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A game theoretic decision model for organic food supplier evaluation in the global supply chains
Journal of Cleaner Production 242 (2020) 118536
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Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro
A game theoretic decision model for organic food supplier evaluation in the global supply chains Henry Lau a, Paul K.C. Shum a, Dilupa Nakandala a, Youqing Fan a, *, Carman Lee b a b
School of Business, Western Sydney University, Australia Department of Industrial Systems Engineering, Hong Kong Polytechnic University, Hunghom, HKSAR, China
a r t i c l e i n f o
a b s t r a c t
Article history: Received 7 May 2019 Received in revised form 18 September 2019 Accepted 20 September 2019 Available online 24 September 2019
Sourcing organic food products from global green supply chains (GGSCs) spreads across country borders and interacting among a wide spectrum of intermediary stakeholders, which adds complexity and incentivises profit-seeking stakeholders to engage in food adulteration. This phenomenon is frequently observed among green and organic products with credence attributes that are difficult to authenticate in both the prior- and post-purchase stages in both developed and developing countries. The case study that we investigate is a small-sized supermarket chain operator in Hong Kong, with an increasingly expanding range of organic food products. Whilst there is a proven set of hybrid multi-criterion decision making (MCDM) methods and model that have been implemented successfully in Australia, unfortunately, unable to assure total success in optimizing the selection of organic food suppliers when applying to this supermarket chain in Hong Kong. Even the obvious solution of certification and product labeling is not sufficient to solve this organic food credibility problem. Our study proposes a newly discovered criterion as postulated by the game theoretic framework, offering a more optimal and authentic solution to the organic food authentication problem. Apart from adopting the mixed strategy of random monitoring, fines should be imposed to penalise dishonest suppliers, instead of awarding bonuses to suppliers operating with integrity. An even better solution is to institutionalise these control mechanisms by the governing authorities with continuous monitoring and imposition of penalties. © 2019 Published by Elsevier Ltd.
Handling editor: M.T. Moreira Keywords: MCDM Game theory Organic food Supplier selection Global green supply chain
1. Introduction Organic farming is a preferred alternative approach to conventional agriculture. The production process and outputs of organic agriculture contribute to sustainable development (Gan et al., 2016; Magistris and Gracia, 2016), human health, ecosystem and soil (Chekima et al., 2017; Laureti and Benedetti, 2018). The original goal is to implement total commitment to triple bottom line (TBL) (Elkington, 1997) agenda at all organisational levels in organic farming and retailing SC that integrate the economic, environmental, and social facets of sustainable agricultural practices (Savory, 2016). Organic food products contribute to cleaner production by minimizing environmental impact and maximizing efficient allocation of resources (Vega-Zamora et al., 2019). Organic consumers believe in the important health and environment benefits that are more advantageous than the conventional food
* Corresponding author. School of Business, Western Sydney University, Australia. E-mail address: [email protected] (Y. Fan). https://doi.org/10.1016/j.jclepro.2019.118536 0959-6526/© 2019 Published by Elsevier Ltd.
products (Magistris and Gracia, 2008; Singh and Verma, 2017). Therefore, consumption of organic foods becomes the most popular sustainable behaviour, with consequential sustained growth over the past decade (D’Amico et al., 2016; Laureti and Benedetti, 2018). Movement of organic food products requires green supply chain management (GSCM), which is distinguished from traditional SCM in terms of its more expansive perspectives and a ‘win-win’ commitment towards achieving multiple TBL objectives (Elkington, 1997, 1998), engaging a wider spectrum of stakeholders (Kumar and Chandrakar, 2012). The prevailing approach in the business sector is to implement the ISO14001 as a standard for evaluating the TBL performance of suppliers operating in a sustainable SC (Buyukozkan and Cifci, 2011). For organic food supplier evaluation, the multicriterion decision making (MCDM) methods are more applicable to optimise the multiple TBL objectives. However, when evaluating the organic supplier performance, since the credence attributes are not detectable via sensory recognition, the global GSCs is more liable to information asymmetry and quality uncertainty. This information asymmetry between the demand and supply sides exposes the GSCs to opportunistic behaviour and fraud.
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Organic products have several specific credence attributes and claims, e.g. green friendly, production, source, content, that are difficult to determine (Roe and Sheldon, 2007; Voon et al., 2011) in both the prior- and post-purchase stages, due to practicalities or lack of technical expertise (Ford et al., 1988). Inability to verify the promised organic characteristics is the main cause of consumer skepticism and suspicion of fraud materialised at various stages, e.g. in production controls, certification processes, distribution channels, and points of sale, of the GSC (Giannakas, 2002). Research studies (Choe et al., 2009) recognise that trust may be the only factor to mitigate consumers’ fears of opportunism among suppliers and sellers. Third-party certifiers are the key agents to institutionalise certification as a credible signal for credence quality. Though the certifiers add value to the GSCs, implementation of certification is a costly process for assuring compliance with standards, and subject to bias and oversight (Manzini and Accorsi, 2013). Therefore, a tendency towards selecting more lenient certifiers is observed (Ali et al., 2017). Profit seeking certifiers may compromise on the credibility of the certification process when the number of certifying firms increases and competition intensifies (Carriquiry and Babcock, 2007). Consequently, relevant information that may give rise to fraudulent activities can be hidden by certifiers to compromise the certification outcome (Jahn et al., 2005). The role of monitoring and enforcement can be better performed by government authority. Thus, government regulation and public control are necessary to minimise the undesirable effects of market failure. We shall now review the literature on food supplier selection criteria. The game theoretic framework provides a rationale to introduce the cost of monitoring as a key criterion of the set of organic food supplier selection criteria in the context of organic food GSCs. Later, we shall describe the proposed model embedded with the newly discovered criterion as postulated by the game theoretic framework, the data collection and analytical procedure using the three MCDM methods, followed by the numerical application to the supermarket chain case study in the final section. 2. Literature review Assessing and selecting suppliers on the basis of a competitive set of criteria can improve the desirable properties of the purchased goods and services. However, Luo et al. (2009) argue that a universal set of criteria applicable for all supplier selection problems does not exist. Choice of the criteria depends on characteristics of the products/services and the specific industry. For organic food supplier selection, the ten major criteria (product, quality, food safety, price, delivery, serviceability, commercial position, supplier relationship, risk factors, and CSR) identified by Lau et al. (2018) does not internalise the credence attributes of organic food to evaluate supplier performance, resulting in erroneous assessment outcomes during implementation. This incomplete supplier selection can undermine the reputation of the retail stores, thus widening the high intention and low purchase behaviour gap that continues to slow down the transition of the organic sector from a niche market to a mainstream position (Latacz-Lohman and Foster, 1997). To address this problem, our study investigates the three major barriers, i.e. premium price, unavailability, and trust that are causing the intention/behaviour gap. Previous studies found that high price premium and unavailability are the two main causes for the intention/behaviour gap (Aschemann-Witzel and Niebuhr Aagaard, 2014; Latacz-Lohman and Foster, 1997). However, distrust is another even stronger limiting factor. Distrust on organic food certifications is very common and has a large negative effect on consumers’ purchasing decisions (Gan et al., 2016; Goh and Balaji, 2016; Nuttavuthisit &
Thǿgersen 2017; Vega-Zamora et al., 2019). One observed solution in China is the government regulation to control the organic certification processes and discipline the scandals and fraud cases. This drastic measure and the introduction of state-certified organic logo (General Administration of Quality Supervision Inspection and Quarantine of the People’s Republic of China, 2012) help alleviating the level of distrust among the consumers, thus stimulating the growth and development of the organic market in China. Besides, it is high priority for the suppliers and retailers to cultivate consumer trust to energise further growth and development in the organic food market (Gan et al., 2016; Nuttavuthisit & Thǿgersen 2017). Based on literature review and a pilot focus group study, our study finds that trust is the most important factor among these three barriers. Thereafter, our target sample is designed to control the effects of the two other major barriers, i.e. premium price and availability, that have previously been found negatively impacting on the actual purchasing behaviour (Aschemann-Witzel and Niebuhr Aagaard, 2014). Consumers with high level of income and education are willing to pay premium price and purchase more frequently (Boztepe, 2016; Pearson et al., 2013). Availability of a wide range of organic products is not a problem issue in this supermarket chain. Once the premium price and availability barriers have been controlled by design, our study explores the causal factors that contribute to high level of trust. The game theory conveys insight into the weakness of the current organic certification systems and the non-involvement of the retail sector, as attested in the literature (Chkanikova and Kogg, 2018; Chkanikova and Lehner, 2015). Based on the theoretical framework of game theory, the conventional approach of supplier selection cannot avoid the phenomenon outlined above in which conventional food is sold as organic food. Our study recommends strengthening the monitoring mechanism to supplement certification and organic labeling in supplier selection. The desirability of introducing government regulation and public control can be revealed in the game theoretical framework. Game theory strategy can be classified into two main types: (i) a pure strategy, where a particular strategy is chosen; and (ii) a mixed strategy, where a strategy is selected randomly from a set of available strategies, with each strategy being selected at a predetermined frequency. Though mixed strategy has not been applied in the GSCM decision context, where the course of action, e.g. facility capacity, would not be alterable, these game theory concepts can still be relevant in the investigation of how the different stakeholders with different bargaining power in the organic food markets interacting with each other to influence the game equilibrium (Zhu et al., 2018), and the associated market failure phenomenon. Mixed strategy is more applicable to this organic food supplier selection case study with undeterminable scenarios where no pure strategy equilibrium exists. Nash (1950) introduced mixed strategies with which at least one equilibrium exists in every strategic game. Fudenberg and Tirole (1991) developed a simultaneous move monitoring game, where an employer either monitors or does not monitor a worker, and the worker either works or shirks, and was followed by further research studies (Andreozzi, 2004; Avenhaus et al., 2002) with extended models and applications. The unique Nash equilibrium is found in mixed strategies, where the employer monitors the worker with a calculated probability, and the worker shirks with a calculated probability, depending on the parameter values in the payoff matrix. Furthermore, Avenhaus et al. (2002) found that the introduction of fines could deter non-compliance, whereas awarding bonuses to encourage compliance would only increase the probability of non-compliance, contrary to the conventional thinking in commercial practices in GSCM.
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3. Methodology Our preliminary result indicates that trust is the primary factor among the three barriers, consistent with the findings of recent research studies on trust in the purchasing decisions on organic food products (Gan et al., 2016), or distrust as a barrier to the growth and development of the organic food sector (Gan et al., 2016; Goh and Balaji, 2016; Nuttavuthisit & Thǿgersen 2017; VittersØ & Tangeland, 2015). Consequently, our target sample is designed to control the effects of the other two major barriers, i.e. premium price and unavailability. From the supermarket chain’s perspective, the top management team agrees that premium price and availability are not problem issues in their positioned market segment. Only when the level of trust on organic food products can be elevated would a higher level of sales transactions be realised (Kim et al., 2008), and the associated profit improvement. The pilot focus group study was followed by an in-depth thematic analysis of the qualitative interviews to discover how the interaction of trust and other key factors would impact positively on the organic food purchasing decisions. Each interview lasted between 20 min to an hour. Our research team began with openended questions, followed with semi-structured questions that related to the research questions, as listed below: 1. What is your attitude towards purchasing green and organic food products? 2. Do you aware of what organic food production, certification and labeling are? 3. What are the benefits of consuming organic foods? 4. Does the quality of organic food products relate to your health benefits? 5. Do you think it is worth paying the price premium on organic food products? If so, why? 6. What make you decide to purchase organic food products? 7. In your organic purchasing decisions, do you consider subjective norms and social influence from friends, relatives, and social media that endorse organic foods? 8. Besides purchasing in this supermarket, which retail outlets do you shop regularly for organic food products? Does it waste your time to shop around? Our study followed the three-stage procedure commonly adopted in the literature (Braun and Clarke, 2006; Miles et al., 2014) which include data familiarization, coding, theme development and revision. The primary purpose of this inductive procedure unfolds the dominant themes emerged from the interview data (Thomas, 2003), in line with the research questions and theoretical frameworks drawn from the literature. The next step is the assessment of the organic food suppliers which comprises three stages: (i) development of a hybrid MCDM supplier selection model, with criteria and sub-criteria assessed by the MCDM methods, namely fuzzy AHP, TOPSIS, and ELECTRE; (ii) coordination of a case study to test the feasibility of the proposed model; (iii) application of the game theory’s mixed strategy model to calculate the relative cost of monitoring for all the organic food suppliers under consideration, and incorporated into the hybrid MCDM
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supplier selection model. This MCDM model has previously been applied to a supermarket chain case study in Australia (Lau et al., 2018). After implementing these MCDM methods and model to the present smallsized supermarket chain in Hong Kong, the feedback from the management team was that this project was successfully executed. However, it did not completely resolve the authenticity problems among the organic food suppliers. This motivates the research team to investigate the reason why consumers decide either to buy or not to buy, but not engage in monitoring and appraising the organic food that they purchase. After a few rounds of brainstorming sessions among the expert team members (academia and practitioners), interviews were conducted among various stakeholders, especially the consumers, of the organic food GSC. The underlying reasons turn out to be the issue of trust and cost of monitoring that is likely to be incurred by various stakeholders. In response, the criterion ‘cost of monitoring’ as revealed from the mixed strategy is incorporated into the hybrid MCDM fresh food supplier selection model, with criteria and sub-criteria listed in Table 6.
