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Article

Developing a Sustainability Measurement for Innovation Performance for the Food Industry

by
Fontip Leesatapornwongsa
1,
Natcha Thawesaengskulthai
2,3,* and
Ronnakorn Vaiyavuth
4
1
Technopreneurship and Innovation Management Program, Graduate School, Chulalongkorn University, Bangkok 10330, Thailand
2
Department of Industrial Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand
3
Human–Robot Collaboration and Systems Integration Research Unit, Chulalongkorn University, Bangkok 10330, Thailand
4
School of Integrated Innovation, Chulalongkorn University, Bangkok 10330, Thailand
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(24), 16714; https://doi.org/10.3390/su152416714
Submission received: 31 October 2023 / Revised: 30 November 2023 / Accepted: 4 December 2023 / Published: 11 December 2023
(This article belongs to the Special Issue Entrepreneurship, Innovation, and Sustainability)
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Abstract

:
While the significance of innovation management in sustaining competitiveness for businesses is widely recognized, there is an absence of research in the food industry regarding the measurement of sustainability outcomes resulting from innovation efforts. This research endeavors to construct a comprehensive measurement framework for assessing innovation performance. The primary objective is to examine the measures to evaluate the success the success of product innovation management concerning the sustainability objectives of organizations This study employs Confirmatory Factor Analysis (CFA) on an online survey of 354 Thai food manufacturers to investigate the robust evaluation of innovation performance in the food sector. The key findings reveal that the proposed measurement instrument for innovation management performance has been validated. All sustainability dimensions, including social, economic, and environmental sustainability, have been validated as the appropriate measurement for sustainability as a result of innovation management. The social aspect has the most influence, followed by the economic and environmental aspects. In conclusion, this study offers valuable insights for practitioners aiming to evaluate their innovation management practices by employing the developed measurement scale. By assessing and enhancing their innovation management approaches, organizations can strive for sustainability during dynamic environmental challenges and uncertainties.

1. Introduction

The potential for organizational growth is dependent upon the organization’s ability for generating novel concepts and effectively leveraging them to achieve sustained competitive advantage [1]. Innovation success can give a business an edge over its competitors [2]. In recent years, scholars have emphasized the importance of innovation in securing enduring competitive advantages for organizations [3]. Companies benefit from innovation and create market-differentiating products [4]. Regarding the growing need for sustainability and the presence of management strategies, companies constantly encounter challenges with developing innovation without overlooking the sustainability issues [5].
Academic attention is currently directed towards the examination of strategies employed by system stakeholders to accelerate sustainability transitions in response to growing environmental consciousness and stricter regulations [6]. The concept of business sustainability may be described as the ability to generate resources that can be utilized to adapt to fluctuations in production factors, replace outdated assets, and make investments in order to maintain competitiveness [7]. In addition, it is imperative to take into account the dimensions of the triple bottom line, which incorporate environmental and socioeconomic considerations [8]. Sustainability innovations enhance the competitive advantage of corporations. The implementation of sustainability improvements has the potential to produce advantageous outcomes for a corporation [9]. Many scholars [6,9,10,11,12] integrate sustainability with a firm business model as a sustainability business model. The sustainability business model focuses on sustainability themes to drive corporate decisions [11]. Innovative business models give organizations a competitive edge in sustainability performance, considering sustainable development goals [13].
The evaluation and measurement of innovation success enables firms to evaluate their performance and the effectiveness of their innovation process and policies [14]. A considerable proportion of firms do not use metrics to assess innovation, and those that do express dissatisfaction with the existing measures [15]. In the present situation, it is essential to consider the assessment of innovation performance. However, the measuring of innovation is a challenging aspect of managing organizational innovation, despite its significant importance [4].
While innovation management techniques affect company innovation performance [16] and numerous studies have shown the significance of innovation in relation to sustainable development and sustainability [17], prior studies have mostly focused on its impact on financial [18,19,20] or operational performance [20,21,22]. The connection between sustainability and innovation may be established by emphasizing the advantageous outcomes of sustainable research and development (R&D), as suggested by Michelino et al. [23]. Moreover, there is a growing trend among corporations to adopt the concepts of corporate sustainability, which encompasses the integration of economic, environmental, and social aspects [24].
This research has the potential to provide insights into the factors influencing product innovation performance. Given the significant impact of innovations in the food sector of the dynamic and fiercely competitive business environment, as highlighted by Horvat et al. [25], the primary emphasis of this study will be on this particular industry. The objective of our research is to examine the measures employed to evaluate the sustainability effects of innovation management. Data were given from surveys conducted in the food sector.
The paper is structured as follows. A review of the literature on innovation management and sustainability supporting the development of a measurement scale is presented in the section that follows. Section 3 describes the research methods affecting the design of the study, which is based on a survey within the context of food industry. Section 4 presents the results, and Section 5 and Section 6 provide the discussion, implications, and conclusion.

2. Literature Review

2.1. Innovation Management and Sustainability

Innovation is valued in academia and business; thus, many studies focus on how to manage it to maximize its benefits. Innovation management is a program or technique that facilitates a corporation manage and offer decision-making information [26]. Innovation management is defined as the start of a company’s departure from traditional management practices, processes, and concepts [27]. It includes managerial practices that support companies to reach their goals and boost their success [28] by creating and applying management practices, processes, structures, and techniques [28]. Innovation management advises managers on how to increase and sustain competitiveness. Innovation management inevitably involves company changes. Managers can change how they direct, decide, organize, and motivate teams [29]. Additionally, innovation management system is essential for company innovation. The system should (a) consider related factors like customers’ needs, the company’s strategies, resources, and technological opportunities, (b) set innovation process goals based on these factors, and (c) manage and control each step of the innovation process [30].
The concept of sustainability has substantial significance and is now experiencing considerable development within the domain of business transformation. This occurrence has a significant impact on innovation [31]. It is generally acknowledged by academics, industry experts, and government officials that innovation is a crucial factor in achieving sustainability. This is because sustainable development is an urgent matter that demands reforms and immediate action from businesses, governments, and society broadly [17]. In order to achieve the ambitious sustainable development goals, it is imperative to implement substantial modifications in policy, technology, and innovation paradigms [32]. Attaining sustainability requires the implementation of fundamental, disruptive, and system-wide innovation [33]. The World Business Council for Sustainable Development asserts that innovation plays a central role in the establishment of a sustainable human society [34].
Numerous studies have emphasized the significance of the relationship between innovation and the attainment of sustainable development objectives. The values-based perspective on innovation management is more effectively advocated by discussions on sustainability-oriented innovation and responsible innovation [35]. From 2010 to 2019, the quantity of articles devoted to innovation and sustainability has increased significantly. The bibliometric analysis identifies the journals, authors, and papers that have had the greatest impact on the field under study. Based on the findings of this study, it is possible to generate a novel research direction in the expansive domain of sustainability and the burgeoning domain of innovation by integrating their defining characteristics into a single concept such as “sustainable innovation” [36]. Furthermore, an investigation employing meta-analysis to examine the influence of innovation on sustainability performance within organizations, including environmental, economic, and social aspects, unveiled that innovation has a favorable effect. Economic innovation exhibited the most robust and advantageous correlations with sustainability performance, while environmental innovation demonstrated the same pattern [37].

