mix? The research covers all coal mining companies included in the official statistics. It adopts a long-term perspective (1990–2020), dating from the beginning of the systemic transformation in Poland. The research involved the use of multivariate financial analysis methods, including the logit model for predicting the degree of financial threat, as well as taxonomic methods for assessing the dissimilarity of structures and their concentration. The general conclusion of the research is that there has been a lack of consistency (follow-up) between the forms and effects of restructuring in coal mining companies in Poland on the one hand and changes in the composition of the country's energy mix as a result of the energy transition on the other. In particular, this means that such restructuring, being neither effective nor efficient, has failed to accelerate change in the energy mix.

**Keywords:** restructuring; energy policy; hard coal mining; energy transition

#### **1. Introduction**

The countries of the European Union are characterised by different structures of electricity production, differing availability of fossil fuels and different states of advancement in the use of renewable energy sources [1,2]. Poland is an example of a country with significant coal resources and a still high degree of dependence in terms of electricity production on hard coal and lignite. Therefore, one of the biggest challenges in the ongoing energy transition in Poland is the restructuring of the hard coal mining sector. Dealing with this challenge requires an extensive, long-term analysis of the directions and effects of the changes already made in this sector. Objective diagnosis of former achievements may become the basis for building policies and programs for simultaneous restructuring of the coal mining and energy sectors.

In the article, the research problem focuses on identifying consistency (follow-up) between the forms and effects of restructuring of coal mining companies in Poland on the one hand and changes in the composition of the country's energy mix on the other. This problem, in turn, provided the main platform for the research goals, namely a multidimensional assessment of the forms and effects of such restructuring in the context of the ongoing energy transition. This main goal is made more specific by the five sub-goals.

Therefore, the key questions posed in the research are as follows: has restructuring enabled the hard coal mining industry to reach a sufficient level of efficiency to compete

**Citation:** Kaczmarek, J.; Kolegowicz, K.; Szymla, W. Restructuring of the Coal Mining Industry and the Challenges of Energy Transition in Poland (1990–2020). *Energies* **2022**, *15*, 3518. https://doi.org/10.3390/ en15103518

Academic Editor: Ben McLellan

Received: 10 April 2022 Accepted: 9 May 2022 Published: 11 May 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

on a sustainable basis on the open market, and to what extent, if at all, were the assumed restructuring objectives achieved in the context of changes in the energy mix? These questions, in turn, determine whether the content of the main research hypothesis is verified. The main hypothesis was supported by three partial hypotheses (see Section 1.5).

The research conducted in the present article led to the general conclusion that there was a lack of coherence (follow-up) between the forms and effects of the restructuring of coal mining companies in Poland on the one hand and changes in the energy mix as a result of energy transition on the other. More specifically, this means that such restructuring, being inefficient did not accelerate change in the energy mix. Coal mining companies have not achieved sustainable profitability and competitiveness on the open coal market. Restructuring did not bring about any significant changes in the structure of employment, assets, capital expenditure or sales, nor did it trigger major technical and technological progress. The effects of restructuring revealed a number of important temporal caesura in this process, thanks to which it can be divided into different periods. The changes observed in the energy mix occurred as a result of having to cover higher energy demand with the increased use of non-carbon energy sources.

The methodology and results of the research as well as a discussion of its findings provide the structure of the article. In the first step, the results of the literature and a previously known research review are presented. The authors also define the relationships between the following triad of terms: transformation, structural changes and restructuring, and the research problem is embedded in an international context (restructuring of coal mining in EU countries), after which the research goals, hypothesis and methods forming the research framework are presented. The research results themselves have been arranged in order, beginning with an analysis of the degree of implementation of the restructuring objectives, followed by a cause-and-effect analysis of the results, concluding with an analysis of changes in the structures and analysed relationships. In the next step, the results were discussed in depth, which provided further confirmation of the research hypotheses. The final conclusions are presented in the summary.

The article is part of a series of publications on the restructuring and development of enterprises and industries with particular emphasis on the fuel and energy sector. The current research is a diagnosis of the state of dependence (follow-up/consistency) of the effects of coal mining restructuring and changes in the energy sector. The next stage will include a comparative analysis of the effectiveness limits (relation of effects and inputs) of all energy generation sources. Combining the current diagnosis with such an efficiency analysis (non-parametric approach) will allow for the formulation of the main elements of the new energy transition policy.

#### *1.1. Restructuring and Structural Changes*

In the present article, restructuring is treated as a different phenomenon from structural change. The year 1990 marked the beginning of the implementation in Poland of a set of reforms aimed at systemic transformation, including economic transformation [3] (pp. 102–103). It is commonly believed that the transformation positively contributed to economic development [4–6]. It was supported by structural changes, the factors and growth-related dependencies of which created a paradigm in economics [7] (pp. 10–18). These changes, induced by the transformation, gradually turned into autonomous processes [8] (pp. 45–54), [9,10].

In the above context, transformation involves the reconstruction of the structure of the economy, and restructuring, as an integral element the former, has become one of the levels of transformation in Poland [11] (p. 153). Generally speaking, restructuring concerns changes taking place in economic structures such as enterprises (a micro approach). As regards internal development opportunities, these fundamental, external factors have destabilised enterprises [12] (p. 114). To regain their equilibrium, enterprises have had to adjust [13] by adopting restructuring strategies [14] (pp. 260–269), [15] (pp. 391–397). Their goal is to eliminate discrepancies between changes in the environment and the path of an enterprise's development. However, not only must they adapt to such changes, but they must also anticipate them [16] (pp. 50–62), [17] (pp. 81–92), [18] (pp. 301–308).

In its essence, restructuring is a multi-faceted and complex process [19–21]. The effects of restructuring are not immediately felt, but rather are spread out overtime [22] (pp. 10–23), [23]. A distinction should be made between inputs that are primary and effects that are secondary [24]. The effects of restructuring are not always positive either [25].

As was noted above, the aim of a company's restructuring is to adapt to a changing environment. The future state or states of this environment are undefined and cannot be predicted with any certainty. As a consequence, an enterprise operates with an element of risk, and the sublimation of such a critical state constitutes a crisis for an enterprise [26,27] (pp. 42–48). Enterprises are exposed and vulnerable to a diverse range of risks, and this is especially true of Polish enterprises [28] (pp. 10–31). An open crisis usually results from a long-lasting "smouldering" crisis, i.e., from the accumulation of causes that are not neutralised and which assume a structural, systemic form. The symptoms of a company in crisis, which themselves constitute an interplay of various factors, are reflected in its performance, and ultimately in its results and financial condition [29]. At the present time, these are detected using early warning systems [30], which make use of discriminant analysis methods [31], in particular logit models [32] (pp. 60–74). Such a model was estimated for the present research.

The purpose of assessing structural changes in this article was to quantify not only the scale of changes that take place within particular structural components, but also to determine the degree of structural transformation over time [33]. Economic structures comprise sets of elements in the economic process together with the specific relationships that exist between them [34]. These can be mapped out based on the convergence between the numbers corresponding to these elements [35]. The main goal of measuring structural changes is to determine the absolute and relative differences between the shares of individual structure components in the whole [36] and the total impact of such changes at moments in time on the same or many different objects [37]. The task of analysing structural changes is to show the course and size of structural transformations as well as the dynamics of these changes, and also to identify the structural components that influence these changes [38]. The intensity of structural change was further assessed by determining its course, i.e., by measuring the factor of concentration (absolute concentration).

#### *1.2. Restructuring of Coal Mining in Europe*

Alongside the intensive mining of hard coal, in the years after World War II, mining countries such as Poland also embarked on a period of industrialisation and intensive development. In 1990, England, France, Germany, Belgium and Poland accounted for 90% of all coal extracted in Europe, with Poland producing 56% of the total. However, by 2020, its share had reached 96%. The turning point in the mining industry came in the second half of the 20th century, when oil rapidly overtook coal in economic importance. From that time, restructuring of the coal mining industry in Europe began in haste. Each country has adopted its own different approach based on different tools, the use of which was, and still is, conditioned by many internal and external factors [39].

In France, from 1960 onwards, the government began to pursue a policy aimed at increasing the share of nuclear energy in electricity output, thereby reducing the demand for domestic thermal coal. This change had the direct effect of reducing employment. Despite the fact that the mining industry in France had been owned by the state since 1946, social and regional factors prevented the French government from completely shutting down the mining industry, which only occurred in 2004 (finally two years later in Decision 3632/93/ECSC of the European Coal and Steel Community).

In Belgium, coal mining had been privately owned, but in 1967 the government nationalised the coal industry and embarked on its restructuring. The closure of the mines caused serious social tensions, but production costs were very high, and Belgian coal was unable to compete with imported coal. In addition, there was a high dependence on foreign

labour. The restructuring plan provided mainly for the reduction of employment, and eventually over 10,000 employees decided to leave and rely on redundancy schemes. By 1989, employment had dropped to 6000 people, and the second stage of restructuring came to an end with the closure of the country's last mine in 1992.

In the case of England, coal was replaced by oil as the main source of energy back in the 1960s, while nuclear energy and natural gas began to develop rapidly [40]. The end of the oil crisis (1979) and Margaret Thatcher's ascension to office ushered in the era of what came to be known as "state business" [41]. The country's least profitable mines began to be closed. The aim of restructuring was to make mining more competitive through its mechanisation, cut employment and introduce a business model specifically suited to private enterprises [42]. The end result was the complete privatisation of the mining industry, which had been concluded by 1994. In 2019, the decision was taken to close the country's last colliery, although this did not signal a definitive end to mining in England [43]. In 2020, the government approved the construction of the first new deep-sea coal mine in 30 years [44].

The German coal industry entered a period of crisis in the early 1960s [45]. The federal and regional authorities (the Ruhr region) pursued a consistent policy aimed at increasing productivity. In 1968, a comprehensive restructuring program was launched to adjust the volume of output to shrinking needs and introduce extensive social protection programmes [46]. In the early 1990s, pressure from the European Commission to cut enormous public aid allocated to mining together with forecasts of a decline in demand forced more radical restructuring. The last mines were closed by 2018 [47]. The restructuring path adopted by Germany, which was almost painless, was extended over time and guaranteed social peace, and as a consequence differed from the more drastic steps taken to liquidate the mining industry in Great Britain, which suffered widespread social protests as a result.

In Spain, following the gradual shrinking of the coal mining industry in 1950–1975, the industry enjoyed a period of rapid growth. Government endeavoured to regulate production volume, coal prices and energy sales. The Third National Energy Plan, implemented in 1984, provided for a further increase in coal mining and government subsidies. After Spain joined the EU, pressure increased to reduce subsidies to the coal industry. A plan to cut employment and mining activity was implemented in 1985 and continued into the 1990s, also as a consequence of pressure from the environmental lobby against mining from opencast mines. As a result, by 2020, only one small mine was in operation in Spain.

At the present time, Germany (with a share of 33.3%), France (8.8%), Spain (4.5%) and the United Kingdom (5.1%) are the leading coal importers in Europe and are beginning increasingly to appreciate the role of this raw material in shaping their energy security.

The restructuring of the hard coal mining industry in Poland has been ongoing since 1989, that is, from the beginning of the systemic transformation and the transition to a market economy [48] (p. 242). Currently, Poland faces the challenge of creating a longterm energy policy that will ensure a balance between the security of its energy supplies, the efficiency of economic processes and an appropriate standard of environmental protection [49]. According to the plan, hard coal mining in Poland will come to an end by 2049 (restructuring programs are discussed together with an assessment of the achievement of the objectives in Section 3.1).

#### *1.3. Poland's Energy Transition and Its Implications*

The Polish energy sector is primarily based on the combustion of hard coal and lignite. According to Eurostat databases [50] (all data concerning the structure of electricity generation in Poland and EU countries comes from Eurostat databases) regarding energy in 1990, 96% of electricity and derived heat was produced using coal (56% from hard coal and 40% from lignite). In 2020, this fuel source still accounted for 68% of the country's electricity supply (44% from hard coal and 24% from lignite), which represents a decrease of 28 pp..b.

Electricity generation was completely different in form in the "Old Union" (EU15). In these countries, electricity obtained from coal accounted for only 35% of total output in 1990, and less than 8% in 2020. It should be emphasised, however, that in the case of the EU15, restructuring of the mining industry was completed earlier or is currently at an advanced stage (see Section 1.3). Of course, we need to be aware that these countries have diversified access to fossil fuels, including coal. As a result, in the years 1990–2020, these countries underwent more profound changes than Poland in terms of their energy mix. During this period, although the share of nuclear energy decreased from 33% to 25%, the share of renewable energy sources increased from 14% to 42%, and the share of natural gas rose from 7% to 22%. Therefore, it can be argued that, compared to the EU15 countries, Poland is an example of a country that has failed to adapt to the requirements of energy transition [51] (p. 10).

In this context, however, it is worth looking at the situation in those countries that were admitted to the European Union alongside Poland as part of its fifth enlargement (EU11). In the case of these countries, in 1990, coal accounted for 55% of total electricity generated, and in 2020, its share still remained at 39%. Thus, progress in reducing the importance of coal (by 16 pp.) in the EU11 countries has been slower than in Poland. It should also be noted that in 1990, the share of nuclear energy in the energy mix of many of these countries was significant, amounting on average to 17.5%, and by 2020 this share was as high as 22%. In turn, the share of energy from natural gas increased in this period from 10% to 12%, and from renewable energy it had risen from less than 7% to reach 23%.

The above observations lead to the conclusion that Poland does not differ significantly from other EU11 countries when it comes to the pace of change in its energy mix during this period [52]. Of course, this does not constitute grounds for a generally positive assessment in light of the delays in transformation observed in this group of countries. The reasons for this delay can partly be found in the insufficient financial resources allocated to the transformation, but also, as in the case of Poland, in the considerable influence exerted by the social and political environment [53].

Despite the fact that coal continues to account for a high share of Poland's energy mix, for the most part it is the process of adapting to EU requirements that had, and still has, a decisive influence on the country's energy policy, and thus on a gradual reduction in the share of coal [54]. The foundations of energy sector reform and the energy transition in Poland in general are, primarily, the following:


Along with these arrangements, subsequent phases in the European Emissions Trading System (EU ETS) introduced in 2003 by Directive 2003/87/EC [65] have also been implemented, which in the following years would have a significant impact on the profitability of energy production from conventional sources [66].

Returning to our assessment of changes in the structure of electricity generation sources in Poland, it should be emphasised that although they have been significant in the case of coal (a decrease of 28 pp, i.e., by 29.1%), in absolute values, in the case of actual coal consumption, these changes are no longer so great. According to Eurostat data, in the years 1990–2020, the consumption of coal in the power industry decreased by only 9%. This means that the changes in the energy mix were achieved by covering the increase in energy demand (rising by 16%) through the greater use of non-coal energy sources (8.5 times). This was the case primarily with natural gas (a 16-fold increase) and renewable energy (a 15-fold increase). The use of gas was characterised by large fluctuations connected with the dates when large and medium-sized power plants and combined heat and power plants were commissioned. Gas consumption increased at its fastest rate in the years 1998–2005 (average annual growth of 49%) and in 2015–2020 (average annual growth of 22%). Renewable sources developed much later (apart from the existing large, commercial hydropower plants), with wind playing the most important role. The beginnings of the wind energy sector date back to 1997, but it only began to develop on a larger scale from 2001 onwards (average annual growth of 50%). The most rapidly developing renewable source is photovoltaics, the origins of which in Poland date back to 2011 (average annual growth of 182%), and the annual maximum occurred in 2015 (722%).

To sum up, the position enjoyed by coal as the strategic anchor of Poland's energy security has undoubtedly been unshakeable for decades [67]. This was very much confirmed by the findings of successive documents setting out Poland's energy policy (PEP) over the next decades [68–71]. These policies (adopted in 1990, 1995, 2000 and 2005, respectively) predicted that demand for hard coal and lignite for the energy sector would remain at a similar level or even increase. Moreover, some of these forecasts (PEP 2020, PEP 2025) were based directly on findings from previously approved mining reform programs. This leads to the thesis that the country's energy policy is dependent on the assumptions of reform of only one sector of the economy, i.e., mining. Only the energy policy adopted in 2009 (PEP 2030) [72] anticipated a significant and steady decline in the use of hard coal and lignite in electricity production. According to forecasts, the consumption of hard coal in 2020 would be nearly 30% lower than the 2006 base, and lignite consumption would decline by 26%. Therefore, such a significant reduction in coal consumption should have resulted in a reduction in output in domestic mines and thus have constituted a strong impulse for their restructuring.

#### *1.4. Identified Research Deficits*

The problem of the energy transition has been at the forefront of the European Union's policy in the recent years. The adaptation of economies and industry to changing climate goals is the leitmotif of many scientific and expert studies of a cross-cutting nature. They concern the analysis of changes in all EU countries or groups thereof [73–75], or locally concern only individual countries, including, for example, Germany [46], Spain [76] and Denmark [77]. The object of similar research was and still is also Poland. So far, detailed studies have focused on the determinants of the energy transition and its pace [78], its costs [79] or its impact on individual industry sectors [80].

The fact that Poland is a traditionally hard coal mining country forces research on the restructuring process of the entire coal mining industry and individual mining companies.

