Next Article in Journal
An Empirical Mode Decomposition-Based Hybrid Model for Sub-Hourly Load Forecasting
Previous Article in Journal
Calculation Methods of High-Voltage Direct Current (HVDC) Line Sag Considering Meteorology
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Energies
Volume 17
Issue 2
10.3390/en17020306
energies-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
Correction published on 13 June 2024, see Energies 2024, 17(12), 2891.
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Switching Behavior in the Polish Energy Market—The Importance of Resistance to Change

by
Ilona Lipowska
1,*,
Marcin Lipowski
1,
Dariusz Dudek
2 and
Radosław Mącik
1
1
Institute of Management and Quality Sciences, Maria Curie-Sklodowska University, Pl. M. Curie-Skłodowskiej 5, 20-031 Lublin, Poland
2
Faculty of Management, Lublin University of Technology, Nadbystrzycka 38, 20-618 Lublin, Poland
*
Author to whom correspondence should be addressed.
Energies 2024, 17(2), 306; https://doi.org/10.3390/en17020306
Submission received: 10 December 2023 / Revised: 4 January 2024 / Accepted: 6 January 2024 / Published: 8 January 2024 / Corrected: 13 June 2024
(This article belongs to the Section C: Energy Economics and Policy)
Browse Figures
Versions Notes

Abstract

:
(1) Background: Consumer switching behavior was one of the expected outcomes of the ongoing competitive reform in the European electricity industry. The aim of this study is to analyze the factors that affect the intention of consumers to switch from their incumbent supplier, who has a strong market position. This article follows the trend of describing and analyzing the situation of incumbent energy suppliers after energy market liberalization. (2) Methods: The research goal was to verify seven research hypotheses regarding switching intentions on the Polish energy market. For this purpose, structural equation modeling analysis using AMOS 29 was conducted. The survey was carried out in 2020, before the SARS-CoV-2 pandemic in Poland, and the research sample consisted of 1216 adults (clients of the incumbent supplier). (3) Results: Special attention was given to customers’ resistance to change, as it was found to be a factor that can impact customers’ decisions to switch, both directly and indirectly. Resistance to change can affect the trust that customers have in their current supplier’s brand, their perception of the costs involved in switching, and their intention to switch. The research also highlighted the importance of brand trust and perceived ease of change in the customers’ decision-making process. (4) Conclusions: An important factor that reduces switching behavior is the customer’s resistance to change; brand trust is a valuable asset that also reduces switching intention.

1. Introduction

According to the World Bank [1], the global movement to reform electric power markets started during the 1980s and has advanced considerably since then. Competitive reform of the European electricity industry is still in progress [2]. The main goal of these reforms was to introduce market competition in the electricity industry through restructuring, privatization, and re-regulation [3]. Electricity companies, particularly in Europe and the US, have been facing the transformational challenges of the energy transition for the past decade as a result of the rapid increase in the supply of renewable power and new players emerging to meet the requirements of a changing marketplace [4]. Researchers have underlined the great expectations that have been created due to the introduction of competition into retail electricity supply [5]. Consumer switching was one of the expected results of stronger retail competition [6]. However, incumbent energy suppliers have been perceived as incumbent coalitions that “defend the status quo against challengers” [7] (p. 57). Poland is a country with a moderately concentrated energy market [8]. To make an accurate assessment, we need to consider two variants of the HHI. The Herfindahl–Hirschman Index (HHI) is a common measure of the market concentration of an industry and is used to determine market competitiveness. The first variant is calculated based on the amount of electricity that is fed into the grid. This variant shows a high degree of market concentration. The second variant is calculated based on the installed capacity, which indicates an average degree of market concentration [9]. Almost three decades ago, S.M. Keaveney [10] (p. 71) wrote that “customer switching behavior damages market share and profitability of service firms and yet has remained virtually unexplored in the marketing literature.” Although many interesting studies and publications on customer switching behavior have been presented since then, this topic still deserves research attention due to rapid market changes. In most European countries, the energy industry continues to undergo dramatic changes [11]. We strongly believe that the energy market, with its specificity, requires further analysis of factors affecting customer switching behavior. In Poland, the liberalization of the energy market has started relatively recently. The transformation process of converting the Polish economy into a market-oriented economy began in the early 1990s. One of the last sectors to undergo this transformation is the energy sector [12]. The major step towards the liberalization of the Polish energy market was the amendment to the Energy Law Act that entered into force on 3 May 2005. On 1 July 2007, each customer (household) obtained the right to purchase energy from the seller of their choice [13]. However, changing energy suppliers is still not a mass phenomenon. Although much research on different energy markets indicates that changing energy suppliers can be one of the easiest ways to save money on energy bills [14], many consumers do not take advantage of the lower energy prices available in liberalized retail markets [15]. There is a significant difference between the cheapest and most expensive contracts available on the market, so consumers have the option to switch or renegotiate contracts with their existing supplier to benefit from the lowest prices [16]. Some interesting conclusions were presented by Giulietti, Waddams, and Waterson [17], who pointed out that incumbent energy suppliers retained considerable market power despite the process of liberalization in the energy market. However, consumers’ switching decisions demand time and patience—according to researchers, the accumulation of switching experiences can reduce transaction costs for consumers and promote consumer switching [18]. The final decision on switching to another supplier is a two-step decision: first, a consumer needs to take the step of considering switching (analyzing the market alternatives), and next, a consumer decides whether to switch from the current supplier or not [18]. Since retail energy markets have opened, regulators and competition authorities have placed increasing emphasis on the importance of active consumers who check and shop around for better deals to ensure a well-functioning market. Electricity supply, like many other markets, involves a ‘default’ relationship: consumers remain with the same supplier unless they take action to switch [19].
A major aim of deregulation in energy markets is to increase competition among retailers and thereby enrich consumer choice [14]. Switching suppliers is likely to occur when consumers perceive the benefits of switching as exceeding the costs. Such a decision is under the influence of both financial (or economic) and psychological (or behavioral) factors; the former are relatively straightforward in any analysis of consumer decisions, while the latter are relatively complex [14].
The research results presented in this paper refer to the time before the COVID-19 pandemic, which we consider an advantage and not a weakness due to the avoidance of potential periodic market disturbances related to the pandemic and temporary government regulations (subsidies on the energy market). This study aims to analyze the relationships among resistance to change, brand trust, perceived switching costs, perceived ease of change, and switching intention. The methodology used is a quantitative study through a questionnaire with a sample of 1216 customers from Poland in 2020. To achieve this objective, SEM (structural equation modeling) analysis using AMOS 29 was conducted. Our article follows the trend of describing and analyzing the situation of incumbent energy suppliers in various European countries after energy market liberalization [20,21,22]. Keeping in mind the essence of the incumbent supplier, we gave special research attention to customers’ resistance to change as a factor, directly and indirectly, impacting customers’ switching decisions on the energy market.
The paper is structured in the following manner: The Section 1 introduces the theme, objectives, and relevance of the investigation. The Section 2 presents a theoretical background, which emphasizes some useful theories, and presents seven research hypotheses. The Section 3 presents both the methodology and the research model. The Section 4 describes the sample used and analyzes the empirical results. Finally, the Section 5 covers the discussion.