2.2. Sustainabiilty in the Food Industry

The food industry is dynamic as consumer demands evolve. The industry needs to develop and adapt in order to meet them. In recent years, the food industry has grown in scale and significance. On the contrary, the food industry exerts a substantial influence on the environment [38]. The environmental impacts and resource depletion associated with food production are substantial and can be examined [39]. The importance of food packaging waste management is recognized by the International Panel of Experts on Sustainable Food Systems. The preservation of biodiversity, water, and land on a global scale gives rise to concerns regarding the ability to fulfil forthcoming global needs. Over the next half-century, population expansion, urbanization, and wage increases will drive the expansion of the food processing industry, and thus, transforming global food supply chains is required. A considerable quantity of biodegradable waste and remainders with high biochemical and chemical oxygen demand are discarded by the food industry. Due to this, waste disposal regulations have become more stringent globally over the past ten years [40].
Food operations that are industrialized utilize mass production. In contemporary food supply chains, production, financing, and marketing are interconnected on a global scale [41,42,43]. Technological advancements, globalization, and altering consumer preferences and marketing have all contributed to economic, social, and environmental difficulties [44,45]. Green and sustainable alternatives for minimizing energy consumption in food production, processing, and packaging have been developed in response to this critical issue [38]. A collaborative effort is required among farmers, manufacturing enterprises, policymakers, retailers, consumers, and investors in order to develop and execute agri-food production processes and products that are environmentally sustainable [39].
There is a growing consumer awareness regarding various aspects of food products, including their origin, inputs, the labor standards enforced by food corporations and farmers, animal welfare, and environmental footprint [43,46]. Additionally, customers value sustainable foods and adhere to strict food safety standards. Consumers who are conscious of the social, economic, and environmental circumstances surrounding the production and distribution of food prefer businesses that adhere to these standards [47]. Simultaneously, the food industry places significant importance on safety and quality, which focuses on supply chain management [42], quality assurance [41,48], and improved tracking and tracing essential concerns [49]. There has been a longstanding public apprehension regarding the sustainability of the food industry, encompassing its ecological, social, and economic ramifications. Fair trade and organic food initiatives are particularly significant [47].
Prior studies have shown that the assessment of sustainability within the realm of food production has three dimensions, namely economic, environmental, and social with the details in Table 1.

3. Research Methods

3.1. Study Design and Questionnaire Development

The aim of this study is to validate the innovation performance from the empirical data. A survey instrument was developed to assess innovation performance. The questions were developed based on a comprehensive examination of the relevant literature and were then refined using the Delphi approach, including six industry experts. This iterative process involved three rounds of examination, ultimately leading to a consensus. Furthermore, additional consultations were conducted with five experts in the field of food innovation to assess the Index of Item Objective Congruence (IOC). This iterative process ensured the finalization of all the questions. The questions were revised from 21 questions to 16 questions with the reduction. The researchers used a seven-point Likert scale to assess the first-order indicators, with values ranging from 1 (indicating no influence) to 7 (indicating a substantial impact) for measuring innovation performance.
The proposed measuring scale is assessed using a particular industry, specifically the Thai food manufacturing sector. The study of a particular industry is useful in evaluating the level of innovation achieved, as it allows the observation of new products that exhibit more uniformity in terms of their technology and economic impacts [55]. The present investigation was carried out via online surveys delivered by email inquiries.

3.2. Sampling and Data Collection

The survey’s target population is Thai food manufacturers. According to the data obtained from the Department of Industrial Works, there are a total of 7046 food manufacturers in Thailand. The sample includes both domestic and multinational original brand manufacturers. This study employed purposive sampling to target managers of related teams in innovation projects and senior executives who support innovation projects. The most often used variation of this method is founded on the rule that the sample size must be greater than 10 times the maximum number of inner or outer model connections pointing at any latent variable in the model [56]. Therefore, the suggested minimum sample size for analyzing innovation performance is 160, with three latent variables and sixteen observed variables. This study’s actual sample size is 354, which exceeds the minimum requirement. The questionnaire was published as a Google form so that respondents could easily respond and submit their responses. The questionnaire was submitted as a Google form to facilitate respondents’ responses and submissions. In addition, the responses were collected electronically, which reduces the possibility of human error if a paper-to-digital conversion is required. We delivered the questionnaire via email to the food manufacturers designated by the Department of Industrial Works. The surveys were gathered throughout the period spanning from January to April of 2023.