3.1. The organic food consumers vs the suppliers Extending the simultaneous move monitoring game to the organic food GSC, the interaction between the consumers and the organic food suppliers is examined first. Assuming suppliers are selling organic products through their own stores and charge at price a; b is the organic production cost; c is the organic vs conventional production cost differential; s is the utility of consuming organic products; f is the utility of consuming non-organic products (s is equal to f under the ‘not monitor’ condition when the consumer cannot differentiate organic vs non-organic products); m is the cost of monitoring the organic food suppliers; p is the penalty imposed on the suppliers for selling non-organic products labeled as organic. As shown in Table 1 below, the Nash equilibrium exists for consumers preferring not to monitor organic products. When the suppliers realise the strategy of the consumers, they opt to cheat by producing non-organic products but selling as organic products to maximise payoff. As motivated by their payoffs in this game scenario, the consumers do not monitor, they just either buy or not buy, consistent with the observations in the organic food markets of Hong Kong (Ng, 2018). Future technological advance in monitoring will be able to reduce the cost of monitoring (m). However, from the consumer perspective, testing for just the presence of pesticides by consumer would be prohibitively costly (McCluskey, 2000). When the cost of monitoring (m) remains high for consumer in general, consumer does not monitor. More feasible solution is the social perspective in setting the national standards, e.g. standardization and regulation of cause organic labels to be easily identified for consumers with bounded rationality. However, when the cost of monitoring remains high and standardization and regulation are lacking or ineffective, from the supplier perspective, the corresponding best strategic response is to produce non-organic food but sell as organic to maximise profitability.
Table 1 The payoff matrix of a monitoring game between organic food consumer and supplier. Monitor
Supplier produces organic Supplier produces non-organic
Not Monitor
Consumer buys
Consumer doesn’t buy
Consumer buys
Consumer doesn’t buy
a-b, s-a-m -p-(b-c)þa, f-a-m
-b, 0-m -p-(b-c), 0-m
a-b, s-a a-(b-c), f-a
-b, 0 -(b-c), 0
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Table 2 The payoff matrix of a monitoring game between a supermarket chain and an organic food supplier. Monitor
Supplier produces organic Supplier produces non-organic
Not Monitor
Consumer buys (a)
Consumer doesn’t buy (b)
Consumer buys (c)
Consumer doesn’t buy (d)
a-b, s-a-m -p-(b-c)þa, f-a-m
-b, 0-m -p-(b-c), 0-m
a-b, s-a a-(b-c), f-a-rep
-b, 0 -(b-c), 0-rep
Note: The strategy payoff (b) is dominated by (a), (d) is dominated by (c); s > a > 0 always.
3.2. The supermarket chain vs the organic food suppliers Next, the interaction between supermarket chain and the organic food suppliers is examined. Again, let a be the supplier’s organic food sales revenue; b be the organic production cost; c be the organic vs conventional production cost differential; s be the retailer’s organic food sales revenue; f be the monetised utility of consuming non-organic products; m be the cost of monitoring the organic food suppliers; rep be the expected costs of reputation damage if the supermarket chain were found to be misrepresenting and selling non-organic as organic products. As shown in Table 2 below, the Nash equilibrium does not exist. This raises the fundamental issue about the solution of this type of game, which covers a large class of games in commercial practices. As revealed in Table 2, if the supermarket chain plays the strategy of not monitoring the incoming organic products, consistent with previous findings in the literature (Chkanikova and Kogg, 2018; Chkanikova and Lehner, 2015), then the supplier will play the strategy of supplying non-organic as organic food to maximise its payoff. Upon realizing the strategy of the supplier, the supermarket chain will change its strategy to monitor so as to maximise its payoff, and in response, the supplier will play the strategy of supplying organic food to maximise its payoff. But then in further response, the supermarket will play the non-monitoring strategy. This predicted set of interactive strategic moves results in a vicious circle. As such, the game theory predicts that a mixed strategy equilibrium, where each player chooses a randomised optimal mixed strategy that maximises own payoff, will be justified. 3.3. The MCDM methods: FAHP, TOPSIS, and ELECTRE The objective is to select the best suppliers on the basis of multiple supplier performance metrics that contribute to the best positioning in retail outlets to maximise customer satisfaction in organic food and corporate profitability. The Analytic Hierarchical Process (AHP) that has popularly been applied in the supplier selection literature (Levary, 2008) is chosen to rank the potential suppliers. However, to overcome the problems associated with the AHP method as suggested by Saaty (1980), the fuzzy extent analysis method (Chang, 1996) has been implemented. The TOPSIS method (Hwang and Yoon, 1981) was the next main classical MCDM method deployed in this study. TOPSIS represents the rationale of human choice, and has been successfully applied in nine application areas (Behzadian et al., 2012), However, Olson (2004) finds that precision of the weights plays a critical role for enhancing the accuracy in TOPSIS. Therefore, this study makes use of FAHP to calculate a more accurate set of weights as input into the TOPSIS to generate the best and most precise ranking outcome efficiently. The project team then ranks the preference order by the performance score (or the closeness coefficient) of all the alternatives in descending order. Furthermore, another MCDM method ELECTRE (ELimination Et ), which is a compensatory MCDM Choix Traduisant la REalite method, is deployed to assess the non-compensatory set of food safety criteria that are not substitutable, i.e. poor rating in one sub-
criterion cannot be compensated for by strong ratings in others (Rowley et al., 2012). This method has been applied in supplier selection decision (Seifi et al., 2014; Wan et al., 2017). 4. Case study The supermarket chain is a small-sized operator, with a continually expanding range of organic food products. However, a number of discouraging news stories have surfaced about the tests of a number of random samples in the Hong Kong organic food market, indicating that a high percentage of labeled organic vegetables in fact contained prohibited pesticide residues that exceeded safety thresholds, equivalent to the maximum level of the European Commission. This supermarket chain does not have its own private standards in food safety and quality management systems. The organic food suppliers are only required to comply with the good agricultural and manufacturing practices and existing food safety regulations. Only the production systems of some larger organic food suppliers are audited and certified by a certification agent. This supermarket chain also does not have in-house food technologists and nutritionists to sanction the organic food products. The model for selecting the best candidates as preferred suppliers is built upon the ten fresh food supplier selection criteria (product, quality, food safety, price, delivery, serviceability, commercial position, supplier relationship, risk factors, and CSR). An internal rating system provides qualitative measure for the criteria and sub-criteria, and functioned as part of the product management database systems for completing the product control process for each product order. For example, quality is one of the ten main criteria, the scores for each order delivery in relation to the subcriterion ‘conformance to specification’ ranged from 1: complete nonconformance to 10: complete conformance. For the subcriterion ‘rejection and return’, the rating is defaulted to a score of 10: 0% rejection and return, and it can be changed to a different score as new evidence of fault is observed, a rejection decision can be made in operations, e.g. 1: 100% rejection and return. For availability, it is quantified as a combination of the order quantity that missed the promise schedule, and the time duration of late delivery multiplied by the ordered quantity to be delivered, both of which are recorded in the logistics and operations management systems. These scores are assigned by the responsible product operations manager in the receiving and various operations processes. However, implementation of this model did not allow this supermarket chain to determine precisely the problematic organic food suppliers. Therefore, the management team engages our research team to conduct follow-up interviews among various stakeholders, especially the consumers, of the organic food GSC of this supermarket chain. Our study targeted the sampled consumers with higher level of individual income (at least HK$30,000 per month) and better educated (professional with undergraduate degree or diploma) so that price premium would not be a major demand problem issue. If the sample is not controlled for these two barrier factors, then pooling separate groups with differential behaviours can give distorting empirical results to the supermarket chain and probably be
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less applicable to the retail stores due to its unique market positioning in higher level of income and education segment. This supermarket chain is able to attract consumers that are willing to pay the price premium and purchase more organic products. The top management team agrees that only when the level of trust on organic food products can be elevated would a higher level of sales transactions be realised (Kim et al., 2008). However, for future research, the sample can be designed to represent the population in general, and develop SEM models to test whether behaviours do differ among various groups that are categorised by varying levels of income, education, and other factors. Sixty consumers were sampled in 2018 from five individual stores of this supermarket chain located at various high-end residential districts. All interviewees were screened to meet the research design specification of the level of income above HK$30k/ month, without recording the level of actual income information. As such, the domestic helpers or maids were excluded. The education level is between diploma and university degree. Majority of the interviewees are female (65%). The average duration of the interview time is 35 min. One of the co-authors and two university graduated research assistants, with previous training and practice in interviewing, qualitative research, and NVivo coding, were responsible for managing the interview process. Arbitrated by the co-author as a supervisor, three rounds of test-coding were crosschecked by the two research assistants as inter-raters to assure coding quality and consistency over time. Based on the thematic analysis of the qualitative interview data collected from the in-depth interviews of these sixty consumers, the findings are summarised below. Some of the qualitative findings are consistent with the previous findings in the literature. For those aspects that the literature is silent, new insights can be drawn from the thematic analysis of the qualitative interview data. 1. Nine key nodes, i.e. trust, income and education, attitude towards purchasing green and organic foods, subjective norm, health and environmental benefits, organic regulation and certification systems, willingness to pay, willingness to spend time and effort to shop for organic foods, and consumer awareness, are the antecedents of the actual purchasing behaviours. Their corresponding references are shown in Table 3. 2. For consumers with higher level of income and university education, the more awareness and receptive to the information about sustainable development and organic food are observed, consistent with the previous literature (Magnusson et al., 2003). This consumer segment has better attitude towards sustainability and purchase green and organic foods, consistent with the previous literature (Krystallis et al., 2006). 3. Consumers with higher level of income and university education prefer to buy higher quality organic food products and
Table 3 The key nodes and their corresponding references. Key Nodes
References
1. 2. 3. 4. 5. 6. 7. 8. 9.
51 60 49 47 58 45 50 43 55
Trust Income and education Attitude towards purchasing green and organic foods Subjective norm Health and environmental benefits Organic regulation and certification systems Willingness to pay Willingness to spend time and effort to shop for organic foods Consumer awareness
4.
5.
6.
7.
8.
9.
10.
11.
12.
5
acknowledge their health benefits. They are also more willing to pay the price premium and spend more time and effort to shop for organic foods. The more intensive the social pressure and subjective norms, the more willingness to pay premium price and spend time/ effort to shop for organic food products. For consumers who are more health and environment conscious, the better attitudes towards purchasing green and organic foods are revealed from the interview data, which is consistent with the previous research findings (Magistris and Gracia, 2008). Relative to the more frequent buyers, consumers who purchase organic foods less frequently express less trust on organic certification and labeling, and distrust the organic food retailers more, especially in the wet market that might mix conventional with organic food products as an easy route to profit. Quality of the organic food products is a subjective measure. However in practice, perceived high quality food products judged by the look and feel implicitly infers more probable health benefits. All the interviewees prefer to shop for organic food products in a supermarket chain where a wide variety of organic food products are available. However, these consumers also shop, though less frequently, in specialty stores and the wet markets that they are familiar with or recommended by their close friends and relatives. It is the additional net health and environmental benefits from organic food consumption, i.e. health and environmental benefits minus the organic food price premium, that influence the organic food purchasing decisions. If the trust factor in terms of organic food regulation and certification can be strengthened, more consumers would purchase organic food products. This empirical evidence is consistent with previous findings in the literature (Tung et al., 2012), signifying the lack of trust as the major factor that accounts for the intention-behaviour gap. The interviewed consumers admit that they would not be able to verify whether the organic food products are genuine or not. Fraudulent cases publicised in the media do sway their organic food purchasing decisions. They become suspicious but could not pinpoint which agents and stages in the GSC that are more susceptible to the wrong-doings, consistent with the previous literature (Giannakas, 2002). When the feeling of distrust emerges, the merits of sustainability and health benefits dissipate, and the rationale behind paying the premium price would principally be weakened. The organic consumers prefer to shop for organic food products in a supermarket chain that they trust. However, when hinted that the organic regulation and certification systems in the exporting countries assign an extensive responsibilities that are mandatory to the primary producers and operators, whilst other agents, e.g. the processors/ manufacturers, distributors, and retailers, in the GSC are less controlled, they respond by desiring that the supermarket chain should take more initiative to monitor and control the organic food suppliers.