2. Theoretical Background

Our study is focused on examining the factors that influence the intention to switch energy suppliers. This type of behavioral intention is defined in the literature as the level of effort one is willing to put in to achieve a particular goal [23]. Behavioral intention (BI) is the probability, or a measure of strength, of one’s intention to perform a specific behavior [24]. Behavioral intentions are central to a range of theories about the determinants of behavior/action [25]. One of these prominent theories is the Theory of Planned Behavior (TPB) by I. Ajzen, which is an extension of the Theory of Reasoned Action (TRA) proposed by M. Fishbein and I. Ajzen. According to the TPB, the behavior of individuals is driven by behavioral intention. The main element of the TPB is therefore behavioral intention, understood as an individual’s intention (readiness) to become involved in a specific behavior—the stronger the intention, the greater the probability of the individual’s involvement in a given activity [23]. Both in the TRA and TPB, behavioral intention constitutes the immediate predictor of the behavior [26]. When pointing out important theories using the BI concept, the Self-Determination Theory by R. Deci and R. Ryan [27,28] should also be mentioned. The basic concepts underlying this theory are one’s autonomy or perceived power/control over one’s behavior [29]. A person is autonomous in their behavior when this behavior is perceived by them as voluntary and fully supported by them; the person is involved in the activity and approves of the values expressed by this activity [30]. Another theory worth indicating is the Self-Efficacy Theory by A. Bandura [31], which concerns an individual’s belief in the possibility of action toward a chosen goal, regardless of any obstacles that may arise. A. Bandura defined perceived self-efficacy as an assessment of one’s abilities to perform a specific task necessary to cope with and overcome future and potential problems [32]. It is a person’s belief in their ability to mobilize motivation, cognitive resources, and actions needed to exercise control over events in one’s own life [33]. The Push–Pull Mooring (PPM) framework starts with the law of migration, which explains why a person migrates from the point of origin to the point of destination [34]. The PPM model is a common approach used to explain why customers switch between different products, services, or even suppliers [35]. In this theoretical approach, the switching intention is defined as a signal of termination of the relationship between the consumer or customer and the old service provider [36].
All of these theories help understand customer switching behavior. The article discusses how changing energy suppliers is a specific form of switching behavior. Switching behavior refers to the way customers shift from one supplier to another [37]. Consumer switching refers to consumers stopping the use of one brand of products or services and switching to another [38]—in the context of this study, it involves switching from one energy supplier to another. Consumers develop their intention to switch when relative advantages from a new or existing energy supplier outweigh those of their current energy supplier. In the context of consumer behavior, switching refers to a consumer making a competing choice on the next purchase occasion rather than the previously purchased choice [39]. However, in the case of a client–brand relationship based on a long-term contract, each next purchase is not as spontaneous as in the case of a purchase not resulting from a signed contract.
In our research model (Figure 1), we took into consideration a set of factors discussed below in more detail. The selection process for factors began by considering the low tendency of Polish consumers to switch suppliers. In Poland, the level of willingness to switch energy providers is gradually increasing, though it is still shallow compared to other EU countries. The direction of change is well-defined in EU policy, but the implementation differs among member states. This led us to choose brand trust as an external factor due to the strength of incumbent suppliers in Poland. Additionally, we wanted to identify less obvious internal factors on the consumer’s side. Therefore, we selected resistance to change, subjective perception of switching costs, and ease of switching suppliers as the other factors.
The starting point in our research model is resistance to change (or change aversion), which is usually analyzed in the context of organizational change [40]. However, resistance to change can be applied not only to a person as an employee but also, in a more general sense, to an individual as a consumer [41]. Resistance to change is widely recognized as the main reason for failure when it comes to change initiatives [42]. According to research on organizational change, resistance is any behavior that tries to keep the status quo. It is equivalent to inertia, and its main goal is to avoid change [43]. Amarantou et al. [44] indicated the lack of a commonly accepted definition for resistance to change, and an overview of the literature reveals that resistance occurs to protect an individual from the consequences of an existing or perceived change. According to the literature, the factors that cause resistance to change can be divided into two groups: individual factors and situational factors [45]. Hubbart [46] wrote about the myriad of sources of variation that may lead to change aversion—three of them are explained in more detail below. One of the important reasons for consumers’ change aversion can be their fear of change. “Change” is often linked with the idea of leaving something familiar behind and stepping into the unknown. Liu et al. [47] indicated the vital role of fear of change in the customers’ traditional behaviors and among the people who are resisting innovation. The next factor affecting change aversion is the necessity of leaving their comfort zone, which can be perceived as both frightening and generating some risk of potential failure or disappointment [48]. The next important factor is having an emotional attachment to the past [49]. According to the attachment approach, consumers form connections with objects or brands over time due to their personal history of usage [50]. We believe that this may be particularly important when abandoning the current energy supplier in favor of cooperation with a new company. In our research model, we analyzed change aversion in the context of switching energy suppliers. The resistance to change can have a significant impact on the energy market, which is based on long-term contracts and customers who are accustomed to their current service provider. This is because they prefer the comfortable status quo provided by their current provider. The specificity of the energy market makes this issue particularly interesting to study. In this light, we hypothesize that:
H1: 
Resistance to change has a positive impact on brand trust.
H2: 
Resistance to change has a negative impact on actual supplier switching intention.
H3: 
Resistance to change has a negative impact on perceived ease of change.
H4: 
Resistance to change has a positive impact on perceived switching costs.
The importance of trust has been extensively examined in the relationship marketing literature [51]. According to Chaudhuri and Holbrook [52] (p. 82), brand trust is defined as “the willingness of the average consumer to rely on the ability of the brand to perform its stated function.” Doney and Cannon [53] emphasized that brand trust is the degree to which customers believe that a brand can provide the required value. Brand trust is a measure of the level of customer confidence in a brand, and it connects brand promise, customized experience, and corporate culture [54]. A trustworthy brand is one that consistently keeps its promise [55]. Some researchers have noted a decline in consumer trust in brands globally in recent decades [56]. What is important in our research approach is that brand trust helps reduce customer uncertainty and vulnerability [57]. Building and maintaining brand trust is seen as a crucial concern in many corporate strategies due to the high cost of obtaining new customers and retaining the existing ones, which is connected to long-term relationships and profitability [58,59]. Extensive literature over decades of research indicates that brand trust is a key factor in the development of brand loyalty and that brand trust plays an important role in long-term customer relationships [52,60,61,62,63,64]. In the context of our research, among the multitude of benefits of brand trust, the key one is higher loyalty.
In this light, we hypothesize that:
H5: 
Brand trust has a negative impact on actual supplier switching intention.
Switching costs have been very attractive for both marketing academics and practitioners [65], and marketing professionals indicate the strategic importance of switching costs [66]. However, some researchers indicated that consumer perceptions of switching costs are slightly industry-specific [67]. Switching cost perception is an important concept in the study of consumer loyalty and its implications for organizations [68]. El-Manstrly [69] (p. 144) noted that switching costs have “a profound explanatory effect on customer loyalty.” Switching costs are defined as “the one-time costs that customers associate with the process of switching from one provider to another” [70] (p. 260). These costs may include dimensions of both monetary expenses and non-monetary costs [71]. Consumers perceive switching costs as the total cost of search costs, transaction costs, learning costs, loss of loyal customer discounts, customer habit, emotional costs, and cognitive effort—all together, these costs create the barrier to switching the actual provider [72]. In effect, the perception of switching costs makes the customer’s decision to change service provider more costly [73]. The vast literature indicates the positive impact of switching costs on consumer loyalty [67,74,75]. Switching costs are perceived as relevant antecedents of customer loyalty [76]. However, we do not plan to verify this well-established impact, and we instead focus our research attention on another relationship that perceived switching costs can create.
In this light, we hypothesize that:
H6: 
Perceived switching costs have a negative impact on the perceived ease of changing the actual energy supplier.
By analogy to the positive impact of perceived ease of use on the intention to use in the Technology Acceptance Model (TAM) by F.D. Davis [77,78], we suggest that a positive perception of how easy it is to change the actual energy supplier will strengthen the intention to switch to another energy supplier. Certain researchers have analyzed the link between the TAM and the Self-Efficacy Theory by A. Bandura [31]. He et al. [79] (p. 1) noted that “when people view technology as easy to use (perceived ease of use), they are more confident and competent in adopting the technology (self-efficacy).” According to Bandura [31], there are four factors shaping perceived self-efficacy: performance accomplishments, vicarious experience, verbal persuasion, and emotional arousal. As an information source, experience plays an important role [80]. Experience-based decisions are defined as decisions emanating from direct or vicarious reinforcements that were received by a person in the past [81]. By observing others, people create knowledge about decisions and behavior and learn how to decide and behave [82]. Observations can lead to avoiding mistakes when unknown or unfamiliar decisions have to be made and behaviors have to be chosen [83]. By analogy to Davis’s [77] definition of perceived ease of use, we understand perceived ease of change as the extent to which changing an actual energy supplier is perceived by a person as being easy to implement and free of effort.
In this light, we hypothesize that:
H7: 
Perceived ease of change has a positive impact on actual supplier switching intention.