3.3. Analyses

The researcher employed Confirmatory Factor Analysis (CFA) using the statistical software AMOS version 20 to investigate the substantial assessment of innovation performance within the food industry. CFA is used to measure both independent and dependent variables. CFA is one of the structural equation modeling (SEM) techniques. This analysis is broadly used in many areas such as econometrics, marketing, psychology, sociology, and education [57]. Exploratory factor analysis was established as an empirical approach of analysis when investigators had no prior understanding of the structure of survey responses. Exploratory factor analysis is employed by researchers when there is a lack of prior knowledge on the underlying elements that may influence a given set of questions. This statistical technique facilitates the identification of the most suitable explanatory model based on observed data, after its collection. Researchers can utilize CFA to simplify a suboptimal model into a simple and reliable form, enhancing conceptual and statistical precision. This makes CFA useful for theoretical testing and construction. It tests factor structure theories systematically. Moreover, CFA allows researchers to test and evaluate multiple models of the causes underlying a collection of questions to choose the best measurement model to explain responses [58].
Therefore, in this study, CFA is employed to assess the appropriateness of the proposed model. The CFA assesses the extent to which each observed variable correlates with or fits into an expected underlying variable. The evaluation of the measurement model also involves assessing the reliability and validity of the measurements by considering various characteristics of the variables, such as correlations and variances. The factor loadings of latent variables were determined which is it is recommended to have a minimum standardized loading of 0.6 [59]. Hence, this study examined variables with factor loadings below or approximately 0.6. The study refitted the models to the data results to have more precise estimates. The improvement of model fitness can be achieved through various techniques such as partitioning or aggregating variables, as well as by establishing correlations among errors, in conjunction with the elimination of variables. Modification indices (M.I.) can be employed to address the factor loading and error term issues. The modification indices can be accessed through the AMOS software. A high residual correlation is indicative of a strong variable correlation. Correlations have the potential to diminish the discriminant validity of a variable. All variables were evaluated based on correlation residuals that were equal to or greater than 2.0 [60].
The likelihood ratio chi-square statistic ( χ 2 ) is employed to assess the overall fit. A significant chi-square value relative to the degrees of freedom indicates a substantial difference between the observed matrix and the chi-square reference matrix. When the value is low and the significance threshold exceeds 0.05, there is no statistically significant difference between the observed and chi-square reference matrices with small sample sizes of less than 200 [61]. In order to mitigate sensitivity, the normed chi-square ( χ 2 /df) method was employed, revealing significant discrepancies between the observed and estimated matrices. A suitable value for this ratio is 5.0 or lower [62]. Comparative fit index (CFI) is a statistical measure that assesses the fit of an estimated model by comparing it to a null or independent model. The range of values falls within the interval of 0 to 1.0. When larger values are indicative of a stronger level of goodness of fit, CFI is particularly well-suited for modeling development methodologies that involve small sample sizes. Incremental fit index (IFI) demonstrates minimal sensitivity to variations in sample size. Values greater than 0.90 are considered acceptable for this index, even though it has the potential to exceed this threshold [63]. Tucker–Lewis fit index (TLI) incorporates a simplicity measure along with a comparison index that evaluates the difference between defined and null models [64]. A threshold value greater than 0.90 was proposed as an indicator of the level of fit [65]. Values below 0.90 are suitable for complex models, while values exceeding 0.95 indicate a high level of agreement [66]. Goodness-of-fit index (GFI) provides an assessment of the overall level of fit without considering the degrees of freedom [67,68]. The goodness-of-fit index is derived from the level of simplicity exhibited by the estimated model. The calculation involves determining the proportion of the estimated population covariance matrix that is explained by the weighted fraction of the sample covariance variance [62]. The chi-square statistic in large samples is adjusted by employing the root mean square error of approximation (RMSEA) [59]. A range of 0.05–0.08 was suggested as an indicator of a close fit [63]. Values below 0.05 suggest a strong alignment between the model and the degrees of freedom. It is recommended that RMSEA values of 0.08 or lower indicate a reasonable approximation error, whereas values exceeding 0.1 should be avoided when using models [69]. The goodness-of-fit criteria is summarized in Table 2.

4. Results

The online survey was sent to the food manufacturers listing in the Department of Industrial Works via email. A total of 354 individuals total completed the survey. Table 1 displays the frequency distribution and percentage of respondent demographics by gender, age, work position, and work experience, as well as the number of food product innovations developed annually.

4.1. Normality of Distributions

The data in Table 3 demonstrates that the average performance of product innovation management in the food manufacturing industry has variation over 16 variables. The average score for each of these indicators ranges from 6.06 (for indicators EC4 and SS4) to 6.36 (for indicator SS3). After examining the skewness and kurtosis, it is determined that the distribution is normal, as shown by all 16 indicators having skewness values less than 2 and kurtosis values less than 7 [70]. This indicates that the distribution is biased to the left. This conclusion implies that a substantial number of the samples have innovation outcomes scores that exceed the variables’ mean. There are five indicators with negative kurtosis values (−0.14, −0.17, −0.45, −0.30, and −0.01, respectively): EC2, EC4, ES4, ES6, and SS4. The results of these measurements vary significantly. In other words, the distribution curve is located below the typical normal curve. The majority of this variable’s indicators have positive kurtosis. This implies that the majority of the indicators for the variables have a more concentrated distribution, resulting in a steeper curve than the normal distribution.

4.2. Measurement Model Assessment of the Innovation Management Performance for Food Manufacturing

Three factors comprise the innovation management performance for food manufacturing: economic sustainability, environmental sustainability, and social sustainability. Figure 1 is a graphical representation of the CFA.
The results of the analysis are detailed in Table 4 and the model is illustrated as Figure 2. The standardized estimates of factor loadings, which restrict the variances of the constructs to 1, are the regression weights of the variables loading into the designated constructs. These loadings are critical factors to consider in CFA. The error between the calculated true values and the observed data is illustrated by measurement errors. R2 represents the information that the provided measure is explained by the variable. Then, 1-R2, which represents proportions of unexplained variation, represents standardized estimates for measurement errors.
The goodness-of-fit parameters illustrate that the model does well fit the data: χ 2 = 222.970, df = 101, p = 0.000, χ 2 /df = 2.208, CFI = 0.971, IFI = 0.972, TLI = 0.966, GFI = 0.928, and RMSEA = 0.058. The goodness-of-fit indices should be higher than 0.9, and all of them are greater than 0.9 as summarized in Table 5.