Previous studies have strongly touched upon the theoretical aspect of the mining restructuring process and the creation of restructuring programs [81]. The research was limited only to a review (qualitative description) of newly created and already implemented programs [82]. Sometimes, they only reviewed the objectives, legitimacy and complementarity of the tasks performed [83]. To some extent, the research also concerned the assessment of the degree of implementation of mining restructuring programs. However, it was carried out only from the perspective of assessing the assumed effects [84], sometimes also in relation to political conditions [85]. The research conducted so far has attempted to periodise the restructuring process, but its basis was only the periods derived from

legal acts and related to subsequent programs. Such a division did not take into account endogenous factors and interdependencies between them. Many times, the authors of publications focused on the basic production, technical, economic [86] and financial [87] indicators and their absolute change [88]. However, they have not developed and used any multidimensional, discriminatory models. This is a strong deficit, especially regarding the high rank of these models (especially logit ones) and the intense growth of interest in their application. Moreover, no studies concerned changes in economic structures and the assessment of their impact on the measures as dependent variables [89]. The analysis of mining enterprises [90] so far has focused mainly on the assessment of changes in employment [91,92] or changes in sales and production [93]. These analyses, however, were fragmentary and did not take into account the complexity of the assessment of the complex restructuring process. A significant part of the publication focuses on the assessment of the technical conditions of extraction [94] and the assessment of changes in production parameters. Some authors have turned their attention to the designation of possible restructuring strategies [95,96] and assessed management actions [97]. However, their studies do not contain an analysis of the economic and financial effects of the restructuring measures taken. In the past, attempts have also been made to assess financial ratios [98], but only in absolute terms. Furthermore, there was no attempt to perform a comprehensive analysis and evaluation of changes using a synthetic model. Sojda [99] made such an attempt at statistical analysis, but in an isolated approach, concerning only a few, selected financial indicators. However, he did not analyse the cause-and-effect implications.

Each determinant as a category, relation or measure of the assessment of economic results and financial condition, being a target in restructuring programs, can be the basis for calculating the change in the structure that characterises it. Few studies, however, attempted such a detailed analysis, despite the fact that structural research is crucial for most scientific disciplines [100,101]. Usually, such research concerns only change in the structure of employment [102,103], investments, assets or sold production. However, there is no research on the directions of transformations, assessment of the dynamics (intensity) of structural changes and indication of those components of structures that intensify or anticipate the changes to the greatest extent. This is a significant deficit because restructuring is closely related to structural changes [104] (pp. 11–12), which restore the ability to develop [105]. This development concerns not only the potential (size), but especially the structures [106]. These are its components and determinants [107] (p. 19), which forces the necessity to fill this deficiency in the research on the complex structure of coal mining enterprises.

There is a particularly clear deficit in research at the interface between mining and energy transition. Although the issue of EU climate policy appears in the above-mentioned studies (especially in those published after 2008—the 3 × 20 climate and energy package), it usually constitutes only a research background. So far, the authors have not addressed the problem that can be described by the question: are the pace and effects of the restructuring of the coal mining sector sufficient from the point of view of the desired pace of energy transition? This problem is the focus of the research in this article.

The review of the literature revealed a number of significant research gaps at the theorycognitive, methodological and empirical levels. In the first case, no parallel approach has been developed for assessing the course and effects of the restructuring of the coal mining industry and changes in the energy sector from the perspective of its transformation, and, as a consequence, changes in the energy mix. At the methodological level, there is clearly lacking a method for measuring structural changes in coal mining, as well as their direction and variability or their correlation with operational effectiveness. Moreover, until now, researchers have not used aggregated measures to assess a company's financial condition, which are the most specific barometers of its operational health. This concerns in particular indicators predicting the financial risk of a going concern. Nor has any comprehensive, multi-layered method been developed for assessing the effects of restructuring, based on the relationship between causes (operating economics) and effects (financial results), which

is important from the point of view of assessing the mechanism by which these results are achieved. Meanwhile, at the empirical level, no research results have been published that cover a longer timeframe, i.e., the period from the beginning of the economic transformation in Poland (1990) up to the present day. Moreover, most of the available research results were obtained with the use of a research sample, which limits the formulation of general and universal conclusions regarding the coal mining sector as a whole.

#### *1.5. Research Problem, Goals and Properties of Research*

The research problem that emerges from the above-identified deficits is the consistency (follow-up) between the course and effects of the restructuring of coal mining companies in Poland and changes in the composition of the country's energy mix. It shows the main goal of the research, which is to provide a multidimensional assessment of the forms and effects of therestructuring of coal mining companies in the context of the ongoing energy transition. The main goal was achieved through the implementation of the following sub-goals:


The research problem and research objectives are sublimated within the main hypothesis: (H) The effective and efficient restructuring of coal mining companies results in accelerated changes in the energy mix. This in turn gives rise to the following questions: has the restructuring carried out so far ensured a level of efficiency sufficient for the hard coal mining industry to compete on sustainable terms on the open market, and to what extent, if at all, have the objectives of restructuring been achieved in the context of changes in the energy mix?

The main hypothesis was supported by three partial hypotheses resulting from the partial goals and the "atomisation" of the research problem:

**H1.** *The restructuring of coal mining companies resulted in the achievement of the primary goal of sustainable profitability and increased productivity, with labour as the main factor*;

**H2.** *Hard coal restructuring has brought about significant changes in the structure of employment, assets, capital expenditure and sales*;

**H3.** *The effects of restructuring coal mining companies based on a multidimensional approach are determined by time intervals characterised by elements of homogeneity (time series periodisation)*.

The research covered all the enterprises included in the "Mining and coal mining" section of the PKD (PKD—Polish Classification of Activities). This constitutes an exhaustive compilation of all enterprises included in the official statistics. These are long-term studies and cover the period 1990–2020. These two factors provide a firm basis for formulating general conclusions and assessments that extend beyond the specific problem of the research sample. They also make it possible to identify real, and, as a consequence, long-term, trends and changes in structures as well as interdependencies (correlations) and regression relationships.

The research framework included two initial planes of inquiry, supported by appropriate methodological pillars. The first involved a multidimensional assessment of restructuring based on the features of a certain economic model. This model reveals the mechanism used by coal mining companies to achieve results, understood as a combination of causes (economics of operation) and effects (financial results). The second level of research provides insights into the intensity of changes in the structures of coal mining companies and their interdependence with changes in operating efficiency. The resulting third level of research consisted in assessing the effects and forms of restructuring of coal mining companies according to the degree of consistency with the effects of implementing the goals of energy transition examined in the two previous planes.

For the needs of the research goals, hypotheses and framework, numerous research methods were used, including versions of multivariate financial analysis, together with an estimated logit model for predicting the degree of financial risk to a going concern (a universal barometer of an enterprise's financial condition ensuring a dynamic measurement). In turn, to study changes in structures, the authors used taxonomic methods to assess the structural dissimilarities (changes in intensity) and their concentration. The occurrence of dependencies in terms of the economic effects of coal mining restructuring, changes in structures, efficiency and transition of the energy mix was investigated by means of statistical correlation and regression measures (see the Materials and Methods section).

The added value of the research lies in the following: (1) its uniqueness in terms of its subject (covering all the enterprises in the sector) as well as time (it is a long-term study, dating from the beginning of the economic transformation); (2) its use of a wide range of research methods (including logistic regression and structure taxonomy) and multi-layered analyses; (3) the fact that it assesses the coherence (follow-up) between the forms and effects of the restructuring of coal mining companies with changes in the energy mix resulting from energy transition. It is also important to emphasise the universality of both the methods used and in particular the structure of the RS—thanks to which it can be used in an international context (in coal-mining countries)—and thus also the universal applicability of the research itself and the comparability of the obtained results, something which had not been achieved previously.

#### **2. Materials and Methods**

The research presented in the article covered all enterprises included in the PKD classification section 10/5—"Mining and coal mining" (PKD—Polish Classification of Activities; PKD section 10 up until 2006 and PKD section 5 from 2007—a change in the classification). These are, for the most part, large, multi-plant enterprises. They constitute a full set of the enterprises included in the official statistics. The research was a long-term project, launched back in 1990 (the beginning of the economic transformation in Poland) and ending in 2020. As a consequence, they cover a period of 31 years, during which coal mining companies underwent deep and extensive restructuring. Thanks to the availability of comparable figures and information, the structural changes were assessed during the period 1992–2020 (for fixed assets from 2004).

As regards the ordering of the numerical data, it should be pointed out that the PKD section entitled "Mining and coal mining" classifies enterprises into two groups, i.e., hard coal mining and lignite mining. The former group occupies a dominant position in terms of the number of people it employs (95.9%), total assets (96.6%), sales revenues (96.7%) and net financial result (99.5%). This is due to the fact that lignite mines are a branch of energy companies (power plants), and only one small mine (operating to satisfy heating needs) functions as an independent enterprise. Generally speaking, this means that coal mining companies determine the results of the entire "Mining and coal extraction" section.

To provide a synthetic assessment of the financial condition of restructured coal mining enterprises, the author made use of a proprietary logit model [108] (Table 1), which was developed in response to the shortcomings of hitherto available models, i.e., their historicity (obsolescence), limited number of training sets and overestimated predictions, limitations in their application (dynamic measurements) and traditional estimation techniques (pairwise comparability only) [109] (pp. 31–42), [110]. The research did not apply any foreign models which had a suboptimal structure and which were inadequate with regards to the operating conditions of enterprises in Poland [111] (pp. 129–172).

The logistic regression models (logit models) are classic tools for predicting the degree of financial threat [112]. However, when compared to newer-generation methods (such as neural networks or random forests), they are more transparent and their results are easier to interpret and compare. In addition, those models in many cases achieve a comparable predictive ability [113]. Other advantages of the logistic regression model are the lack of assumptions on the probabilistic nature of explanatory variables and a user-friendly interpretation of the estimated model parameters.

The model was estimated on the basis of a training data set consisting of 13,047 active production companies and 1377 failed entities. These were all production enterprises covered by the official statistics in Poland (companies with more than 9 employees). The total number of observation objects for the years 2007–2012 amounted to 130,204. The companies were matched using the case-control technique [114] (pp. 145–162). The explanatory variables (out of a total of 29 financial indicators) were selected by means of step methods and the best subset method [115]. The criterion for assessing the fit of the model to the data was the AIC (Akaike Information Criterion) measure. The logistic regression model used was Firth [116,117]. This model involves changing the form of the standard likelihood function *L (θ)* to the form *L* ∗ (*θ*) = *L*(*θ*)|*I<sup>θ</sup>* | 1/2, where *<sup>I</sup><sup>θ</sup>* is the information matrix and *θ* is the vector of structural parameters [118]. This is the penalised likelihood function. Firth's logistic regression model has a Bayesian counterpart [119]. It is equivalent to the classical model with Jeffreys non-informative prior distribution superimposed on the parameters [120,121]. As a result of this modification, the estimation of the parameters in this model is almost unbiased (a particular improvement is observed in small samples), while the confidence intervals are characterised by better probabilistic properties. The predictive capacity of the model was measured using the sensitivity, specificity and the AUC (Area Under Curve) measure as the area under the Receiver Operating Characteristic (ROC) curve. The value of AUC = 0.914 confirms that the estimated model has very high predictive capabilities.

The measure derived from the model (FTD) expresses the degree of financial risk to a going concern and the danger of bankruptcy (annual advance, calibration against the bankruptcy rate, value given per 10,000 companies). It can be treated as a barometer of a company's financial condition [122,123]—the more favourable the situation, the lower the value of the FTD measure. In addition, it has two specific properties: it allows for a dynamic analysis and relativises its result in relation to the risk of bankruptcy.


**Table 1.** Estimated prediction logit model of the financial threat degree.

Source: [124] (pp. 117–131).

To analyse the interdependence of time series (as well as regression interdependencies), a critical significance level of α = 0.05 was adopted, compared with a *p*-value test probability. A value lower than the critical level of significance means that one can proceed ad hoc, as if the null hypothesis that no correlation exists has been rejected. The degree of correlation as a numerical value is given in the text only when the condition of *p*-value < α is met. Detailed results of the regression analysis are provided in Appendix C. The correlation was measured using the Pearson r coefficient (degrees of correlation: <0.1 slight; 0.1–0.3 weak; 0.3–0.5 average; 0.5–0.7 strong; 0.7–0.9 very strong; >0.9 an almost full correlation). The measure of variability applied was the standard deviation and the coefficient of variation (the ratio of the standard deviation to the mean).

A linear regression model was adopted to analytically illustrate the relationship between an explained (dependent) variable and an explanatory (independent) variable and to determine the nature of this relationship [125]. The choice of a linear regression model was based on its simplicity, expressed by the easy interpretability of the results. Nevertheless, before choosing the linear model, an analysis of the distribution of observations was carried out using scatter diagrams. The point clouds took on a pattern indicating their distribution with respect to a straight line. Moreover, the analysis of the variables did not reveal the existence of outliers that would interfere with the determination of this model. The fit for the regression equation was established using the determination coefficient R2 (fit levels: 0.0–0.5 unsatisfactory, 0.5–0.6 weak, 0.6–0.8 satisfactory, 0.8–0.9 good 0.9–1.0 very good).

In their detailed assessment of the potential and financial results of coal mining enterprises, the authors resorted to numerous indicators of financial analysis covering the following areas: potential, results, debt, financial liquidity, profitability, effectiveness, and efficiency. Because these are commonly known and have been widely described in the available literature on the subject, they are not explained in detail in the present article [126–128] (a general description is included in the Appendix A).

To assess structural changes, economic structures were used that enabled an evaluation of the transformations resulting from mining restructuring processes, i.e., regarding employment, net fixed assets, investment outlays, sales and the structure of electricity generation as an interdependent element. The intensity of change (NPS), also referred to as the degree of structural dissimilarity, was measured by means of a taxonomic approach. This made it possible to determine the absolute and relative differences between the shares of individual structural components and the total impact of these changes in the same or many different structural elements at comparable moments in time [129,130]. Using the taxonomy of structures, it is possible to determine, more unambiguously and comprehensively than by means of a traditional over time comparison of individual structural indicators, the degree of transformation of the structures under study and to carry out a periodisation of their development with regards to the criterion of the degree of intensity (dynamics) of structural changes [131]. Thus, taxonomy makes it possible to determine the directions of structural change [132], its dynamics (intensity) and its determinants (also as anticipation). In addition, it allows the evaluation of the similarity of structures to each other and over time.

$$\text{NPS} = 1 - \sum\_{i=1}^{n} \min(p\_{i0}, p\_{i1}) \tag{1}$$

where:

*NPS*—intensity of structural change (dissimilarity),

*min*—minimum value of the components of the structure,

*pi*0—share of the i-th component of the structure in time t0,

*pi*1—share of the i-th component of the structure in time t1.

The NPS measure assumes values in the range of h0, 1i where a value equal to zero means no structural changes while the value 1 denotes a complete change in the structure [133]. The NPS parameter makes it possible to determine the intensity of structural changes in relation to past periods, but not to determine the direction of change. The latter is gauged by means of the concentration measure, understood as the degree of deconcentration or consolidation of a given phenomenon in one component of the structure. For this purpose, the degree of structural concentration was measured by means of the Herfindahl-Hirschman index (HHI). The latter is calculated as the sum of the squared shares of all the components of the structure:

$$\text{HHI} = \sum\_{i=1}^{N} x^2 \tag{2}$$

where:

*x*—component share,

*N*—number of components.

The HHI index lies within the range of h0, 1i. The higher the HHI value, the greater the concentration. It is calculated in such a way that the value of the components has a greater impact on the value of the HHI than their number [134].

#### **3. Results**

#### *3.1. Extent to Which the Goals of Coal Mining Restructuring Programs in Poland Have Been Achieved*

Coal mining companies in Poland were already being broken up and then commercialised in the first years of the systemic transformation. This strategy was expected to create competition on the market and give impetus to bottom-up restructuring [135]. These changes took place against a backdrop of top-down government coal pricing, the abolition of subsidies and, later, the introduction of temporary export restrictions, as well as barriers raised by new public laws. All these factors resulted in the systematic deterioration of the financial condition of mining enterprises, which soon gave rise to social unrest. To prevent escalation, successive governments took steps to improve the economic health of the mines. These actions were reflected in a number of government-sponsored mining restructuring programs. These steps were taken for various economic, social and political reasons [136]. In the years 1990–2020, nine programs, together with various modifications, were drawn up, adopted and implemented (a list of them is included in the Appendix B) [137].

An analysis of these restructuring programs reveals a radical shift in their main objectives, an increase in the number of specific objectives and a high degree of deconcentration. A reorientation was observed away from goals heavily focused on protecting mining companies from bankruptcy, towards the goals of privatisation, rationalisation of resource exploitation, profitability and energy security.

The general objective of restructuring was to ensure the independent, market-driven adjustment of coal mining companies to the conditions of a free market economy. This is because these concerns had been established during the years of a centralised economy characterised by an overdeveloped non-productive infrastructure, low levels of operating efficiency, high energy consumption and technological backwardness. The first steps were taken in May 1992. It was assumed that, when competing with each other, only a few inefficient mining enterprises would be liquidated. Unfortunately, the financial state of most mining enterprises deteriorated with each year that passed, leading to the financial collapse of 1998. In the years 1991–2002, none of the implemented mining restructuring programs achieved its main goal, that is, they failed to ensure the economic efficiency of the hard coal mining industry [138]. The reasons for this failure were, inter alia, unfavourable changes and structural dependencies between the main factors: employment, assets, capital expenditure, output and sales and electricity generation.