3. Materials and Methods

In Figure 1, we have considered four factors to explain the intention of energy supplier switching. The survey was carried out at the beginning of 2020, before the SARS-CoV-2 pandemic in Poland, and the research sample consisted of 1216 adults (Table 1). Since global crises such as COVID-19 and the war in Ukraine were disruptive to the energy market in Europe and the customers’ relationships with it, the authors believe that the survey data before such events serve to answer the present research questions more reliably. The Polish energy market has been experiencing disruptions that have resulted in government interventions that strongly influence the level of energy prices. The subsidy system was valid until the end of 2023. However, due to a change in government, there is no clear information on plans for 2024.
All latent variables were adapted from the literature. The behavioral intention in the research model is the customer’s intention to switch the actual energy supplier. Switching intention (SI): SI1: I was considering changing my current energy supplier; SI2: I am planning to change my current energy supplier; SI3: There is a good chance that I will change the energy supplier. Resistance to change (RtC): RtC1: I do not like changes; RtC2: I am afraid of changing my energy supplier; RtC3: I got used to my energy supplier.
Brand trust (BT): BT1: I know that the offer proposed by the supplier is the best for me; BT2: My current provider keeps me safe; BT3: I know that the offer proposed by the supplier takes into account my needs; BT4: I know that I can always count on my supplier’s help and advice on matters related to electricity.
Perceived switching costs (PSC): PSC1: The costs of terminating the contract with the current supplier are too high; PSC2: It’s a waste of time to switch energy suppliers; PSC3: Changing energy suppliers requires too much effort.
Perceived ease of change (PEofC): PEofC1: I know the procedure for changing energy suppliers; PEofC2: The procedure for switching energy suppliers is not complicated; PEofC3: Changing energy suppliers doesn’t take too much time.

4. Results

4.1. Research Sample Characteristics

The respondents were customers of one of the largest energy suppliers in Poland, an incumbent supplier. This incumbent supplier owns approximately one-third of the domestic market shares (energy sold to end users). We used the quota sampling method—the household size was an important criterion for the research sample structure. The data collection was conducted in Poland. The formal research was carried out using a paper questionnaire, which was distributed to the respondents in the customer service offices of one of the largest energy companies in Poland. During the questionnaire distribution, it was assumed that every 10th client would be invited to the survey. There were a small number of refusals during the study. After the research, the interviewers checked the quality and completeness of each questionnaire. Incomplete questionnaires were not included in the research sample.
It is worth paying special attention to the last three characteristics of the research sample. Firstly, over half of the respondents have been with their current energy supplier for more than 10 years. Secondly, nearly 90% of the respondents have had their current energy supplier as their only energy supplier throughout their lives. Finally, almost 75% of the respondents reported contacting their current energy supplier once a year or less. All these characteristics indicate that Polish customers are relatively inactive in the electricity market. Significantly, the energy supplier analyzed in the study is an incumbent supplier. We agree with He and Reiner [14] that it is important to study inactive consumers in the energy market with little or no experience of switching.