5. Discussion and Implications

While prior studies have mostly focused on innovation performance impact on finance, it may not cover all long-term goals of business which is sustainability. This research work has facilitated a thorough understanding of the sustainability of the food manufacturing sector. Firms in each industry may have different concerns about its sustainability. Therefore, this study focuses on the food industry.
The research conducted by Mastos, Gotzamani, and Kafetzopoulos [54] on the Measurement Instrument for Sustainability in Food Supply Chains has contributed to a thorough comprehension of sustainability measurement in the food industry. In this study, we develop the measurement for innovation performance in the sustainability aspect on their proposed model with building upon other previous qualitative research works by Hřebíček, Popelka, Štencl, and Trenz [50], Tsolakis, Anastasiadis and Srai [51], Ahmad and Wong [52], and Vu, Chan, Lim, and Chiu [53]. Therefore, we significantly enhance the measurement by including other value indications.
Based on our examination of sustainability measurement in the food industry and the data we gathered from Thai food manufacturers through surveys, we empirically confirmed that every aspect of sustainability is important for overall innovation performance. With factor loadings of 0.99, 0.97, and 0.96, respectively, we can conclude that social sustainability has the greatest influence, followed by economic and environmental aspects.
Social sustainability includes the product safety, the inclusion of the product’s label information, the health and safety of employees and their professional growth, environmental accidents, and the economic and environment impact at the social level. It is important to ensure the safety of the product and take responsibility for the health and safety of customers [50,52,53,54]. Also, it is essential to provide customers with comprehensive label information regarding the products and services offered, including details about the raw materials used and the nutritional composition [50,52]. Health and safety measures refer not only to customers, but also to employees [50,52,53,54]. Employees should be offered professional development suggestions, including training and guidance on career advancement [50,51,52,53]. The environmental accidents should be minimized in order to decrease the organization’s environmental fines [54]. At the social level, it is crucial to consider the influence on the economy and the environment. This includes the development of socially good products and the implementation of community-supporting initiatives, which are essential for achieving social sustainability [50,52,53].
Mastos, Gotzamani, and Kafetzopoulos [54], Tsolakis, Anastasiadis, and Srai [51], and Ahmad and Wong [52] have posited that the indicators of profit growth rate, profit margin cash flow, and return on investment (ROI) might serve as measures of innovation success in the context of economic sustainability. Our analysis reveals that the profit growth rate may be conceptualized within a larger framework of economic value, including a wider definition of financial outcomes such as increased revenue or reduced costs. Furthermore, when considering the profit margin cash flow, it is possible to conceptualize it as a measure of financial stability and health, which may provide a more comprehensive assessment [53,54]. Finally, we have identified trading opportunities and determined that tracing back through the production processes serves as a novel means of measuring indicators. Effective innovation management may lead to an expansion of trade opportunities for businesses, so it can contribute to the overall sustainability of businesses [53]. In addition, tracing back through the production process is crucial for enhancing production efficiency. This includes measures such as minimizing the consumption of raw materials and streamlining the time required for invention creation [51].
Last but not least, for environmental sustainability, our findings suggest that in addition to the factors of water consumption, waste management, and energy efficiency proposed by Mastos, Gotzamani, and Kafetzopoulos [54], Tsolakis, Anastasiadis, and Srai [51], Ahmad and Wong [52], Vu, Chan, Lim, and Chiu [53], there are other notable considerations. Specifically, we have identified the reduction in chemical consumption, the usage of eco-friendly materials, the reduction in air pollution, and the promotion of biodiversity as significant contributors to this aspect of sustainability. One of the primary sustainable outcomes of effective innovation management is the use of environmentally friendly resources, together with the implementation of efficient material consumption practices and the deliberate selection of suppliers that share a similar environmental concern [50,52,53,54]. Additionally, the outcome encompasses the mitigation of air pollution and the preservation of biodiversity, shown by the absence of pollution or the introduction of invasive species or pests [50,52]. Furthermore, there should not be an impact on housing nor a change in the ecosystem [50].
This study represents the first endeavor to provide an entire set of sustainability indicators for the Thai food manufacturing sector. Furthermore, the majority of prior studies conducted in the food industry, not limited to the Thai context, have utilized a restricted number of indicators and have primarily employed a qualitative methodology. The sustainability indicators included in this study were derived from a synthesis of prior research conducted in the food sector. To verify the model, a quantitative approach known as Confirmatory Factor Analysis (CFA) was employed. As of the present time, there has been no report of such a contribution in the existing body of scholarly literature. While the indicators were initially developed to be confirmed within the context of the Thai food manufacturing sector, they may potentially be adapted to and used in different sectors and regions with some adjustments.
Businesses, with the ultimate objective of ensuring their long-term survival, attempt to effectively manage innovation in anticipation of attaining this aim. This research offers practical implications for practitioners seeking to evaluate their innovation management approach by adopting this measurement scale. By evaluating their innovation management practice, organizations may gain insights into the effectiveness of their innovation management and aim for ongoing development in order to attain sustainability, especially in the midst of environmental changes and uncertainties. In addition, from a practical perspective, it can be inferred that directing attention towards social sustainability initiatives might lead to the most substantial improvement in innovation performance for Thai food manufacturers. Organizations could focus on the development of more precise and efficient approaches for sustainable innovation within their respective industries.

6. Conclusions and Future Research

Scholars have increasingly emphasized the importance of innovation in achieving long-term competitive advantages for organizations. The effect of innovation management systems on company innovation performance has been widely studied, with a particular emphasis on its significance for sustainable development. However, businesses have challenges in fostering innovation while also addressing sustainability issues. The existing studies have primarily concentrated on the influence of innovation on financial or operational performance. The main objective of our research is to investigate the measures used in assessing sustainability outcomes resulting from innovation management in response to sustainability consciousness. Businesses require a measurement tool that can assess how well they develop and manage innovation in the long term. The main focus of this research belongs to the food industry, which is widely acknowledged as an important sector that presents considerable difficulties in attaining effective innovation. These challenges arise from the characteristics that are unique to this industry.
Based on an examination of the relevant academic literature on sustainability within the food industry, it can be argued that there are three distinct aspects of sustainability: economic, environmental, and social. These dimensions play a significant role in contributing to the achievement of food business objectives. The CFA approach was employed to quantitatively validate each indicator for every dimension in our study. We discovered that social sustainability has the greatest impact, with a small difference in level arising from the economic and environmental aspects. In order to achieve social sustainability, it is essential to enhance the health and safety of customers, provide comprehensive product label information, prioritize the health and safety of workers, foster their career development, mitigate environmental accidents, and provide the impact on the economy and environment at the social level. Economic sustainability encompasses many key aspects, including the growth of economic value, the enhancement of an organization’s financial stability and health, the expansion of trade opportunities, and the capacity to trace the production process. Environmental sustainability comprises several indicators consisting reducing water and chemical consumption, effectively managing waste, optimizing energy utilization in the production process, using eco-friendly materials, minimizing air pollution, and encouraging biodiversity conservation.
The limitation of this study is that that the measuring indicators used are derived from previous research conducted within the food industry. Additionally, the scope of this study is limited to companies within the food sector in Thailand, including firms of various sizes and other characteristics, e.g., years of operation, the amount of funds allocated to innovation activities. Future research possibly involves a qualitative investigation aimed at identifying previously unexplored indicators derived from existing research or including relevant data from other sectors. In addition, future work may be directed towards examining individual business sizes in order to ascertain if there are any noticeable inconsistencies or variations in the significance of each indicator for different company sizes. Furthermore, it is essential to conduct a comprehensive analysis of the suggested measurement on a worldwide scale, rather than confining it to a particular country. Moreover, the applicability of the majority of these indicators can be extended beyond the current industry under consideration, and thus, there is potential to apply this measurement to other sectors. Finally, the future research has the potential to integrate the measurement of innovation performance with the measurement of innovation management utilizing structural equation modelling (SEM) to examine the relationship between product innovation management dimensions and innovation performance. SEM enables both direct and indirect examinations of model relationships, making it a highly effective tool capable of simultaneously examining multiple relationships. This comprehensive approach would enable a holistic understanding of how innovation can be effectively managed, and its outcomes accurately assessed.