With the aim not only of saving mining enterprises from bankruptcy, but also of averting a crisis in an industrially important region of the country, the Polish government adopted the idea of consolidation. In 2003, the largest mining company in Europe (Kompania W˛eglowa S.A, Katowice, Poland) was established. Then, in 2015, the remaining mining concerns were merged with energy companies, while inefficient mines were transferred to a separate enterprise earmarked for liquidation (Spółka Restrukturyzacji Kopal´n S.A., Bytom, Poland).

The year 2006 marked a particular turning point. Up until then, the rationalisation of employment was constantly listed among the specific objectives of each program, which was justified by the industry's high employment levels, which in turn was due to technological backwardness and an excessive focus on non-mining activities. In the first 6 years of restructuring alone, employment declined significantly (by approximately 41%, i.e., 140,000 employees), which, however, did not result in any social unrest. The post-2006 programs no longer provided for any cutbacks in employment, the more so as the employment level planned in the previous programs had only been achieved in two years, while in the remaining years, employment levels were slightly hight than the targets set.

The reasons for discontinuing employment reduction plans were as follows: [139,140]


Overall, after 2006, restructuring programs no longer listed any specific measures [141]. Instead, these programs focused on the strategic directions of mining development, the aim of which was to set priorities in the strategies and action plans of enterprises. Natural breakdowns became a way to further reduce employment. Unfortunately, the unfavourable age structure of the industry (an "aging" workforce) was becoming an ever more acute problem. In 2008, employment increased in almost all employee groups (excluding administration employees).

The objective of the program for 2007–2010 was the rational and effective management of coal deposits. The employment policy pursued in 2007–2015 focused on optimising the use of internal reserves and ensuring the correct balance between wage increases and economic results. Council Decision No. 787 of the European Union of 10 December 2010, which limited the time allotted to supporting unprofitable mines and adopted increasingly restrictive standards regarding carbon dioxide emissions and environmental protection, had a decisive impact on the restructuring process. These solutions have become the core determinants shaping the functioning of the mining industry. In 2015, the government continued to assume that hard coal mining would be competitive in the new market conditions.

In April 2016, Polska Grupa Górnicza (PGG) was established to strengthen Poland's energy security [8] and function as a recovery plan for the failing Kompania W˛eglowa S.A. Another major crisis in the mining industry forced the government to conclude an agreement (24 September 2020) with trade unions and other interest groups and set 2049 as the final date for the closure of the mines.

The goal of most of the restructuring programmes was to make the mining industry profitable, but in only three programs was this actually the main objective (Table 1). The dominant features of all the programmes were the following: adjusting production capacity to demand, cutting mining costs, rationalising employment, increasing labour productivity and improving working conditions. All of them likewise envisaged a systematic reduction in production, mainly through the liquidation of highly inefficient mines, as well as those mines facing the most serious problems and natural hazards and thus incurring high output costs. The remaining goals of the restructuring programs involved changes in organisational structures, production volume and financial conditions. The former mainly involved privatisation, mining infrastructure and mine closures. Changes at the management level consisted in the creation of new supervisory structures. Changes in production resulted from a reduction in coal mining due to weaker demand, which in turn was a consequence of lower energy consumption in industry. Another important task was to restructure technological processes by modernising machinery and mining methods. Financial restructuring focused on improving profitability and reducing the indebtedness of mining enterprises. Efficiency was to be improved through investments in equipment and machines (longwall and road shearers or coal streams), as well as other equipment in the longwall complex in the form of powered supports, longwall conveyors and other devices (Table 2).




1993–2020 (Appendix B), materials of the Ministry of Economy. Available online: www.gov.pl (accessed on 2 February 2022).

78

#### *3.2. Assessment of the Results of Coal Mining Enterprises*

**Partial hypothesis H1.** *The restructuring of coal mining companies resulted in the achievement of the primary goal of sustainable profitability and increased productivity, with labour as the main factor*. *Energies* **2022**, *15*, x FOR PEER REVIEW 15 of 46

> The overall results of coal mining companies in Poland in the years 1990–2020 were very poor. Accumulated revenues of PLN 729.6 billion generated a net profit of only PLN 1.1 billion, i.e., 0.15% (a weak correlation between results and revenues, i.e., r = 0.25). Until 1998, revenues grew rapidly, but costs increased at a much faster rate, which only exacerbated loses, despite essentially linear price growth. The subsequent suppression of costs yielded positive results that lasted until 2013 despite a resurgence in cost dynamics. Unfortunately, this trend continued despite worsening price conditions, which led to losses in 2014–2016. Costs have stabilised in recent years, and this trend has continued despite declining revenues, resulting in the highest ever annual losses (PLN—4.4 billion in 2020). The share of exports was subject to cyclical changes, with this share at its lowest when results had relatively stabilised, with an average, indeed for the most part negative, correlation with financial results (r = −0.45) (Figure 1). The overall results of coal mining companies in Poland in the years 1990–2020 were very poor. Accumulated revenues of PLN 729.6 billion generated a net profit of only PLN 1.1 billion, i.e., 0.15% (a weak correlation between results and revenues, i.e., r = 0.25). Until 1998, revenues grew rapidly, but costs increased at a much faster rate, which only exacerbated loses, despite essentially linear price growth. The subsequent suppression of costs yielded positive results that lasted until 2013 despite a resurgence in cost dynamics. Unfortunately, this trend continued despite worsening price conditions, which led to losses in 2014–2016. Costs have stabilised in recent years, and this trend has continued despite declining revenues, resulting in the highest ever annual losses (PLN—4.4 billion in 2020). The share of exports was subject to cyclical changes, with this share at its lowest when results had relatively stabilised, with an average, indeed for the most part negative, correlation with financial results (r = −0.45) (Figure 1).

**Figure 1.** Financial results of coal mining enterprises in 1990–2020 (bn PLN). Note: net financial result and export—right axis. Source: authors' research based on GUS in Warsaw (Statistical Head Office in Warsaw, Poland)—databases of limited access. Available online: https://stat.gov.pl/en/databases/ (accessed on 4 February 2022); Pont Info Warsaw—Gospodarka SŚDP—commercial databases. Available online: http://baza.pontinfo.com.pl/index.php (accessed on 2 February 2022). **Figure 1.** Financial results of coal mining enterprises in 1990–2020 (bn PLN). Note: net financial result and export—right axis. Source: authors' research based on GUS in Warsaw (Statistical Head Office in Warsaw, Poland)—databases of limited access. Available online: https://stat.gov.pl/en/ databases/ (accessed on 4 February 2022); Pont Info Warsaw—Gospodarka SSDP—commercial ´ databases. Available online: http://baza.pontinfo.com.pl/index.php (accessed on 2 February 2022).

Labour costs accounted for constantly the largest share in the overall cost structure (average—41.9%, coefficient of variation 10.0%). Energy consumption costs remained relatively stable (average 5.7%), while the costs of materials decreased (average annual rate −1.9%, final share—10.0%) and the share of external services varied (average 19.0%, coefficient of variation 19.9%). Due to its dominant share in costs, the labour factor requires in-depth analysis. The undoubted outcome of the restructuring of Polish coal mining companies in the Labour costs accounted for constantly the largest share in the overall cost structure (average—41.9%, coefficient of variation 10.0%). Energy consumption costs remained relatively stable (average 5.7%), while the costs of materials decreased (average annual rate −1.9%, final share—10.0%) and the share of external services varied (average 19.0%, coefficient of variation 19.9%). Due to its dominant share in costs, the labour factor requires in-depth analysis.

years 1990–2020 was a reduction in employment, declining from 393,900 at the beginning of this period to 78,500 at its end. (−80.1%). Employment shrank by an average rate of −5.2%, with the most significant decline occurring up until 1998, and the process as a whole finished in 2016. Revenues from sales, varying periodically but increasing in the long-term, contrasted with a dwindling workforce, which resulted in higher labour productivity (an average annual rate of 14.4%), which collapsed just after 2016, based only on a reduction in the number of employees. However, unit labour costs are of greater importance when it comes to assessing how efficiently an enterprise's labour resources The undoubted outcome of the restructuring of Polish coal mining companies in the years 1990–2020 was a reduction in employment, declining from 393,900 at the beginning of this period to 78,500 at its end. (−80.1%). Employment shrank by an average rate of −5.2%, with the most significant decline occurring up until 1998, and the process as a whole finished in 2016. Revenues from sales, varying periodically but increasing in the long-term, contrasted with a dwindling workforce, which resulted in higher labour productivity (an average annual rate of 14.4%), which collapsed just after 2016, based only on a reduction in the number of employees. However, unit labour costs are of greater

> are being used. Unfortunately, this was not characterised by any upward trend (an average annual rate of −0.02%), and this measure of productivity remained relatively unchanged in 2020 compared to 1990. On average, therefore, PLN 100 in remuneration costs

importance when it comes to assessing how efficiently an enterprise's labour resources are being used. Unfortunately, this was not characterised by any upward trend (an average annual rate of −0.02%), and this measure of productivity remained relatively unchanged in 2020 compared to 1990. On average, therefore, PLN 100 in remuneration costs produced PLN 245.8 in sales revenues. *Energies* **2022**, *15*, x FOR PEER REVIEW 16 of 46

> The main production and technical factor driving change in the coal industry was the volume of production. It decreased in an essentially linear fashion (average annual rate −3.3%), declining in total by 63.1%, and only in brief periods was it propped up by successive restructuring plans. An almost total correlation existed between this factor and the number of people employed in the industry (r = 0.96). In general, technical efficiency followed a similar path to that of labour, although at a much slower rate (average annual rate of 2.1%). What is especially noticeable is that after 2016, first technical efficiency and then labour efficiency deteriorated significantly. The correlation between technical efficiency and unit labour costs slightly exceeded the lower limit of the average (r = 0.39) (Figure 2). The main production and technical factor driving change in the coal industry was the volume of production. It decreased in an essentially linear fashion (average annual rate −3.3%), declining in total by 63.1%, and only in brief periods was it propped up by successive restructuring plans. An almost total correlation existed between this factor and the number of people employed in the industry (r = 0.96). In general, technical efficiency followed a similar path to that of labour, although at a much slower rate (average annual rate of 2.1%). What is especially noticeable is that after 2016, first technical efficiency and then labour efficiency deteriorated significantly. The correlation between technical efficiency and unit labour costs slightly exceeded the lower limit of the average (r = 0.39) (Figure 2).

**Figure 2.** Employed persons, labour efficiency and labour costs productivity of coal mining enterprises in 1990–2020. Note: output and technical efficiency—right axis. Source: as in Figure 1. **Figure 2.** Employed persons, labour efficiency and labour costs productivity of coal mining enterprises in 1990–2020. Note: output and technical efficiency—right axis. Source: as in Figure 1.

Fixed and current assets grew fairly evenly until 2013, after which both declined. Investment expenditures increased linearly until 2011, but the ratio of investment expenditure to depreciation only increased in the years 1999–2011, after which the ratio decreased. By 2014, it was less than unity, so even a simple renewal of assets failed to be achieved. In the case of current assets, fluctuations intensified after 2011 as part of an overall upward trend, while, in general, receivables grew faster than inventories. Recently, a decline in receivables has brought about a reduction in short-term liabilities, but at the same time a disproportionate increase in receivables. The period up until 2002 saw an alarmingly rapid increase in debt, especially short-Fixed and current assets grew fairly evenly until 2013, after which both declined. Investment expenditures increased linearly until 2011, but the ratio of investment expenditure to depreciation only increased in the years 1999–2011, after which the ratio decreased. By 2014, it was less than unity, so even a simple renewal of assets failed to be achieved. In the case of current assets, fluctuations intensified after 2011 as part of an overall upward trend, while, in general, receivables grew faster than inventories. Recently, a decline in receivables has brought about a reduction in short-term liabilities, but at the same time a disproportionate increase in receivables.

term debt, which rose above what was deemed a safe level of liquidity at which liabilities can be settled. Moreover, ever higher losses led to negative equity in the years 1999– 2002—coal mining companies began to suffer dramatic financial collapse (a debt level of 123.9% in 2002). The debt relief provided for these concerns through the restructuring process resulted in medium-term stabilisation, but after 2012 liabilities began to rise once more (reaching a debt level of 74.5% in 2020). Two further periods of losses reduced the value of equity, leading to a situation in which the value of the net assets of the coal mining industry as a whole stood at only PLN 8.5 billion in 2020, i.e., 1/3 of its annual revenues. What is more, since 2003, value transfers to the economic system (a component of GDP) gradually decreased (with significant fluctuations), from 59.1% of the rate of value added The period up until 2002 saw an alarmingly rapid increase in debt, especially shortterm debt, which rose above what was deemed a safe level of liquidity at which liabilities can be settled. Moreover, ever higher losses led to negative equity in the years 1999–2002—coal mining companies began to suffer dramatic financial collapse (a debt level of 123.9% in 2002). The debt relief provided for these concerns through the restructuring process resulted in medium-term stabilisation, but after 2012 liabilities began to rise once more (reaching a debt level of 74.5% in 2020). Two further periods of losses reduced the value of equity, leading to a situation in which the value of the net assets of the coal mining industry as a whole stood at only PLN 8.5 billion in 2020, i.e., 1/3 of its annual revenues. What is more, since 2003, value transfers to the economic system (a component of GDP)

to 26.6% in 2020. (Figure 3).

gradually decreased (with significant fluctuations), from 59.1% of the rate of value added to 26.6% in 2020. (Figure 3). *Energies* **2022**, *15*, x FOR PEER REVIEW 17 of 46

**Figure 3.** Short and long-term liabilities, equity and value-added margin of coal mining enterprises in 1990–2020. Note: value-added margin—right axis. Source: as in Figure 1. **Figure 3.** Short and long-term liabilities, equity and value-added margin of coal mining enterprises in 1990–2020. Note: value-added margin—right axis. Source: as in Figure 1.

The asset-capital structure (ACSR) is generally viewed as negative, for at least several reasons. Firstly, in the first four years of restructuring, the ACSR index decreased 7.5-fold, and in the years 1999–2002, it stood at unprecedented, negative levels (negative equity). Secondly, during the years of improvement and relative stabilisation (up until 2012), its average value nevertheless remained extremely low (an average of 0.17 compared to the reference value 1.0). Thirdly, various factors show no indications of any improvement in the asset and capital structure. The asset-capital structure (ACSR) is generally viewed as negative, for at least several reasons. Firstly, in the first four years of restructuring, the ACSR index decreased 7.5-fold, and in the years 1999–2002, it stood at unprecedented, negative levels (negative equity). Secondly, during the years of improvement and relative stabilisation (up until 2012), its average value nevertheless remained extremely low (an average of 0.17 compared to the reference value 1.0). Thirdly, various factors show no indications of any improvement in the asset and capital structure.

This image of a flawed overall structure overlaps with assessments of static and dynamic liquidity and solvency. An almost total correlation can be observed between current and quick liquidity ratios (r = 0.99), close to the level (average value: current liquidity—0.71, quick ratio—0.58) due to the relatively small impact of inventories and above all significantly different *in minus* from the reference values. The correlation between changes in current liquidity and the ACSR ratio was very high (r = 0.81), which is proof of the transfer of defective asset and capital relations from the overall level to the level of working capital management. The operating cash flow and cash flow coverage ratios were negative, which occurred again in 2020. Their level was critically low: PLN 1 of revenues generated on average a financial surplus of PLN 0.03, while PLN 1 covered PLN 0.06 of total liabilities. The solvency ratio based only on the net financial result and depreciation was also very low (on average 0.10), and the correlation with current liquidity was average (r = 0.46). With relatively stable inventory and receivable conversion cycles in terms of their length, the working capital cycle was always negative (due to the length of the shortterm liabilities cycle) and reached its peak value of −278 days in 2002, and then, as a result of debt reduction, it was reduced to an average of −63 days. Efficiency can be measured in terms of profitability and productivity. An assessment This image of a flawed overall structure overlaps with assessments of static and dynamic liquidity and solvency. An almost total correlation can be observed between current and quick liquidity ratios (r = 0.99), close to the level (average value: current liquidity—0.71, quick ratio—0.58) due to the relatively small impact of inventories and above all significantly different *in minus* from the reference values. The correlation between changes in current liquidity and the ACSR ratio was very high (r = 0.81), which is proof of the transfer of defective asset and capital relations from the overall level to the level of working capital management. The operating cash flow and cash flow coverage ratios were negative, which occurred again in 2020. Their level was critically low: PLN 1 of revenues generated on average a financial surplus of PLN 0.03, while PLN 1 covered PLN 0.06 of total liabilities. The solvency ratio based only on the net financial result and depreciation was also very low (on average 0.10), and the correlation with current liquidity was average (r = 0.46). With relatively stable inventory and receivable conversion cycles in terms of their length, the working capital cycle was always negative (due to the length of the short-term liabilities cycle) and reached its peak value of −278 days in 2002, and then, as a result of debt reduction, it was reduced to an average of −63 days.

of operating profitability in the branch reveals three periods characterised by similar properties: the period up to 2002, when coal mining companies were highly unprofitable concerns; the 2003–2012 period, characterised by positive results with considerable volatility and the 2013–2020 period, when the profitability of these enterprises was shaken twice, and which ended in further major losses. Cost productivity provides support for profitability. Unfortunately, this was not the case with the surveyed companies. Firstly, this was because until 2002 the value of this indicator was lower than 1 (mean value— 0.96, minimum—0.84). This situation repeated itself in the years 2019–2020. Secondly, throughout this entire period, an expenditure stream equal to PLN 1 yielded average revenues of just PLN 1.01. Thirdly, with some fluctuations (a coefficient of variation 9.6%), cost productivity was on a downward path (average annual rate of −1.1%). Asset Efficiency can be measured in terms of profitability and productivity. An assessment of operating profitability in the branch reveals three periods characterised by similar properties: the period up to 2002, when coal mining companies were highly unprofitable concerns; the 2003–2012 period, characterised by positive results with considerable volatility and the 2013–2020 period, when the profitability of these enterprises was shaken twice, and which ended in further major losses. Cost productivity provides support for profitability. Unfortunately, this was not the case with the surveyed companies. Firstly, this was because until 2002 the value of this indicator was lower than 1 (mean value—0.96, minimum—0.84). This situation repeated itself in the years 2019–2020. Secondly, throughout this entire period, an expenditure stream equal to PLN 1 yielded average revenues of just PLN 1.01.