Expectedly, among the nine key nodes, trust plays a key role in mediating several input factors (health and environmental benefits, attitude towards purchasing green and organic foods, organic regulation and certification systems, consumer awareness) and the output factors of willingness to pay and willingness to spend time/ effort to shop for organic foods, which are causally linked to the actual purchasing behaviours. To raise the expenditure on green
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and organic foods, industry and government policy interventions to strengthen the level of trust among consumers are important catalysts. As such, our findings are consistent with the recent literature, and trust should be advocated as the most important factor for promoting the growth and development in the organic food market (Gan et al., 2016; Nuttavuthisit & Thǿgersen 2017). As revealed in the empirical results that GSCM impacts positively on export performance via the mediating role of environmental performance (AlGhwayeen and Abdallah, 2018), similarly, GSCM facilitates the organic food market expansion via the mediating role of trust on the legitimacy of organic food certifications. For future research, the qualitative results and insights gained from the thematic analysis of this study can guide the design and development of a structural equation modeling (SEM) model. With a larger survey sample, the empirical results validated by the SEM model can become a valuable tool for the practitioners operating in the organic food market, and possibly be generalised to other credence environmental friendly products, to promote consumer trust and expand the corresponding markets. As revealed by the application of game theory and the findings of our thematic analysis of the qualitative interviews, the underlying key reason turns out to be the cost of monitoring that is likely to be incurred by various stakeholders. The game theory explains why consumers decide either buy or do not buy, but do not incline to monitor and appraise the organic food that they purchase. The mixed strategy concept also explains why the supermarket chain and the organic food suppliers should interact with each other by randomly selecting an appropriate strategy to maximise their payoffs. The game theory conveys an insight into the important role of the monitoring mechanism to assure quality and trust should not be overlooked, even in the presence of certification and organic labeling. Based on the mixed strategy framework, the conventional procedure and criteria for supplier selection cannot circumvent the phenomenon in which conventional food is sold as organic food. 4.1. Application of the game theory After a series of brainstorming sessions among the expert team members (academia and practitioners), combining the data extracted from the internal database systems and the interview data collected from various stakeholders of the organic food GSC, the assessment of the organic food suppliers was re-examined again. A set of relative cost of monitoring was calculated based on the payoff matrix applicable to each organic food supplier. Applying game theory to this small-sized supermarket chain, the payoff matrix can be represented as shown in Table 4 below. Let a ¼ $50,000. b ¼ $35,000, c ¼ $20,000, s ¼ $100,000, m ¼ $80,000, f ¼ $55,000, p ¼ $20,000, rep ¼ $200,000. Since (b) is dominated by (a), (d) is dominated by (c) (note that s > a > 0 always), columns (b) and (d) are eliminated for further analysis, giving the following payoff matrix, as shown in Table 5. Let X be the probability of supplier producing organic food, and Y be the probability of the supermarket chain monitoring the organic food supplier. Using this payoff matrix, the expected payoff to the supplier is equal to the probability of each of the four
Table 5 The payoff matrix of a monitoring game between this supermarket chain and an organic food supplier.
Supplier produces organic Supplier produces non-organic
Monitor
Not Monitor
Consumer buys (a)
Consumer buys (c)
$15k, -$30k -$20k, -$75k
$15k, $100k $35k, -$160k
outcomes multiplied by its corresponding payoff: On the other hand, the expected payoff to the supermarket chain is equal to the probability of each of the four outcomes multiplied by its corresponding payoff: The mixed strategy for the supermarket chain is to randomly monitor the organic food supplier with a probability of 39.5%, and for the supplier to produce and deliver organic food with a probability of 36.4%. Similar data inputs and analysis are implemented for all the other organic food suppliers, and their scores are represented in the cost of monitoring criterion for further evaluation via the nominated MCDM methods. 4.2. Application of the MCDM methods A project team is responsible for defining the organic food supplier selection problem, designing and scheduling the associated tasks. Based on the data mined from the corporate survey database of this supermarket chain, the internal supplier management systems, and external databases, the weightings of the parent criteria and children sub-criteria for each supplier are calculated with the application of the FAHP method, as shown in Table 6 below. Given that the huge negative impact of food safety events are extremely costly in terms of potential loss in sales and damage to the brand image of the supermarket chains, and other social costs, e.g. health and productivity of consumers and the public in general, incentives exist for the supermarket chain to disqualify potential suppliers that have a high probability of delivering unsafe products. Since any sub-criterion within the food safety criterion is not compensatory or substitutable for any other food safety subcriteria, the non-compensatory MCDM method ELECTRE is particularly applicable for disqualifying organic food suppliers that did not reach a standard threshold. Applying ELECTRE, the numerical net concordance and discordance indices are shown in Table 7. The ranking relation is ordered in preference as A, G, E, I, B, C, D, L, and H. Suppliers F, J, and K are disqualified due to their poor food safety ranking, which could potentially cause unbearable costs of sales loss and damage to the brand image of this supermarket chain. Due to the large number of organic food products and suppliers, the pairwise comparisons for the FAHP method entail far too complex cognitive processing for the experts. Furthermore, the data collected for each criterion have values that differ widely from other criteria, and thus cannot be suitably represented in the simple FAHP format. TOPSIS avoids this issue by circumventing the tedious pairwise comparisons of alternatives among the large number of
Table 4 The payoff matrix of a monitoring game between this supermarket chain and an organic food supplier. Monitor
Supplier produces organic Supplier produces non-organic
Not Monitor
Consumer buys (a)
Consumer doesn’t buy (b)
Consumer buys (c)
Consumer doesn’t buy (d)
$15k, -$30k -$20k, -$75k
0, -$80k -$20k, -$80k
$15k, $100k $35k, -$160k
0, 0 0, -$160k
Note: The strategy payoff (b) is dominated by (a), (d) is dominated by (c); s > a > 0 always.
H. Lau et al. / Journal of Cleaner Production 242 (2020) 118536
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Table 6 The normalised weights of the sub-criteria with respect to their parent criterion. Objective
Primary criteria (Weights)
Secondary sub-criteria (Weights)
Superior Fresh Food Supplier Product (0.089) Performance Quality (0.114)
Availability; Reliability; Value-added activities (cutting, trimming, grading, and packing); Packaging; Warranty; Innovation (0.31, 0.17, 0.10, 0.15, 0.15, 0.12) Conformance to specification; Rejection and return rate; Process capability; Quality management systems; Quality improvement; Quality assessment technique (0.29, 0.30, 0.12, 0.14, 0.10, 0.05) Certified organic & ISO9001 (Product management) is certified organic; Procedural compliance and periodic audits at pre- and post-farm safety (0.144) stages; Continuous food safety training; Traceability (0.22, 0.24, 0.21, 0.19, 0.14) Cost of monitoring Probability of organic food supplier producing non-organic food; Frequency of sampling organic products for (0.238) laboratory testing (0.25, 0.75) Price (0.067) Price representation; Price decreased ratio; Product cost; Total logistics management cost; Tariff and taxes; Quantity discounts; Payment terms; Shelving and listing fees (0.05, 0.08, 0.21, 0.15, 0.14, 0.14, 0.17, 0.06) Delivery (0.094) Delivery on time; Lead time; Compliance with quantity and packaging standards; Use of standard cradles; Transportation facility, e.g. refrigerated trucks; Delivery performance; Ability and willingness to expedite order; Delivery frequency (0.14, 0.08, 0.11, 0.12, 0.21, 0.14, 0.16, 0.04) Serviceability (0.041) Service quality; Flexibility in product mix, volume, and lead time; Responsiveness to demand changes; Ability to modify product/service; Information sharing, EDI capability, and systems compatibility; Technical support; Management of complaints; Administration for return, sell-back, and reverse logistics; Value-added service (0.26, 0.11, 0.16, 0.15, 0.05, 0.11, 0.02, 0.03, 0.11) Commercial position Market reputation; Market share; Performance history; Asset specificity and infrastructure; Financial position and (0.056) stability; Technological capability (0.26, 0.11, 0.31, 0.12, 0.13, 0.06) Supplier relationship Relationship connectors; Duration of relationship; Annual average amount of past business over the last three years; (0.036) Communication and conflict resolution; Long-term commitment (0.23, 0.22, 0.19, 0.25, 0.11) Risk factors (0.058) Geographical location; Political stability and government policies; Exchange rates and economic position; Labor relations; Terrorism and crime rate (0.24, 0.18, 0.25, 0.19, 0.14) CSR (0.063) ISO-14001 certification; Eco-labeling; Stakeholder relations and community recognition; Pay rates, labor conditions and work environment; Compliance to international human rights (0.15, 0.12, 0.32, 0.25, 0.16)
Table 7 The ranking relations of the net concordance and discordance index matrices. Supplier
Net concordance index
Net discordance index
A E I B C L H F J K
3.73 2.86 2.38 2.04 1.58 1.34 0.03 0.41 1.86 3.47
31.13 22.28 15.79 11.32 7.81 7.81 1.10 6.08 12.13 17.16
organic food suppliers, and accommodates the decision matrix of wide ranging values in its efficient computational routine. Results of the normalised decision matrix are generated. Combined with the weights, the weighted normalised decision matrix is calculated, followed by the determination of the positive and negative ideal solutions. In the final step, the relative closeness to the ideal solution is determined, as shown in tables below. The differential ranking orders of the preferred suppliers between ‘without’ and ‘with’ the inclusion of the cost of monitoring criterion are shown in Tables 8 and 9. With the inclusion of the new criterion (cost of monitoring), the relative rank positions of the list of preferred suppliers shift in response to the re-calculation of the positive (Dþ) and the negative (D-) ideal solutions, and their relative closeness to the ideal solution (V) of each potential supplier. Without the inclusion of the cost of monitoring, all the seven potential suppliers are ranked on the basis of V, in the order of supplier E, A, B, I, H, C, L. After the inclusion of the cost of monitoring, all the seven potential suppliers are ranked on the basis of V, generating the order of supplier I, E, A, B, C, H, L. The result reveals that for the first four organic food suppliers, when the cost of monitoring criterion is introduced in the organic food supplier evaluation model, supplier I shifts its rank from 4th to 1st, and the rank positions of the other suppliers E, A, and B shift to the next lower rank. The main reason is
Table 8 The positive (Dþ) and the negative (D-) ideal solutions, and their relative closeness to the ideal solution (V) of each potential supplier, without the inclusion of the cost of monitoring criterion. Supplier
Dþ DV Rank
A
E
I
B
C
L
H
0.078 0.119 0.603 2
0.063 0.114 0.645 1
0.081 0.104 0.562 4
0.073 0.100 0.580 3
0.099 0.083 0.457 6
0.125 0.047 0.273 7
0.098 0.094 0.491 5
due to the lower cost of monitoring measured for supplier I. From the supermarket’s perspective, supplier I has regular and complete organic certification, laboratory results, and operating in a jurisdiction that has well-established organic standards and regulatory framework. Therefore, the cost of monitoring and ongoing verification are relatively lower. To make further progress on the organic food supplier selection results, apart from informing the organic food suppliers the ten criteria and sub-criteria in greater details, the suppliers are recommended to restructure their organic production to comply with minimal organic standards and regulatory framework, complete organic certification on a regular basis, and accompanied with auditable laboratory results. Training and development sessions are offered to target lower ranked suppliers. The supermarket chain also informs the suppliers the random monitoring process, and the associated penalties for disqualified deliveries. Six months after the implementation of the newly proposed hybrid MCDM method, a performance review on the organic food supplier selection process confirmed a noticeable reduction in the safety incidents and an improvement in the level of customer satisfaction. For the organic sector in Hong Kong, a more cost-effective alternative is to institutionalise the control mechanisms in the relevant government authorities to monitor continuously and impose penalties on the offenders. Furthermore, we recommend the Hong Kong Government to follow other developed countries to introduce legislation and standards to regulate the organic sector.