4.2. Study Results

Our research goal was to verify research hypotheses regarding switching intentions in the Polish energy market. For this purpose, SEM (structural equation modeling) analysis using AMOS 29 was conducted. The CFA models were performed using maximum likelihood (ML) estimation. The ML estimation method has been described as being well-suited to theory testing and development [84]. The estimates presented relate to the standardized regression weights. Table 2 presents convergent validity and discriminant validity. The model fit for our research model is as follows: CMIN/DF 6.057, GFI 0.945, AGFI 0.920, RMSEA 0.064 (LO 90 0.059—HI 90 0.070), and PCLOSE 0.000.
In this study, AMOS 29.0 was used to test the goodness of fit of the model. It was generally considered that the model with a root mean square error of approximation (RMSEA) less than 0.08 and a goodness-of-fit index (GFI) and adjusted goodness-of-fit index (AGFI) greater than 0.9 had a better goodness of fit. All the measured variables met the requirements.
Figure 2 presents the summary of the research results and Table 3 presents the verification of research hypotheses.
Our additional interest was the indirect effect(s) on SI through certain variables. Further analysis shows the existence of an indirect effect of resistance to change on perceived ease of change through perceived switching costs (two-tailed p values = 0.05), perceived switching costs on switching intention through perceived ease of change (two-tailed p values = 0.05), and resistance to change on switching intention through brand trust (two-tailed p values = 0.002). A detailed list of standardized effects (direct, indirect, and total effects) is presented in Table 4.

5. Discussion

The purpose of this study was to analyze factors that influence customers to switch energy suppliers, focusing on an established supplier with a dominant market position. The set of analyzed factors included the following ones: resistance to change, brand trust, perceived switching costs, perceived ease of change, and switching intention. Special research attention was dedicated to customers’ resistance to change as a factor that both directly and indirectly influences customers’ switching decisions in the energy market.
We reached several conclusions throughout the study. Regarding our research model, consumers’ intentions to switch energy suppliers were strongly and directly influenced by three factors. These factors are listed below in order of decreasing importance in shaping behavioral intention. The most important factor was the customers’ resistance to change (path estimate = −0.26; p-value = 0.000). The negative relationship between purchase intention and resistance to change has been well-identified in the literature. The second factor reducing the intention to switch energy suppliers was brand trust (path estimate = −0.19; p-value = 0.000). Such an impact is grounded in the literature on brand trust and customer loyalty. The third factor that has a direct positive influence on switching intention is the perceived ease of change (path estimate = 0.17; p-value = 0.000). The Technology Acceptance Model was adapted to incorporate the impact of perceived ease of use on the intention to use, changing the relationship between these variables.
Our research model began with the aforementioned resistance to change. Apart from directly shaping behavioral intention, the next two hypotheses explore the impact of resistance to change, which have been confirmed—on brand trust (path estimate = 0.53; p-value = 0.000) and perceived switching costs (path estimate = 0.24; p-value = 0.000). The only unconfirmed hypothesis in our research model was the nonsignificant relationship between resistance to change and perceived ease of change. We believe that this potential influence requires further research. It is important to understand the factors that can influence the perceived ease of change, as this perception has a significant impact on the intention to change. Perceived ease of change is similar to perceived ease of use, but it is important to determine if a slightly different set of items that define this variable will affect the relationship significantly.
Our results on the negative impact of switching costs on perceived ease of change (path estimate = −0.10; p-value = 0.009) contribute to the already well-established literature dedicated to switching costs. We explained the significance of these types of customer costs in terms of cooperation with an incumbent energy supplier.
Taking into consideration all the findings mentioned above, we should emphasize the possibility of influencing each of the three variables that directly shape switching intention in our research model. Every consumer has an inherent resistance to change that remains beyond the direct influence of service providers. The influence of service providers on brand trust is evident through various marketing activities. We believe that our research findings could be beneficial not only for existing energy providers but also for other service providers who have not yet focused on selling energy but hold a strong market presence through the sale of other services, such as telecommunications services. In Poland, there are at least a few service providers with a strong brand that have extended their starting portfolio of telecommunications services with financial services and commodities such as energy or gas. According to our model, brand trust has a significant impact on reducing switching intention, so it is crucial for service suppliers to have a strong brand with a high level of customer trust. The long-term process of building brand strength from the very beginning by a new player on the energy market guarantees that existing suppliers will maintain their market advantage resulting from, among others, a strong brand that customers trust. It is reasonable to expect competition from strong brands expanding to include energy supply. We believe that the presented results will contribute to a better understanding of the situation of incumbent suppliers in the gradually liberalizing energy market. This is our research contribution: to emphasize the continuing strong market position of established (incumbent) suppliers in relation to new market entrants, not only due to their strong brands but also their customers’ resistance to change. The research findings highlight the significance of a customer’s internal readiness to switch. We focused on certain factors that directly affect the customer instead of just considering the market. The article brings attention to an important and non-obvious factor—the psychological reactance to change—that provides an advantage to incumbent suppliers.
The level of switching intention is strongly influenced by resistance to change on the one hand and strong trust in the brand of a well-known supplier on the other. As intended by government regulators, one of the main goals of the deregulation of energy markets was to increase market competition [86]. However, increased competition among retailers is insufficient; an active consumer attitude is also necessary. Hampton et al. [87] (p. 4) paid research attention to the concept of customer engagement in retail electricity markets—according to these researchers, consumers can engage in this market “by making informed decisions due to deeper liberalization surrounding the choice of electricity supplier or tariff.” We can confidently apply the presented results to long-term service contracts in Poland, such as insurance and banking. Telecom services are based on long-term agreements, but Polish consumers are considerably more willing to switch providers. In our opinion, low attachment to service providers in the Polish telecom market may be due to intensive promotional activities directed at new customers and the lack of an incumbent supplier. In 2013, one of the main telecommunications providers acquired the former monopolist and underwent a rebranding process, resulting in a name change that no longer identified the company with the incumbent provider. Undoubtedly, the specificity of the service is also important—energy meets the characteristics of a typical commodity with relatively little differentiation in the perception of customers.
Although important issues emerged from this work, some limitations should be taken into account, as they suggest directions for further research. The first limitation concerns the fact that our research group consists of customers from only one specific energy supplier in Poland. Second, the country’s energy policy could impact the decision to switch suppliers by regulating individual consumer prices. As previously stated, the pricing system for individual customers in Poland before the outbreak of the war in Ukraine was subject to much less government intervention compared to the current system that will remain in effect until at least the end of 2023. Third, in the presented model, we consciously chose not to consider the positive impact of perceived switching costs on consumer loyalty, which is well-defined in the literature. Naturally, when deciding on a specific set of factors shaping the intention to switch, certain variables were not included in the research model. Some of the non-included factors are price-related factors (e.g., perceived relative price) or customer satisfaction [86,88]. Future research could also extend empirical testing to these dependencies (the impact of resistance to change on perceived ease of change), which has not been confirmed in our research approach. It is worth noting that our analysis was carried out under the assumption that consumers have a single energy supplier. However, interesting research exists beyond this typical condition [89]. Finally, we are also aware of the intention–behavior gap that has been identified in the scientific literature [25]. The intention–behavior gap is a phenomenon in which people do not always follow through with their intended actions. It refers to the ability to turn intentions into actual behaviors [90]. In our study, we analyzed the intention to switch energy suppliers, and the results suggested a strong intention–behavior relationship.
Our research can be further explored in the absence of government subsidies for households on energy bills, which have occurred due to persistently high inflation. Another aspect to be studied is the significance of incumbent suppliers’ contribution to the production of energy from renewable sources. It is crucial to gain further insight into the market position of incumbent suppliers due to the changes taking place in the energy market that are posing increasing challenges for these companies.