Thirdly, with some fluctuations (a coefficient of variation 9.6%), cost productivity was on a downward path (average annual rate of −1.1%). Asset productivity only grew at any significant rate until 1997, and began to decline after 2003, assuming values below 1 (an average value of 0.81, minimum—0.55). The decline in these values was more pronounced than in the case of cost productivity. The correlation between asset productivity and the operating profitability of assets was slight (r = 0.08), while there was an almost complete correlation between cost productivity and the operating profitability of sales (r = 0.99) (Figure 4). productivity only grew at any significant rate until 1997, and began to decline after 2003, assuming values below 1 (an average value of 0.81, minimum—0.55). The decline in these values was more pronounced than in the case of cost productivity. The correlation between asset productivity and the operating profitability of assets was slight (r = 0.08), while there was an almost complete correlation between cost productivity and the operating profitability of sales (r = 0.99) (Figure 4).

*Energies* **2022**, *15*, x FOR PEER REVIEW 18 of 46

**Figure 4.** Productivity of assets (AP) and costs (CP), and operating return on assets (oROA) and sales (oROS) of coal mining enterprises in 1990–2020. Note: productivity of assets and costs—right axis. Source: as in Figure 1. **Figure 4.** Productivity of assets (AP) and costs (CP), and operating return on assets (oROA) and sales (oROS) of coal mining enterprises in 1990–2020. Note: productivity of assets and costs—right axis. Source: as in Figure 1.

**Proof of partial hypothesis H1.** The above-mentioned results for the years 1990–2020 provide a legitimate and unquestionable basis for providing a negative verification of the partial hypothesis (H1). In other words, the restructuring of coal mining companies has not achieved its basic goal of sustainable profitability and increasing productivity, including with regards to its main factor, i.e., labour productivity. □ **Proof of partial hypothesis H1.** The above-mentioned results for the years 1990–2020 provide a legitimate and unquestionable basis for providing a negative verification of the partial hypothesis (H1). In other words, the restructuring of coal mining companies has not achieved its basic goal of sustainable profitability and increasing productivity, including with regards to its main factor, i.e., labour productivity.

#### *3.3. Assessment of the Structural Changes of Coal Mining Enterprises 3.3. Assessment of the Structural Changes of Coal Mining Enterprises*

**Partial hypothesis H2.** *Hard coal restructuring has brought about significant changes in the structure of employment, assets, capital expenditure and sales.* **Partial hypothesis H2.** *Hard coal restructuring has brought about significant changes in the structure of employment, assets, capital expenditure and sales*.

A reduction in underground employment of 75% and a reduction of 85% above ground did not significantly change the employment structure (details in Appendix C) in coal mining companies (NPS = 0.11 compared to 1992). The increase in structural variability observed in 1992–1996 slowed down to a complete stop. The limited volatility of the employment structure is also confirmed by the low volatility of the HHI concentration index (0.459 in 1992 and 0.453 in 2020). The relatively high value of concentration resulted from a significant share of underground workers (63.73% in 1992; 64.37% in 2020). No correlation was observed between the intensity of change in the employment structure and the rate of change in production (r = 0.004), which confirms the lack of any correlation between changes in the employment structure and changes in the volume of production (no cause-and-effect relationship) (Figure 5). A reduction in underground employment of 75% and a reduction of 85% above ground did not significantly change the employment structure (details in Appendix C) in coal mining companies (NPS = 0.11 compared to 1992). The increase in structural variability observed in 1992–1996 slowed down to a complete stop. The limited volatility of the employment structure is also confirmed by the low volatility of the HHI concentration index (0.459 in 1992 and 0.453 in 2020). The relatively high value of concentration resulted from a significant share of underground workers (63.73% in 1992; 64.37% in 2020). No correlation was observed between the intensity of change in the employment structure and the rate of change in production (r = 0.004), which confirms the lack of any correlation between changes in the employment structure and changes in the volume of production (no cause-and-effect relationship) (Figure 5).

Changes in the volume of hard coal sales varied from year to year, and only in 3 out of the 28 years during this period (1996, 1997, 2004) did actual production exceed the targets. In the remaining years, no mining output and sales targets were set, and, since 2017, sales volume has not been planned at all. The significant decline in sales and mining output

**Thousands**

**Thousands**

50

300

**Thousands**

350

was reflected in changes in the sales structure (commercial power engineering, industrial power engineering, industrial and municipal heating plants, other industrial customers, coking plants and other domestic customers). A strong correlation was observed between changes in the sales structure and the sales volume, which was mainly a consequence of the declining share of other domestic recipients, with the exception of the commercial power and heating industries (industry and coking plants). These changes are confirmed in the strong upward trend in the HHI index since 2016. In 2020, the sale of coal to the commercial power industry accounted for as much as 67% of total sales (37% in 1992) (Figure 6). *Energies* **2022**, *15*, x FOR PEER REVIEW 19 of 46 0.02 0.04 0.06 0.08 100 150 200 250 Employment (th per.) NPS (1992=100) NPS (y/y) HHI

*Energies* **2022**, *15*, x FOR PEER REVIEW 19 of 46

**Figure 5.** Employment, intensity of structure changes (NPS 1992 = 100%, NPS y/y), concentration (HHI) for coal mining enterprises in 1992–2020. Comments: employment—left axis, HHI—standardised quantity. Comparable data available only since 1992. Source: own study based on data provided by ARP S.A. Katowice. Available online: https://polskirynekwegla.pl/ (accessed on 10 February 2022). **Figure 5.** Employment, intensity of structure changes (NPS 1992 = 100%, NPS y/y), concentration (HHI) for coal mining enterprises in 1992–2020. Comments: employment—left axis, HHI—standardised quantity. Comparable data available only since 1992. Source: own study based on data provided by ARP S.A. Katowice. Available online: https://polskirynekwegla.pl/ (accessed on 10 February 2022). quence of the declining share of other domestic recipients, with the exception of the commercial power and heating industries (industry and coking plants). These changes are confirmed in the strong upward trend in the HHI index since 2016. In 2020, the sale of coal to the commercial power industry accounted for as much as 67% of total sales (37% in 1992) (Figure 6).

tween changes in the sales structure and the sales volume, which was mainly a conse-

0.20 80 HHI **Figure 6.** Coal sales, intensity of sales structure transformations ((NPS 1992 = 100%, NPS y/y), concentration (HHI) for coal mining enterprises in 1992–2020. Comments: coal sales—left axis. Other remarks as to Figure 5. Source: as in Figure 5.

0.10 40 The restructuring programs implemented in the years 1993–2007 set specific investment outlay targets to adapt mines to the ongoing technical and technological changes, adjust extracted and mechanically process coal to market needs and reconstruct and mod-

0.00

0.05

0.15

0.1

0

20

60

**Thousands**

8).

ernise machinery and devices. On the other hand, investments in the drilling of mine workings exceeded spending targets for the years 1995–1997 and 2004–2006. Since 2007, neither the size nor the structure of investment outlays has been planned, which was reflected in a greater deconcentration of the structure of investment outlays. purchases of machinery and equipment and other things) and the lack of any clear direction. The biggest structural change (y/y) occurred in 2004, when there was a sharp reduction in other forms of capital expenditure. Since 2005, the pace and scale of change (annual average NPS = 0.077) in the structure of investment outlays has been waning. (Figure 7).

flected in a greater deconcentration of the structure of investment outlays.

**Figure 6.** Coal sales, intensity of sales structure transformations ((NPS 1992 = 100%, NPS y/y), concentration (HHI) for coal mining enterprises in 1992–2020. Comments: coal sales—left axis. Other

The restructuring programs implemented in the years 1993–2007 set specific investment outlay targets to adapt mines to the ongoing technical and technological changes, adjust extracted and mechanically process coal to market needs and reconstruct and modernise machinery and devices. On the other hand, investments in the drilling of mine workings exceeded spending targets for the years 1995–1997 and 2004–2006. Since 2007, neither the size nor the structure of investment outlays has been planned, which was re-

In 1996 and 2004, structural change increased in intensity, which was a consequence of a significant increase in expenditure on the purchase of machinery and equipment. The value of the HHI indicator confirms the high degree of diversification in investment outlays (which covered pits, coal mechanical processing plants, environmental protection,

*Energies* **2022**, *15*, x FOR PEER REVIEW 20 of 46

remarks as to Figure 5. Source: as in Figure 5.

In 1996 and 2004, structural change increased in intensity, which was a consequence of a significant increase in expenditure on the purchase of machinery and equipment. The value of the HHI indicator confirms the high degree of diversification in investment outlays (which covered pits, coal mechanical processing plants, environmental protection, purchases of machinery and equipment and other things) and the lack of any clear direction. The biggest structural change (y/y) occurred in 2004, when there was a sharp reduction in other forms of capital expenditure. Since 2005, the pace and scale of change (annual average NPS = 0.077) in the structure of investment outlays has been waning (Figure 7). A weak correlation was observed between the pace and scale of changes in the structure of investment outlays and the value of net fixed assets (r = 0.25). An average correlation existed between the NPS of the investment structure and the rate of change in the value of net fixed assets (r = 0.31), and a slight correlation was noted with the NPS of the structure of assets (r = 0.08), with no regression relationship specified. This indicates that the purpose of the investments was not to further modernise the property structure but rather to continue current operations, and their main focus was the maintenance of mine workings (38% in 2020).

**Figure 7.** Investment outlays for mining enterprises in Poland in 1992–2020; (**a**) Capital expenditure, intensity of structure changes (NPS 1992 = 100, NPS y/y), and concentration (HHI); (**b**) Regression curve for investment outlays and net fixed assets values; (**c**) Regression curve of the intensity of changes in the structure of investment outlays and the rate of changes in the net fixed assets. Notes: investment outlays—left axis. Remaining remarks as to Figure 5. Source: as in Figure 5. **Figure 7.** Investment outlays for mining enterprises in Poland in 1992–2020; (**a**) Capital expenditure, intensity of structure changes (NPS 1992 = 100, NPS y/y), and concentration (HHI); (**b**) Regression curve for investment outlays and net fixed assets values; (**c**) Regression curve of the intensity of changes in the structure of investment outlays and the rate of changes in the net fixed assets. Notes: investment outlays—left axis. Remaining remarks as to Figure 5. Source: as in Figure 5.

An analysis of the variability of the structure of fixed assets (land, buildings, premises and civil engineering facilities, machinery and technical equipment, means of transport and other fixed assets) reveals slight changes (annual average NPS = 0.018). The high and sustained level of concentration (average HHI = 0.49) was due to a constantly high share A weak correlation was observed between the pace and scale of changes in the structure of investment outlays and the value of net fixed assets (r = 0.25). An average correlation existed between the NPS of the investment structure and the rate of change in the value of

of buildings, premises and water engineering facilities (average—63.73%). A very strong negative correlation (r = −0.86) and a satisfactory fit of the regression curve (R<sup>2</sup> = 0.75) for the value of assets and the volume of coal output means that an increase of PLN 1000 PLN

correlation (r = 0.24) was observed between the intensity of changes in the structure of assets and the change in the structure of employment. This confirms the lack of any common restructuring policy for these areas. An average correlation with the pace and scale of changes in the asset structure and total productivity (r = 0.41), a weak correlation with productivity per underground worker (r = 0.13) and a weak correlation with the rate of change in productivity (r = 0.14) clearly confirm that the restructuring of fixed assets did not result in any improvement in the operating efficiency of mining enterprises (Figure net fixed assets (r = 0.31), and a slight correlation was noted with the NPS of the structure of assets (r = 0.08), with no regression relationship specified. This indicates that the purpose of the investments was not to further modernise the property structure but rather to continue current operations, and their main focus was the maintenance of mine workings (38% in 2020).

An analysis of the variability of the structure of fixed assets (land, buildings, premises and civil engineering facilities, machinery and technical equipment, means of transport and other fixed assets) reveals slight changes (annual average NPS = 0.018). The high and sustained level of concentration (average HHI = 0.49) was due to a constantly high share of buildings, premises and water engineering facilities (average—63.73%). A very strong negative correlation (r = −0.86) and a satisfactory fit of the regression curve (R <sup>2</sup> = 0.75) for the value of assets and the volume of coal output means that an increase of PLN 1000 PLN in the value of assets resulted in a decrease in output of 0.16 thousand tonnes. A weak correlation (r = 0.24) was observed between the intensity of changes in the structure of assets and the change in the structure of employment. This confirms the lack of any common restructuring policy for these areas. An average correlation with the pace and scale of changes in the asset structure and total productivity (r = 0.41), a weak correlation with productivity per underground worker (r = 0.13) and a weak correlation with the rate of change in productivity (r = 0.14) clearly confirm that the restructuring of fixed assets did not result in any improvement in the operating efficiency of mining enterprises (Figure 8). *Energies* **2022**, *15*, x FOR PEER REVIEW 22 of 46

**Figure 8.** Net fixed assets in coal mining companies in Poland in 1992–2020; (**a**) Net fixed assets in 1992–2020, intensity of structure changes (NPS 2004 = 100, NPS y/y), and concentration (HHI) in 2004–2020; (**b**) The regression curve of the net fixed assets and the hard coal output in Poland in 1992–2020. Notes: net fixed assets—left axis. Remaining remarks as to Figure 5. Source: as in Figure 5. **Figure 8.** Net fixed assets in coal mining companies in Poland in 1992–2020; (**a**) Net fixed assets in 1992–2020, intensity of structure changes (NPS 2004 = 100, NPS y/y), and concentration (HHI) in 2004–2020; (**b**) The regression curve of the net fixed assets and the hard coal output in Poland in 1992–2020. Notes: net fixed assets—left axis. Remaining remarks as to Figure 5. Source: as in Figure 5.

**Proof of partial hypothesis H2.** The results outlined above provide an unequivocal basis for nullifying the partial hypothesis (H2). This means that the restructuring of hard coal enterprises did not help to achieve the basic goal of rationalising employment. No corre-

A basic goal of every restructuring program was a reduction in liabilities, first so as to prevent insolvency and liquidation and then to ensure growth. All the programs provided for debt reduction through the implementation of composition and bank settlement proceedings, spreading out and postponing overdue payments or cancelling overdue interest and payments owed to the state. The introduction of special laws regulating the restructuring of hard coal mining debt had the effect of drastically lowering liabilities by

the technical and technological changes that took place during this period. □

**Proof of partial hypothesis H2.** The results outlined above provide an unequivocal basis for nullifying the partial hypothesis (H2). This means that the restructuring of hard coal enterprises did not help to achieve the basic goal of rationalising employment. No correlation was observed between changes in the employment structure, the size and rate of coal output and changes in the structure of fixed assets, and mines were not adapted to the technical and technological changes that took place during this period.

A basic goal of every restructuring program was a reduction in liabilities, first so as to prevent insolvency and liquidation and then to ensure growth. All the programs provided for debt reduction through the implementation of composition and bank settlement proceedings, spreading out and postponing overdue payments or cancelling overdue interest and payments owed to the state. The introduction of special laws regulating the restructuring of hard coal mining debt had the effect of drastically lowering liabilities by over 62% in 2003. Overall, thanks to debt cutting measures based on special provisions introduced in 1998 [142] and 2003 [143], over PLN 18 billion (EUR 3.9 billion) of the debt owed by the hard coal mining industry was written off [144] (see conclusions in Section 3.2).

*3.4. Assessment of Changes in the Structure of Electricity Generation vs. Hard Coal Mining*

**Partial hypothesis H3.** *The effects of restructuring coal mining companies based on a multidimensional approach are determined by time intervals characterised by elements of homogeneity (time series periodisation)*.

Changes introduced in the structure of electricity generation in Poland (hard coal, lignite, natural gas, heating oil, RES) were small in scale (NPS = 0.097, annual average 0.012) up until 2010 (compared to 1990). In the years 2010–2020, the intensity of change in the generation structure was already twice as high (NPS = 0.286 in 2020, 0.032 annually on average) as in the previous 20 years. A very high and negative correlation was observed between changes in the structure of electricity generation (NPS) and (1) sales of steam coal (r = 0.66, unsatisfactory regression fit R<sup>2</sup> = 0.43), (2) coal output (r = 0.88, a satisfactory regression fit R<sup>2</sup> = 0.78) and (3) the volume of steam coal production (r = 0.91, a good regression fit R<sup>2</sup> = 0.84).

The regression relationship outlined above should be interpreted in such a way that a change in the structure of energy production in 1990–2020 by 1% (NPS) resulted in a decrease in thermal coal production by 1.09%, i.e., in a decline in thermal coal production amounting to 2,332,000 tonnes. (Figure 9). This indicates that external factors (i.e., nonmining factors) exerted a significant influence on the volume of coal mined and the nature of changes in its output (mainly with regards to the ways in which increased demand for electricity was covered from sources other than coal.

For the purposes of reinforcing and interpreting the results obtained so far, the authors applied the model system of inequalities [145]. This approach is based on the existence of interdependencies between the dynamics underpinning the main categories that determine the economic condition of an enterprise and its rational (efficient) functioning. Changes in the structure of employment and fixed assets (the resources of enterprises) were first assessed, followed by an evaluation of the changes in the sale and mining of steam coal, total productivity and productivity per underground worker (as effects of operation), as well as the structure of energy production and investments (as external stimulants of changes). A very strong and negative correlation was observed between changes in the structure of the energy mix and sales (SCE) (r = −0.84, a matching regression relationship R <sup>2</sup> = 0.71) on the one hand and coal mining (MCE) (r = <sup>−</sup>0.92, matching the relationship regression R<sup>2</sup> = 0.84) on the other. A strong, negative correlation was observed between changes in the structure of electricity generation (EN) and changes in the employment structure (r = <sup>−</sup>0.70, adjustment of the regression relationship R<sup>2</sup> = 0.50) as well as changes in the structure of fixed assets (r = 0.70, adjustment of the regression relationship R<sup>2</sup> = 0.50). In the remaining cases, the correlation was weak or slight (Figure 10).

3.2).

*(time series periodisation).*

gression fit R<sup>2</sup> = 0.84).

electricity was covered from sources other than coal.

over 62% in 2003. Overall, thanks to debt cutting measures based on special provisions introduced in 1998 [142] and 2003 [143], over PLN 18 billion (EUR 3.9 billion) of the debt owed by the hard coal mining industry was written off [144] (see conclusions in Section

*3.4. Assessment of Changes in the Structure of Electricity Generation vs. Hard Coal Mining* 

**Partial hypothesis H3.** *The effects of restructuring coal mining companies based on a multidimensional approach are determined by time intervals characterised by elements of homogeneity* 

Changes introduced in the structure of electricity generation in Poland (hard coal, lignite, natural gas, heating oil, RES) were small in scale (NPS = 0.097, annual average 0.012) up until 2010 (compared to 1990). In the years 2010–2020, the intensity of change in the generation structure was already twice as high (NPS = 0.286 in 2020, 0.032 annually on average) as in the previous 20 years. A very high and negative correlation was observed between changes in the structure of electricity generation (NPS) and (1) sales of steam coal (r = 0.66, unsatisfactory regression fit R<sup>2</sup> = 0.43), (2) coal output (r = 0.88, a satisfactory regression fit R<sup>2</sup> = 0.78) and (3) the volume of steam coal production (r = 0.91, a good re-

The regression relationship outlined above should be interpreted in such a way that a change in the structure of energy production in 1990–2020 by 1% (NPS) resulted in a decrease in thermal coal production by 1.09%, i.e., in a decline in thermal coal production amounting to 2,332,000 tonnes. (Figure 9). This indicates that external factors (i.e., non-

**Figure 9.** Electricity production sources in Poland in 1990–2020; (**a**) The structure of electricity generation in Poland in 1990, 2000, 2010 and 2020; (**b**) The regression curve of the intensity of changes in the structure of electricity generation and the coal production in Poland in 1990–2020 Source: own study based on Eurostat. Available online: https://ec.europa.eu/eurostat/data/database (accessed on 22 February 2022). **Figure 9.** Electricity production sources in Poland in 1990–2020; (**a**) The structure of electricity generation in Poland in 1990, 2000, 2010 and 2020; (**b**) The regression curve of the intensity of changes in the structure of electricity generation and the coal production in Poland in 1990–2020 Source: own study based on Eurostat. Available online: https://ec.europa.eu/eurostat/data/database (accessed on 22 February 2022). relationship regression R<sup>2</sup> = 0.84) on the other. A strong, negative correlation was observed between changes in the structure of electricity generation (EN) and changes in the employment structure (r = −0.70, adjustment of the regression relationship R<sup>2</sup> = 0.50) as well as changes in the structure of fixed assets (r = 0.70, adjustment of the regression relationship R<sup>2</sup> = 0.50). In the remaining cases, the correlation was weak or slight (Figure 10).

**Figure 10.** Strength of dependence of selected interdependencies for coal mining enterprises in Poland. Notes: NPS—intensity of structure changes, FA—fixed assets; EMPN—employment, IO—investment outlays; TP—total productivity, PpU—productivity per underground worker; EG—energy generation; PCE—steam coal output; SCE—sale of steam coal. The colour of the arrow indicates the degree of correlation: <0.1 weak (no colour); 0.1–0.3 weak (blue); 0.3–0.5 average (green); 0.5–0.7 high (yellow); 0.7–0.9 very high (orange); >0.9 almost full (red). Source: own study based on data provided by ARP S.A. Katowice branch; own study based on data from hard coal mining restructuring programs in Poland in the years 1993–2020 (Appendix B), materials of the Ministry of Economy. Available online: www.gov.pl (accessed on 2 February 2022), and ARP Katowice. Available online: https://polskirynekwegla.pl/ (accessed on 10 February 2022). The shape of the steam coal sales curve made it possible to distinguish three distinct **Figure 10.** Strength of dependence of selected interdependencies for coal mining enterprises in Poland. Notes: NPS—intensity of structure changes, FA—fixed assets; EMPN—employment, IO—investment outlays; TP—total productivity, PpU—productivity per underground worker; EG—energy generation; PCE—steam coal output; SCE—sale of steam coal. The colour of the arrow indicates the degree of correlation: <0.1 weak (no colour); 0.1–0.3 weak (blue); 0.3–0.5 average (green); 0.5–0.7 high (yellow); 0.7–0.9 very high (orange); >0.9 almost full (red). Source: own study based on data provided by ARP S.A. Katowice branch; own study based on data from hard coal mining restructuring programs in Poland in the years 1993–2020 (Appendix B), materials of the Ministry of Economy. Available online: www.gov.pl (accessed on 2 February 2022), and ARP Katowice. Available online: https://polskirynekwegla.pl/ (accessed on 10 February 2022).

periods: (1) 1992–2002 (a decrease in sales with a simultaneous slight change in the structure of electricity generation; annual average NPS = 0.006 and NPS = 0.035 compared to 1992), (2) 2003- 2008 (an increase in sales with a simultaneous weak increase in the inten-

NPS = 0.073 compared to 1992) and (3) 2009–2020 (a clear downward trend in sales with an equally strong trend towards structural change; annual average NPS = 0.029 and NPS = 0.286 compared to 1992). In addition, sales declined significantly in 2019–2020, which was accompanied by significant changes in the structure of electricity generation com-

pared to 1992 (Figure 11).

The shape of the steam coal sales curve made it possible to distinguish three distinct periods: (1) 1992–2002 (a decrease in sales with a simultaneous slight change in the structure of electricity generation; annual average NPS = 0.006 and NPS = 0.035 compared to 1992), (2) 2003–2008 (an increase in sales with a simultaneous weak increase in the intensity of changes in the structure of electricity generation, annual average NPS = 0.017 and NPS = 0.073 compared to 1992) and (3) 2009–2020 (a clear downward trend in sales with an equally strong trend towards structural change; annual average NPS = 0.029 and NPS = 0.286 compared to 1992). In addition, sales declined significantly in 2019–2020, which was accompanied by significant changes in the structure of electricity generation compared to 1992 (Figure 11). *Energies* **2022**, *15*, x FOR PEER REVIEW 25 of 46

**Figure 11.** The rate of changes in the sales of steam coal volume, and the variability of changes in the structure of electricity generation in Poland (NPS 1992 = 100, NPS y/y) in 1992–2020. Source: as in Figure 10. **Figure 11.** The rate of changes in the sales of steam coal volume, and the variability of changes in the structure of electricity generation in Poland (NPS 1992 = 100, NPS y/y) in 1992–2020. Source: as in Figure 10.

The above results lead to the conclusion that in the years 1992–2020, a strong and negative correlation existed between changes in the structure of electricity generation and steam coal sales volume (r = 0.66), with this structure having a noticeable impact on the sales volume (R<sup>2</sup> = 0.43). An analysis of this relationship in all three of these periods yields the following results: The above results lead to the conclusion that in the years 1992–2020, a strong and negative correlation existed between changes in the structure of electricity generation and steam coal sales volume (r = 0.66), with this structure having a noticeable impact on the sales volume (R<sup>2</sup> = 0.43). An analysis of this relationship in all three of these periods yields the following results:


**Proof of partial hypothesis H3.** In conclusion, an assessment of changes in the structure

When discussing the results of coal mining companies that have undergone continuous restructuring, several factors need to be distinguished [146]. The first is the composition of the determinants of the outcome, especially in operational terms, and concerns not only absolute differences in revenues and costs but also their dynamics and the cost struc-

economic prosperity, which made it impossible to limit such expenditure in periods of falling prices, which in turn led to poorer results and higher losses [149]. Another problem was that operating costs were burdened with capital expenditure; faced with problems of low creditworthiness, enterprises made use of a widening result field and increased their

of electricity generation in the years 1992–2020 as well as indicators of changes in hard coal sales for electricity needs (the NPS indicator) confirms partial hypothesis (H3). This means that the restructuring period can be divided into intervals of time with their own homogenous characteristics. □ **Proof of partial hypothesis H3.** In conclusion, an assessment of changes in the structure of electricity generation in the years 1992–2020 as well as indicators of changes in hard coal sales for electricity needs (the NPS indicator) confirms partial hypothesis (H3). This means that the restructuring period can be divided into intervals of time with their own homogenous characteristics.

**4. Discussion**

#### **4. Discussion**

When discussing the results of coal mining companies that have undergone continuous restructuring, several factors need to be distinguished [146]. The first is the composition of the determinants of the outcome, especially in operational terms, and concerns not only absolute differences in revenues and costs but also their dynamics and the cost structure [147,148]. The labour factor exerted considerable pressure on costs during times of economic prosperity, which made it impossible to limit such expenditure in periods of falling prices, which in turn led to poorer results and higher losses [149]. Another problem was that operating costs were burdened with capital expenditure; faced with problems of low creditworthiness, enterprises made use of a widening result field and increased their costs through investments, which also could not be quickly brought under control during times of recession.

In the years 1990–2020, productivity suffered a linear and substantial decline [150] with the rapidly shrinking workforce (with a positive or negative deviation from production dynamics) [151]. The costs connected with cutting employment constituted the main component of cost-cutting measures in mining in general [152]. Labour productivity, measured in terms of revenues, increased overall, but from the point of view of efficiency, this indicator turns out to be misleading, as it is based on a quantitative reduction in the volume of production and labour [153]. After 2003, total cost productivity began to decline, and asset productivity at an even faster pace. In these circumstances, the next recessions (2014–2016 and the even sharper downturn of 2019–2020) were inevitable, and they were triggered by a slight correction in coal prices.

Low productivity and operational efficiency are quickly and quite strongly reflected in levels of solvency and liquidity as well as in working capital cycles [154]. Debt increases rapidly, and an enterprise's ability to pay its liabilities decreases. In these conditions, enterprises are no longer capable of self-financing and expect public assistance in the form of subventions and government subsidies [155]. Plans to shift away from direct government-initiated restructuring after 2006 remained a fact for only a few years and became utter fiction after 2013 [156]. To keep coal mining companies operating in order to supply power plants with raw material, further large subsidies were needed [157].

Using the logit model for predicting the degree of financial risk for a going concern (FTD), a synthesis was constructed of partial assessments of the results of coal mining enterprises. This model is a multidimensional tool for assessing the financial situation of an enterprise, as was previously mentioned, and functions as a kind of barometer of this financial condition [158]. The results of the findings are, firstly, that the level of risk for coal mining companies significantly exceeds the results of all industrial enterprises (including those involved in the mining of other natural resources). On average, this indicator was 1.8 times higher than for industry as a whole. Secondly, this is a highly volatile measure (volatility coefficient of 63.7%), which is particularly evident when considering the stabilisation of the industry observed since 2009. Thirdly, the course of the FTD curve makes it possible to distinguish three characteristic periods in the course and effects of restructuring in the coal mining industry: (1) 1990–2002 (achieving financial independence and profitability), (2) 2003–2013 (relative stabilisation and departure from direct restructuring measures), (3) 2014–2020 (a return to conditions of crisis). Particularly visible are the effects of the debt reduction (measures implemented in 2002–2003) and the return of crisis (2019–2020) (Figure 12).

In conclusion, the results of restructuring coal mining companies in 1990–2020 assessed by means of the multi-component FTD measure is further evidence in favour of adopting partial hypothesis (H3). This means that it is possible to periodise the tested time series, which was reinforced by the previously demonstrated occurrence of close (convergent) time intervals distinguished according to the intensity of changes in the structure of electricity generation in connection with changes in hard coal output for energy purposes (see Section 3.4).

crisis (2019–2020) (Figure 12).

**Figure 12.** Financial threat degree of going concern (FTD) against key economic criteria for coal mining enterprises in 1990–2020. Note: net operating profit (NOP)—right axis. Source: as in Figure 1.

costs through investments, which also could not be quickly brought under control during

In the years 1990–2020, productivity suffered a linear and substantial decline [150] with the rapidly shrinking workforce (with a positive or negative deviation from production dynamics) [151]. The costs connected with cutting employment constituted the main component of cost-cutting measures in mining in general [152]. Labour productivity, measured in terms of revenues, increased overall, but from the point of view of efficiency, this indicator turns out to be misleading, as it is based on a quantitative reduction in the volume of production and labour [153]. After 2003, total cost productivity began to decline, and asset productivity at an even faster pace. In these circumstances, the next recessions (2014–2016 and the even sharper downturn of 2019–2020) were inevitable, and they

Low productivity and operational efficiency are quickly and quite strongly reflected in levels of solvency and liquidity as well as in working capital cycles [154]. Debt increases rapidly, and an enterprise's ability to pay its liabilities decreases. In these conditions, enterprises are no longer capable of self-financing and expect public assistance in the form of subventions and government subsidies [155]. Plans to shift away from direct government-initiated restructuring after 2006 remained a fact for only a few years and became utter fiction after 2013 [156]. To keep coal mining companies operating in order to supply

Using the logit model for predicting the degree of financial risk for a going concern (FTD), a synthesis was constructed of partial assessments of the results of coal mining enterprises. This model is a multidimensional tool for assessing the financial situation of an enterprise, as was previously mentioned, and functions as a kind of barometer of this financial condition [158]. The results of the findings are, firstly, that the level of risk for coal mining companies significantly exceeds the results of all industrial enterprises (including those involved in the mining of other natural resources). On average, this indicator was 1.8 times higher than for industry as a whole. Secondly, this is a highly volatile measure (volatility coefficient of 63.7%), which is particularly evident when considering the stabilisation of the industry observed since 2009. Thirdly, the course of the FTD curve makes it possible to distinguish three characteristic periods in the course and effects of restructuring in the coal mining industry: (1) 1990–2002 (achieving financial independence and profitability), (2) 2003–2013 (relative stabilisation and departure from direct restructuring measures), (3) 2014–2020 (a return to conditions of crisis). Particularly visible are the effects of the debt reduction (measures implemented in 2002–2003) and the return of

power plants with raw material, further large subsidies were needed [157].

times of recession.

were triggered by a slight correction in coal prices.

No restructuring can be a stand-alone undertaking [159], especially when it concerns an entire economic sector. The key tasks of restructuring in the case of the hard coal mining industry were to achieve profitability, modernise mining technology, increase productivity and efficiency and promote efficient management practices [160]. Unfortunately, the longterm effects of these measures have been negative. Moreover, the future situation will be conditioned by the interplay of a number of factors, which will give rise to two opposing relationships:


A more detailed discussion of the effects of the restructuring programmes launched in the branch after 1990, led to the following conclusions:


**Proof of main hypothesis.** The research findings presented so far, including the verification of sub-hypotheses H1–H3 and their initial discussion, have denied the validity of the main hypothesis. This means that the restructuring of hard coal enterprises, being neither effective nor efficient, failed to accelerate changes in the energy mix.