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Table 9 The positive (Dþ) and the negative (D-) ideal solution, and their relative closeness to the ideal solution (V) of each potential supplier, with the inclusion of the cost of monitoring criterion. Supplier
Dþ DV Rank
A
E
I
B
C
L
H
0.131 0.133 0.504 3
0.102 0.135 0.569 2
0.089 0.168 0.654 1
0.128 0.112 0.467 4
0.126 0.109 0.465 5
0.175 0.054 0.235 7
0.164 0.103 0.387 6
5. Conclusion Organic food products are sourced from GSCs, which span across country borders and involve a wide spectrum of stakeholders with diverse needs and commercial motives. The complexities of the organic food GSC offer room and incentive for profit-seeking stakeholders to engage in and cover up food adulteration (Agriculture and Agri-Food Canada, 2010; Caswell and Mojduszka, 1996). This phenomenon can also be observed in Hong Kong organic food markets. As we explored the case study of the supermarket chain in Hong Kong, managing the complex trade-offs among the multiple TBL objectives (economic, social, and environmental) and assuring authenticity among the organic food suppliers are the most problematic issues facing the top management team. Our study aims to discover the key factors and mechanism on the supply side that can assure a more optimal solution to the green and organic food authentication problem, matching the demand side expectations among the organic consumers with the purpose of escalating the level of trust in order to promote the growth and development of the organic food sector. We focused on bridging the research gap related to the green or sustainable SC literature by generating theoretical and empirical evidence to minimise the negative effects of information asymmetry and quality uncertainty in the organic food GSCs. The obvious solution of certification and product labels is not sufficient to solve the food credibility problem since many suppliers are still incentivised to sell non-organic food products as organic at a premium price. Built upon the earlier hybrid MCDM methods and model (Lau et al., 2018), and combined with a newly introduced criterion as revealed by the mixed strategy of game theory, a more complete authenticity solution to the organic food problem is proposed. The results of this case study demonstrate that an equilibrium exists at which the profits of the GSC members and the multiple CSR objectives can be optimised. As such, the supermarket chain can adopt the new hybrid MCDM methods and model for its organic food supplier selection process to maximise customer satisfaction and corporate profitability. The game theoretic mixed strategy of randomly monitoring the organic food supplier according to its corresponding probability of compliance should be implemented. Furthermore, to keep the organic food suppliers honest, a fine should be imposed on dishonest suppliers. A more efficient and effective alternative is to institutionalise government authorities to engage in continuous monitoring and imposing penalties. The costs of monitoring and compliance would be charged against the suppliers and/or the retailers.
Funding details The research is funded by Western Sydney University School of Business Research Development Funding (2017)
References Agriculture and Agri-Food Canada, 2010. Market Trends: Organics. International Market Bureau. AAFC No. 11263E. Al-Ghwayeen, W., Abdallah, A., 2018. Green supply chain management and export performance: the mediating role of environmental performance. J. Manuf. Technol. Manag. 29 (7), 1233e1252. Ali, M., Zhan, Y., Alam, S., Tse, Y., Tan, K., 2017. Food supply chain integrity: the need to go beyond certification. Ind. Manag. Data Syst. 117 (8), 1589e1611. Andreozzi, L., 2004. Rewarding policemen increases crime. Another surprising result from the inspection game. Public Choice 121 (1e2), 69e82. Aschemann-Witzel, J., Niebuhr Aagaard, E., 2014. Elaborating on the attitudeebehaviour gap regarding organic products: young Danish consumers and in-store food choice. Int. J. Consum. Stud. 38 5, 550e558. Avenhaus, R., Stengel, B.V., Zamir, S., 2002. Inspection games. In: Aumann, R., Hart, S. (Eds.), Handbook of Game Theory with Economic Applications, vol. 3. North-Holland, Amsterdam, pp. 1947e1987. Behzadian, M., Otaghsara, K., Yazdani, M., Ignatius, J., 2012. A state-of the-art survey of TOPSIS applications. Expert Syst. Appl. 39 (17), 13051e13069. Boztepe, A., 2016. Green marketing and its impact on consumer buying behavior. Eur. J. Econ. Political Stud. 5 (1), 5e21. Braun, V., Clarke, V., 2006. Using thematic analysis in psychology. Qual. Res. Psychol. 3 (2), 77e101. Buyukozkan, G., Cifci, G., 2011. A novel fuzzy multi-criteria decision framework for sustainable supplier selection with incomplete information. Comput. Ind. 62 (2), 164e174. Carriquiry, M., Babcock, B., 2007. Reputations, market structure, and the choice of quality assurance systems in the food industry. Am. J. Agric. Econ. 89 (1), 12e23. Caswell, J., Mojduszka, E., 1996. Using informational labeling to influence the market for quality in food products. Am. J. Agric. Econ. 78 (5), 1248e1253. Chang, D.Y., 1996. Applications of the extent analysis method on fuzzy AHP. Eur. J. Oper. Res. 95 (3), 649e655. Chekima, B., Oswald, A., Wafa, S.A., Wafa, S.K., Chekima, K., 2017. Narrowing the gap: factors driving organic food consumption. J. Clean. Prod. 166 (November), 1438e1447. Chkanikova, O., Lehner, M., 2015. Private eco-brands and green market development: towards new forms of sustainability governance in the food retailing. J. Clean. Prod. 107, 74e84. Chkanikova, O., Kogg, B., 2018. Sustainability governance service providers: the role of third-party product certification in facilitating corporate life cycle management. Int. J. Life Cycle Assess. 23 (7), 1383e1395. Choe, Y., Park, J., Chung, M., Moon, J., 2009. Effect of the food traceability system for building trust: price premium and buying behavior. Inf. Syst. Front. 11 (2), 167e179. D’Amico, M., Vita, G.D., Monaco, L., 2016. Exploring environmental consciousness and consumer preferences for organic wines without sulfites. J. Clean. Prod. 120 (May), 64e71. Elkington, J., 1997. Cannibals with Forks: the Triple Bottom Line of 21st Century Business. Capstone, Oxford. Elkington, J., 1998. Partnerships from cannibals with forks: the triple bottom line of 21st century business. Environ. Qual. Manag. 8 (1), 37e51. Ford, G., Smith, D., Swasy, J., 1988. An empirical test of the search, experience and credence attributes framework. Adv. Consum. Res. 15, 239e244. Fudenberg, D., Tirole, J., 1991. Game Theory. MIT Press, Cambridge, MA. Gan, C., Chang, Z., Tran, M., Cohen, D., 2016. Consumer attitudes toward the purchase of organic products in China. Int. J. Bus. Econ. 15 (2), 117e144. General Administration of Quality Supervision Inspection and Quarantine of the People’s Republic of China 2012, ’Organic Products’ GB/T 19630.1-19630.4, 2011, National Standard of the People’s Republic of China, Beijing. Giannakas, K., 2002. Information asymmetries and consumption decisions in organic food product markets. Can. J. Agric. Econ. Rev. Can. D’Agroeconomie. 50 (1), 35e50. Goh, S., Balaji, M., 2016. Linking green skepticism to green purchase behaviour. J. Clean. Prod. 131, 629e638. Hwang, C.L., Yoon, K., 1981. Multiple Attribute Decision Making: Methods and Applications. Springer, Heidelberg. Jahn, G., Schramm, M., Spiller, A., 2005. The reliability of certification: quality labels as a consumer policy tool. J. Consum. Policy 53e73. Kim, D., Ferrin, D., Rao, H., 2008. A trust-based consumer decision-making model in electronic commerce: the role of trust, perceived risk, and their antecedents. Decis. Support Syst. 44 (2), 544e564. Krystallis, A., Fotopoulos, C., Zotos, Y., 2006. Organic consumers’ profile and their willingness to pay (WTP) for selected organic food products in Greece. J. Int. Consum. Mark. 19 (1), 81e106. Kumar, R., Chandrakar, R., 2012. Overview of green supply chain management: operation and environmental impact at different stages of the supply chain. Int. J. Eng. Adv. Technol. 1 (3), 1e6. Latacz-Lohman, U., Foster, C., 1997. From “niche” to “mainstream”- strategies for marketing organic food in Germany and the UK. Br. Food J. 99 (8), 275e282. Lau, H., Nakandala, D., Shum, P., 2018. A business process decision model for freshfood supplier evaluation. Bus. Process Manag. J. 24 (3), 716e744. Laureti, T., Benedetti, I., 2018. Exploring pro-environmental food purchasing behaviour: an empirical analysis of Italian consumers. J. Clean. Prod. 172 (January), 3367e3378.
H. Lau et al. / Journal of Cleaner Production 242 (2020) 118536 Levary, R.R., 2008. Using the analytic hierarchy process to rank foreign suppliers based on supply risks. Comput. Ind. Eng. 55 (2), 535e542. Luo, X., Wu, C., Rosenberg, D., Barnes, D., 2009. Supplier selection in agile supply chains: an information-processing model and an illustration. J. Purch. Supply Manag. 15 (4), 249e262. Magistris, T., Gracia, A., 2008. The decision to buy organic food products in Southern Italy. Br. Food J. 110 (9), 929e947. Magistris, T., Gracia, A., 2016. Consumers’ willingness-to-pay for sustainable food products: the case of organically and locally grown almonds in Spain. J. Clean. Prod. 118 (April), 97e104. Magnusson, M., Arvola, A., Hursti, U., Aberg, L., Sjoden, P., 2003. Choice of organic food is related to perceived consequences for human health and to environmentally friendly behavior. Appetite 40 (2), 109e117. Manzini, R., Accorsi, R., 2013. The new conceptual framework for food supply chain assessment. J. Food Eng. 115 (2), 251e263. McCluskey, J., 2000. A game theoretic approach to organic foods: an analysis of asymmetric information and policy. Agric. Resour. Econ. 29 (1), 1e9. Miles, M., Huberman, A., Saldana, J., 2014. Qualitative Data Analysis: A Methods Sourcebook, third ed. Sage publications, Thousand Oaks, CA. Nash, J., 1950. Equilibrium points in n-person games. Proc. Natl. Acad. Sci. 36, 48e49. Ng, P., 2018. Clampdown on ’organic’ Veg Urged. The Standard, 26 Mar 2018. viewed 26 Mar 2018. http://www.thestandard.com.hk/section-news.php? id¼194119&sid¼47384769. Nuttavuthisit, K., Thǿgersen, J., 2017. The importance of consumer trust for the emergence of a market for green products: the case of organic food. J. Bus. Ethics 140 (2), 323e337. Olson, D.L., 2004. Comparison of weights in TOPSIS models. Math. Comput. Model. 40, 721e727. Pearson, D., Henryks, J., Sultan, P., Anisimova, T., 2013. Organic food: exploring purchase frequency to explain consumer behavior. J. Org. Syst. 8 (2), 50e63.
9
Roe, B., Sheldon, I., 2007. Credence good labeling: the efficiency and distributional implications of several policy approaches. Am. J. Agric. Econ. 89 (4), 1020e1033. Rowley, H., Peters, G., Lundie, S., Moore, S., 2012. Aggregating sustainability indicators: beyond the weighted sum. J. Environ. Manag. 11, 24e33. Saaty, T.L., 1980. The Analytic Hierarchy Process. McGraw-Hill, New York. Savory, A., 2016. Holistic Management: A Commonsense Revolution to Restore Our Environment, third ed. Island Press, Washington, DC. Seifi, M., Markabi, M., Sarbijan, M., 2014. An improved fuzzy-based ELECTRE I method for supplier selection. Int. J. Oper. Logist. Manag. 3 (4), 387e398. Singh, A., Verma, P., 2017. Factors influencing Indian consumers’ actual buying behaviour towards organic food products. J. Clean. Prod. 167 (November), 473e483. Thomas, D., 2003. A General Inductive Approach for Qualitative Data Analysis. School of Population Health University of Auckland, New Zealand. Tung, S., Shih, C., Wei, S., Chen, Y., 2012. Attitudinal inconsistency toward organic food in relation to purchasing intention and behavior: an illustration of Taiwan consumers. Br. Food J. 114 (7), 997e1015. Vega-Zamora, M., Torres-Ruiz, F., Parras-Rosa, M., 2019. Towards sustainable consumption: keys to communication for improving trust in organic foods. J. Clean. Prod. 216 (April), 511e519. VittersØ, G., Tangeland, T., 2015. The role of consumers in transitions towards sustainable food consumption: the case of organic food in Norway. J. Clean. Prod. 92, 91e99. Voon, J., Sing, K., Agrawal, A., 2011. Determinants of willingness to purchase organic food : an exploratory study using structural equation modeling. Int. Food Agribus. Manag. Rev. 14 (2), 103e120. Wan, S., Xu, G., Dong, J., 2017. Supplier selection using ANP and ELECTRE II in interval 2-tuple linguistic environment. Inf. Sci. 385e386, 19e38. Zhu, Q., Li, X., Zhao, S., 2018. Cost-sharing models for green product production and marketing in a food supply chain. Ind. Manag. Data Syst. 118 (4), 654e682.