Author Contributions

Conceptualization, I.L. and M.L.; Data curation, M.L.; Formal analysis, I.L., M.L. and R.M.; Investigation, D.D.; Methodology, M.L.; Project administration, M.L. and D.D.; Resources, I.L.; Software, M.L. and R.M.; Supervision, I.L. and M.L.; Validation, I.L., M.L. and R.M.; Visualization, I.L. and M.L.; Writing—original draft, I.L., M.L. and R.M.; Writing—review and editing, I.L., M.L. and R.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

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.

References

  1. Besant-Jones, J.E. Reforming Power Markets in Developing Countries: What Have We Learned? World Bank: Washington, DC, USA, 2006. [Google Scholar]
  2. Mollard, M. Switching costs and the pricing strategies of incumbent suppliers on the British retail electricity market. In Proceedings of the 4th Conference on Applied Infrastructure Research, Berlin, Germany, 15–17 November 2005. [Google Scholar]
  3. Joskow, P.L. Lessons learned from electricity market liberalisation. Energy J. 2008, 29, 9–42. [Google Scholar] [CrossRef]
  4. Henderson, J.; Sen, A. The Energy Transition: Key Challenges for Incumbent and New Players in the Global Energy System; OIES Paper: ET; The Oxford Institute for Energy Studies: Oxford, UK, 2021; pp. 1–27. [Google Scholar]
  5. Defeuilley, C. Retail competition in electricity markets. Energy Policy 2009, 37, 377–386. [Google Scholar] [CrossRef]
  6. Yang, M.; Chi, Y.; Mamaril, K.; Berry, A.; Shi, X.; Zhu, L. Communication-Based Approach for Promoting Energy Consumer Switching: Some Evidence from Ofgem’s Database Trials in the United Kingdom. Energies 2020, 13, 5179. [Google Scholar] [CrossRef]
  7. Heiskanen, E.; Apajalahti, E.-L.; Matschoss, K.; Lovio, R. Incumbent energy companies navigating energy transitions: Strategic action or bricolage? Environ. Innov. Soc. Transit. 2018, 28, 57–68. [Google Scholar] [CrossRef]
  8. Hajiyev, N. Oligopoly Trends in Energy Markets: Causes, Crisis of Competition, and Sectoral Development Strategies. Int. J. Energy Econ. Policy 2021, 11, 392–400. [Google Scholar] [CrossRef]
  9. Gawin, R. National Report of the President of URE; Energy Regulatory Office: Warsaw, Poland, 2023. [Google Scholar]
  10. Keaveney, S.M. Customer Switching Behavior in Service Industries: An Exploratory Study. J. Mark. 1995, 59, 71–82. [Google Scholar] [CrossRef]
  11. Walsh, G.; Groth, M.; Wiedmann, K.-P. An Examination of Consumers’ Motives to Switch Energy Suppliers. J. Mark. Manag. 2005, 21, 421–440. [Google Scholar] [CrossRef]
  12. Kulczycka, J.; Lipińska, A. Barriers to liberalization of the Polish energy-sector. Appl. Energy 2003, 76, 229–238. [Google Scholar] [CrossRef]
  13. Brzeziński, S.; Zborowski, K.; Pietrasieński, P. The effect of the liberalization of electricity market in Poland. Pol. J. Manag. Stud. 2013, 7, 152–160. [Google Scholar]
  14. He, X.; Reiner, D. Why Consumers Switch Energy Suppliers: The Role of Individual Attitudes. Energy J. 2017, 38, 25–54. [Google Scholar] [CrossRef]
  15. Deller, D.; Giulietti, M.; Loomes, G.; Price, C.W.; Moniche, A.; Jeon, J.Y. Switching Energy Suppliers: It’s Not All About the Money. Energy J. 2021, 42, 1–26. [Google Scholar] [CrossRef]
  16. von der Fehr, N.-H.M.; Banet, C.B.; Le Coq, C.; Pollitt, M.; Willems, B. Retail Energy Markets under Stress; Lessons Learnt for the Future of Market Design; Centre on Regulation in Europe (CERRE): Brussels, Belgium, 2022; pp. 1–50. [Google Scholar]
  17. Giulietti, M.; Price, C.W.; Waterson, M. Consumer Choice and Competition Policy: A Study of UK Energy Markets. Econ. J. 2005, 115, 949–968. [Google Scholar] [CrossRef]
  18. Murakami, R. Consumer switching behavior and bundling: An empirical study of Japan’s retail energy markets. Int. J. Econ. Policy Stud. 2022, 16, 443–463. [Google Scholar] [CrossRef]
  19. Flores, M.; Price, C.W. The Role of Attitudes and Marketing in Consumer Behaviours in the British Retail Electricity Market. Energy J. 2018, 39, 153–180. [Google Scholar] [CrossRef]
  20. Nicolli, F.; Vona, F. Energy market liberalization and renewable energy policies in OECD countries. Energy Policy 2019, 128, 853–867. [Google Scholar] [CrossRef]
  21. Kungl, G. Stewards or sticklers for change? Incumbent energy providers and the politics of the German energy transition. Energy Res. Soc. Sci. 2015, 8, 13–23. [Google Scholar] [CrossRef]
  22. Salm, S.; Wüstenhagen, R. Dream team or strange bedfellows? Complementarities and differences between incumbent energy companies and institutional investors in Swiss hydropower. Energy Policy 2018, 121, 476–487. [Google Scholar] [CrossRef]
  23. Ajzen, I. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 1991, 50, 179–211. [Google Scholar] [CrossRef]
  24. Fishbein, M.; Ajzen, I. Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research; Addison-Wesley: Boston, MA, USA, 1975. [Google Scholar]
  25. Conner, M.; Norman, P. Understanding the intention-behavior gap: The role of intention strength. Front. Psychol. 2022, 13, 923464. [Google Scholar] [CrossRef]
  26. Konerding, U. Formal models for predicting behavioral intentions in dichotomous choice situations. Methods Psychol. Res. 1999, 4, 1–32. [Google Scholar]
  27. Deci, E.L.; Ryan, R.M. The general causality orientations scale: Self-determination in personality. J. Res. Personal. 1985, 19, 109–134. [Google Scholar] [CrossRef]