Author Contributions

Conception of the presented idea, F.L., N.T. and R.V.; supervision, methodology, and results, N.T. and R.V.; data collection, F.L.; writing—original draft preparation, F.L.; writing—review and editing, N.T. and R.V. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

COA No. 004/2566. Date of approval: 10 January 2023. The Research Ethics Review Committee for Research Involving Human Subjects: The Second Allied Academic Group in Social Sciences, Humanities, and Fine and Applied Arts at Chulalongkorn University, based on the Declaration of Helsinki, the Belmont report, CIOMS guidelines, and the Principle of the international conference on harmonization—Good clinical practice (ICH-GCP) has approved the execution of the aforementioned research project.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Flynn, M.; Dooley, L.; O’Sullivan, D.; Cormican, K. Idea management for organisational innovation. Int. J. Innov. Manag. 2003, 07, 417–442. [Google Scholar] [CrossRef]
  2. Gök, O.; Peker, S. The impact of marketing’s innovation-related capabilities on a firm’s innovation performance. Int. J. Innov. Manag. 2020, 24, 2050054. [Google Scholar] [CrossRef]
  3. Nybakk, E. Learning orientation, innovativeness and financial performance in traditional manufacturing firms: A higher-order structural equation model. Int. J. Innov. Manag. 2012, 16, 1250029. [Google Scholar] [CrossRef]
  4. Almeida, F. Measuring innovation through a crowd source initiative. Int. J. Innov. Sci. 2019, 11, 471–488. [Google Scholar] [CrossRef]
  5. Globocnik, D.; Rauter, R.; Baumgartner, R.J. Synergy or conflict? The relationships among organisational culture, sustainability-related innovation performance, and economic innovation performance. Int. J. Innov. Manag. 2020, 24, 2050004. [Google Scholar] [CrossRef]
  6. Hernández-Chea, R.; Jain, A.; Bocken, N.M.P.; Gurtoo, A. The Business Model in Sustainability Transitions: A Conceptualization. Sustainability 2021, 13, 5763. [Google Scholar] [CrossRef]
  7. Barbieri, J.C.; de Vasconcelos, I.F.G.; Andreassi, T.; de Vasconcelos, F.C. Innovation and sustainability: New models and propositions. Rev. Adm. Empresas 2010, 50, 146–154. [Google Scholar] [CrossRef]
  8. Elkington, J. Enter the triple bottom line. In The Triple Bottom Line: Does It All Add up; Routledge: London, UK, 2004; Volume 11, pp. 1–16. [Google Scholar]
  9. Hermundsdottir, F.; Aspelund, A. Sustainability innovations and firm competitiveness: A review. J. Clean. Prod. 2021, 280, 124715. [Google Scholar] [CrossRef]
  10. Pateli, A.G.; Giaglis, G.M. A research framework for analysing eBusiness models. Eur. J. Inf. Syst. 2004, 13, 302–314. [Google Scholar] [CrossRef]
  11. Stubbs, W.; Cocklin, C. Conceptualizing a “Sustainability Business Model”. Organ. Environ. 2008, 21, 103–127. [Google Scholar] [CrossRef]
  12. Wirtz, B.W.; Pistoia, A.; Ullrich, S.; Göttel, V. Business models: Origin, development and future research perspectives. Long Range Plan. 2016, 49, 36–54. [Google Scholar] [CrossRef]
  13. Nosratabadi, S.; Mosavi, A.; Shamshirband, S.; Kazimieras Zavadskas, E.; Rakotonirainy, A.; Chau, K.W. Sustainable Business Models: A Review. Sustainability 2019, 11, 1663. [Google Scholar] [CrossRef]
  14. Edler, J.; Cunningham, P.; Gök, A. Handbook of Innovation Policy Impact; Edward Elgar Publishing: Cheltenham, UK, 2016. [Google Scholar]
  15. Cohn, M.; Ford, D. Introducing an Agile Process to an Organization. Computer 2003, 36, 74–78. [Google Scholar] [CrossRef]
  16. Albors-Garrigos, J.; Igartua, J.I.; Peiro, A. Innovation management techniques and tools: Its impact on firm innovation performance. Int. J. Innov. Manag. 2018, 22, 1850051. [Google Scholar] [CrossRef]
  17. Silvestre, B.S.; Ţîrcă, D.M. Innovations for sustainable development: Moving toward a sustainable future. J. Clean. Prod. 2019, 208, 325–332. [Google Scholar] [CrossRef]
  18. Ernst, H. Success Factors of New Product Development: A Review of the Empirical Literature. Int. J. Manag. Rev. 2002, 4, 1–40. [Google Scholar] [CrossRef]
  19. Terziovski, M.; Morgan, J.P. Management practices and strategies to accelerate the innovation cycle in the biotechnology industry. Technovation 2006, 26, 545–552. [Google Scholar] [CrossRef]
  20. Capitanio, F.; Coppola, A.; Pascucci, S. Product and Process Innovation in the Italian Food Industry. Agribusiness 2010, 26, 503–518. [Google Scholar] [CrossRef]
  21. Adams, R.; Bessant, J.; Phelps, R. Innovation management measurement: A review. Int. J. Manag. Rev. 2006, 8, 21–47. [Google Scholar] [CrossRef]
  22. Blindenbach-Driessen, F.; van den Ende, J. Innovation in project-based firms: The context dependency of success factors. Res. Policy 2006, 35, 545–561. [Google Scholar] [CrossRef]
  23. Michelino, F.; Cammarano, A.; Celone, A.; Caputo, M. The Linkage between Sustainability and Innovation Performance in IT Hardware Sector. Sustainability 2019, 11, 4275. [Google Scholar] [CrossRef]
  24. Hansen, E.G.; Grosse-Dunker, F.; Reichwald, R. Sustainability innovation cube—A framework to evaluate sustainability-oriented innovations. Int. J. Innov. Manag. 2009, 13, 683–713. [Google Scholar] [CrossRef]
  25. Horvat, A.; Behdani, B.; Fogliano, V.; Luning, P.A. A systems approach to dynamic performance assessment in new food product development. Trends Food Sci. Technol. 2019, 91, 330–338. [Google Scholar] [CrossRef]