Contents lists available at ScienceDirect
Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro
A game theoretic decision model for organic food supplier evaluation in the global supply chains Henry Lau a, Paul K.C. Shum a, Dilupa Nakandala a, Youqing Fan a, *, Carman Lee b a b
School of Business, Western Sydney University, Australia Department of Industrial Systems Engineering, Hong Kong Polytechnic University, Hunghom, HKSAR, China
a r t i c l e i n f o
a b s t r a c t
Article history: Received 7 May 2019 Received in revised form 18 September 2019 Accepted 20 September 2019 Available online 24 September 2019
Sourcing organic food products from global green supply chains (GGSCs) spreads across country borders and interacting among a wide spectrum of intermediary stakeholders, which adds complexity and incentivises profit-seeking stakeholders to engage in food adulteration. This phenomenon is frequently observed among green and organic products with credence attributes that are difficult to authenticate in both the prior- and post-purchase stages in both developed and developing countries. The case study that we investigate is a small-sized supermarket chain operator in Hong Kong, with an increasingly expanding range of organic food products. Whilst there is a proven set of hybrid multi-criterion decision making (MCDM) methods and model that have been implemented successfully in Australia, unfortunately, unable to assure total success in optimizing the selection of organic food suppliers when applying to this supermarket chain in Hong Kong. Even the obvious solution of certification and product labeling is not sufficient to solve this organic food credibility problem. Our study proposes a newly discovered criterion as postulated by the game theoretic framework, offering a more optimal and authentic solution to the organic food authentication problem. Apart from adopting the mixed strategy of random monitoring, fines should be imposed to penalise dishonest suppliers, instead of awarding bonuses to suppliers operating with integrity. An even better solution is to institutionalise these control mechanisms by the governing authorities with continuous monitoring and imposition of penalties. © 2019 Published by Elsevier Ltd.
Handling editor: M.T. Moreira Keywords: MCDM Game theory Organic food Supplier selection Global green supply chain
1. Introduction Organic farming is a preferred alternative approach to conventional agriculture. The production process and outputs of organic agriculture contribute to sustainable development (Gan et al., 2016; Magistris and Gracia, 2016), human health, ecosystem and soil (Chekima et al., 2017; Laureti and Benedetti, 2018). The original goal is to implement total commitment to triple bottom line (TBL) (Elkington, 1997) agenda at all organisational levels in organic farming and retailing SC that integrate the economic, environmental, and social facets of sustainable agricultural practices (Savory, 2016). Organic food products contribute to cleaner production by minimizing environmental impact and maximizing efficient allocation of resources (Vega-Zamora et al., 2019). Organic consumers believe in the important health and environment benefits that are more advantageous than the conventional food
* Corresponding author. School of Business, Western Sydney University, Australia. E-mail address: [email protected] (Y. Fan). https://doi.org/10.1016/j.jclepro.2019.118536 0959-6526/© 2019 Published by Elsevier Ltd.
products (Magistris and Gracia, 2008; Singh and Verma, 2017). Therefore, consumption of organic foods becomes the most popular sustainable behaviour, with consequential sustained growth over the past decade (D’Amico et al., 2016; Laureti and Benedetti, 2018). Movement of organic food products requires green supply chain management (GSCM), which is distinguished from traditional SCM in terms of its more expansive perspectives and a ‘win-win’ commitment towards achieving multiple TBL objectives (Elkington, 1997, 1998), engaging a wider spectrum of stakeholders (Kumar and Chandrakar, 2012). The prevailing approach in the business sector is to implement the ISO14001 as a standard for evaluating the TBL performance of suppliers operating in a sustainable SC (Buyukozkan and Cifci, 2011). For organic food supplier evaluation, the multicriterion decision making (MCDM) methods are more applicable to optimise the multiple TBL objectives. However, when evaluating the organic supplier performance, since the credence attributes are not detectable via sensory recognition, the global GSCs is more liable to information asymmetry and quality uncertainty. This information asymmetry between the demand and supply sides exposes the GSCs to opportunistic behaviour and fraud.
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Organic products have several specific credence attributes and claims, e.g. green friendly, production, source, content, that are difficult to determine (Roe and Sheldon, 2007; Voon et al., 2011) in both the prior- and post-purchase stages, due to practicalities or lack of technical expertise (Ford et al., 1988). Inability to verify the promised organic characteristics is the main cause of consumer skepticism and suspicion of fraud materialised at various stages, e.g. in production controls, certification processes, distribution channels, and points of sale, of the GSC (Giannakas, 2002). Research studies (Choe et al., 2009) recognise that trust may be the only factor to mitigate consumers’ fears of opportunism among suppliers and sellers. Third-party certifiers are the key agents to institutionalise certification as a credible signal for credence quality. Though the certifiers add value to the GSCs, implementation of certification is a costly process for assuring compliance with standards, and subject to bias and oversight (Manzini and Accorsi, 2013). Therefore, a tendency towards selecting more lenient certifiers is observed (Ali et al., 2017). Profit seeking certifiers may compromise on the credibility of the certification process when the number of certifying firms increases and competition intensifies (Carriquiry and Babcock, 2007). Consequently, relevant information that may give rise to fraudulent activities can be hidden by certifiers to compromise the certification outcome (Jahn et al., 2005). The role of monitoring and enforcement can be better performed by government authority. Thus, government regulation and public control are necessary to minimise the undesirable effects of market failure. We shall now review the literature on food supplier selection criteria. The game theoretic framework provides a rationale to introduce the cost of monitoring as a key criterion of the set of organic food supplier selection criteria in the context of organic food GSCs. Later, we shall describe the proposed model embedded with the newly discovered criterion as postulated by the game theoretic framework, the data collection and analytical procedure using the three MCDM methods, followed by the numerical application to the supermarket chain case study in the final section. 2. Literature review Assessing and selecting suppliers on the basis of a competitive set of criteria can improve the desirable properties of the purchased goods and services. However, Luo et al. (2009) argue that a universal set of criteria applicable for all supplier selection problems does not exist. Choice of the criteria depends on characteristics of the products/services and the specific industry. For organic food supplier selection, the ten major criteria (product, quality, food safety, price, delivery, serviceability, commercial position, supplier relationship, risk factors, and CSR) identified by Lau et al. (2018) does not internalise the credence attributes of organic food to evaluate supplier performance, resulting in erroneous assessment outcomes during implementation. This incomplete supplier selection can undermine the reputation of the retail stores, thus widening the high intention and low purchase behaviour gap that continues to slow down the transition of the organic sector from a niche market to a mainstream position (Latacz-Lohman and Foster, 1997). To address this problem, our study investigates the three major barriers, i.e. premium price, unavailability, and trust that are causing the intention/behaviour gap. Previous studies found that high price premium and unavailability are the two main causes for the intention/behaviour gap (Aschemann-Witzel and Niebuhr Aagaard, 2014; Latacz-Lohman and Foster, 1997). However, distrust is another even stronger limiting factor. Distrust on organic food certifications is very common and has a large negative effect on consumers’ purchasing decisions (Gan et al., 2016; Goh and Balaji, 2016; Nuttavuthisit &
Thǿgersen 2017; Vega-Zamora et al., 2019). One observed solution in China is the government regulation to control the organic certification processes and discipline the scandals and fraud cases. This drastic measure and the introduction of state-certified organic logo (General Administration of Quality Supervision Inspection and Quarantine of the People’s Republic of China, 2012) help alleviating the level of distrust among the consumers, thus stimulating the growth and development of the organic market in China. Besides, it is high priority for the suppliers and retailers to cultivate consumer trust to energise further growth and development in the organic food market (Gan et al., 2016; Nuttavuthisit & Thǿgersen 2017). Based on literature review and a pilot focus group study, our study finds that trust is the most important factor among these three barriers. Thereafter, our target sample is designed to control the effects of the two other major barriers, i.e. premium price and availability, that have previously been found negatively impacting on the actual purchasing behaviour (Aschemann-Witzel and Niebuhr Aagaard, 2014). Consumers with high level of income and education are willing to pay premium price and purchase more frequently (Boztepe, 2016; Pearson et al., 2013). Availability of a wide range of organic products is not a problem issue in this supermarket chain. Once the premium price and availability barriers have been controlled by design, our study explores the causal factors that contribute to high level of trust. The game theory conveys insight into the weakness of the current organic certification systems and the non-involvement of the retail sector, as attested in the literature (Chkanikova and Kogg, 2018; Chkanikova and Lehner, 2015). Based on the theoretical framework of game theory, the conventional approach of supplier selection cannot avoid the phenomenon outlined above in which conventional food is sold as organic food. Our study recommends strengthening the monitoring mechanism to supplement certification and organic labeling in supplier selection. The desirability of introducing government regulation and public control can be revealed in the game theoretical framework. Game theory strategy can be classified into two main types: (i) a pure strategy, where a particular strategy is chosen; and (ii) a mixed strategy, where a strategy is selected randomly from a set of available strategies, with each strategy being selected at a predetermined frequency. Though mixed strategy has not been applied in the GSCM decision context, where the course of action, e.g. facility capacity, would not be alterable, these game theory concepts can still be relevant in the investigation of how the different stakeholders with different bargaining power in the organic food markets interacting with each other to influence the game equilibrium (Zhu et al., 2018), and the associated market failure phenomenon. Mixed strategy is more applicable to this organic food supplier selection case study with undeterminable scenarios where no pure strategy equilibrium exists. Nash (1950) introduced mixed strategies with which at least one equilibrium exists in every strategic game. Fudenberg and Tirole (1991) developed a simultaneous move monitoring game, where an employer either monitors or does not monitor a worker, and the worker either works or shirks, and was followed by further research studies (Andreozzi, 2004; Avenhaus et al., 2002) with extended models and applications. The unique Nash equilibrium is found in mixed strategies, where the employer monitors the worker with a calculated probability, and the worker shirks with a calculated probability, depending on the parameter values in the payoff matrix. Furthermore, Avenhaus et al. (2002) found that the introduction of fines could deter non-compliance, whereas awarding bonuses to encourage compliance would only increase the probability of non-compliance, contrary to the conventional thinking in commercial practices in GSCM.
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3. Methodology Our preliminary result indicates that trust is the primary factor among the three barriers, consistent with the findings of recent research studies on trust in the purchasing decisions on organic food products (Gan et al., 2016), or distrust as a barrier to the growth and development of the organic food sector (Gan et al., 2016; Goh and Balaji, 2016; Nuttavuthisit & Thǿgersen 2017; VittersØ & Tangeland, 2015). Consequently, our target sample is designed to control the effects of the other two major barriers, i.e. premium price and unavailability. From the supermarket chain’s perspective, the top management team agrees that premium price and availability are not problem issues in their positioned market segment. Only when the level of trust on organic food products can be elevated would a higher level of sales transactions be realised (Kim et al., 2008), and the associated profit improvement. The pilot focus group study was followed by an in-depth thematic analysis of the qualitative interviews to discover how the interaction of trust and other key factors would impact positively on the organic food purchasing decisions. Each interview lasted between 20 min to an hour. Our research team began with openended questions, followed with semi-structured questions that related to the research questions, as listed below: 1. What is your attitude towards purchasing green and organic food products? 2. Do you aware of what organic food production, certification and labeling are? 3. What are the benefits of consuming organic foods? 4. Does the quality of organic food products relate to your health benefits? 5. Do you think it is worth paying the price premium on organic food products? If so, why? 6. What make you decide to purchase organic food products? 7. In your organic purchasing decisions, do you consider subjective norms and social influence from friends, relatives, and social media that endorse organic foods? 8. Besides purchasing in this supermarket, which retail outlets do you shop regularly for organic food products? Does it waste your time to shop around? Our study followed the three-stage procedure commonly adopted in the literature (Braun and Clarke, 2006; Miles et al., 2014) which include data familiarization, coding, theme development and revision. The primary purpose of this inductive procedure unfolds the dominant themes emerged from the interview data (Thomas, 2003), in line with the research questions and theoretical frameworks drawn from the literature. The next step is the assessment of the organic food suppliers which comprises three stages: (i) development of a hybrid MCDM supplier selection model, with criteria and sub-criteria assessed by the MCDM methods, namely fuzzy AHP, TOPSIS, and ELECTRE; (ii) coordination of a case study to test the feasibility of the proposed model; (iii) application of the game theory’s mixed strategy model to calculate the relative cost of monitoring for all the organic food suppliers under consideration, and incorporated into the hybrid MCDM
3
supplier selection model. This MCDM model has previously been applied to a supermarket chain case study in Australia (Lau et al., 2018). After implementing these MCDM methods and model to the present smallsized supermarket chain in Hong Kong, the feedback from the management team was that this project was successfully executed. However, it did not completely resolve the authenticity problems among the organic food suppliers. This motivates the research team to investigate the reason why consumers decide either to buy or not to buy, but not engage in monitoring and appraising the organic food that they purchase. After a few rounds of brainstorming sessions among the expert team members (academia and practitioners), interviews were conducted among various stakeholders, especially the consumers, of the organic food GSC. The underlying reasons turn out to be the issue of trust and cost of monitoring that is likely to be incurred by various stakeholders. In response, the criterion ‘cost of monitoring’ as revealed from the mixed strategy is incorporated into the hybrid MCDM fresh food supplier selection model, with criteria and sub-criteria listed in Table 6.