  28. Ryan, R.M.; Deci, E.L. Self-Determination Theory and the Facilitation of Intrinsic Motivation, Social Development, and Well-Being. Am. Psychol. 2000, 55, 68–78. [Google Scholar] [CrossRef] [PubMed]
  29. Martin, K.D.; Hill, R.P. Life Satisfaction, Self-Determination, and Consumption Adequacy at the Bottom of the Pyramid. J. Consum. Res. 2012, 38, 1155–1168. [Google Scholar] [CrossRef]
  30. Chirkov, V.; Ryan, R.M.; Youngme, K.; Kaplan, U. Differentiating Autonomy from Individualism and Independence: A Self-Determination Theory Perspectiveon Internalization of Cultural Orientations and Weil-Being. J. Personal. Soc. Psychol. 2003, 84, 98. [Google Scholar] [CrossRef]
  31. Bandura, A. Self-efficacy: Toward a Unifying Theory of Behavioral Change. Psychol. Rev. 1977, 84, 191–215. [Google Scholar] [CrossRef] [PubMed]
  32. Bandura, A. Self-Efficacy Mechanism in Human Agency. Am. Psychol. 1982, 37, 122–147. [Google Scholar] [CrossRef]
  33. Wood, R.; Bandura, A. Social Cognitive Theory of Organizational Management. Acad. Manag. Rev. 1989, 14, 361–384. [Google Scholar] [CrossRef]
  34. Bansal, H.S.; Taylor, S.F.; James, Y.S. “Migrating” to new service providers: Toward a unifying framework of consumers’ switching behaviors. J. Acad. Mark. Sci. 2005, 33, 96–115. [Google Scholar] [CrossRef]
  35. Tanuwijaya, E.; Oktavia, T. Analysis of the Factors Influencing Customer Switching Behaviour of The Millennials in Digital Banks. J. Syst. Manag. Sci. 2023, 13, 122–133. [Google Scholar]
  36. Haridasan, A.C.; Fernando, A.G.; Balakrishnan, S. Investigation of consumers’ cross-channel switching intentions: A push-pull-mooring approach. J. Consum. Behav. 2021, 20, 1092–1112. [Google Scholar] [CrossRef]
  37. Akwensivie, D.M. Switching behaviour and customer relationship management-the Iceland experience. Br. J. Mark. Stud. 2014, 2, 89–100. [Google Scholar]
  38. Dong, Z.; Qiu, X. Social Exclusion and Switching Behaviour of Green Products: The Mediating role of Control Demand. In Proceedings of the 5th International Conference on Advances in Energy, Environment and Chemical Science (AEECS 2021) E3S Web of Conferences, Shanghai, China, 26–28 February 2021; Volume 245, p. 02040. [Google Scholar] [CrossRef]
  39. Prasad, M.M.; Kumar, D.P. Factors Influencing the Behavior of the Mobile Phone users to Switch their Service Providers in Andhra Pradesh. Glob. J. Manag. Bus. Res. E Mark. 2016, 16, 1–11. [Google Scholar]
  40. Oreg, S. Personality, context, and resistance to organizational change. Eur. J. Work Organ. Psychol. 2006, 15, 73–101. [Google Scholar] [CrossRef]
  41. Descotes, R.M.; Pauwels-Delassus, V. The impact of consumer resistance to brand substitution on brand relationship. J. Consum. Mark. 2015, 32, 34–42. [Google Scholar] [CrossRef]
  42. Jain, P.; Asrani, C.; Jain, T. Resistance to Change in an Organization. OSR J. Bus. Manag. 2018, 20, 37–43. [Google Scholar]
  43. Pardo del Val, M.; Martínez Fuentes, C. Resistance to change: A literature review and empirical study. Manag. Decis. 2003, 41, 148–155. [Google Scholar] [CrossRef]
  44. Amarantou, V.; Kazakopoulou, S.; Chatzoudes, D.; Chatzoglou, P. Resistance to change: An empirical investigation of its antecedents. J. Organ. Chang. Manag. 2018, 31, 426–450. [Google Scholar] [CrossRef]
  45. Hafizh Damawan, A.; Azizah, S. Resistance to Change: Causes and Strategies as an Organizational Challenge. Adv. Soc. Sci. Educ. Humanit. Res. 2019, 395, 49–53. [Google Scholar]
  46. Hubbart, J.A. Organizational Change: The Challenge of Change Aversion. Adm. Sci. 2023, 13, 162. [Google Scholar] [CrossRef]
  47. Liu, X.; Wang, F.; Wong, C. Impact of Traditional Behavior of Customers, Employees, and Social Enterprises on the Fear of Change and Resistance to Innovation. Front. Psychol. 2022, 13, 923094. [Google Scholar] [CrossRef]
  48. Endrejat, P.C. When to challenge employees’ comfort zones? The interplay between culture fit, innovation culture and supervisors’ intellectual stimulation. Leadersh. Organ. Dev. J. 2021, 42, 1104–1118. [Google Scholar] [CrossRef]
  49. Kumar, S.S. Challenges of Managing an Organizational Change. Adv. Manag. 2012, 5, 1–3. [Google Scholar]
  50. Lambert-Pandraud, R.; Laurent, G. Why do Older Consumers Buy Older Brands? The Role of Attachment and Declining Innovativeness. J. Mark. 2010, 74, 104–121. [Google Scholar] [CrossRef]
  51. Hsu, C.; Cai, L.A. Brand Knowledge, Trust and Loyalty—A Conceptual Model of Destination Branding. In Proceedings of the International CHRIE Conference-Refereed Track, San Francisco, CA, USA, 31 July 2009; p. 12. [Google Scholar]
  52. Chaudhuri, A.; Holbrook, M.B. The chain of effects from brand trust and brand affect to brand performance: The role of brand loyalty. J. Mark. 2001, 65, 81–93. [Google Scholar] [CrossRef]
  53. Doney, P.M.; Cannon, J.P. An examination of the nature of trust in buyer–seller relationships. J. Mark. 1997, 61, 35–51. [Google Scholar]
  54. Amoah, J.; Bashiru Jibril, A.; Bankuoru Egala, S.; Keelson, S.A. Online brand community and consumer brand trust: Analysis from Czech millennials. Cogent Bus. Manag. 2022, 9, 2149152. [Google Scholar] [CrossRef]
  55. Munuera-Aleman, J.L.; Delgado-Ballester, E.; Yague-Guillen, M.J. Development and Validation of a Brand Trust Scale. Int. J. Mark. Res. 2003, 45, 1–18. [Google Scholar] [CrossRef]
  56. Rajavi, K.; Kushwaha, T.; Steenkamp, J.-B.E.M. In Brands We Trust? A Multicategory, Multicountry Investigation of Sensitivity of Consumers’ Trust in Brands to Marketing-Mix Activities. J. Consum. Res. 2019, 46, 651–670. [Google Scholar] [CrossRef]