  26. Kimberly, J.R. Managerial innovation. Handb. Organ. Des. 1981, 1, 104. [Google Scholar]
  27. Hamel, G. The why, what, and how of management innovation. Harv. Bus. Rev. 2006, 84, 72. [Google Scholar]
  28. Birkinshaw, J.; Hamel, G.; Mol, M.J. Management Innovation. Acad. Manag. Rev. 2008, 33, 825–845. [Google Scholar] [CrossRef]
  29. Hamel, G. The future of management. Hum. Resour. Manag. Int. Dig. 2008, 16, 6. [Google Scholar] [CrossRef]
  30. García-Morales, V.J.; Lloréns-Montes, F.J.; Verdú-Jover, A.J. Influence of personal mastery on organizational performance through organizational learning and innovation in large firms and SMEs. Technovation 2007, 27, 547–568. [Google Scholar] [CrossRef]
  31. Seebode, D.; Jeanrenaud, S.; Bessant, J. Managing innovation for sustainability. RD Manag. 2012, 42, 195–206. [Google Scholar] [CrossRef]
  32. Leach, M.; Rockström, J.; Raskin, P.; Scoones, I.; Stirling, A.C.; Smith, A.; Thompson, J.; Millstone, E.; Ely, A.; Arond, E.; et al. Transforming Innovation for Sustainability. Ecol. Soc. 2012, 17, 6. [Google Scholar] [CrossRef]
  33. Gobble, M.M. Innovation and sustainability. Res. Technol. Manag. 2012, 55, 64–67. [Google Scholar] [CrossRef]
  34. Dormann, J.; Holliday, C. Innovation, Technology, Sustainability and Society; World Business Council For Sustainable Development: Geneva, Switzerland, 2002. [Google Scholar]
  35. Ivanov, K. Values-based business model innovation–The case of Ecosia and its business model. Int. J. Innov. Manag. 2022, 26, 2240002. [Google Scholar] [CrossRef]
  36. Maier, D.; Maier, A.; Așchilean, I.; Anastasiu, L.; Gavriș, O. The Relationship between Innovation and Sustainability: A Bibliometric Review of the Literature. Sustainability 2020, 12, 4083. [Google Scholar] [CrossRef]
  37. Kuzma, E.; Padilha, L.S.; Sehnem, S.; Julkovski, D.J.; Roman, D.J. The relationship between innovation and sustainability: A meta-analytic study. J. Clean. Prod. 2020, 259, 120745. [Google Scholar] [CrossRef]
  38. Chakka, A.K.; Sriraksha, M.S.; Ravishankar, C.N. Sustainability of emerging green non-thermal technologies in the food industry with food safety perspective: A review. LWT 2021, 151, 112140. [Google Scholar] [CrossRef]
  39. Del Borghi, A.; Gallo, M.; Strazza, C.; Del Borghi, M. An evaluation of environmental sustainability in the food industry through Life Cycle Assessment: The case study of tomato products supply chain. J. Clean. Prod. 2014, 78, 121–130. [Google Scholar] [CrossRef]
  40. Despoudi, S.; Bucatariu, C.; Otles, S.; Kartal, C.; Otles, S.; Despoudi, S.; Bucatariu, C.; Kartal, C. Chapter 1—Food waste management, valorization, and sustainability in the food industry. In Food Waste Recovery, 2nd ed.; Galanakis, C.M., Ed.; Academic Press: San Diego, CA, USA, 2021; pp. 3–19. [Google Scholar]
  41. Manning, L.; Baines, R.N.; Chadd, S.A. Quality assurance models in the food supply chain. Br. Food J. 2006, 108, 91–104. [Google Scholar] [CrossRef]
  42. Manning, L.; Baines, R.N.; Chadd, S.A. Deliberate contamination of the food supply chain. Br. Food J. 2005, 107, 225–245. [Google Scholar] [CrossRef]
  43. Trienekens, J.H.; Wognum, P.M.; Beulens, A.J.M.; van der Vorst, J.G.A.J. Transparency in complex dynamic food supply chains. Adv. Eng. Inform. 2012, 26, 55–65. [Google Scholar] [CrossRef]
  44. Yakovleva, N. Measuring the sustainability of the food supply chain: A case study of the UK. J. Environ. Policy Plan. 2007, 9, 75–100. [Google Scholar] [CrossRef]
  45. Zanoni, S.; Zavanella, L. Chilled or frozen? Decision strategies for sustainable food supply chains. Int. J. Prod. Econ. 2012, 140, 731–736. [Google Scholar] [CrossRef]
  46. Cross, P.; Edwards, R.T.; Opondo, M.; Nyeko, P.; Edwards-Jones, G. Does farm worker health vary between localised and globalised food supply systems? Environ. Int. 2009, 35, 1004–1014. [Google Scholar] [CrossRef] [PubMed]
  47. Beske, P.; Land, A.; Seuring, S. Sustainable supply chain management practices and dynamic capabilities in the food industry: A critical analysis of the literature. Int. J. Prod. Econ. 2014, 152, 131–143. [Google Scholar] [CrossRef]
  48. Brown, C.G.; Longworth, J.W.; Waldron, S. Food safety and development of the beef industry in China. Food Policy 2002, 27, 269–284. [Google Scholar] [CrossRef]
  49. Wang, X.; Li, D.; O’brien, C. Optimisation of traceability and operations planning: An integrated model for perishable food production. Int. J. Prod. Res. 2009, 47, 2865–2886. [Google Scholar] [CrossRef]
  50. Hřebíček, J.; Popelka, O.; Štencl, M.; Trenz, O. Corporate performance indicators for agriculture and food processing sector. Acta Univ. Agric. Et Silvic. Mendel. Brun. 2012, 60, 121–132. [Google Scholar] [CrossRef]
  51. Tsolakis, N.; Anastasiadis, F.; Srai, J. Sustainability Performance in Food Supply Netwaorks: Insights from the UK Industry. Sustainability 2018, 10, 3148. [Google Scholar] [CrossRef]
  52. Ahmad, S.; Wong, K.Y. Development of weighted triple-bottom line sustainability indicators for the Malaysian food manufacturing industry using the Delphi method. J. Clean. Prod. 2019, 229, 1167–1182. [Google Scholar] [CrossRef]
  53. Vu, H.M.; Chan, H.K.; Lim, M.K.; Chiu, A.S.F. Measuring business sustainability in food service operations: A case study in the fast food industry. Benchmarking Int. J. 2017, 24, 1037–1051. [Google Scholar] [CrossRef]
  54. Mastos, T.; Gotzamani, K.; Kafetzopoulos, D. Development and Validation of a Measurement Instrument for Sustainability in Food Supply Chains. Sustainability 2022, 14, 5203. [Google Scholar] [CrossRef]
  55. Santarelli, E.; Piergiovanni, R. Analyzing literature-based innovation output indicators: The Italian experience. Res. Policy 1996, 25, 689–711. [Google Scholar] [CrossRef]
  56. Christopher Westland, J. Lower bounds on sample size in structural equation modeling. Electron. Commer. Res. Appl. 2010, 9, 476–487. [Google Scholar] [CrossRef]
  57. Bollen, K.A. Confirmatory factor analysis. In Structural Equations with Latent Variables; John Wiley: Hoboken, NJ, USA, 1989; pp. 226–318. [Google Scholar]
  58. Bryant, F.B.; Yarnold, P.R.; Michelson, E.A. Statistical Methodology. Acad. Emerg. Med. 1999, 6, 54–66. [Google Scholar] [CrossRef] [PubMed]
  59. Hair, J.F. Multivariate Data Analysis; Pearson Prentice Hall: Saddle River, NJ, USA, 2006. [Google Scholar]
  60. Byrne, B.M. Structural equation modeling with AMOS, EQS, and LISREL: Comparative approaches to testing for the factorial validity of a measuring instrument. Int. J. Test. 2001, 1, 55–86. [Google Scholar] [CrossRef]
  61. Kline, R.B. Promise and pitfalls of structural equation modeling in gifted research. In Methodologies for Conducting Research on Giftedness; Thompson, B., Subotnik, R.F., Eds.; American Psychological Association: Worcester, MA, USA, 2010; pp. 147–169. [Google Scholar]
  62. Tabachnick, B.G.; Fidell, L.S.; Ullman, J.B. Using Multivariate Statistics; Pearson: Boston, MA, USA, 2013; Volume 6. [Google Scholar]
  63. Schumacker, R.E.; Lomax, R.G. A Beginner’s Guide to Structural Equation Modeling; Psychology Press: London, UK, 2004. [Google Scholar]
  64. Hair, J.F.; Black, W.C.; Babin, B.J. Multivariate Data Analysis: A Global Perspective; Pearson Education: Boston, MA, USA, 2010. [Google Scholar]
  65. Baumgartner, H.; Homburg, C. Applications of structural equation modeling in marketing and consumer research: A review. Int. J. Res. Mark. 1996, 13, 139–161. [Google Scholar] [CrossRef]
  66. Abdullah, M.A.; Manaf, N.H.A.; Yusuf, M.-B.O.; Ahsan, K.; Azam, S.M.F. Determinants of customer satisfaction on retail banks in New Zealand: An empirical analysis using structural equation modeling. Glob. Econ. Financ. J. 2014, 7, 63–82. [Google Scholar] [CrossRef]
  67. Byrne, B.M. Structural Equation Modeling with Mplus: Basic Concepts, Applications, and Programming; Routledge: London, UK, 2013. [Google Scholar]
  68. Hair, J.F.; Gabriel, M.; Patel, V. AMOS covariance-based structural equation modeling (CB-SEM): Guidelines on its application as a marketing research tool. Braz. J. Mark. 2014, 13, 44–55. [Google Scholar]
  69. Browne, M.W.; Cudeck, R. Alternative ways of assessing model fit. Sociol. Methods Res. 1992, 21, 230–258. [Google Scholar] [CrossRef]
  70. West, S.G.; Finch, J.F.; Curran, P.J. Structural equation models with nonnormal variables: Problems and remedies. In Structural Equation Modeling: Concepts, Issues, and Applications; Sage Publications, Inc: Thousand Oaks, CA, USA, 1995; pp. 56–75. [Google Scholar]
Sustainability 15 16714 g001
Figure 1. Measurement model assessment of innovation management performance for food manufacturing.
Figure 1. Measurement model assessment of innovation management performance for food manufacturing.
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Figure 2. Measurement model of innovation management performance for food manufacturing.
Figure 2. Measurement model of innovation management performance for food manufacturing.
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Table 1. Sustainability measurement and description.
Table 1. Sustainability measurement and description.
DimensionMeasurementDescriptionSource
Economic
sustainability		Economic valueIncrease economic value, such as by increasing income or reducing expenses.[50,51,52]
Financial healthIncrease the organization’s financial stability and health.[53,54]
Trade opportunityIncrease trade opportunity.[53]
TraceabilityTrace back through the production process to improve production efficiency, such as reducing raw materials used and reducing innovation development time.[51]
Government supportReceive financial assistance from the government.[50]
Environment
sustainability		Water and chemical consumptionReduce the consumption of water and chemicals.[50,51,52,53,54]
Waste managementReduce or manage waste.[50,51,52,53,54]
Energy efficiencyEfficiently use energy in production and reduce the use of non-renewable energy.[50,51,52,53,54]
Efficiency of material consumption and eco-friendly materialUse eco-friendly materials and have efficient material consumption, and select suppliers that operate with the same environmental consciousness.[50,52,53,54]
Air pollutionReduce air pollution.[50,52]
BiodiversitySupport biodiversity, such as by not causing pollution or invasion of weeds or pests, having no effect on housing, nor changing the ecosystem.[50]
Social
sustainability		Product safetyIncrease product safety and take responsibility for the health and safety of customers.[50,51,52,53,54]
LabellingLabel information about products and services about raw materials and nutrients to customers.[50,52]
Employee’s health and safetyEmployees are healthy and safety.[50,52,53,54]
Career developmentProvide employees with career development opportunities, such as training and career advice.[50,51,52,53]
Human rights and lawsProvide employment stability, equal opportunity, and consider human rights and other laws.[50,52,53]
Workplace accidentsReduce workplace accidents involving employees.[54]
Accidents related to environmentReduce accidents related to the environment and reduce the company’s environmental penalties.[54]
Economic and environmental effects at the social levelImpact to economic and environment at the social level, such as the creation of socially beneficial products and community-supporting operations.[50,52,53]
CorruptionReduce corruption.[50]
Public policyAlign with public policy.[50]
Table 2. Summarized goodness-of-fit criteria.
Table 2. Summarized goodness-of-fit criteria.
Goodness of FitLevel of Acceptable Fit
Chi square/Degree of Freedom ( χ 2 /df)<5.0
Comparative fit index (CFI)>0.90
Incremental fit index (IFI)>0.90
Tucker–Lewis index (TLI)>0.90
Goodness-of-fit index (GFI)>0.90
Root Mean Square Error of Approximation (RMSEA)<0.08
Table 3. Mean, SD, skew, and kurtosis on innovation performance.
Table 3. Mean, SD, skew, and kurtosis on innovation performance.
DimensionPerformance of Product Innovation Management for Food ManufacturingMeanSDSkewKurtosis
Economic
sustainability		EC1: Increase economic value, such as increasing income or reducing expenses.6.160.88−0.921.05
EC2: Increase the organization’s financial stability and health.6.270.79−0.79−0.14
EC3: Increase trade opportunity.6.300.80−1.020.94
EC4: Trace back through the production process to improve production efficiency, such as reducing raw materials used and reducing innovation development time.6.060.89−0.66−0.17
Environment
sustainability		ES1: Reduce the consumption of water and chemicals.6.130.95−1.121.93
ES2: Reduce or manage waste.6.320.81−1.191.88
ES3: Efficiently use energy in production and reduce the use of non-renewable energy.6.140.94−0.990.83
ES4: Use eco-friendly materials and have efficient material consumption, and select suppliers that operate with the same environmental consciousness.6.110.93−0.69−0.45
ES5: Reduce air pollution.6.290.85−1.130.95
ES6: Support biodiversity, such as by not causing pollution or invasion of weeds or pests, having no effect on housing, nor changing the ecosystem.6.090.90−0.68−0.30
Social
sustainability		SS1: Increase product safety and take responsibility for the health and safety of customers.6.350.81−1.080.58
SS2: Label information about products and services about raw materials and nutrients to customers.6.170.88−0.930.86
SS3: Employees are healthy and safety.6.360.80−1.443.28
SS4: Provide employees with career development opportunities, such as training and career advice.6.060.88−0.63−0.01
SS5: Reduce accidents related to the environment and reduce the company’s environmental penalties.6.190.93−1.141.44
SS6: Impact to economic and environment at the social level, such as the creation of socially beneficial products and community-supporting operations.6.100.96−0.830.05
Table 4. Results of factor loadings and residuals for innovation management for food manufacturing.
Table 4. Results of factor loadings and residuals for innovation management for food manufacturing.
ConstructItemsStandardized LoadingsStandard
Error		t-ValueR2
Economic
sustainability		EC10.752 a 0.602
EC20.8240.06216.0970.539
EC30.7940.06215.4200.723
EC40.7760.07015.0260.695
Environment
sustainability		ES10.734 a 0.656
ES20.8500.06016.2940.661
ES30.8340.07015.9470.645
ES40.8100.06915.4600.656
ES50.8130.06315.5160.688
ES60.8030.06715.3160.584
Social
sustainability		SS10.810 a 0.494
SS20.8300.06118.3460.543
SS30.7640.05716.3450.649
SS40.7030.06414.6160.936
SS50.7370.06715.5540.909
SS60.8060.06717.5970.986
χ 2 = 222.970, df = 101, p = 0.000, χ 2 /df = 2.208, CFI = 0.971, IFI = 0.972, TLI = 0.966, GFI = 0.928, and RMSEA = 0.058. Note: a The corresponding parameter has been set equal to 1 (unstandardized) to fix the measurement scale.
Table 5. Goodness-of-fit indices for CFA of innovation management performance for food manufacturing.
Table 5. Goodness-of-fit indices for CFA of innovation management performance for food manufacturing.
Goodness-of-Fit MeasureCriterionModelResult
Chi square ( χ 2 ) 222.970
Degree of Freedom (df) 101
χ 2 /df<5.02.208Good fit
CFI>0.900.971Good fit
IFI>0.900.972Good fit
TLI>0.900.966Good fit
GFI>0.900.928Good fit
RMSEA<0.080.058Good fit
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Leesatapornwongsa, F.; Thawesaengskulthai, N.; Vaiyavuth, R. Developing a Sustainability Measurement for Innovation Performance for the Food Industry. Sustainability 2023, 15, 16714. https://doi.org/10.3390/su152416714

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Leesatapornwongsa F, Thawesaengskulthai N, Vaiyavuth R. Developing a Sustainability Measurement for Innovation Performance for the Food Industry. Sustainability. 2023; 15(24):16714. https://doi.org/10.3390/su152416714

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Leesatapornwongsa, Fontip, Natcha Thawesaengskulthai, and Ronnakorn Vaiyavuth. 2023. "Developing a Sustainability Measurement for Innovation Performance for the Food Industry" Sustainability 15, no. 24: 16714. https://doi.org/10.3390/su152416714

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