3.1. The organic food consumers vs the suppliers Extending the simultaneous move monitoring game to the organic food GSC, the interaction between the consumers and the organic food suppliers is examined first. Assuming suppliers are selling organic products through their own stores and charge at price a; b is the organic production cost; c is the organic vs conventional production cost differential; s is the utility of consuming organic products; f is the utility of consuming non-organic products (s is equal to f under the ‘not monitor’ condition when the consumer cannot differentiate organic vs non-organic products); m is the cost of monitoring the organic food suppliers; p is the penalty imposed on the suppliers for selling non-organic products labeled as organic. As shown in Table 1 below, the Nash equilibrium exists for consumers preferring not to monitor organic products. When the suppliers realise the strategy of the consumers, they opt to cheat by producing non-organic products but selling as organic products to maximise payoff. As motivated by their payoffs in this game scenario, the consumers do not monitor, they just either buy or not buy, consistent with the observations in the organic food markets of Hong Kong (Ng, 2018). Future technological advance in monitoring will be able to reduce the cost of monitoring (m). However, from the consumer perspective, testing for just the presence of pesticides by consumer would be prohibitively costly (McCluskey, 2000). When the cost of monitoring (m) remains high for consumer in general, consumer does not monitor. More feasible solution is the social perspective in setting the national standards, e.g. standardization and regulation of cause organic labels to be easily identified for consumers with bounded rationality. However, when the cost of monitoring remains high and standardization and regulation are lacking or ineffective, from the supplier perspective, the corresponding best strategic response is to produce non-organic food but sell as organic to maximise profitability.
Table 1 The payoff matrix of a monitoring game between organic food consumer and supplier. Monitor
Supplier produces organic Supplier produces non-organic
Not Monitor
Consumer buys
Consumer doesn’t buy
Consumer buys
Consumer doesn’t buy
a-b, s-a-m -p-(b-c)þa, f-a-m
-b, 0-m -p-(b-c), 0-m
a-b, s-a a-(b-c), f-a
-b, 0 -(b-c), 0
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Table 2 The payoff matrix of a monitoring game between a supermarket chain and an organic food supplier. Monitor
Supplier produces organic Supplier produces non-organic
Not Monitor
Consumer buys (a)
Consumer doesn’t buy (b)
Consumer buys (c)
Consumer doesn’t buy (d)
a-b, s-a-m -p-(b-c)þa, f-a-m
-b, 0-m -p-(b-c), 0-m
a-b, s-a a-(b-c), f-a-rep
-b, 0 -(b-c), 0-rep
Note: The strategy payoff (b) is dominated by (a), (d) is dominated by (c); s > a > 0 always.
3.2. The supermarket chain vs the organic food suppliers Next, the interaction between supermarket chain and the organic food suppliers is examined. Again, let a be the supplier’s organic food sales revenue; b be the organic production cost; c be the organic vs conventional production cost differential; s be the retailer’s organic food sales revenue; f be the monetised utility of consuming non-organic products; m be the cost of monitoring the organic food suppliers; rep be the expected costs of reputation damage if the supermarket chain were found to be misrepresenting and selling non-organic as organic products. As shown in Table 2 below, the Nash equilibrium does not exist. This raises the fundamental issue about the solution of this type of game, which covers a large class of games in commercial practices. As revealed in Table 2, if the supermarket chain plays the strategy of not monitoring the incoming organic products, consistent with previous findings in the literature (Chkanikova and Kogg, 2018; Chkanikova and Lehner, 2015), then the supplier will play the strategy of supplying non-organic as organic food to maximise its payoff. Upon realizing the strategy of the supplier, the supermarket chain will change its strategy to monitor so as to maximise its payoff, and in response, the supplier will play the strategy of supplying organic food to maximise its payoff. But then in further response, the supermarket will play the non-monitoring strategy. This predicted set of interactive strategic moves results in a vicious circle. As such, the game theory predicts that a mixed strategy equilibrium, where each player chooses a randomised optimal mixed strategy that maximises own payoff, will be justified. 3.3. The MCDM methods: FAHP, TOPSIS, and ELECTRE The objective is to select the best suppliers on the basis of multiple supplier performance metrics that contribute to the best positioning in retail outlets to maximise customer satisfaction in organic food and corporate profitability. The Analytic Hierarchical Process (AHP) that has popularly been applied in the supplier selection literature (Levary, 2008) is chosen to rank the potential suppliers. However, to overcome the problems associated with the AHP method as suggested by Saaty (1980), the fuzzy extent analysis method (Chang, 1996) has been implemented. The TOPSIS method (Hwang and Yoon, 1981) was the next main classical MCDM method deployed in this study. TOPSIS represents the rationale of human choice, and has been successfully applied in nine application areas (Behzadian et al., 2012), However, Olson (2004) finds that precision of the weights plays a critical role for enhancing the accuracy in TOPSIS. Therefore, this study makes use of FAHP to calculate a more accurate set of weights as input into the TOPSIS to generate the best and most precise ranking outcome efficiently. The project team then ranks the preference order by the performance score (or the closeness coefficient) of all the alternatives in descending order. Furthermore, another MCDM method ELECTRE (ELimination Et ), which is a compensatory MCDM Choix Traduisant la REalite method, is deployed to assess the non-compensatory set of food safety criteria that are not substitutable, i.e. poor rating in one sub-
criterion cannot be compensated for by strong ratings in others (Rowley et al., 2012). This method has been applied in supplier selection decision (Seifi et al., 2014; Wan et al., 2017). 4. Case study The supermarket chain is a small-sized operator, with a continually expanding range of organic food products. However, a number of discouraging news stories have surfaced about the tests of a number of random samples in the Hong Kong organic food market, indicating that a high percentage of labeled organic vegetables in fact contained prohibited pesticide residues that exceeded safety thresholds, equivalent to the maximum level of the European Commission. This supermarket chain does not have its own private standards in food safety and quality management systems. The organic food suppliers are only required to comply with the good agricultural and manufacturing practices and existing food safety regulations. Only the production systems of some larger organic food suppliers are audited and certified by a certification agent. This supermarket chain also does not have in-house food technologists and nutritionists to sanction the organic food products. The model for selecting the best candidates as preferred suppliers is built upon the ten fresh food supplier selection criteria (product, quality, food safety, price, delivery, serviceability, commercial position, supplier relationship, risk factors, and CSR). An internal rating system provides qualitative measure for the criteria and sub-criteria, and functioned as part of the product management database systems for completing the product control process for each product order. For example, quality is one of the ten main criteria, the scores for each order delivery in relation to the subcriterion ‘conformance to specification’ ranged from 1: complete nonconformance to 10: complete conformance. For the subcriterion ‘rejection and return’, the rating is defaulted to a score of 10: 0% rejection and return, and it can be changed to a different score as new evidence of fault is observed, a rejection decision can be made in operations, e.g. 1: 100% rejection and return. For availability, it is quantified as a combination of the order quantity that missed the promise schedule, and the time duration of late delivery multiplied by the ordered quantity to be delivered, both of which are recorded in the logistics and operations management systems. These scores are assigned by the responsible product operations manager in the receiving and various operations processes. However, implementation of this model did not allow this supermarket chain to determine precisely the problematic organic food suppliers. Therefore, the management team engages our research team to conduct follow-up interviews among various stakeholders, especially the consumers, of the organic food GSC of this supermarket chain. Our study targeted the sampled consumers with higher level of individual income (at least HK$30,000 per month) and better educated (professional with undergraduate degree or diploma) so that price premium would not be a major demand problem issue. If the sample is not controlled for these two barrier factors, then pooling separate groups with differential behaviours can give distorting empirical results to the supermarket chain and probably be
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less applicable to the retail stores due to its unique market positioning in higher level of income and education segment. This supermarket chain is able to attract consumers that are willing to pay the price premium and purchase more organic products. The top management team agrees that only when the level of trust on organic food products can be elevated would a higher level of sales transactions be realised (Kim et al., 2008). However, for future research, the sample can be designed to represent the population in general, and develop SEM models to test whether behaviours do differ among various groups that are categorised by varying levels of income, education, and other factors. Sixty consumers were sampled in 2018 from five individual stores of this supermarket chain located at various high-end residential districts. All interviewees were screened to meet the research design specification of the level of income above HK$30k/ month, without recording the level of actual income information. As such, the domestic helpers or maids were excluded. The education level is between diploma and university degree. Majority of the interviewees are female (65%). The average duration of the interview time is 35 min. One of the co-authors and two university graduated research assistants, with previous training and practice in interviewing, qualitative research, and NVivo coding, were responsible for managing the interview process. Arbitrated by the co-author as a supervisor, three rounds of test-coding were crosschecked by the two research assistants as inter-raters to assure coding quality and consistency over time. Based on the thematic analysis of the qualitative interview data collected from the in-depth interviews of these sixty consumers, the findings are summarised below. Some of the qualitative findings are consistent with the previous findings in the literature. For those aspects that the literature is silent, new insights can be drawn from the thematic analysis of the qualitative interview data. 1. Nine key nodes, i.e. trust, income and education, attitude towards purchasing green and organic foods, subjective norm, health and environmental benefits, organic regulation and certification systems, willingness to pay, willingness to spend time and effort to shop for organic foods, and consumer awareness, are the antecedents of the actual purchasing behaviours. Their corresponding references are shown in Table 3. 2. For consumers with higher level of income and university education, the more awareness and receptive to the information about sustainable development and organic food are observed, consistent with the previous literature (Magnusson et al., 2003). This consumer segment has better attitude towards sustainability and purchase green and organic foods, consistent with the previous literature (Krystallis et al., 2006). 3. Consumers with higher level of income and university education prefer to buy higher quality organic food products and
Table 3 The key nodes and their corresponding references. Key Nodes
References
1. 2. 3. 4. 5. 6. 7. 8. 9.
51 60 49 47 58 45 50 43 55
Trust Income and education Attitude towards purchasing green and organic foods Subjective norm Health and environmental benefits Organic regulation and certification systems Willingness to pay Willingness to spend time and effort to shop for organic foods Consumer awareness
4.
5.
6.
7.
8.
9.
10.
11.
12.
5
acknowledge their health benefits. They are also more willing to pay the price premium and spend more time and effort to shop for organic foods. The more intensive the social pressure and subjective norms, the more willingness to pay premium price and spend time/ effort to shop for organic food products. For consumers who are more health and environment conscious, the better attitudes towards purchasing green and organic foods are revealed from the interview data, which is consistent with the previous research findings (Magistris and Gracia, 2008). Relative to the more frequent buyers, consumers who purchase organic foods less frequently express less trust on organic certification and labeling, and distrust the organic food retailers more, especially in the wet market that might mix conventional with organic food products as an easy route to profit. Quality of the organic food products is a subjective measure. However in practice, perceived high quality food products judged by the look and feel implicitly infers more probable health benefits. All the interviewees prefer to shop for organic food products in a supermarket chain where a wide variety of organic food products are available. However, these consumers also shop, though less frequently, in specialty stores and the wet markets that they are familiar with or recommended by their close friends and relatives. It is the additional net health and environmental benefits from organic food consumption, i.e. health and environmental benefits minus the organic food price premium, that influence the organic food purchasing decisions. If the trust factor in terms of organic food regulation and certification can be strengthened, more consumers would purchase organic food products. This empirical evidence is consistent with previous findings in the literature (Tung et al., 2012), signifying the lack of trust as the major factor that accounts for the intention-behaviour gap. The interviewed consumers admit that they would not be able to verify whether the organic food products are genuine or not. Fraudulent cases publicised in the media do sway their organic food purchasing decisions. They become suspicious but could not pinpoint which agents and stages in the GSC that are more susceptible to the wrong-doings, consistent with the previous literature (Giannakas, 2002). When the feeling of distrust emerges, the merits of sustainability and health benefits dissipate, and the rationale behind paying the premium price would principally be weakened. The organic consumers prefer to shop for organic food products in a supermarket chain that they trust. However, when hinted that the organic regulation and certification systems in the exporting countries assign an extensive responsibilities that are mandatory to the primary producers and operators, whilst other agents, e.g. the processors/ manufacturers, distributors, and retailers, in the GSC are less controlled, they respond by desiring that the supermarket chain should take more initiative to monitor and control the organic food suppliers.