  57. Cardoso, A.; Gabriel, M.; Figueiredo, J.; Oliveira, I.; Rêgo, R.; Silva, R.; Oliveira, M.; Meirinhos, G. Trust and Loyalty in Building the Brand Relationship with the Customer: Empirical Analysis in a Retail Chain in Northern Brazil. J. Open Innov. Technol. Mark. Complex. 2022, 8, 109. [Google Scholar] [CrossRef]
  58. Anderson, E.W.; Mittal, V. Strengthening the Satisfaction-Profit Chain. J. Serv. Res. 2000, 3, 107–120. [Google Scholar] [CrossRef]
  59. Amanah, D.; Handoko, B.; Hafas, H.R.; Hermansyur; Harahap, D.A. Customer Retention: Switching Cost and Brand Trust Perspectives. Palarch’s J. Archaeol. Egypt/Egyptol. 2021, 18, 3552–3561. [Google Scholar]
  60. Lau, G.T.; Lee, S.H. Consumers’ trust in a brand and the link to brand loyalty. J. Mark. Manag. 1999, 4, 341–370. [Google Scholar] [CrossRef]
  61. Rudzewicz, A.; Strychalska-Rudzewicz, A. The Influence of Brand Trust on Consumer Loyalty. Eur. Res. Stud. J. 2021, 24, 454–470. [Google Scholar] [CrossRef] [PubMed]
  62. Gielens, K.; Steenkamp, J.-B.E. Branding in the era of digital (dis)intermediation. Int. J. Res. Mark. 2019, 36, 367–384. [Google Scholar] [CrossRef]
  63. Zhang, S.; Peng, M.Y.-P.; Peng, Y.; Zhang, Y.; Ren, G.; Chen, C.-C. Expressive Brand Relationship, Brand Love, and Brand Loyalty for Tablet PCs: Building a Sustainable Brand. Front. Psychol. 2020, 11, 231. [Google Scholar] [CrossRef]
  64. Hao, A.W.; Paul, J.; Trott, S.; Guo, C.; Wu, H.-H. Two decades of research on nation branding: A review and future research agenda. Int. Mark. Rev. 2019, 38, 46–69. [Google Scholar] [CrossRef]
  65. Sahin, A.; Kitapci, H.; Zehir, C. Creating Commitment, Trust and Satisfaction for a Brand: What is the Role of Switching Costs in Mobile Phone Market? Procedia-Soc. Behav. Sci. 2013, 99, 496–502. [Google Scholar] [CrossRef]
  66. Hess, M.; Enric, R.J. Managing Customer Switching Costs: A Framework for Competing in the Networked Environment. Manag. Res. 2003, 1, 93–110. [Google Scholar]
  67. Yen, Y.-S. Can perceived risks affect the relationship of switching costs and customer loyalty in e-commerce? Internet Res. 2010, 20, 210–224. [Google Scholar] [CrossRef]
  68. Barroso, C.; Picón, A. Multi-dimensional analysis of perceived switching costs. Ind. Mark. Manag. 2012, 41, 531–543. [Google Scholar] [CrossRef]
  69. El-Manstrly, D. Enhancing customer loyalty: Critical switching cost factors. J. Serv. Manag. 2016, 27, 144–169. [Google Scholar] [CrossRef]
  70. Park, J.-G.; Park, K.; Lee, J. A firm’s post-adoption behavior: Loyalty or switching costs? Ind. Manag. Data Syst. 2014, 114, 258–275. [Google Scholar] [CrossRef]
  71. Wang, C.-Y. Customer participation and the roles of self-efficacy and adviser-efficacy. Int. J. Bank Mark. 2019, 37, 241–257. [Google Scholar] [CrossRef]
  72. Zhu, G.; Ao, S.; Dai, J. Estimating the switching costs in wireless telecommunication market. Nankai Bus. Rev. Int. 2011, 2, 213–236. [Google Scholar] [CrossRef]
  73. Thompson, S.A.; Loveland, J.M.; Loveland, K.E. The impact of switching costs and brand communities on new product adoption: Served-market tyranny or friendship with benefits. J. Prod. Brand Manag. 2019, 28, 140–153. [Google Scholar] [CrossRef]
  74. Jones, M.A.; Reynolds, K.E.; Mothersbaugh, D.L.; Beatty, S.E. The positive and negative effects of switching costs on relational outcomes. J. Serv. Res. 2007, 9, 335–355. [Google Scholar] [CrossRef]
  75. Blut, M.; Beatty, S.E.; Evanschitzky, H.; Brock, C. The Impact of Service Characteristics on the Switching Costs–Customer Loyalty Link. J. Retail. 2014, 90, 275–290. [Google Scholar] [CrossRef]
  76. Shi, W.; Ma, J.; Ji, C. Study of social ties as one kind of switching costs: A new typology. J. Bus. Ind. Mark. 2015, 30, 648–661. [Google Scholar] [CrossRef]
  77. Davis, F.D. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Q. 1989, 13, 319–340. [Google Scholar] [CrossRef]
  78. Davis, F.D.; Bagozzi, R.P.; Warshaw, P.R. User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Manag. Sci. 1989, 35, 982–1003. [Google Scholar] [CrossRef]
  79. He, Y.; Chen, Q.; Kitkuakul, S. Regulatory focus and technology acceptance: Perceived ease of use and usefulness as efficacy. Cogent Bus. Manag. 2018, 5, 1459006. [Google Scholar] [CrossRef]
  80. Dong, X.; Chang, Y.; Wang, Y.; Yan, J. Understanding usage of Internet of Things (IOT) systems in China: Cognitive experience and affect experience as moderator. Inf. Technol. People 2017, 30, 117–138. [Google Scholar] [CrossRef]
  81. Yechiam, E.; Aharon, I. Experience-based decisions and brain activity: Three new gaps and partial answers. Front. Psychol. 2012, 2, 390. [Google Scholar] [CrossRef] [PubMed]
  82. Bandura, A. Social Foundations of Thought and Action: A Social Cognitive Theory; Prentice-Hall: Englewood Cliffs, NJ, USA, 1986. [Google Scholar]
  83. Almeida, F.; Curado, M. The role of observation, cognition, and imagination in Keynes’s approach to decision-making. EconomiA 2019, 20, 15–26. [Google Scholar] [CrossRef]
  84. Cheng, J.-H.; Lu, K.-L. Enhancing effects of supply chain resilience: Insights from trajectory and resource-based perspectives. Supply Chain Manag. Int. J. 2017, 22, 329–340. [Google Scholar] [CrossRef]
  85. Henseler, J.; Ringle, C.M.; Sarstedt, M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci. 2015, 43, 115–135. [Google Scholar] [CrossRef]