A number of studies on mining conducted so far have concerned only the analysis, and in part the evaluation, of the changes that have taken place in the context of a single programme being implemented, or have concerned a limited scope, number and type of measures. The most extensive research on changes in mining was conducted by Makieła [166], Turek and Jonek-Kowalska [173]. They focused only on selected, isolated areas, without trying to build a comprehensive assessment. This deficit was filled by the research in the article through the use of a logit model, which allowed, in synthetic terms, a comprehensive, objective and multidimensional assessment of financial health and risk in the long term. This assessment showed that the restructuring of coal mining companies failed to achieve the primary objective of sustainable profitability and productivity growth. A multidimensional diagnosis of the employment structure and the assessment of differences between individual plants in the hard coal mining industry, carried out by Frankowski et al. [176], included only a detailed analysis of the area of employment and its structure. This article strengthens the method of evaluating the changes in the structure of employment using the taxonomy of structures and evaluating the interdependence with other resources of the enterprises, showing negligible changes in the structure of employment, assets, capital expenditures and sales. Gumi´nski [168], in his research, focused on studies on selected objects of analysis (questionnaire surveys), which as a result prevented the identification of endogenous and exogenous factors affecting the changes that occurred. This missing area was filled in the article using taxonomic analysis, which was supported by regression analysis. A definite weakness of previous studies is the short time horizon, which covered only short periods, mostly limited to specific restructuring programmes,

and the low cross-sectionality of the analysis. The long-term studies conducted allowed the determination of the time intervals with the features of homogeneity (periodisation of the time series). The added value of the research in the article is the analysis of the full restructuring period and all restructuring programmes, as well as the wide range of measures analysed. This article reinforces the assessment by conducting research that provides clear findings in this regard, which were verified using objective assessment tools. As in the past, the authors have referred in their research mainly to the analyses concerning the development of the renewable energy market [181–183], technologies related to the production of energy from RES [184], the socio-environmental effects of energy use and the importance of the conditions related to the limits of pollutant emissions [185], accepting (postulatively) the direction of decarbonisation in energy production [186] and departing from the issue of the necessary coherence of restructuring changes in the mining and power industries. This relationship was demonstrated in the conducted research by confirming the high correlation between changes in the energy mix and changes in the structures characterising mining companies. The results of the research described in the article indicate a very important issue. In order to accelerate the change in the energy mix, the restructuring of the coal mining industry should support the restructuring of the energy sector.

If we consider the last of the above conclusions in a broader context, it was thought [68] that energy security (as defined in 1990) required meeting three conditions simultaneously: ensuring security of the energy supply, maintaining socially justified energy prices and minimising environmental damage. To guarantee energy security understood in this way, it was necessary, inter alia, to ensure (as was confirmed in the goals of subsequent energy policies (PEP)) the possibility of satisfying demand for coal from domestic sources. This was to be achieved by measures aimed at modernising technologies connected with mining and preparing coal for electricity purposes, as well as by creating incentive mechanisms encouraging the maintenance and development of suitable production capacities [72] (p. 10). Unfortunately, the results of the present analysis show that underinvestment in the modernisation of production assets and the exploitation of new deposits places in doubt the ability of the country to meet its needs for coal up until 2049. The situation is serious even despite the predicted gradual decline in demand for coal for electricity generation purposes (set to decline by over 40% in 2040 compared to 2020), according to the forecasts presented in PEP 2040 [49].

On the other hand, the ongoing energy transition in Europe, which is taking place in an expanding common European energy market (Regulation 2019/943/EU) [187], poses another threat to Poland's energy security. Delays in the simultaneous reform of the power engineering and coal mining sectors, as well as prolonging the use of coal as the country's basic fuel while other EU countries are rapidly developing renewable energy sources, may result in the displacement of domestic energy from the power system due to the "merit order" effect [188–190]. This means that priority in the energy system is given to energy with the lowest variable cost of production. In a common energy market where neighbouring countries engage in large scale production from renewable sources, imported energy will be the first to be accepted into the Polish energy system. Renewable energy is characterised by negligible variable costs (it does not use fuel and does not require manpower). This will put the conventional energy sector in a difficult economic situation.

Much will also depend on the pace of development of the Polish RES sector. The key question seems to be whether Poland will maintain the relatively high pace of RES development, already presented in the article. The government's forecasts included in the Energy Policy of Poland by 2040 [49] assume an increase in electricity production from RES by only 13% during the current decade (until 2030). This should increase the share of renewable energy sources in electricity production to 32%. Growth is expected to occur—at different rates—in all renewable sources, as evidenced by official [49] and independent studies [191,192]. However, the development of RES is highly dependent on legal solutions and may be quickly stopped or accelerated [191,193] depending on the political will of the government.

Returning to the conventional energy sector, on the basis of the aforementioned regulations regarding the internal energy market, in 2025 EU member states will no longer be able to support producers of coal-generated electricity via, for example, a system of capacity payments, which currently constitutes an important source of financing for conventional power plants [194,195]. Indirectly, this will have a significant impact on mining through a wave-like reduction in demand for coal—along with the exclusion of subsequent power plants from the system. The financial condition of coal mining companies will deteriorate further, and they will be cut off from funds needed for investments. As a result, the Polish economy will become dependent on energy imports, despite theoretically having significant domestic energy resources at its disposal. This is another argument in favour of not slowing down the pace of energy transition, despite the problems caused by the war in Ukraine and subsequent increases in gas prices, which are treated as a transition fuel for a target zero-emission economy. In conclusion, it can be argued that by lagging behind in the energy transition process, Poland faces a significant threat to its energy security in the medium term.

Every research project has certain limitations. These can be mitigated by ensuring the coherence and consistency of the research framework, as well as by using advanced research methods and databases and applying multiple arguments and long-term timeframes. In the present research, above all else, there were no problems regarding the representativeness of the study (the study population comprised all enterprises in the sector). One limitation was the lack of data availability to conduct structure taxonomy and regression analyses for all measures and the full period (31 years). An additional limitation was the heterogeneity, large cross-sectional variation and discrepancies in the secondary data sets, the mitigation of which was attempted by accessing the primary data. In addition, the depth structural and ownership changes (liquidations, mergers of mines and mining companies) made it difficult to maintain comparability of analysis. The abandonment since 2007 of quantitative target setting in restructuring programmes has weakened the clear assessment of the effects achieved. In part, this problem was solved in the study by using taxonomic analysis to determine the intensity of changes occurring and objective comparisons. As a consequence, generalising conclusions can be drawn with a high level of reliability. This also applies to the trends and dependencies observed in the research due to its very long and exhaustive time horizon (31 years). Methodological limitations were overcome by applying multisectional sub-measures, a multi-dimensional logistic regression measure and taxonomic measures of structure variability. Of course, other methods may produce different results, but it can be argued that the general trends, relationships and structures would not be significantly different. Furthermore, there is no limitation on the degree of applicability of the research. It is intended as a universal diagnostic tool. Both the methods used and the research framework enable its use in a broad, international application. This represents the value of universality of the research and comparability of the results obtained, which has not been achieved so far.

The conclusion that the restructuring of hard coal enterprises, being neither effective nor efficient, did not accelerate changes in the energy mix, provides the platform for further research. The latter will focus on the task of identifying and measuring the factors that influenced the dynamic though still insufficient development of non-coal energy sources, in particular renewable energy sources. Specifically, this research will address:


• selection of energy generation sources for the mix as a relativisation of their efficiency to the energy security factor.

The present research, which has the character of a diagnosis of the state of dependence of the effects of coal mining restructuring and changes in the power industry, as indicated, is the first stage of a widely conducted study. The next step will involve a comparative analysis of the efficiency boundaries (the relationship between effects and expenditures) of all generation sources. Combining the current diagnosis with such an efficiency analysis (non-parametric approach) will allow the formulation of the main building blocks for a new energy transition policy. Already now, however, after the first stage of the diagnosis concerning hard coal mining, the main pillars (directions) of this policy can be formulated:


The results of the research conducted so far, the diagnosis made on their basis and the above-mentioned pillars of the future energy policy prove that the pace of Poland's energy transition should be accelerated. The prospect of abandoning coal by 2049 is too distant, not only because of environmental factors, but also in view of the country's energy security. In the conditions of the single EU energy market, the merit order effect is in force, and, at the same time, given that state support for the capacity market is now prohibited and prices of emission allowances are above EUR 60, conventional energy may be forced out of the market, and the ability to generate revenues sufficient to cover operating costs will be lost.

#### **5. Conclusions**

The research results presented and discussed in the present article yield several general conclusions. First, coal mining companies have not achieved sustainable profitability and competitiveness on the open coal market. Secondly, restructuring has not brought about any significant structural changes in any of the basic economic categories: employment, assets, capital expenditure and sales. Third, no significant technical and technological progress has been achieved.

The main dependency shown in the article is that a 1 pp. change in the structure of electricity generation resulted in a 2.04 pp reduction in coal output and a 1.09 pp decrease in sales of steam coal. When assessing this dependency, it should be borne in mind that coal output, especially in terms of its consumption for energy purposes, only declined by a few percent in the years 1990–2020. This means that the changes in the energy mix were only possible by covering higher energy demand by increasing the use of non-coal energy sources (8.5 times, including 15 times RES). What is more, the share of these sources only began to increase rapidly (exponentially) at the beginning of the 21st century (gas), and later only in the case of energy from renewable sources (wind farms), and from photovoltaics the most, but only after 2011.

The linear decline in hard coal production as well as the exponential share of renewable energy sources in the total energy mix further highlights the argument presented in the detailed conclusions, namely that the restructuring of hard coal enterprises, being neither effective nor efficient, failed to accelerate changes in the energy mix. In particular, this means that there was no consistency (follow-up) between the forms and effects of restructuring applied to coal mining companies in Poland and modifications in the country's energy mix in response to the energy transition.

The negative assessment of restructuring presented above is reinforced by the fact that in 1990–2020, very significant sums to the amount of EUR 57.3 billion (9.3% of GDP in 2020) were set aside in the budget for subventions, subsidies and other encumbrances for the sector. Moreover, the cost of maintaining the hard coal mining industry until 2049 will require an additional approximately EUR 69.7 billion (11.3% of GDP in 2020). Such expenditure, both incurred already and still planned, should be considered wasted in the sense that it failed to accelerate the dynamics of the energy transition.

**Author Contributions:** Conceptualization, J.K., K.K. and W.S.; methodology, J.K. and K.K.; validation, W.S.; investigation, J.K., K.K. and W.S.; resources, J.K., K.K. and W.S.; data curation, J.K., K.K. and W.S.; writing—original draft preparation, J.K. (transition and restructuring; productivity and effectiveness; financial and threat prediction analysis), K.K. (coal mining restructuring in Europe and polish government restructuring programs; structure and concentration analysis) and W.S. (energy transition in EU, energy mix sources, polish energy policy); writing—review and editing, J.K., K.K. and W.S.; visualization, J.K. and K.K.; supervision, J.K. Authorship is limited only to those who have contributed substantially to the research and article. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research and publication was funded by a subvention granted to the Cracow University of Economics.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Restrictions apply to the availability of these data. Data were obtained from: GUS Warszawa (Statistical Head Office in Warsaw)—databases are of limited access and are available (Statistics Poland Databases; available online: https://stat.gov.pl/en/databases/ accessed on 4 February 2022) for a fee and with the permission of GUS, Warsaw; Pont Info Warsaw (Poland), Gospodarka SSDP—commercial databases are available (Gospodarka S ´ SDP; available online: ´ http: //baza.pontinfo.com.pl/index.php accessed on 2 February 2022) for a fee and with the permission of Pont Info, Warsaw; EUROSTAT—unlimited public access databases (available online: https: //ec.europa.eu/eurostat/data/database accessed on 22 February 2022); Agencja Rozwoju Przemysłu (ARP—Industrial Development Agency)—commercial databases are available (available online: https://polskirynekwegla.pl/ accessed on 10 February 2022) for a fee and with the permission of ARP.

**Conflicts of Interest:** The authors declare no conflict of interest. The funder had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

#### **Appendix A**

The financial standing and restructuring effect evaluation measures for coal mining enterprises:


The intensity of structural changes standing and restructuring effect evaluation measures for coal mining enterprises:

	- − fixed assets FA,
	- − employment EMPN,
	- − investment outlays IO,
	- − total productivity TP,
	- − productivity per underground worker PpU,
	- − energy generation EG,
	- − power coal mining PCM,
	- − sale of energy coal SCE.

#### **Appendix B**

Hard coal restructuring programs:


*2003–2006 with the use of anti-crisis acts and initiation of privatization of some mines)* przyj˛ety przez Rad˛e Ministrów w dniu 20 listopada 2002 roku (z korektami wynikaj ˛acymi z Porozumienia strony rz ˛adowej ze stron ˛a zwi ˛azkow ˛a z dnia 11 grudnia 2002 roku oraz korektami wynikaj ˛acymi ze stanu prawnego sektora na dzie´n 10 stycznia 2003 roku)*,* przyj˛ety przez Rad˛e Ministrów w dniu 28 stycznia 2003 roku.


*Energies* **2022**, *15*, 3518

**Appendix C**

**Table A1.** Intensity of changes in the structure of employment (NPS) in mining enterprises in Poland in 1992–2020.






**Table A3.** Intensity of changes in the structure (NPS) of the sales in mining enterprises in Poland in 1992–2020.


*Energies* **2022**, *15*, 3518




**Table A5.** Intensity of changes in the structure (NPS) of electricity generation in Poland in 1990–2020.

**1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020** 0.000 0.013 0.015 0.017 0.011 0.011 0.018 0.017 0.019 0.021 0.033 0.030 0.035 0.035 0.043 0.054 0.078 0.089 0.073 0.086 0.097 0.113 0.139 0.131 0.154 0.177 0.185 0.196 0.202 0.246 0.286 0.013 0.000 0.003 0.005 0.009 0.022 0.028 0.026 0.021 0.027 0.045 0.039 0.045 0.049 0.051 0.065 0.090 0.100 0.079 0.089 0.109 0.113 0.139 0.131 0.155 0.178 0.186 0.197 0.204 0.247 0.287 0.015 0.003 0.000 0.003 0.008 0.021 0.028 0.027 0.021 0.027 0.045 0.038 0.044 0.050 0.052 0.067 0.092 0.102 0.081 0.091 0.110 0.113 0.139 0.130 0.154 0.177 0.185 0.196 0.202 0.246 0.286 0.017 0.005 0.003 0.000 0.010 0.024 0.030 0.029 0.023 0.029 0.047 0.040 0.047 0.052 0.054 0.069 0.093 0.104 0.083 0.092 0.112 0.112 0.139 0.131 0.154 0.177 0.185 0.197 0.203 0.247 0.286 0.011 0.009 0.008 0.010 0.000 0.015 0.022 0.021 0.014 0.020 0.037 0.030 0.038 0.043 0.046 0.061 0.086 0.096 0.075 0.085 0.104 0.110 0.137 0.129 0.152 0.174 0.182 0.194 0.200 0.244 0.284 0.011 0.022 0.021 0.024 0.015 0.000 0.009 0.009 0.015 0.013 0.024 0.023 0.028 0.030 0.039 0.051 0.073 0.083 0.069 0.082 0.093 0.109 0.135 0.127 0.150 0.173 0.182 0.192 0.198 0.242 0.282 0.018 0.028 0.028 0.030 0.022 0.009 0.000 0.005 0.018 0.013 0.017 0.021 0.025 0.026 0.035 0.047 0.064 0.075 0.065 0.078 0.089 0.105 0.132 0.124 0.148 0.170 0.178 0.190 0.196 0.240 0.280 0.017 0.026 0.027 0.029 0.021 0.009 0.005 0.000 0.015 0.010 0.019 0.019 0.023 0.024 0.033 0.047 0.067 0.076 0.063 0.077 0.087 0.103 0.131 0.123 0.147 0.169 0.176 0.189 0.195 0.239 0.279 0.019 0.021 0.021 0.023 0.014 0.015 0.018 0.015 0.000 0.008 0.028 0.020 0.029 0.034 0.037 0.054 0.077 0.086 0.065 0.075 0.094 0.101 0.129 0.121 0.145 0.167 0.175 0.188 0.195 0.239 0.278 0.021 0.027 0.027 0.029 0.020 0.013 0.013 0.010 0.008 0.000 0.020 0.013 0.022 0.026 0.029 0.047 0.070 0.079 0.059 0.073 0.087 0.099 0.127 0.119 0.142 0.165 0.173 0.186 0.192 0.236 0.275 0.033 0.045 0.045 0.047 0.037 0.024 0.017 0.019 0.028 0.020 0.000 0.016 0.015 0.016 0.027 0.041 0.052 0.061 0.056 0.071 0.080 0.097 0.125 0.116 0.139 0.161 0.169 0.182 0.188 0.232 0.272 0.030 0.039 0.038 0.040 0.030 0.023 0.021 0.019 0.020 0.013 0.016 0.000 0.010 0.014 0.019 0.037 0.060 0.069 0.049 0.063 0.076 0.092 0.120 0.111 0.135 0.156 0.164 0.177 0.183 0.227 0.267 0.035 0.045 0.044 0.047 0.038 0.028 0.025 0.023 0.029 0.022 0.015 0.010 0.000 0.007 0.013 0.028 0.051 0.061 0.043 0.057 0.068 0.086 0.114 0.105 0.128 0.151 0.159 0.171 0.177 0.221 0.261 0.035 0.049 0.050 0.052 0.043 0.030 0.026 0.024 0.034 0.026 0.016 0.014 0.007 0.000 0.015 0.029 0.046 0.055 0.041 0.056 0.065 0.085 0.113 0.104 0.127 0.149 0.157 0.169 0.176 0.220 0.260 0.043 0.051 0.052 0.054 0.046 0.039 0.035 0.033 0.037 0.029 0.027 0.019 0.013 0.015 0.000 0.020 0.042 0.053 0.033 0.047 0.061 0.076 0.104 0.096 0.119 0.141 0.150 0.162 0.169 0.213 0.252 0.054 0.065 0.067 0.069 0.061 0.051 0.047 0.047 0.054 0.047 0.041 0.037 0.028 0.029 0.020 0.000 0.029 0.041 0.022 0.034 0.047 0.062 0.091 0.083 0.106 0.129 0.137 0.150 0.157 0.201 0.240 0.078 0.090 0.092 0.093 0.086 0.073 0.064 0.067 0.077 0.070 0.052 0.060 0.051 0.046 0.042 0.029 0.000 0.012 0.031 0.034 0.046 0.062 0.093 0.093 0.111 0.129 0.137 0.150 0.156 0.201 0.239 0.089 0.100 0.102 0.104 0.096 0.083 0.075 0.076 0.086 0.079 0.061 0.069 0.061 0.055 0.053 0.041 0.012 0.000 0.035 0.038 0.039 0.056 0.096 0.096 0.115 0.123 0.130 0.143 0.150 0.194 0.233 0.073 0.079 0.081 0.083 0.075 0.069 0.065 0.063 0.065 0.059 0.056 0.049 0.043 0.041 0.033 0.022 0.031 0.035 0.000 0.017 0.034 0.044 0.072 0.064 0.088 0.111 0.119 0.132 0.138 0.183 0.223 0.086 0.089 0.091 0.092 0.085 0.082 0.078 0.077 0.075 0.073 0.071 0.063 0.057 0.056 0.047 0.034 0.034 0.038 0.017 0.000 0.024 0.030 0.061 0.061 0.080 0.098 0.106 0.120 0.126 0.170 0.209 0.097 0.109 0.110 0.112 0.104 0.093 0.089 0.087 0.094 0.087 0.080 0.076 0.068 0.065 0.061 0.047 0.046 0.039 0.034 0.024 0.000 0.034 0.074 0.073 0.092 0.099 0.095 0.115 0.121 0.160 0.199 0.113 0.113 0.113 0.112 0.110 0.109 0.105 0.103 0.101 0.099 0.097 0.092 0.086 0.085 0.076 0.062 0.062 0.056 0.044 0.030 0.034 0.000 0.041 0.046 0.062 0.068 0.078 0.102 0.108 0.144 0.181 0.139 0.139 0.139 0.139 0.137 0.135 0.132 0.131 0.129 0.127 0.125 0.120 0.114 0.113 0.104 0.091 0.093 0.096 0.072 0.061 0.074 0.041 0.000 0.020 0.027 0.044 0.066 0.090 0.094 0.132 0.169 0.131 0.131 0.130 0.131 0.129 0.127 0.124 0.123 0.121 0.119 0.116 0.111 0.105 0.104 0.096 0.083 0.093 0.096 0.064 0.061 0.073 0.046 0.020 0.000 0.024 0.050 0.065 0.087 0.092 0.129 0.164 0.154 0.155 0.154 0.154 0.152 0.150 0.148 0.147 0.145 0.142 0.139 0.135 0.128 0.127 0.119 0.106 0.111 0.115 0.088 0.080 0.092 0.062 0.027 0.024 0.000 0.029 0.051 0.073 0.077 0.114 0.150 0.177 0.178 0.177 0.177 0.174 0.173 0.170 0.169 0.167 0.165 0.161 0.156 0.151 0.149 0.141 0.129 0.129 0.123 0.111 0.098 0.099 0.068 0.044 0.050 0.029 0.000 0.029 0.049 0.060 0.090 0.127 0.185 0.186 0.185 0.185 0.182 0.182 0.178 0.176 0.175 0.173 0.169 0.164 0.159 0.157 0.150 0.137 0.137 0.130 0.119 0.106 0.095 0.078 0.066 0.065 0.051 0.029 0.000 0.029 0.034 0.069 0.109 0.196 0.197 0.196 0.197 0.194 0.192 0.190 0.189 0.188 0.186 0.182 0.177 0.171 0.169 0.162 0.150 0.150 0.143 0.132 0.120 0.115 0.102 0.090 0.087 0.073 0.049 0.029 0.000 0.035 0.053 0.092 0.202 0.204 0.202 0.203 0.200 0.198 0.196 0.195 0.195 0.192 0.188 0.183 0.177 0.176 0.169 0.157 0.156 0.150 0.138 0.126 0.121 0.108 0.094 0.092 0.077 0.060 0.034 0.035 0.000 0.048 0.091 0.246 0.247 0.246 0.247 0.244 0.242 0.240 0.239 0.239 0.236 0.232 0.227 0.221 0.220 0.213 0.201 0.201 0.194 0.183 0.170 0.160 0.144 0.132 0.129 0.114 0.090 0.069 0.053 0.048 0.000 0.044 0.286 0.287 0.286 0.286 0.284 0.282 0.280 0.279 0.278 0.275 0.272 0.267 0.261 0.260 0.252 0.240 0.239 0.233 0.223 0.209 0.199 0.181 0.169 0.164 0.150 0.127 0.109 0.092 0.091 0.044 0.000

*Energies* **2022**, *15*, 3518

**Table A6.** Results of the univariate regression analysis (significant results only).


#### **References**


#### *Article* **Controlling in the Process of Development of the Energy and Heating Sector Based on Research of Enterprises Operating in Poland**

**Janusz Nesterak 1,\*, Marta Kołodziej-Hajdo <sup>2</sup> and Michał J. Kowalski 3,\***


**Abstract:** Enterprises operating in the energy and heating (E&H) sector play a particularly important role in the economy of each country. At the same time, the conditions in which they currently operate mean that the managers of these organizations have many decision-making problems that they have to deal with. They can be supported by the introduction of well-functioning controlling. This forces scientists to conduct extended research aimed at determining the current and future directions of development of controlling in E&H sector enterprises. At the moment, this is not a frequent field of research exploration. The area of research concerns issues related to the use of controlling in E&H sector enterprises. The objective of this paper is to present the results of the research obtaining knowledge on to what extent companies in the energy and heating sector use controlling tools helping them to increase efficiency of enterprises and effectiveness of the decisions made by managers. The scientific problem is looking for a way in which controlling can increase the efficiency of enterprises in the E&H sector and how it can improve the effectiveness of decisions made by managers. The general conclusion of the research is that it seems necessary to strengthen the role of controlling aimed at its transformation from reporting controlling to management controlling. In view of the challenges of the global economy related to the energy crisis, controlling should be used to a greater extent in the E&H industry to increase the efficiency of basic processes and to effectively implement modern management tools.

**Keywords:** controlling; controlling tools; energy sector; energy and heating; E&H

#### **1. Introduction**

Increasingly, economists are of the opinion that maximizing shareholder returns should not be the main goal of companies, given the complex environment in which they operate and the interactions that exist with various stakeholders. Maximizing the positive impact of enterprises on stakeholders is a new approach that contributes to the metamorphosis of business strategies of enterprises in various fields, especially those that generate negative social and environmental expansions, such as enterprises in the energy and heating sector (E&H) [1,2].

The increase in energy demand, the level of its consumption, climate change caused by economic and social development, limited resources, and the political situation in the world brought the recognition of the strategic importance of the companies from the E&H sector. This is also reflected in specific legal regulations and instruments regulating the activities of enterprises in this sector in the EU. The economic development of countries depends on access to energy. Looking at it through the prism of numbers, it should be

**Citation:** Nesterak, J.; Kołodziej-Hajdo, M.; Kowalski, M.J. Controlling in the Process of Development of the Energy and Heating Sector Based on Research of Enterprises Operating in Poland. *Energies* **2023**, *16*, 773. https:// doi.org/10.3390/en16020773

Academic Editor: Seung-Hoon Yoo

Received: 30 November 2022 Revised: 3 January 2023 Accepted: 5 January 2023 Published: 9 January 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

stated that over the period 2010–2020, the global demand for electricity increased from 64.4 thousand PJ (petajoule) up to 82.0 thousand PJ, or 27.3% over eleven years. In Poland, the trend was also upward. Electricity consumption in Poland in 2010 amounted to 427 PJ, and in 2020 it was 494 PJ, which is 15.7% more. As for heat energy, global consumption increased from 11.5 thousand PJ in 2010 to 12.9 thousand PJ in 2020, so by 12.2%. During this period, Poland saw a decrease in demand from 274 PJ in 2010 to 235 PJ in 2020 (a change of −14.2%).

The energy sector is not uniform. It consists of companies that constitute the elements of value chain of energy supplied to the end user. In addition, the characteristics of the energy market determine its dissimilarity in comparison with traditional commodity markets, as well as with the financial market [3,4]. From the institutional side, the electricity market in 2020 in Poland was dominated by the three largest entities (PGE Polska Grupa Energetyczna S.A., ENEA S.A., TAURON Polska Energia S.A.), which accounted for approximately 62% of electricity production [5]. In the case of thermal energy production, the market is more diversified. In Poland, at the end of 2020, there were 387 companies operating on the regulated heat market that held concessions for the production, transmission and distribution as well as heat trading [6].

Energy companies, while conducting business activity, are also obliged to ensure the continuity of energy supplies, while being supervised by governments. They are also subject to other systemic regulations at national and international levels. Energy sector companies are important for the economy because, on the one hand, they are obliged to ensure energy security, and on the other hand, they can lead to energy poverty, because the increase in energy prices directly affects consumers' access to energy [7,8].

On the one hand, companies operating in the E&H sector play a particularly important role in the economy of each country; on the other hand, the conditions in which these entities currently operate mean that the managers of these organizations have many decisionmaking problems they have to deal with. The solution is to introduce well-functioning controlling to these enterprises, which supports managers in making the right decisions. This forces scientists to conduct extended research aimed at determining the current and future directions of development of controlling in enterprises of the energy sector [9,10]. At the moment the vast majority of authors focus only on selected aspects of controlling, its instruments, or the process of implementation in enterprises of the E&H sector. Therefore, the area of research presented in this paper concerns issues related to the use of controlling in E&H sector enterprises.

The objective of this paper is to present the results of the research obtaining knowledge to what extent companies in the Energy and Heating sector use controlling tools helping them to increase efficiency of enterprises and effectiveness of the decisions made by managers.

The scientific problem is looking for a way in which controlling can increase the efficiency of enterprises in the E&H sector and how it can improve the effectiveness of decisions made by managers.

The research presented in this paper has been discussed in six parts. After the Section 1, Section 2.1 presents a literature review on the concept of controlling and its role in an enterprise management. Section 2.2 elaborates on the current scientific achievements in the area of research on the use of controlling in E&H sector enterprises. Section 3 discusses the applied research procedure and data sources. The results of the research are presented in Section 4. Finally, in Section 5, a discussion and conclusions from the conducted research are formulated.

#### **2. Theoretical Framework**

#### *2.1. Review of the Research in the Field of Controlling*

The concept of controlling does not have a uniform definition in the world literature. Different schools of thought around the world have different views, and numerous authorities in the field of management indicate helplessness in trying to organize this concept. Preissler rightly states that "everyone has their own ideas about what controlling means or should mean, only that everyone thinks something different" [11]. The multiplicity of definitions is also caused by the use of controlling in many different enterprises with different organizational or financial situations. The fact that controlling may concern many functional areas also affects the multitude of concepts. One of the reasons for many different theories may be the ambiguity of the English word "to control".

The Americans and Germans had the greatest influence on shaping the term controlling. The American approach to controlling assigns it the task of monitoring current results and constantly comparing them to planned assumptions, as well as forecasting and providing managers with various information that can serve the implementation of the company's goals. For this reason, the American controller is more identified with the finance and accounting department, and his duties are similar to those of the European chief accountant. Controlling plays an advisory role in the management process. For German scientists, controlling is a more comprehensive process that includes a wide range of instruments for managing and controlling the company's finances, while the controller's attention is focused on all departments of the company. Controlling not only supports the functions of the management process, but even creates them; hence, it can be said that it is a management system. The differences between these two fundamental approaches to controlling are presented in Table 1.


**Table 1.** Controlling functions according to the German and American schools.

W—Function support, P—Taking over some functions, T—Creating a function, G—Depth of a function; Source: own elaboration based on: [12–19].

In the literature on the subject, controlling is understood ambiguously as: a philosophy [20–22], system [23–25], management method [26,27], or management tool [28–31]. It is associated with management systems and perceived as "managerial control" [32–34], or as "management control and accounting" [35–37]. Many scientists believe that the idea of controlling is now also expressed in management accounting [38] and [23] (p. 19). More and more frequently among researchers of the subject, the concept of controlling is sometimes called performance management [39–43]. The multitude of definitions, terms and interpretations indicate various areas and functions of controlling, presenting different ways of understanding it both by management theoreticians and practitioners. Research [44–47] presents detailed characteristics of controlling, its tasks, possible competencies and importance for the management system.

Understanding controlling as a tool to support managers determines its positive features, among which are: lowering costs, increasing profits, increasing work efficiency, and developing effective strategies and structures that facilitate the decision-making process by improving the information system or profitability analysis in various functional areas of the company [48].

The concept of controlling in its contemporary meaning was created in German countries and it is perceived as a subsystem supporting organization management [23]. Similarly, it is characterized as support for planning and coordination subsystems [49], or as coordination of the management system in terms of solving various types of decisionmaking tasks [50].

In his research, Mocanu states that controlling is an important method supporting management, it is one of the most frequently used management methods, and it is associated with a relatively large polemic related to its different perceptions [51]. The issue of controlling, due to its high applicability, is often taken up in scientific research. The works carried out by foreign and Polish scientists concern enterprises with various types of activity and from various sectors of the economy. Research conducted by Abdel-Kader and Luther [52] seeks to find out why companies adopt different practices in the field of management accounting and what the process of change in this area looks like [53]. There are also studies on the impact of variously understood managerial control strategies on the financial results of the surveyed organizations [54–61]. Schäffer and Binder present extensive considerations pointing to the development and use of management accounting and management control research in German-speaking countries [62].

Research on the use of controlling in the economy is conducted by many scientists from various foreign universities. The leaders are mainly scientists from research centres in Germany and the USA, conducting multidimensional research on enterprises using controlling [63–71]. The ICV (Controller-Verein e.V.—Wörthsee, Germany), founded in 1975, is of great importance for expanding knowledge on controlling solutions in the economy, which prepares numerous reports containing guidelines for controlling [72]. Extensive information on the development of controlling tools in the practice of German enterprises can be obtained on numerous Internet portals [73–76]. In Poland, the issue of controlling is a field of exploration pursued by many scientists. These studies are conducted mainly at the Cracow University of Economics, the Wrocław University of Economics, and the Wrocław University of Technology, with the support of scientists from other academic centres in Poland [77–88].

#### *2.2. Controlling in Companies from the Energy and Heating Sector*

Scientific considerations over the last few decades also concern the use of controlling in management within the energy sector enterprises. However, their number is relatively small, compared to the research on typically productive sectors of the economy. In manufacturing enterprises, a distinction is usually made between the primary activity related to the production of specific products and services and the auxiliary activity, the primary task of which is to support the core activity. However, the organization of manufacturing processes in energy sector enterprises is more complicated and, therefore, model controlling solutions are significantly diversified, from the method supporting management [89,90] to the implementation of its selected tools [91].

Scientific considerations regarding the energy sector indicate that Polish enterprises are often characterized by a management approach based on management accounting and budgeting. They use traditional tools in the form of budgets and financial evaluation indicators more often than more advanced controlling instruments [92–95]. A common controlling tool, implemented in practice, are models of systematic cost accounting, the implementation of which is to help in providing relevant and useful information about the costs of the company's activity [96]. In Polish research, we will also find references to the possibility of using problem-based cost accounting in energy sector enterprises, such as ecology cost accounting [97] or quality cost accounting [98]. An analogy can also be found in scientific research conducted on the example of Slovak enterprises, also from the energy sector [99].

The increase in the demand for information on the costs of energy production and transmission reported by the management staff, and on the other hand, the growing complexity of the processes of supplying individual types of energy, the significant diversity of energy consumers and products, and the increase in the share of indirect costs, became the basis for the development of management accounting instruments used in enterprises of the energy sector, especially in relation to activity-based costing [100–104].

As indicated by the researchers of the subject, companies from the energy sector are also looking for more advanced controlling tools that respond to the new approach to management—management by value. This idea considers the process of creating company value in terms of activities. According to this concept, energy company is as a whole composed of processes and activities aimed at creating value. Few studies in this area in the energy sector indicate that the integration of activity-based costing and the EVA method increases the effectiveness of enterprise management. The combination of these methods makes it possible to identify activities that reduce a company value, which should be modified or eliminated [103,105,106].

In the literature on the subject, there are also studies indicating that limited energy resources and energy security concerns in relation to alternative energy sources prompt the use of product life cycle analysis (LCA) to analyse sensitivity and compare the level of costs for different energy sources. The research points out that the concept of a product life cycle management (LCM) aims to minimize the environmental and socio-economic burdens associated with the product throughout its life cycle. It is emphasized that one of the directions of development of controlling in the energy sector should be the use of this tool by investors and decision makers in the process of making decisions regarding sustainable development [107–109].

Additionally, of interest are the studies indicating the potential of personnel controlling and the system of human capital valuation indicators in energy enterprises [45]. Some studies emphasize the importance of financial controlling and its impact on the results and efficiency of operations [110], or the possibility of using the balanced scorecard (BSC) as an instrument of strategic controlling, which can effectively support the management processes of a company operating in the E&H industry [111].

Few studies in the energy sector include: verification of the levels of use and use of BSC, the impact of individual characteristics, the most commonly used metrics and the characteristics of its effective implementation. Despite the widespread use of this instrument in many sectors of the economy, as scientific considerations show, it is not a popular controlling instrument in energy enterprises. The authors point to the need to implement BSC in these entities, for example through benchmarking on other industries where BSC was used, which should lead to faster and better results in the energy sector [112,113].

In the processes supporting project management in energy enterprises, real and adequate use should be made of project controlling [82]. The subject of consideration of researchers is also the use of controlling from the conceptual and organizational side in the process of restructuring enterprises from the energy sector [89,114–116].

Due to the nature of the E&H sector, scientists pay attention to another research area, which is the scope and effectiveness of implementing operational tools and strategic controlling in energy companies towards sustainable development and corporate social responsibility [117].

In recent years, the field of exploration has been the practice of management accounting in the field of obtaining environmental information for managers, the so-called environmental management accounting [118,119]. Many researchers [120,121] draw attention to the use of environmental reporting, with particular emphasis on the energy sector, asking how accounting and management systems introduced in enterprises can reduce their negative impact on sustainable development [122].

The literature on the subject also indicates the need to develop controlling tools for managing carbon dioxide emissions, not only in energy sector companies (so-called carbon accounting) [123,124].

In recent decades, we have been dealing with regulations' changes in many areas, including the energy sector, not only in Poland, but also in other countries. When talking about regulation, we mean widely existing (and sometimes emerging) law, other legal requirements, standards and generally recognized guidelines. Currently, scientific considerations indicate the need to conduct theoretical and empirical research in the field of controlling, management accounting in the area of regulation to which the energy system is subject. Many of these regulatory changes come with disclosure and transparency requirements. So, there is a need to test them causally, not just to document it [125].

Research conducted by Kowalewski and Lelusz [126] showed that, in the opinion of managers, often the main reason for implementing controlling in an enterprise from the energy sector is the need to systematize planning and analyses. However, the main barrier to its implementation is the insufficient knowledge of employees on this subject. According to the managers, the most important positive effects of the implementation of controlling in the E&H sector are: the organization of the division of tasks and goals to be achieved in individual organizational units as well as the increase in the professionalism of employees. The research also looks for factors determining the success of the implementation of controlling in companies from the energy sector [127].

Due to the strategic nature of this sector for the economy, E&H is subject to many legal regulations, indicated in the Energy Law [128]. The obligations of companies in this sector include the preparation of a number of industry reports indicated by the Energy Regulatory Office. Reporting obligations are also imposed by the Accounting Act [129], tax laws or requirements of other public and local government institutions. This applies to reporting not only financial data, but also qualitative and non-financial data. This multidimensional reporting system requires ordering and synchronization, which is ensured by well-implemented and updated reporting controlling. Hence, in the further part of the work, the results of the original research presenting the current state of the use of controlling in enterprises of the energy and heating sector in Poland are presented.

#### **3. Data and Descriptive Statistics**

#### *Research Procedure and Data*

The research procedure was adapted to the research objectives of this paper.

In the years 2013–2022, the research was conducted with the aim to determine the state of maturity of the use of controlling in enterprises operating in Poland. In the empirical research, a non-probability method of sampling (purposive sampling) was used to enable obtaining the results that were as representative as possible for the E&H registered companies. E&H firms from the companies register databases were approached. The respondents were the management representatives, including both the top management level, as well as directors or operational managers and controllers of E&H companies. The respondents were mainly employees who understood the problems posed in the research survey.

The surveys were carried out using a business intelligence IT system called Business Navigator [130] by Archman sp. z o.o. from Krakow and the Google survey system [131]. The Business Navigator system has a survey module that supports large-scale research and enables the presentation of results in any layout. Its key functionalities include: fully independent user-defined surveys and directing them to selected respondents; defining serial surveys, created and sent automatically by the system after previously defining the sending parameters; and managing access to the results for selected people. The survey was initially prepared in a spreadsheet and then transposed to the Business Navigator IT system and the Google survey system. The next step was to register the respondent's e-mail address, after obtaining his consent to participate in the study. From the IT system, the questionnaire was directly and automatically sent by e-mail to the respondent with

a request to complete it. The time of starting work on the survey was recorded in the system, thanks to which it was possible to obtain information about the number of survey participants on an ongoing basis. The respondent was able to stop filling in the survey at any time, save the results, and return to it at a convenient time. After completing the questionnaire, the respondent received an automatic e-mail informing him that sending the questionnaire was successful. As a token of gratitude for their time, the respondents received an electronic version of the monograph entitled "Controlling. Assessment System of Performance Responsibility Centres." [132].

First, the questionnaire was sent to a selected group of 30 respondents (pilot studies), people who worked in controlling positions. They were asked to complete the questionnaire and indicate their comments, both substantive and technical, regarding the transparency and understanding of the questionnaire. The purpose of such action was the need to verify the research tool in such a way that it was detailed on the one hand, but on the other hand did not lead to its negative reception by the respondents. The collected comments were used to develop the second version of the questionnaire, slightly reduced in size, which was sent to 884 respondents who declared their willingness to participate in the research. The database of potential respondents was developed on the basis of initial interviews with employees of the managerial level and those employed in the financial and controlling departments of organizations operating in Poland. A fully completed questionnaire was submitted by 289 respondents (32.7% of the total research frame). A total of 595 (67.3%) surveys were not returned, with 195 (22.0%) respondents starting surveys but not completing them. Some of the respondents who did not complete the questionnaire indicated that after reading the detailed questions, they were not able to answer the questions in a professional and reliable way. They argued that they have a lack of knowledge on the subject of the conducted research.

The survey consisted of six thematic areas of various volumes. In addition, in order to obtain knowledge about the respondent, a detailed specification about them and the company they represent was included. In the survey, it was also decided to use the formula of open questions, allowing the respondents to comment more extensively on specific problems. The commentaries supplemented the answers with valuable—from the research point of view—opinions of people completing the survey.

This publication presents the results of research conducted in the areas of: organization of controlling functions, accounting recording solutions, cost accounting and cost management tools, management reporting and budgeting. The survey consisted of 103 questions, the vast majority of which were closed questions, and the respondents could add comments to their answers each time. The presented results include selected questions considered important in the course of the conducted analyses.

In the first step, the collected empirical material was analysed. The aim of the research was to analyse the controlling solutions used in companies in the energy and heating industry against the background of solutions used in enterprises of other industries. The analysis began with the presentation of the research sample. The collected empirical material covering 289 companies operating in Poland was presented, including 51 entities included in the energy and heating (E&H) industry. It should be noted that only the binding answers provided by the respondents were analysed. Missing answers and or "I do not know" answers were omitted. The energy and heating sector companies were characterized against the background of other companies (Others). Then, selected areas of controlling were analysed, looking for features characteristic of solutions used in energy and heating.

The empirical material was analysed and statistically inferred. The analyses were conducted primarily with the use of contingency tables as well as multiple-response and dichotomy tables; the descriptive statistics of defined variables were inspected and tests concerning the analysis of variance were performed. The analysis of the relationship between the variables was tested with Pearson's chi2 and maximum likelihood chi2 statistics, taking into account Yates' corrections for lower expected numbers. In addition, the assessment of the strength of the relationship between the variables was identified by Pearson's convergence coefficient and Spearman's rank correlation coefficient.

#### **4. Empirical Framework**

#### *4.1. Characteristics of Companies in the Energy and Heating Industry*

The analysed sample included companies with various characteristics. The identified size classes of entities were similar in terms of numbers: large companies—40%, mediumsized companies—29%, and small companies—31%. The sample was dominated by entities with over 15 years of experience on the market, as mature companies constituted 65% of all respondents. Companies with dominant production activity (42%) were represented similarly to companies with dominant service activity (46%), while companies with dominant commercial activity represented 12% of the total. Most of the analysed entities operated in conditions of high competition (45%) and offered specialist products (57%), acting for the mass client (75%).

E&H companies accounted for 18% of the analysed sample. Table 2 presents the characteristics of the sample broken down by the E&H sector and other entities. Companies from the E&H industry did not differ from others in terms of size and time of operation on the market, as well as in terms of the subject of activity. The homogeneity of these features in the two surveyed groups is important from the point of view of the analysis of controlling solutions observed in the E&H sector against the background of the entire sample. E&H companies in the analysed sample operated in conditions of low and medium competition much more often than in the Others group. This relationship was clear and observed at any low level of confidence. Similarly clear differences were observed for the dominant capital feature. E&H companies in the analysed sample definitely more often represented entities with dominant public capital. There were also surpluses of the observed frequencies over the expected ones for offering a mass product for both narrow and mass customers, with the statistics confirming the significance of this relationship for 0.05 < *p* < 0.1.

#### *4.2. Organization of the Controlling Function in the Energy and Heating Industry*

The results of the analyses concerning the organization of the controlling function are presented in Table 3. The vast majority of the analysed E&H companies distinguished the controlling function in the organizational structure. 84% of the analysed entities declared institutional separation of this function. This share is definitely higher than in the case of other companies (58%) and the entire sample (62%). It was observed that E&H companies form controlling structures. The need to use controlling tasks seems to be present in the E&H industry, regardless of the size and time of operation of the entity on the market. Among the eight companies in which the controlling unit was not separated, there were mainly entities of medium size and average experience in the market; in three of them; the performance of controlling tasks was declared in a non-institutional form, by assigning tasks to other organizational units; and in three companies, the performance of the function was declared by an outsourced service. It is noteworthy that the implementation of controlling tasks in the E&H industry generally requires the creation of complex multistation structures. The functioning of such structures was declared by as many as 51% of the surveyed companies and 40% of multi-position structures; only in 9% of the cases was it declared that the controlling task was handled by a single position separated in the organizational structure.


**Table 2.** Features of the research sample.

*Energies* **2023**, *16*, 773

E&H

Expected count B 19.8 18.4 10.9 1.9 51 14.5 36.5 51 21.2 6.2 23.6 51 A-B 2.2 −2.4 −2.9 3.1 0 6.5 −6.5 0 0.8 3.8 −4.6 0


**Table 2.** *Cont.*


*Energies* **2023**, *16*, 773


**Table 3.** Organization of the controlling function in the surveyed entities.

Source: own study.

The results regarding the location of controlling tasks in the organizational structure seem surprising. Three out of four analysed companies in the E&H industry distinguish the controlling unit on the line position as one of the company's functions. The results of the conducted analyses clearly indicate that this feature distinguishes the analysed E&H companies from the other analysed entities. The differences are significant and their statistical significance has been clearly confirmed. The results are surprising, because modern controlling tasks seem to be staff orientation in the organizational structure, which was recorded only in 25% of E&H companies, compared to 55% in other companies, taking into account that a significant part of them does not distinguish controlling structures at all.

Statistical analysis shows that the direct subordination of controlling structures to the organizational structure is also a feature that distinguishes E&H companies from others. E&H companies more often than other surveyed entities subordinate their controlling unit to chief accountants and directly to the management board. Significantly less often, however, it is subordinated to the financial director. The significance of these differences in this area between the studied E&H entities and Others was confirmed statistically at a low level of confidence. This relationship seems to be worth mentioning, as it indicates that the controlling unit very often supports the reported tasks assigned to accounting departments. An interesting analysis would also be supplementing the applications with verification of how often the position of CFO is separated in E&H companies.

#### *4.3. Accounting Records of Economic Events*

Table 4 presents the results regarding the scope of separating the centres of responsibility. 75% of the surveyed companies from the E&H sector indicated that they create responsibility centres. This result is higher than in Others entities, where it is about 58%. It has been observed that E&H companies create use more responsibility centres.


**Table 4.** Responsibility centres in the audited entities.

Source: own study.

The analysis of solutions in the field of recording economic events was carried out primarily in terms of the method of separating the centres of responsibility. In the survey, respondents were asked what type of responsibility centres are created, indicating the following options: department, employee, process, type of cost, type of activity, device, task/project, investment sentence and order. The question also contained an

open answer, other, where the respondent could indicate any number of other centres of responsibility used.

On the basis of the answers obtained, a synthetic variable counting how many different types of responsibility centres were identified was defined. The results obtained in this regard, broken down by E&H and Others, are presented in Table 5. The results indicate that the surveyed E&H companies distinguish definitely more types of responsibility centres than other entities. The significance of these differences was confirmed by the conducted statistical analyses. Further analysis was carried out at the level of selected types of responsibility centres. Significant differences were identified primarily in relation to investment tasks. The results in this regard are presented in Table 5. Companies from the E&H sector definitely more often than other analysed entities identify centres dedicated to the implemented investments in the records system. The differences in this respect between E&H and Others are clear and reported as statistically significant at any low level of confidence. This seems to be directly related to the specificity of E&H activities, which usually require significant investments in production or network infrastructure related to energy supply.

**Table 5.** Number of different types of responsibility centres in the surveyed entities.


**Panel A: Descriptive Statistics**

Source: own study.

It should be assumed that separating a given type of responsibility centre requires the organization of dedicated recording tools, most often an account segment in the records of economic events. The analysis of the tools for recording economic events presented in Table 6 shows that, in most cases, E&H companies keep records of costs directly in the financial and accounting system, without separating data relevant for management reporting outside these systems. This feature does not distinguish E&H companies from practices recorded in other entities.

The way of document circulation also distinguishes the E&H industry from the rest. Nearly 73% of the companies declared the lack of any tools supporting the circulation of documents and indicated the paper circulation of documents. This ratio is comparable to that recorded in the Others group, where it is 74%. It should be added, however, that these data were collected 5 years ago; hence, it should be assumed that the current state of use of systems supporting the circulation of documents is greater in both groups. Irrespectively, what draws attention in the obtained results is the fact that E&H companies use ERP systems directly for the circulation of documents. Both reported dependencies—more frequent use of ERP systems to handle more extensive cost accounting than observed in the Others segment and more frequent use of ERP for document circulation may be important when formulating requirements for these systems for the E&H industry.


**Table 6.** Accounting record tools used in the surveyed entities.

Source: own study.

#### *4.4. Cost Accounting and Cost Management Tools*

Further analyses of controlling solutions focused on the applied cost accounting. The results of selected analyses are presented in Table 7. Based on the observations made, it is difficult to identify cost accounting solutions dedicated or preferred in the E&H industry. The observations made indicate that respondents from E&H pointed to the use of variable costing much less often than other market segments. The results confirm the statistical significance of these differences at any low level of significance. There was an excess of the observed numbers over the expected ones in the case of full cost accounting in E&H companies, but the analyses did not confirm that this segment differed from the others in a statistically significant way. The use of problem costing, including activity-based costing in the observed E&H group, was low and amounts to about 8%. The observed use of these cost accounts in the Others group was also low and amounts to approximately 15%. Statistical analysis, however, does not allow accepting the hypothesis that the use of these cost management tools differs significantly in both analysed groups.

Differences between the groups were noted in terms of cost accounting techniques and tools used. E&H companies more often apply tasks related to cost settlements directly to ERP systems; similar dependencies have already been identified in other areas. The use of dedicated tools (controlling systems) or even a spreadsheet for tasks related to cost settlement is observed less often in the E&H industry than in other analysed entities.

In the conducted research, the types of cost settlements were analysed. Respondents were asked both what cost settlements are performed and what are the basis for these settlements. Selected results are summarized in Tables 7 and 8. The conducted analysis showed that respondents most often indicated the use of one type of cost carrier, average 1.5, with a choice of: natural units (pcs., kg, etc.), direct costs, internally defined price lists, transfer pricing rates, agreed percentages, normative costs, person-hours according to registered working time and others with the option of indicating any other settlement bases.

*Energies* **2023**, *16*, 773



**Table 8.** Number of types of billing keys used.

Source: own study.

There were no significant differences in the number of cost carrier types used between the companies classified in the E&H and Others groups. However, differences in the frequency of use of particular types of carriers were identified. Companies in the E&H industry use time-based cost settlements more often than observed in other entities. The excess of observed observations over expected observations is significant and confirms the statistical significance of the conclusion.

There were also no differences as to the introduction of controlling reporting models for the profit and loss account in the form of a multi-block margin account. Only 36% of respondents from the E&H industry indicated that the companies they represent create this type of model, in the group of other companies, the corresponding indicator is 46%. The small number of observations recorded in this respect for E&H does not allow us to confirm the statistical significance of the observed differences.

#### *4.5. Role and Tasks of Controlling*

The scope of activities undertaken by the controlling structures created in the surveyed enterprises was also analysed. The survey indicated typical activities carried out by the controlling department, asking the respondents to indicate whether the given activities are undertaken and what percentage of time the controllers spend on their performance. The analysed tasks included: supporting strategic planning, budgeting and cost control, providing information from the environment, providing management tools, coordination of planning and control, monitoring goals, operational tasks of employees, budgets and implementation of strategic tasks, internal and external reporting, creating analyses and reports on the company's environment and internal financial statements, implementation of new and optimization of existing IT tools, support for planning and financial control, and other tasks. Selected obtained results are presented in Table 9. The obtained results allowed us to identify the relationships presented below.

The tasks of controlling in E&H companies more often than in other analysed entities concern budgeting. Budget management as a task of controllers was indicated more often in a statistically significant way in E&H companies compared to Others companies. At the same time, it was noted that respondents in E&H companies indicated that a greater percentage of controllers' working time was devoted to this task. For E&H companies, there were surpluses of the observed numbers over the expected ones in the category above 50% of working time and 25–50% of working time is devoted to budgeting and supervision over the implementation of the budget. Companies from the Others group more often spend up to 25% of their working time on this type of activity. The significance of these dependencies was confirmed at the level of *p* = <10%, but it is not without significance here that the respondents to the questions asking for an estimate of the time spent on particular activities often did not answer or chose the answer "I do not know", as a result of which the collected material was often insufficient to carry out full inference.