Expectedly, among the nine key nodes, trust plays a key role in mediating several input factors (health and environmental benefits, attitude towards purchasing green and organic foods, organic regulation and certification systems, consumer awareness) and the output factors of willingness to pay and willingness to spend time/ effort to shop for organic foods, which are causally linked to the actual purchasing behaviours. To raise the expenditure on green
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and organic foods, industry and government policy interventions to strengthen the level of trust among consumers are important catalysts. As such, our findings are consistent with the recent literature, and trust should be advocated as the most important factor for promoting the growth and development in the organic food market (Gan et al., 2016; Nuttavuthisit & Thǿgersen 2017). As revealed in the empirical results that GSCM impacts positively on export performance via the mediating role of environmental performance (AlGhwayeen and Abdallah, 2018), similarly, GSCM facilitates the organic food market expansion via the mediating role of trust on the legitimacy of organic food certifications. For future research, the qualitative results and insights gained from the thematic analysis of this study can guide the design and development of a structural equation modeling (SEM) model. With a larger survey sample, the empirical results validated by the SEM model can become a valuable tool for the practitioners operating in the organic food market, and possibly be generalised to other credence environmental friendly products, to promote consumer trust and expand the corresponding markets. As revealed by the application of game theory and the findings of our thematic analysis of the qualitative interviews, the underlying key reason turns out to be the cost of monitoring that is likely to be incurred by various stakeholders. The game theory explains why consumers decide either buy or do not buy, but do not incline to monitor and appraise the organic food that they purchase. The mixed strategy concept also explains why the supermarket chain and the organic food suppliers should interact with each other by randomly selecting an appropriate strategy to maximise their payoffs. The game theory conveys an insight into the important role of the monitoring mechanism to assure quality and trust should not be overlooked, even in the presence of certification and organic labeling. Based on the mixed strategy framework, the conventional procedure and criteria for supplier selection cannot circumvent the phenomenon in which conventional food is sold as organic food. 4.1. Application of the game theory After a series of brainstorming sessions among the expert team members (academia and practitioners), combining the data extracted from the internal database systems and the interview data collected from various stakeholders of the organic food GSC, the assessment of the organic food suppliers was re-examined again. A set of relative cost of monitoring was calculated based on the payoff matrix applicable to each organic food supplier. Applying game theory to this small-sized supermarket chain, the payoff matrix can be represented as shown in Table 4 below. Let a ¼ $50,000. b ¼ $35,000, c ¼ $20,000, s ¼ $100,000, m ¼ $80,000, f ¼ $55,000, p ¼ $20,000, rep ¼ $200,000. Since (b) is dominated by (a), (d) is dominated by (c) (note that s > a > 0 always), columns (b) and (d) are eliminated for further analysis, giving the following payoff matrix, as shown in Table 5. Let X be the probability of supplier producing organic food, and Y be the probability of the supermarket chain monitoring the organic food supplier. Using this payoff matrix, the expected payoff to the supplier is equal to the probability of each of the four
Table 5 The payoff matrix of a monitoring game between this supermarket chain and an organic food supplier.
Supplier produces organic Supplier produces non-organic
Monitor
Not Monitor
Consumer buys (a)
Consumer buys (c)
$15k, -$30k -$20k, -$75k
$15k, $100k $35k, -$160k
outcomes multiplied by its corresponding payoff: On the other hand, the expected payoff to the supermarket chain is equal to the probability of each of the four outcomes multiplied by its corresponding payoff: The mixed strategy for the supermarket chain is to randomly monitor the organic food supplier with a probability of 39.5%, and for the supplier to produce and deliver organic food with a probability of 36.4%. Similar data inputs and analysis are implemented for all the other organic food suppliers, and their scores are represented in the cost of monitoring criterion for further evaluation via the nominated MCDM methods. 4.2. Application of the MCDM methods A project team is responsible for defining the organic food supplier selection problem, designing and scheduling the associated tasks. Based on the data mined from the corporate survey database of this supermarket chain, the internal supplier management systems, and external databases, the weightings of the parent criteria and children sub-criteria for each supplier are calculated with the application of the FAHP method, as shown in Table 6 below. Given that the huge negative impact of food safety events are extremely costly in terms of potential loss in sales and damage to the brand image of the supermarket chains, and other social costs, e.g. health and productivity of consumers and the public in general, incentives exist for the supermarket chain to disqualify potential suppliers that have a high probability of delivering unsafe products. Since any sub-criterion within the food safety criterion is not compensatory or substitutable for any other food safety subcriteria, the non-compensatory MCDM method ELECTRE is particularly applicable for disqualifying organic food suppliers that did not reach a standard threshold. Applying ELECTRE, the numerical net concordance and discordance indices are shown in Table 7. The ranking relation is ordered in preference as A, G, E, I, B, C, D, L, and H. Suppliers F, J, and K are disqualified due to their poor food safety ranking, which could potentially cause unbearable costs of sales loss and damage to the brand image of this supermarket chain. Due to the large number of organic food products and suppliers, the pairwise comparisons for the FAHP method entail far too complex cognitive processing for the experts. Furthermore, the data collected for each criterion have values that differ widely from other criteria, and thus cannot be suitably represented in the simple FAHP format. TOPSIS avoids this issue by circumventing the tedious pairwise comparisons of alternatives among the large number of
Table 4 The payoff matrix of a monitoring game between this supermarket chain and an organic food supplier. Monitor
Supplier produces organic Supplier produces non-organic
Not Monitor
Consumer buys (a)
Consumer doesn’t buy (b)
Consumer buys (c)
Consumer doesn’t buy (d)
$15k, -$30k -$20k, -$75k
0, -$80k -$20k, -$80k
$15k, $100k $35k, -$160k
0, 0 0, -$160k
Note: The strategy payoff (b) is dominated by (a), (d) is dominated by (c); s > a > 0 always.
H. Lau et al. / Journal of Cleaner Production 242 (2020) 118536
7
Table 6 The normalised weights of the sub-criteria with respect to their parent criterion. Objective
Primary criteria (Weights)
Secondary sub-criteria (Weights)
Superior Fresh Food Supplier Product (0.089) Performance Quality (0.114)
Availability; Reliability; Value-added activities (cutting, trimming, grading, and packing); Packaging; Warranty; Innovation (0.31, 0.17, 0.10, 0.15, 0.15, 0.12) Conformance to specification; Rejection and return rate; Process capability; Quality management systems; Quality improvement; Quality assessment technique (0.29, 0.30, 0.12, 0.14, 0.10, 0.05) Certified organic & ISO9001 (Product management) is certified organic; Procedural compliance and periodic audits at pre- and post-farm safety (0.144) stages; Continuous food safety training; Traceability (0.22, 0.24, 0.21, 0.19, 0.14) Cost of monitoring Probability of organic food supplier producing non-organic food; Frequency of sampling organic products for (0.238) laboratory testing (0.25, 0.75) Price (0.067) Price representation; Price decreased ratio; Product cost; Total logistics management cost; Tariff and taxes; Quantity discounts; Payment terms; Shelving and listing fees (0.05, 0.08, 0.21, 0.15, 0.14, 0.14, 0.17, 0.06) Delivery (0.094) Delivery on time; Lead time; Compliance with quantity and packaging standards; Use of standard cradles; Transportation facility, e.g. refrigerated trucks; Delivery performance; Ability and willingness to expedite order; Delivery frequency (0.14, 0.08, 0.11, 0.12, 0.21, 0.14, 0.16, 0.04) Serviceability (0.041) Service quality; Flexibility in product mix, volume, and lead time; Responsiveness to demand changes; Ability to modify product/service; Information sharing, EDI capability, and systems compatibility; Technical support; Management of complaints; Administration for return, sell-back, and reverse logistics; Value-added service (0.26, 0.11, 0.16, 0.15, 0.05, 0.11, 0.02, 0.03, 0.11) Commercial position Market reputation; Market share; Performance history; Asset specificity and infrastructure; Financial position and (0.056) stability; Technological capability (0.26, 0.11, 0.31, 0.12, 0.13, 0.06) Supplier relationship Relationship connectors; Duration of relationship; Annual average amount of past business over the last three years; (0.036) Communication and conflict resolution; Long-term commitment (0.23, 0.22, 0.19, 0.25, 0.11) Risk factors (0.058) Geographical location; Political stability and government policies; Exchange rates and economic position; Labor relations; Terrorism and crime rate (0.24, 0.18, 0.25, 0.19, 0.14) CSR (0.063) ISO-14001 certification; Eco-labeling; Stakeholder relations and community recognition; Pay rates, labor conditions and work environment; Compliance to international human rights (0.15, 0.12, 0.32, 0.25, 0.16)
Table 7 The ranking relations of the net concordance and discordance index matrices. Supplier
Net concordance index
Net discordance index
A E I B C L H F J K
3.73 2.86 2.38 2.04 1.58 1.34 0.03 0.41 1.86 3.47
31.13 22.28 15.79 11.32 7.81 7.81 1.10 6.08 12.13 17.16
organic food suppliers, and accommodates the decision matrix of wide ranging values in its efficient computational routine. Results of the normalised decision matrix are generated. Combined with the weights, the weighted normalised decision matrix is calculated, followed by the determination of the positive and negative ideal solutions. In the final step, the relative closeness to the ideal solution is determined, as shown in tables below. The differential ranking orders of the preferred suppliers between ‘without’ and ‘with’ the inclusion of the cost of monitoring criterion are shown in Tables 8 and 9. With the inclusion of the new criterion (cost of monitoring), the relative rank positions of the list of preferred suppliers shift in response to the re-calculation of the positive (Dþ) and the negative (D-) ideal solutions, and their relative closeness to the ideal solution (V) of each potential supplier. Without the inclusion of the cost of monitoring, all the seven potential suppliers are ranked on the basis of V, in the order of supplier E, A, B, I, H, C, L. After the inclusion of the cost of monitoring, all the seven potential suppliers are ranked on the basis of V, generating the order of supplier I, E, A, B, C, H, L. The result reveals that for the first four organic food suppliers, when the cost of monitoring criterion is introduced in the organic food supplier evaluation model, supplier I shifts its rank from 4th to 1st, and the rank positions of the other suppliers E, A, and B shift to the next lower rank. The main reason is
Table 8 The positive (Dþ) and the negative (D-) ideal solutions, and their relative closeness to the ideal solution (V) of each potential supplier, without the inclusion of the cost of monitoring criterion. Supplier
Dþ DV Rank
A
E
I
B
C
L
H
0.078 0.119 0.603 2
0.063 0.114 0.645 1
0.081 0.104 0.562 4
0.073 0.100 0.580 3
0.099 0.083 0.457 6
0.125 0.047 0.273 7
0.098 0.094 0.491 5
due to the lower cost of monitoring measured for supplier I. From the supermarket’s perspective, supplier I has regular and complete organic certification, laboratory results, and operating in a jurisdiction that has well-established organic standards and regulatory framework. Therefore, the cost of monitoring and ongoing verification are relatively lower. To make further progress on the organic food supplier selection results, apart from informing the organic food suppliers the ten criteria and sub-criteria in greater details, the suppliers are recommended to restructure their organic production to comply with minimal organic standards and regulatory framework, complete organic certification on a regular basis, and accompanied with auditable laboratory results. Training and development sessions are offered to target lower ranked suppliers. The supermarket chain also informs the suppliers the random monitoring process, and the associated penalties for disqualified deliveries. Six months after the implementation of the newly proposed hybrid MCDM method, a performance review on the organic food supplier selection process confirmed a noticeable reduction in the safety incidents and an improvement in the level of customer satisfaction. For the organic sector in Hong Kong, a more cost-effective alternative is to institutionalise the control mechanisms in the relevant government authorities to monitor continuously and impose penalties on the offenders. Furthermore, we recommend the Hong Kong Government to follow other developed countries to introduce legislation and standards to regulate the organic sector.
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Table 9 The positive (Dþ) and the negative (D-) ideal solution, and their relative closeness to the ideal solution (V) of each potential supplier, with the inclusion of the cost of monitoring criterion. Supplier
Dþ DV Rank
A
E
I
B
C
L
H
0.131 0.133 0.504 3
0.102 0.135 0.569 2
0.089 0.168 0.654 1
0.128 0.112 0.467 4
0.126 0.109 0.465 5
0.175 0.054 0.235 7
0.164 0.103 0.387 6
5. Conclusion Organic food products are sourced from GSCs, which span across country borders and involve a wide spectrum of stakeholders with diverse needs and commercial motives. The complexities of the organic food GSC offer room and incentive for profit-seeking stakeholders to engage in and cover up food adulteration (Agriculture and Agri-Food Canada, 2010; Caswell and Mojduszka, 1996). This phenomenon can also be observed in Hong Kong organic food markets. As we explored the case study of the supermarket chain in Hong Kong, managing the complex trade-offs among the multiple TBL objectives (economic, social, and environmental) and assuring authenticity among the organic food suppliers are the most problematic issues facing the top management team. Our study aims to discover the key factors and mechanism on the supply side that can assure a more optimal solution to the green and organic food authentication problem, matching the demand side expectations among the organic consumers with the purpose of escalating the level of trust in order to promote the growth and development of the organic food sector. We focused on bridging the research gap related to the green or sustainable SC literature by generating theoretical and empirical evidence to minimise the negative effects of information asymmetry and quality uncertainty in the organic food GSCs. The obvious solution of certification and product labels is not sufficient to solve the food credibility problem since many suppliers are still incentivised to sell non-organic food products as organic at a premium price. Built upon the earlier hybrid MCDM methods and model (Lau et al., 2018), and combined with a newly introduced criterion as revealed by the mixed strategy of game theory, a more complete authenticity solution to the organic food problem is proposed. The results of this case study demonstrate that an equilibrium exists at which the profits of the GSC members and the multiple CSR objectives can be optimised. As such, the supermarket chain can adopt the new hybrid MCDM methods and model for its organic food supplier selection process to maximise customer satisfaction and corporate profitability. The game theoretic mixed strategy of randomly monitoring the organic food supplier according to its corresponding probability of compliance should be implemented. Furthermore, to keep the organic food suppliers honest, a fine should be imposed on dishonest suppliers. A more efficient and effective alternative is to institutionalise government authorities to engage in continuous monitoring and imposing penalties. The costs of monitoring and compliance would be charged against the suppliers and/or the retailers.
Funding details The research is funded by Western Sydney University School of Business Research Development Funding (2017)
References Agriculture and Agri-Food Canada, 2010. Market Trends: Organics. International Market Bureau. AAFC No. 11263E. Al-Ghwayeen, W., Abdallah, A., 2018. Green supply chain management and export performance: the mediating role of environmental performance. J. Manuf. Technol. Manag. 29 (7), 1233e1252. Ali, M., Zhan, Y., Alam, S., Tse, Y., Tan, K., 2017. Food supply chain integrity: the need to go beyond certification. Ind. Manag. Data Syst. 117 (8), 1589e1611. Andreozzi, L., 2004. Rewarding policemen increases crime. Another surprising result from the inspection game. Public Choice 121 (1e2), 69e82. Aschemann-Witzel, J., Niebuhr Aagaard, E., 2014. Elaborating on the attitudeebehaviour gap regarding organic products: young Danish consumers and in-store food choice. Int. J. Consum. Stud. 38 5, 550e558. Avenhaus, R., Stengel, B.V., Zamir, S., 2002. Inspection games. In: Aumann, R., Hart, S. (Eds.), Handbook of Game Theory with Economic Applications, vol. 3. North-Holland, Amsterdam, pp. 1947e1987. Behzadian, M., Otaghsara, K., Yazdani, M., Ignatius, J., 2012. A state-of the-art survey of TOPSIS applications. Expert Syst. Appl. 39 (17), 13051e13069. Boztepe, A., 2016. Green marketing and its impact on consumer buying behavior. Eur. J. Econ. Political Stud. 5 (1), 5e21. Braun, V., Clarke, V., 2006. Using thematic analysis in psychology. Qual. Res. Psychol. 3 (2), 77e101. Buyukozkan, G., Cifci, G., 2011. A novel fuzzy multi-criteria decision framework for sustainable supplier selection with incomplete information. Comput. Ind. 62 (2), 164e174. Carriquiry, M., Babcock, B., 2007. Reputations, market structure, and the choice of quality assurance systems in the food industry. Am. J. Agric. Econ. 89 (1), 12e23. Caswell, J., Mojduszka, E., 1996. Using informational labeling to influence the market for quality in food products. Am. J. Agric. Econ. 78 (5), 1248e1253. Chang, D.Y., 1996. Applications of the extent analysis method on fuzzy AHP. Eur. J. Oper. Res. 95 (3), 649e655. Chekima, B., Oswald, A., Wafa, S.A., Wafa, S.K., Chekima, K., 2017. Narrowing the gap: factors driving organic food consumption. J. Clean. Prod. 166 (November), 1438e1447. Chkanikova, O., Lehner, M., 2015. Private eco-brands and green market development: towards new forms of sustainability governance in the food retailing. J. Clean. Prod. 107, 74e84. Chkanikova, O., Kogg, B., 2018. Sustainability governance service providers: the role of third-party product certification in facilitating corporate life cycle management. Int. J. Life Cycle Assess. 23 (7), 1383e1395. Choe, Y., Park, J., Chung, M., Moon, J., 2009. Effect of the food traceability system for building trust: price premium and buying behavior. Inf. Syst. Front. 11 (2), 167e179. D’Amico, M., Vita, G.D., Monaco, L., 2016. Exploring environmental consciousness and consumer preferences for organic wines without sulfites. J. Clean. Prod. 120 (May), 64e71. Elkington, J., 1997. Cannibals with Forks: the Triple Bottom Line of 21st Century Business. Capstone, Oxford. Elkington, J., 1998. Partnerships from cannibals with forks: the triple bottom line of 21st century business. Environ. Qual. Manag. 8 (1), 37e51. Ford, G., Smith, D., Swasy, J., 1988. An empirical test of the search, experience and credence attributes framework. Adv. Consum. Res. 15, 239e244. Fudenberg, D., Tirole, J., 1991. Game Theory. MIT Press, Cambridge, MA. Gan, C., Chang, Z., Tran, M., Cohen, D., 2016. Consumer attitudes toward the purchase of organic products in China. Int. J. Bus. Econ. 15 (2), 117e144. General Administration of Quality Supervision Inspection and Quarantine of the People’s Republic of China 2012, ’Organic Products’ GB/T 19630.1-19630.4, 2011, National Standard of the People’s Republic of China, Beijing. Giannakas, K., 2002. Information asymmetries and consumption decisions in organic food product markets. Can. J. Agric. Econ. Rev. Can. D’Agroeconomie. 50 (1), 35e50. Goh, S., Balaji, M., 2016. Linking green skepticism to green purchase behaviour. J. Clean. Prod. 131, 629e638. Hwang, C.L., Yoon, K., 1981. Multiple Attribute Decision Making: Methods and Applications. Springer, Heidelberg. Jahn, G., Schramm, M., Spiller, A., 2005. The reliability of certification: quality labels as a consumer policy tool. J. Consum. Policy 53e73. Kim, D., Ferrin, D., Rao, H., 2008. A trust-based consumer decision-making model in electronic commerce: the role of trust, perceived risk, and their antecedents. Decis. Support Syst. 44 (2), 544e564. Krystallis, A., Fotopoulos, C., Zotos, Y., 2006. Organic consumers’ profile and their willingness to pay (WTP) for selected organic food products in Greece. J. Int. Consum. Mark. 19 (1), 81e106. Kumar, R., Chandrakar, R., 2012. Overview of green supply chain management: operation and environmental impact at different stages of the supply chain. Int. J. Eng. Adv. Technol. 1 (3), 1e6. Latacz-Lohman, U., Foster, C., 1997. From “niche” to “mainstream”- strategies for marketing organic food in Germany and the UK. Br. Food J. 99 (8), 275e282. Lau, H., Nakandala, D., Shum, P., 2018. A business process decision model for freshfood supplier evaluation. Bus. Process Manag. J. 24 (3), 716e744. Laureti, T., Benedetti, I., 2018. Exploring pro-environmental food purchasing behaviour: an empirical analysis of Italian consumers. J. Clean. Prod. 172 (January), 3367e3378.
H. Lau et al. / Journal of Cleaner Production 242 (2020) 118536 Levary, R.R., 2008. Using the analytic hierarchy process to rank foreign suppliers based on supply risks. Comput. Ind. Eng. 55 (2), 535e542. Luo, X., Wu, C., Rosenberg, D., Barnes, D., 2009. Supplier selection in agile supply chains: an information-processing model and an illustration. J. Purch. Supply Manag. 15 (4), 249e262. Magistris, T., Gracia, A., 2008. The decision to buy organic food products in Southern Italy. Br. Food J. 110 (9), 929e947. Magistris, T., Gracia, A., 2016. Consumers’ willingness-to-pay for sustainable food products: the case of organically and locally grown almonds in Spain. J. Clean. Prod. 118 (April), 97e104. Magnusson, M., Arvola, A., Hursti, U., Aberg, L., Sjoden, P., 2003. Choice of organic food is related to perceived consequences for human health and to environmentally friendly behavior. Appetite 40 (2), 109e117. Manzini, R., Accorsi, R., 2013. The new conceptual framework for food supply chain assessment. J. Food Eng. 115 (2), 251e263. McCluskey, J., 2000. A game theoretic approach to organic foods: an analysis of asymmetric information and policy. Agric. Resour. Econ. 29 (1), 1e9. Miles, M., Huberman, A., Saldana, J., 2014. Qualitative Data Analysis: A Methods Sourcebook, third ed. Sage publications, Thousand Oaks, CA. Nash, J., 1950. Equilibrium points in n-person games. Proc. Natl. Acad. Sci. 36, 48e49. Ng, P., 2018. Clampdown on ’organic’ Veg Urged. The Standard, 26 Mar 2018. viewed 26 Mar 2018. http://www.thestandard.com.hk/section-news.php? id¼194119&sid¼47384769. Nuttavuthisit, K., Thǿgersen, J., 2017. The importance of consumer trust for the emergence of a market for green products: the case of organic food. J. Bus. Ethics 140 (2), 323e337. Olson, D.L., 2004. Comparison of weights in TOPSIS models. Math. Comput. Model. 40, 721e727. Pearson, D., Henryks, J., Sultan, P., Anisimova, T., 2013. Organic food: exploring purchase frequency to explain consumer behavior. J. Org. Syst. 8 (2), 50e63.
9
Roe, B., Sheldon, I., 2007. Credence good labeling: the efficiency and distributional implications of several policy approaches. Am. J. Agric. Econ. 89 (4), 1020e1033. Rowley, H., Peters, G., Lundie, S., Moore, S., 2012. Aggregating sustainability indicators: beyond the weighted sum. J. Environ. Manag. 11, 24e33. Saaty, T.L., 1980. The Analytic Hierarchy Process. McGraw-Hill, New York. Savory, A., 2016. Holistic Management: A Commonsense Revolution to Restore Our Environment, third ed. Island Press, Washington, DC. Seifi, M., Markabi, M., Sarbijan, M., 2014. An improved fuzzy-based ELECTRE I method for supplier selection. Int. J. Oper. Logist. Manag. 3 (4), 387e398. Singh, A., Verma, P., 2017. Factors influencing Indian consumers’ actual buying behaviour towards organic food products. J. Clean. Prod. 167 (November), 473e483. Thomas, D., 2003. A General Inductive Approach for Qualitative Data Analysis. School of Population Health University of Auckland, New Zealand. Tung, S., Shih, C., Wei, S., Chen, Y., 2012. Attitudinal inconsistency toward organic food in relation to purchasing intention and behavior: an illustration of Taiwan consumers. Br. Food J. 114 (7), 997e1015. Vega-Zamora, M., Torres-Ruiz, F., Parras-Rosa, M., 2019. Towards sustainable consumption: keys to communication for improving trust in organic foods. J. Clean. Prod. 216 (April), 511e519. VittersØ, G., Tangeland, T., 2015. The role of consumers in transitions towards sustainable food consumption: the case of organic food in Norway. J. Clean. Prod. 92, 91e99. Voon, J., Sing, K., Agrawal, A., 2011. Determinants of willingness to purchase organic food : an exploratory study using structural equation modeling. Int. Food Agribus. Manag. Rev. 14 (2), 103e120. Wan, S., Xu, G., Dong, J., 2017. Supplier selection using ANP and ELECTRE II in interval 2-tuple linguistic environment. Inf. Sci. 385e386, 19e38. Zhu, Q., Li, X., Zhao, S., 2018. Cost-sharing models for green product production and marketing in a food supply chain. Ind. Manag. Data Syst. 118 (4), 654e682.