  86. Erdogan, M.R.; Camgoz, S.M.; Karan, M.B.; Berument, M.H. The switching behavior of large-scale electricity consumers in The Turkish electricity retail market. Energy Policy 2022, 160, 112701. [Google Scholar] [CrossRef]
  87. Hampton, H.; Foley, A.; Del Rio, D.F.; Smyth, B.; Laverty, D.; Caulfield, B. Customer engagement strategies in retail elec-tricity markets: A comprehensive and comparative review. Energy Res. Soc. Sci. 2022, 90, 102611. [Google Scholar] [CrossRef]
  88. Hussain, S.; Seet, P.-S.; Ryan, M.; Iranmanesh, M.; Cripps, H.; Salam, A. Determinants of switching intention in the electricity —An integrated structural model approach. J. Retail. Consum. Serv. 2022, 69, 103094. [Google Scholar] [CrossRef]
  89. Watson, N.E.; Huebner, G.M.; Fell, M.J.; Shipworth, D. Two energy suppliers are better than one: Survey experiments on consumer engagement with local energy in GB. Energy Policy 2020, 147, 111891. [Google Scholar] [CrossRef]
  90. Sheeran, P.; Webb, T.L. The Intention–Behavior Gap. Soc. Personal. Psychol. Compass 2016, 10, 503–518. [Google Scholar] [CrossRef]
Energies 17 00306 g001
Figure 1. Conceptual research model. Note: (+) positive impact, (−) negative impact.
Figure 1. Conceptual research model. Note: (+) positive impact, (−) negative impact.
Energies 17 00306 g001
Energies 17 00306 g002
Figure 2. Summary of the research results. Note: ** p < 0.001; * p < 0.05; ns—nonsignificant. Model fit: CMIN/DF 6.057, GFI 0.945, AGFI 0.920, RMSEA 0.064 (LO 90 0.059—HI 90 0.070), PCLOSE 0.000.
Figure 2. Summary of the research results. Note: ** p < 0.001; * p < 0.05; ns—nonsignificant. Model fit: CMIN/DF 6.057, GFI 0.945, AGFI 0.920, RMSEA 0.064 (LO 90 0.059—HI 90 0.070), PCLOSE 0.000.
Energies 17 00306 g002
Table 1. Research sample characteristics.
Table 1. Research sample characteristics.
CharacteristicsNumber of RespondentsPercentage of Sample
GenderFemale61050.2
Male60649.8
Age (years)18–2915813.0
30–3930024.7
40–4936029.6
50–5925721.1
60 or more14111.6
Household size
(number of individuals)		1978.0
230925.4
333527.5
432726.9
5 or more14812.2
Period of cooperation with the current energy supplier (years)Less than one 12710.4
One to two675.5
Over two to five14812.2
Over five to ten19416.0
Over ten68055.9
Experience in changing energy suppliersFirst supplier (1)107888.7
Second supplier (2)1109.0
Third supplier161.3
Fourth supplier or more110.9
Frequency of contact with the current energy supplierEvery few years56046.1
Once a year 35529.2
2–4 times a year21317.5
5 or more times a year887.2
Note: (1) the current supplier is the first one the client has cooperated with—he/she has not changed the supplier even once so far; (2) the current supplier is the second one the client has cooperated with—he/she has changed the supplier only once so far.
Table 2. Selected measures of construct reliability and validity.
Table 2. Selected measures of construct reliability and validity.
Discriminant ValidityConvergent ValidityConstruct Reliability
Dimension Criterion of Fornell-LarckerAVECronbach’s
Alfa		CR
RtCBTPSCPEofCSI
Resistance to change RtC0.661 0.4370.6940.699
Brand trustBT0.5610.816 0.6670.8870.889
Perceived switching costsPSC−0.0860.0650.658 0.4330.6550.683
Perceived ease of changing suppliersPEofC0.0060.196−0.0860.747 0.5580.7750.788
Switching intentionSI−0.388−0.3090.0980.1360.8820.7780.9110.913
Note: Bold numbers on the matrix diagonal are square roots from AVE for each construct; numbers off-diagonal are correlations between constructs; this is an alternative form to report the Fornell-Larcker criterion [85] (p. 117).
Table 3. Verification of research hypotheses.
Table 3. Verification of research hypotheses.
Hypothesisp-ValueEstimatesAcceptance or Rejection
H1Resistance to change => Brand trust0.0000.53Acceptance
H2Resistance to change => Supplier switching intention0.000−0.26Acceptance
H3Resistance to change => Perceived ease of change0.0870.06Rejection
H4Resistance to change => Perceived switching costs0.0000.24Acceptance
H5Brand trust => Supplier switching intention0.000−0.19Acceptance
H6Perceived switching costs => Perceived ease of change0.009−0.10Acceptance
H7Perceived ease of change => Supplier switching intention0.0000.17Acceptance
Table 4. Direct, indirect, and total effects.
Table 4. Direct, indirect, and total effects.
RtCPSCPEoCBTSI
PSC
Total Effect0.269 **0000
Direct Effect0.269 **0000
Indirect Effect00000
PEoC
Total Effect0.05 ns−0.109 *000
Direct Effect0.079 ns−0.109 *000
Indirect Effect−0.029 *0000
BT
Total Effect0.549 **0000
Direct Effect0.549 **0000
Indirect Effect00000
SI
Total Effect−0.379 **0.203 *0.2 **−0.162 **0
Direct Effect−0.361 **0.225 **0.2 **−0.162 **0
Indirect Effect0.019 **−0.022 *000
Note: ns—nonsignificant; * two-tailed p value = 0.05; ** two-tailed p value < 0.05.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Lipowska, I.; Lipowski, M.; Dudek, D.; Mącik, R. Switching Behavior in the Polish Energy Market—The Importance of Resistance to Change. Energies 2024, 17, 306. https://doi.org/10.3390/en17020306

AMA Style

Lipowska I, Lipowski M, Dudek D, Mącik R. Switching Behavior in the Polish Energy Market—The Importance of Resistance to Change. Energies. 2024; 17(2):306. https://doi.org/10.3390/en17020306

Chicago/Turabian Style

Lipowska, Ilona, Marcin Lipowski, Dariusz Dudek, and Radosław Mącik. 2024. "Switching Behavior in the Polish Energy Market—The Importance of Resistance to Change" Energies 17, no. 2: 306. https://doi.org/10.3390/en17020306

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop