Tu Lyu
Yulin Guo
Qixiang Geng- School of Business, Qingdao University, Qingdao, China
It is essential to avoid opportunistic behaviors of food supply chain members to guarantee food safety and sustainable supply. This research adopted the perspective of supply chain membership governance to discuss the critical mechanisms of opportunistic behavior avoidance and performance improvement in the food supply chain. Two information-sharing mechanisms (information sharing with customers and information sharing with suppliers) were used as mediating variables to explore the mechanisms of how social control, information sharing, and opportunistic behavior worked on supply chain performance. Furthermore, an online questionnaire survey was conducted to collect 210 data samples from the food manufacturing industry in China, and the structural equation model method was applied to test the research hypotheses. According to the empirical research findings, social control can directly reduce opportunistic behaviors of supply chain members and reduce such behaviors indirectly via the mediating factor of information sharing; social control affects the supply chain performance via the mediating factors of information sharing and opportunistic behavior, instead of directly improving supply chain performance. Two information sharing mechanisms vary in their mechanism of influence. Information sharing with customers reduces opportunistic behaviors, but does not directly improve supply chain performance. Information sharing with suppliers enhances supply chain performance and reduces opportunistic behaviors. This research offers theoretical and practical suggestions for performance improvement and opportunistic behavior avoidance to promote food supply chain management.
1. Introduction
Food safety and sustainable supply have always been a hot topic of great concern to the public both in China and foreign countries, because it is a matter of physical health and social stability. Especially in the context of regular prevention and control of COVID-19, the food supply chain has undertaken severe financial and survival pressures because the human capital flow was restricted, consumer demands have changed, food production facilities and factories have closed, and food trade has been limited (Aday and Aday, 2020; Deconinck et al., 2021; Luckstead et al., 2021). This would easily trigger the occurrence of opportunistic behaviors, such as selling shoddy goods, cheating on the quantity of the goods, and delaying the food supply, which badly hinders the normal running of the food supply chain (Huo et al., 2019; Yadav et al., 2021) and even seriously affects food safety and supply (Kumar and Singh, 2022).
Opportunism is a dishonest breach of business obligations and “self-interest seeking with guile” (Williamson, 1998). Dependence (Gorton et al., 2015), uncertainty (Dahlstrom et al., 2009), justice (Huo et al., 2016b), legal contracts (Son et al., 2021), relational norms (Paswan et al., 2017; Son et al., 2021), buyer coercive and non-coercive powers (Wang et al., 2015) are factors that either increase or decrease the likelihood of opportunism. Scholars have conducted studies on achieving the recovery and stable operation of the food supply chain to reduce the food supply problems arising from chain disruption and mitigate food safety risks (Barman et al., 2021). It is especially worth noting that scholars have made great efforts to reduce the opportunistic behavior of food supply chain member enterprises. For example, Tran et al. (2021) focused on food suppliers’ opportunistic behaviors, such as selling products to others, mixing products, cheating on the supply quantity, cheating on the product quality, refusing to use input provided, and wrongly using money, and presented suggestions for reducing the opportunistic behaviors. Liu (2018) argued that relationship-specific investments could reduce members’ distrust and opportunistic behavior in the food supply chain. Nakandala et al. (2020) pointed out that strengthening mutual trust is an important measure to reduce opportunism in the food supply chain to build shared power and fairness. Xu et al. (2020) maintained that blockchain technology could create a food traceability supply chain to eliminate potential food safety hazards. Similarly, Fu et al. (2020) believed that emerging information technologies such as blockchain and the Internet of things could contribute to easing the constraints of information asymmetry in the food supply chain and reducing opportunistic behaviors.
We assume that the social control mechanism can offer new perspectives for reducing opportunistic behaviors in the supply chain context. Building social control mechanisms to enhance supply chain performance has been confirmed to be a feasible way to maintain favorable relationships between supply chain members (Teller et al., 2016). Different from to use of contractual governance (Jia et al., 2020) or formal control (Li et al., 2010) to reduce opportunism in a supply chain context, which may pay a price of high costs, social control, based on the relationship management of supply chain members, strengthens the mutual trust of upstream and downstream enterprises to seek joint planning and problem solving and improve information exchanges to lower the uncertainty and inventory level (Acharyulu and Shekbar, 2012), thus eventually bringing the likelihood of reducing opportunism. In the supply chain, social control is an effective means to minimize opportunism and improve supply chain performance, which helps supply chain member enterprises to choose favorable partners, lower transaction costs, strengthen the core competitiveness, and thus improve the overall supply chain performance (Cheng, 2021). Meanwhile, Wu et al. (2014) pointed out that social control has the high-level strategy of reducing the “free-riding” behavior of enterprises to achieve mutual understanding and knowledge sharing between upstream and downstream supply chain enterprises, thus enhancing supply chain performance. Li et al. (2010) argued that social control spurs supply chain partners to increase investment in mutual benefit, improving supply chain performance. Falk and Kosfeld (2006) found that trust in social control would raise the hidden cost of opportunistic behaviors among supply chain partners, thus contributing to reducing opportunistic behaviors of enterprises. Beyond that, social control can increase the likelihood of upstream and downstream enterprises working jointly to fulfill the same goal, thus further facilitating enterprise cooperation. Cheng (2021) concluded that profits increase along with the increase in the degree of social control, and social control can increase the safety and credibility of transactions, thereby enhancing the overall performance of the supply chain. Food supply chain enterprises can build social control mechanisms to boost cooperation among member enterprises, lower transaction costs, strengthen information and knowledge sharing, adopt flexible and effective measures to deal with changes in the market environment, and respond quickly to uncertainty, to reduce opportunistic behaviors of supply chain members and enhance the overall performance of the supply chain. Therefore, this research has the primary objective of discussing whether social control mechanisms can restrain food supply chain enterprises and thus achieve the effect of reducing opportunism and enhancing the overall performance of the supply chain.
In the context of regular COVID-19 prevention and control, market information changes rapidly and bears great uncertainty. Food manufacturing enterprises must have quick access to helpful information in the business environment and share it with partners timely (Barman et al., 2021). Therefore, it is highly urgent to build an enterprise information-sharing architecture. Information sharing is needed for the food supply chain. Information sharing can help enterprises to rationally allocate their supply chain resources and cut losses and waste incurred by dynamic environmental changes. Information sharing can also optimize the supply chain structure and propel overall goal fulfillment (Zhou and Benton, 2007). Information sharing among food supply chain enterprises can enhance the overall quality of the supply chain (Ding et al., 2014; Christensen et al., 2021; Liu et al., 2021). Information sharing calls for effective coordination and communication among organizations to cope with the increasingly-complex market environment, which also reveals the core competitive advantage of the supply chain (Shepherd and Günter, 2010). Upstream and downstream manufacturing enterprises should build an intimate and mutually-beneficial relationship, because such a member relationship-based social control mode can help enterprises to exchange information and access external critical information and technology, thereby reducing opportunistic behaviors (You et al., 2018). Especially for food manufacturing enterprises, timely information exchange with supply chain partners can be conducive to improving production plans, stimulating production vitality, improving the food quality and supply capacity, reducing speculative activities caused by information asymmetry between enterprises, and enhancing the efficiency and effectiveness of the supply chain. Accordingly, social control, information sharing, opportunistic behavior, and supply chain performance are interrelated. Our second objective of this research is to investigate the critical effect of information sharing on the relationship between social control, opportunistic behavior, and supply chain management performance.
In summary, this research investigates how social control influences the opportunistic behavior and supply chain performance of enterprises in the food supply chain and attempts to discuss the mediating role of information sharing in the mechanism. Our study contributes to the literature in the following respects. Firstly, from the perspective of social control and information sharing, this study revealed the critical antecedents of reducing opportunistic behavior in food supply chain firms. Secondly, our research contributes to the research by constructing and discovering the mediating mechanism of social control mechanism to improve the performance of the food supply chain; Thirdly, the research further reveals the differences between the two types of information sharing in the process of improving the performance of food supply chain. Our study also provides theoretical references for the performance improvement of the food supply chain from the perspective of social control.
2. Literature review and research hypotheses
Organizational information processing theory (OIPT) suggests that an organization is an open information processing system to collect, analyze, and use information effectively and efficiently, especially when executing complex tasks with extensive levels of uncertainty and interdependence (Srinivasan and Swink, 2018). A better match between the needs and capabilities of information processing can ensure the healthy operation of the enterprise (Li et al., 2019), and such an information processing mechanism is needed to cope with uncertainties and manage unforeseen events that threaten the normal operations of business processes (Wong, C. W. Y. et al., 2020). In addition to strengthening internal information processing, it is an essential way for enterprises to conduct information processing to build communication channels by establishing contacts with other organizations, such as direct contacts between different enterprises to facilitate processes of joint decision making (Galbraith, 1974). Information exchange with suppliers and customers to obtain current and valuable information can improve the visibility of information and increase the effectiveness of enterprise decision-making (Srinivasan and Swink, 2018; Han et al., 2021). Studies suggested that information sharing is an important strategy to enhance information processing capabilities (Wong, C. Y. et al., 2020); therefore, based on the OIPT, this study complied with the research logic of “information acquisition—information processing—performance production” to develop our research model to capture the antecedents and consequences of information sharing.
The food supply chain is witnessing a more fast-changing and chaotic market environment because of the information economy, economic globalization, and the pandemic (Aday and Aday, 2020; Rizou et al., 2020). The difficulty in predicting the market poses more challenges for the stable development of the food supply chain. Enterprises effectively share supplier and customer information with upstream and downstream enterprises can achieve better exchanges and get excessive heterogeneous resources (Tran et al., 2021; Lyu et al., 2023). The utilization of information sharing resources can bring a unique competitive advantage to real-time information exchanges between the enterprise and its upstream enterprises (suppliers) and downstream enterprises (distributors). With this competitive advantage, the supply chain can become more special, because resource endowments from the competitive advantage create a more incredible difficulty for other manufacturing supply chains to imitate (Battistelli et al., 2019). Therefore, information sharing is a necessary information processing mechanism for food supply chain enterprises no matter from the perspective of OIPT theory or the realistic demand of the above analysis.
Prior studies of food supply chains confirmed that a good supply chain relationship quality is a crucial precursor for any stable exchange relationship that ensures relationship continuity and high performance (Odongo et al., 2016; Mesic et al., 2018). Building a good relationship with supply chain members makes it feasible to provide a way for enterprises to access and utilize resources in the supply chain. This way, enterprises can better cooperate with their supply chain partners and develop competitive advantages (Wu et al., 2022). From the perspective of relationship maintenance, food supply chain members can adopt social control measures such as trust accumulation and joint efforts to promote the expansion or stabilization of relational capital (Badraoui et al., 2020), and stimulate knowledge interaction and information sharing among supply chain members (Dawuti, 2014). Accordingly, we connected social control and information sharing to deconstruct the “information acquisition—information processing” relationship.
Performance production in this study includes avoiding or reducing opportunistic behaviors and increasing supply chain performance. Rapid market changes will bring more uncertainty and trigger opportunistic behaviors of food supply chain enterprises (Liu, 2018; Xu et al., 2020). Therefore, food supply chain enterprises must work together to take measures to avoid uncertainty. This research argues that social control and information sharing can be introduced to weaken the influence of environmental changes on opportunistic behaviors. Social control will spur enterprises to constantly detect problems, integrate resources, and allocate resources to reduce opportunistic behavior, thus responding to the market and gratifying customer needs (Wu et al., 2014); Information sharing can keep the supply chain balanced and stable and reduce uncertainty and opportunism (You et al., 2018). Besides, previous studies confirmed that social control and information sharing are drivers to promote supply chain performance in various research scenarios (Anin et al., 2016; Shibin et al., 2020). Therefore, food manufacturing enterprises should implement long-term social control. In addition, the enterprises should continue to strengthen the information sharing between suppliers and customers, thus satisfying the market demand, maintaining the stability and development of the supply chain, and guaranteeing food quality and sustained supply. In summary, the research model shown in Figure 1 reveals the relationships and influence mechanisms of social control, information sharing, opportunistic behavior, and supply chain performance.
Figure 1. Research model.
Social control mechanism consists of trust, joint planning, joint problem solving, and other core contents (Huang et al., 2014), which have been implemented in food supply chain management (Dania et al., 2018). Trust can forge a basis for building cooperative relationships among food supply chain members (Dania et al., 2018). It is helpful to reduce the risk associated with opportunistic behavior (Wu et al., 2014) and significantly increase supply chain operational performance (Ramirez et al., 2021). Joint planning helps the upstream and downstream enterprises of the food supply chain to develop a shared development strategy, development direction, and long-term goals, and integrate limited resources to maintain sustainable behavior (Dania et al., 2018). Joint problem solving encourages upstream and downstream enterprises to work together to adjust uncertainty and conflicts, and enhances the opportunities for communication between the upstream and downstream enterprises to reduce opportunistic behaviors (Cai et al., 2009; Rhee et al., 2014). Thus, the following hypotheses are developed:
H1: Social control positively influences the food supply chain performance.
H2: Social control negatively influences opportunistic behavior.
Distinguishing the information shared by upstream and downstream enterprises can achieve more favorable sharing effects (Alzoubi and Yanamandra, 2020). Guan et al. (2020) found that information sharing between manufacturing enterprises and suppliers can boost the profits of upstream and downstream enterprises, bring scale economies and save operating costs. Prajogo and Olhager (2012) argued that information sharing between manufacturing enterprises and customers could facilitate enterprises to get market information, such as the demand of downstream customers, thus exploiting the customer value and altering product research and development strategies. According to prior studies, this research classifies information sharing into two categories: information sharing with customers and suppliers.
Social control enables enterprises in the supply chain to access critical information from multiple perspectives and channels of various types (Abdullah and Musa, 2014). In particular, food supply chain members have begun to share information about food processing, procurement, inventory, and marketing (Kumar and Singh, 2022). Tajvidi et al. (2020) concluded that social control could provide a strong guarantee for information sharing and lower the risk of necessary information disclosure so enterprises can focus on value co-creation. Wu et al. (2014) found that by establishing a favorable social control mechanism, core enterprises can build a long-term cooperative relationship with upstream and downstream enterprises and raise their information-sharing willingness, thus enhancing the supply chain performance. Information sharing with suppliers and consumers can strengthen the responsiveness of both suppliers and manufacturers (Han et al., 2021), improve the traceability of food sources, strengthen the innovation and flexibility of the supply chain, lower the cost of food production and processing (Reklitis et al., 2021), and safeguard and coordinate the interest of supply chain members (Dania et al., 2018). We thus provided the following hypotheses:
H3: Social control positively influences information sharing (a. information sharing with customers; b. information sharing with suppliers).
The efficient implementation of information sharing can reduce the uncertainty of the supply chain in business operations and help manufacturing enterprises to acquire critical resources, thereby enhancing the dynamic capability and performance of the supply chain (Shan et al., 2020; de Lima et al., 2022; Lyu et al., 2022), and reducing the occurrence of opportunistic behaviors (Huo et al., 2021). In our research context, information sharing among food supply chain members can effectively enhance supply chain performance (Sener et al., 2019; Sundram et al., 2020). Strengthening information exchanges with customers and suppliers can facilitate the timely feedback of end customers’ demands to suppliers and customized production in specific demand scenarios. Especially in the pandemic context, such customized food production and supply can reduce the costs of enterprises, improve the suitability of products to the market, guarantee the sales and market value of food, and ease the burden on manufacturing enterprises. Beyond that, such a full supply chain-based information exchange mode can avoid opportunistic behaviors triggered by information asymmetry and enhance the cooperation efficiency of the supply chain while regulating enterprise behavior. Based on the above analysis, the following hypotheses are presented:
H4: Information sharing (a. information sharing with customers; b. information sharing with suppliers) positively influences the food supply chain performance.
H5: Information sharing (a. information sharing with customers; b. information sharing with suppliers) negatively influences opportunistic behavior.
Given the uncertainty of information exchanges, the supply chain is prone to opportunistic behaviors, which infringe on the stable and long-term cooperative relationship of manufacturing enterprises and incur risks and profit losses (Xu et al., 2020) and have specific manifestations in withholding and distorting information and refusing to fulfill commitments and obligations (Zhang and Chen, 2013). As suppliers and distributors have different rights and positions, enterprises in a weak position should invest more to communicate and build relationships, to weaken enterprises’ motivation to engage in opportunistic behaviors (You et al., 2018). For food supply chain enterprises, such opportunistic behaviors of partners adversely affect the performance of food manufacturing enterprises, and, worse still, may trigger a series of potential problems such as food safety and belated supply (Tran et al., 2021). Based on the above analysis, this research develops the following hypothesis:
H6: Opportunistic behavior negatively influences supply chain performance.
3. Research methodology
3.1. Measurement development
To guarantee that the research data can fully reflect reality and accord with the requirements of reliability and validity, this research used the proven scales in the existing literature as a reference for design and measurement. In particular, the scales used in the Chinese scenario were adjusted to accord with the food supply chain scenario in this research. Based on Claro et al. (2003) and Cai et al. (2009), social control was measured from three dimensions: trust, joint planning, and joint problem solving. Scales of trust and joint planning were designed to have three items, respectively, and the scale of joint planning had four items. Adapted from Flynn et al. (2010) and Prajogo and Olhager (2012), information sharing was measured from two dimensions: information sharing with suppliers and information sharing with customers. The scale of information sharing with customers had three items, while the scale of information sharing with suppliers had four items. Meanwhile, five items were designed based on the research of Huo et al. (2017) and Prajogo and Olhager (2012) to develop supply chain performance scales. Using the research of Luo et al. (2015) and Huo et al. (2016a) for reference, this research designed four items to measure opportunistic behaviors. The enterprise age, size, and property were also used as control variables. The questionnaire adopted a 5-point Likert scale to describe the degree of agreement with statements in the question item (indicating 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, and 5 = Strongly Agree).
3.2. Pilot study
We followed Rasool et al. (2021) to conduct a two-stage pilot test. Firstly, to ensure the research validity, this research invited ten supply chain management personnel as respondents to engage in a pre-survey. On this basis, efforts were made to carefully consider and repeatedly revise the arcane and ambiguous words in the questionnaire and the logical relationship and positional relationship between the sentences in the questionnaire. Second, this study invited 67 MBA students to conduct a small sample pilot test. All these students are department heads or higher-level managers in food processing and manufacturing enterprises. We tested the reliability and validity of the sample data. Results found that the Cronbach’s ɑ coefficient and composite reliability (CR) of each variable are all above 0.7, and average variances extracted (AVE) and factor loading were all above 0.5, indicating that the scale involved in this study could measure the relevant variables more accurately.
3.3. Data collection
This research chose the online survey approach for data collection to avoid the limitations of financial resources, labor power, time, and other objective factors and facilitate the questionnaire distribution and collection. Middle-senior supply chain managers from food processing and manufacturing enterprises were selected as the respondents, because they were familiar with the internal and external processes of enterprise running, including procurement, manufacturing, warehousing, transportation, and other links. In the final survey, a total of 411 samples were collected. However, in the questionnaire filtering, 101 samples were found to have a short questionnaire-filling time (much faster than the expected time), give the same option or just two options for all questionnaire items, or choose obviously-conflicting answers. After the invalid questionnaires were deleted, 310 valid samples were used for future analysis. Thus, the recovery rate of valid questionnaires was 73.0%. The basic information on the enterprise samples is shown in Table 1.
Table 1. Basic information of questionnaire respondents (N = 310).
4. Data analysis and results
4.1. Common method bias and multicollinearity
Since the primary method of data collection utilized by the research questionnaire is self-reporting, the likelihood of there being a common method variance (CMV) has been increased, therefore, we conducted the Harman’s single-factor analysis approach, and the results showed that CMV is not an issue because only one single factor was extracted to explain 31.5% of the variance in the endogenous variables (less than 50%; Hair et al., 2016). We also tested the VIF values to test whether multi-collinearity exists. Results suggested that all the VIF values of variables are between 1.575 and 1.891, far less than 3.3 (Diamantopoulos and Siguaw, 2006), indicating that the multi-collinearity is not an issue needs to be considered in this study.
4.2. Measurement validation
We used the SmartPLS 4 to test the reliability and validity of the data. As shown in Table 2, the CA scores were higher than 0.6, the CR scores were higher than 0.7, and the AVE values were higher than 0.5, which met the requirements specified by Bagozzi and Yi (1988). As shown in Table 3, the factor loadings of all items were higher than the threshold value of 0.7 specified by Chin (1998). Thus, the data had good reliability and convergent validity.
Table 2. Reliability and validity testing results.
Table 3. Cross-loadings.
We used the Fornell–Larcker criterion and heterotrait–monotrait ratio (HTMT) to test discriminant validity. As shown in Table 2, the square root of AVE was greater than the absolute value of the correlation coefficient of its row and column, which met the requirement (Fornell and Larcker, 1981). Moreover, Table 4 indicated that all HTMT values were lower than the threshold value of 0.85 (Kline, 2011). Therefore, the data had good discrimination validity.
Table 4. Heterotrait–Monotrait ratio (HTMT).
4.3. Hypothesis testing
We used the method of partial least squares structural equation modeling (PLS-SEM) and the SmartPLS 4.0 tool to test the structural equation model. PLS was chosen because it helps deal with high-order constructs and complex models, which is suitable for constructing and measuring new theories (Hair et al., 2019). This study tested social control (SC) as a second-order construct with three dimensions: TR, JP, and JSP.
The cumulative explained variance variation of the SCP was 77.6%, and of the OB was 36.5%. As shown in Figure 2, SC has no influence on SCP (β = 0.055, p > 0.05) and has a negative effect on OB (β = −0.231, p < 0.01), indicating that H1 was not supported, and H2 was supported; SC positively impact on CIS (β = 0.645, p < 0.001) and SIS (β = 0.636, p < 0.001), supporting H3a,b; SIS has a positive influence on SCP (β = 0.094, p < 0.01), while CIS did not (β = 0.080, p > 0.05), thus, H4a was not supported and H4b was supported; both CIS (β = −0.171, p < 0.01) and SIS (β = −0.299, p < 0.001) have a negative influence on OB, supporting H5a,b. OB negatively influences SCP (β = −0.749, p < 0.001), supporting H6.
Figure 2. Hypothesis testing results. *p < 0.05, **p < 0.01, ***p < 0.001.
We also tested whether the control variables impact SCP. Results indicated that firm age (β = −0.021), firm size (β = 0.008), industries (β = 0.011), and position of subjects (β = −0.002) have no significant influence on SCP, and the cumulative explained variance variation of the SCP was only 1.4%.
4.4. Post-hoc analysis
To further deepen our understanding of the path of opportunistic behavior and performance improvement in the food supply chain from the perspective of social control, we tested the potential mediators using SmartPLS 4.0 tool in our research model. As shown in Table 5, information sharing with customers (β = −0.110, t-values = 2.625, p < 0.01) and information sharing with the supplier (β = −0.191, t-values = 4.460, p < 0.001) mediate the relationship between social control and opportunistic behavior; information sharing with the supplier (β = 0.060, t-values = 2.600, p < 0.01) and opportunistic behavior (β = 0.173, t-values = 3.047, p < 0.01) mediate the relationship between social control and supply chain performance; opportunistic behavior mediates the relationship between information sharing with the supplier (β = 0.224, t-values = 4.451, p < 0.001)/information sharing with the customer (β = 0.128, t-values = 2.747, p < 0.01) and supply chain performance. Moreover, the multiple mediation effects of information sharing with the customer and opportunistic behavior (β = 0.083, t-values = 2.626, p < 0.01)/information sharing with the supplier and opportunistic behavior (β = 0.143, t-values = 4.416, p < 0.001) between social control and supply chain performance were significant.
Table 5. Mediators testing results.
5. Discussion
5.1. Key findings and theoretical implication
This study focuses on the opportunistic behavior of members in food supply chain management and the performance improvement of manufacturing enterprises. It introduces social control to investigate the direct path of social control’s influence on opportunistic behavior and supply chain performance and the indirect path of social control’s influence via information sharing as the mediating factor. In this research, the following conclusions are drawn.
First, social control can directly reduce the opportunistic behavior of food supply chain members. This conclusion is consistent with the view of Zhang et al. (2019) that social control is an important means to reduce the opportunism of partners. Furthermore, this research also finds that social control has multiple paths of indirectly restricting opportunistic behaviors. To be specific, social control can indirectly restrain opportunistic behaviors of supply chain partners via information sharing with customers and information sharing with suppliers. The mediating path can be represented as: SC → (+)CIS/SIS→(−)OB(β = −0.110, p<0.01/β = −0.191, p<0.001). Therefore, this study enriches the conclusions on the multiple influence paths of social control on the opportunistic behavior of supply chain members.
Furthermore, different from prior studies concluding that social control has a direct positive influence on supply chain performance (e.g., Li et al., 2010), this research finds that social control affects supply chain performance indirectly via various mediating factors instead of directly improving supply chain performance. Social control can reduce opportunistic behaviors via information sharing, thus enhancing supply chain performance. There are multiple mediating paths, that is, SC → (+)CIS/SIS→(−)OB → (−)SCP (β = 0.083, p < 0.01/β = 0.143, p < 0.001). Social control improves supply chain performance by weakening opportunism. So there exists a mediating path SC → (−)OB → (−)SCP (β = 0.172, p < 0.01). Thus, this research demonstrates the mediating effect of information sharing and opportunism in the relationship between social control and food supply chain performance.
Manufacturing enterprises’ information sharing with customers does not directly improve supply chain performance, whereas their information sharing with suppliers enhances supply chain performance. This conclusion is logical in the food supply chain scenario. Information sharing with suppliers can drive food manufacturing enterprises and their suppliers to make exchanges and cooperation concerning information on production, manufacturing, technology, raw materials, and other food production-related aspects. Thus, manufacturing enterprises can improve food processing technology and innovate in food production and circulation to guarantee their competitive advantage and food quality safety. However, information sharing with customers can only help manufacturing enterprises to understand better the market demand, timely track the market changes, and then gain supplementary information about food category development, thus facilitating the manufacturing enterprises in food design, taste improvement, and other fields, so information sharing with customers produces a limited influence on operating cost, food quality and delivery speed of the food supply chain. To further study the information-sharing mechanism, this research introduces a test of the path between information sharing with customers and information sharing with suppliers, and finds that information sharing with suppliers is not a mediating variable between information sharing with customers and supply chain performance improvement, and information sharing with customers and supply chain performance have a particular chain-linked mediating path: CIS → (+)SIS→(−)OB → (−)SCP (β = 0.045, p < 0.01). Specifically, information sharing with customers first affects information sharing with suppliers, acts on opportunistic behaviors, and eventually influences supply chain performance. This demonstrates paths of information sharing with customers to improve supply chain performance. Prior studies take information sharing as a concept (e.g., Huo et al., 2021), and investigate the effect of information sharing with suppliers on boosting the improvement of the overall supply chain quality (Ding et al., 2014; Christensen et al., 2021; Liu et al., 2021). This research deepens the understanding of information sharing in prior studies. It discusses the differences between information sharing with customers and information sharing with suppliers and differences in their influence mechanisms (that is, the influence mechanisms of information sharing with customers and information sharing with suppliers on supply chain performance and their correlations), thereby enriching the conceptual depth and conclusions of existing studies.
Lastly, this research concludes that both information sharing with customers and information sharing with suppliers can significantly reduce the opportunistic behavior of supply chain enterprises and thus enhance supply chain performance. This finding is inconsistent with Wang et al. (2014) that information sharing with suppliers does not correlate with opportunism.
In summary, social control can restrain opportunistic behavior and improve supply chain performance from the following four aspects. First, enterprises would incur severe losses from their “breach of trust.” Laws and regulations can adjust social behaviors, but trust, as the most critical psychological mechanism for effective communication in modern society, still takes an irreplaceable part, and can weaken the opportunistic motivation of upstream and downstream enterprises in the supply chain. Secondly, social control brings a special relationship between enterprises, so enterprise-enterprise relationships are no longer straightforward. Social control can drive upstream and downstream enterprises to make cooperation and daily communication on the premise of mutual benefits, so that the enterprises can be connected more closely, communicate with each other more smoothly, and run more efficiently, thus gaining more heterogeneous resources, cutting the communication cost, and significantly lowering the likelihood of opportunistic behavior triggered by information asymmetry. Thirdly, social control can establish relevant mechanisms and innovative working modes for manufacturing enterprises. Specifically, social control can help enterprises to build flexibly-enhancing, rights-equalizing, and resource-sharing mechanisms. These mechanisms can directly or indirectly enhance the operating efficiency and flexibility of the supply chain and strengthen the cooperation willingness and goal consistency between suppliers and manufacturers, thus significantly improving the supply chain performance. Lastly, social control can facilitate the fulfillment of cooperation between upstream and downstream enterprises in the supply chain. On this basis, design strategies and tactics, timely adjust the strategic orientation and industrial structure, lower losses and supply chain risks, and thus enhance the overall supply chain performance.
5.2. Policy suggestions
This research has great value for the practice of the food manufacturing industry to build and strengthen their relationships with supply chain partners and adopt the information-sharing mechanism to weaken opportunism and improve supply chain performance.
Food supply chain enterprises should pay attention to the supervision of opportunistic behavior, combat opportunism, and promote the food supply chain co-existence and co-prosperity. Especially in the context of the pandemic, people’s food preferences have changed dramatically, with consequent changes in food quotas and supplies for upstream farming, food processing and manufacturing, as well as suppliers and retailers. The pandemic increases the chances of opportunism. Based on our findings, a meaningful way to reduce or even avoid opportunism is to strengthen mutual trust and cooperation between enterprises, form an inter-organizational information sharing channel, and fix it as a common policy followed by member enterprises of the food supply chain.
We suggested that the food manufacturing industry build a social control mechanism based on the relationship of supply chain members. The traditional contract mechanism specifies the rights and obligations of supply chain enterprises. However, in the face of the outbreak of COVID-19, food supply chain enterprises are also faced with numerous difficulties, and relying solely on contracts may not be able to maintain the stability of the food supply chain. A single enterprise is likely to take opportunistic actions due to high profits or basic survival, resulting in the deactivation of the entire food supply chain. As found in this study, enhancing information sharing among supply chain firms through informal social control mechanisms to reduce opportunism and improve performance may be an essential way to increase the overall co-prosperity of the food supply chain in a pandemic environment. This also suggests that enterprises should, in addition to contracts, attach importance to strengthening mutual trust and cooperation with upstream and downstream enterprises to form a close cooperative relationship, deal with emergencies caused by the epidemic and achieve the overall prosperity of the supply chain and the performance of member enterprises.
We also suggested that the food manufacturing industry should consider the effect of information sharing to enhance supply chain management and cooperation. On the one hand, information-sharing platforms can be built so supply chain enterprises can share production, inventory, and transportation data. Enterprises at each node can effectively adjust their production plan for the current or next stage. On the other hand, it is feasible to invest more into the internal information construction of enterprises, strengthen the integration of internal departments of manufacturing enterprises, build new ways of information transmission to alter the original process, improve the flexibility of enterprises, and adopt social control to accelerate the response speed of the supply chain. Given the difference in the effect of information sharing with customers and information sharing with suppliers, manufacturing enterprises should share information strategically and selectively.
6. Conclusion and limitations
Ensuring enterprises’ sustainability and good performance is the inevitable choice in an uncertain environment (Wen et al., 2022). Especially for food supply chain enterprises, it is particularly challenging for them to reduce the harm of opportunistic behavior and maintain stable performance in the context of the pandemic. This study introduced social control and information sharing and built a model based on the OIPT theory. The study confirmed the positive effects of social control and information sharing on improving food supply chain performance and reducing opportunism. Therefore, this study provides a new perspective to explain the growth of supply chain performance and discovers the mediating mechanism of information sharing and the differences between the two types of information sharing. Unavoidably, this research has some limitations. Cross-sectional data were used in this research to show the actual running mechanism of the food supply chain. However, longitudinal tracking research may enrich research conclusions. Beyond that, existing research shows that social control and formal control complement each other (Li et al., 2010). Formal control can be applied to this research to discuss the similarities and differences between social control and formal control in their influence on opportunistic behavior and performance in the supply chain.
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethics statement
Ethical review and approval were not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with national legislation and institutional requirements. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.
Author contributions
TL was responsible for idea generation, manuscript writing and revision. YG was responsible for hypothesis development and data analysis. QG was responsible for data collection. All authors contributed to the article and approved the submitted version.
Funding
This study was supported by the Natural Science Foundation of Shandong Province of China (No. ZR2021QG007) and the Humanities and Social Science Fund of the Ministry of Education of China (No. 19YJC630118).
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
Abdullah, Z., and Musa, R. (2014). The effect of trust and information sharing on relationship commitment in supply chain management. Procedia Soc. Behav. Sci. 130, 266–272. doi: 10.1016/j.sbspro.2014.04.031
Acharyulu, G., and Shekbar, B. R. (2012). Role of value chain strategy in healthcare supply chain management: an empirical study in India. Int. J. Manag. 29, 91–97.
Aday, S., and Aday, M. S. (2020). Impact of COVID-19 on the food supply chain. Food Qual. Safety 4, 167–180. doi: 10.1093/fqsafe/fyaa024
Alzoubi, H. M., and Yanamandra, R. (2020). Investigating the mediating role of information sharing strategy on agile supply chain. Uncertain Supply Chain Manag. 8, 273–284. doi: 10.5267/j.uscm.2019.12.004
Anin, E. K., Essuman, D., and Sarpong, K. O. (2016). The influence of governance mechanism on supply chain performance in developing economies: insights from Ghana. Int. J. Bus. Manag. 11, 252–264. doi: 10.5539/ijbm.v11n4p252
Badraoui, I., Van der Vorst, J. G., and Boulaksil, Y. (2020). Horizontal logistics collaboration: an exploratory study in Morocco’s Agri-food supply chains. Int J Log Res Appl 23, 85–102. doi: 10.1080/13675567.2019.1604646
Bagozzi, R. R., and Yi, Y. (1988). On the evaluation of structural equation models. J. Acad. Mark. Sci. 16, 74–94. doi: 10.1007/bf02723327
Barman, A., Das, R., and De, P. K. (2021). Impact of COVID-19 in food supply chain: disruptions and recovery strategy. Curr. Res. Behav. Sci. 2:100017. doi: 10.1016/j.crbeha.2021.100017
Battistelli, A., Odoardi, C., Vandenberghe, C., Di Napoli, G., and Piccione, L. (2019). Information sharing and innovative work behavior: the role of work-based learning, challenging tasks, and organizational commitment. Hum. Resour. Dev. Q. 30, 361–381. doi: 10.1002/hrdq.21344
Cai, S., Yang, Z., and Hu, Z. (2009). Exploring the governance mechanisms of quasi-integration in buyer–supplier relationships. J. Bus. Res. 62, 660–666. doi: 10.1016/j.jbusres.2008.02.004
Cheng, H. (2021). Do small-and medium-sized manufacturers’ production-related resources influence their export marketing control modes and export performance? J. Bus. Ind. Mark. 36, 1876–1893. doi: 10.1108/JBIM-09-2019-0406
Christensen, F. M. M., Jonsson, P., Dukovska-Popovska, I., and Steger-Jensen, K. (2021). Information sharing for replenishment planning and control in fresh food supply chains: a planning environment perspective. Prod. Plan. Control, 1–22. doi: 10.1080/09537287.2021.2013558
Claro, D. P., Hagelaar, G., and Omta, O. (2003). The determinants of relational governance and performance: how to manage business relationships? Ind. Mark. Manag. 32, 703–716. doi: 10.1016/j.indmarman.2003.06.010
Dahlstrom, R., Haugland, S. A., Nygaard, A., and Rokkan, A. I. (2009). Governance structures in the hotel industry. J. Bus. Res. 62, 841–847. doi: 10.1016/j.jbusres.2007.07.033
Dania, W. A. P., Xing, K., and Amer, Y. (2018). Collaboration behavioural factors for sustainable Agri-food supply chains: a systematic review. J. Clean. Prod. 186, 851–864. doi: 10.1016/j.jclepro.2018.03.148
Dawuti, A. (2014). “Does control matter? Exploring the effects of formal control and social control on knowledge transfer in service supply chain” in Advances in Intelligent Systems and Computing. Vol. 278. eds. Z. Wen and T. Li (Berlin, Heidelberg: Springer), 325–333.
de Lima, F. A., Seuring, S., and Sauer, P. C. (2022). A systematic literature review exploring uncertainty management and sustainability outcomes in circular supply chains. Int. J. Prod. Res. 60, 6013–6046. doi: 10.1080/00207543.2021.1976859
Deconinck, K., Avery, E., and Jackson, L. A. (2021). Food supply chains and Covid-19: impacts and policy lessons. Euro Choices 19, 34–39. doi: 10.1111/1746-692X.12297
Diamantopoulos, A., and Siguaw, J. A. (2006). Formative versus reflective indicators in organizational measure development: a comparison and empirical illustration. Br. J. Manag. 17, 263–282. doi: 10.1111/j.1467-8551.2006.00500.x
Ding, M. J., Jie, F., Parton, K. A., and Matanda, M. J. (2014). Relationships between quality of information sharing and supply chain food quality in the Australian beef processing industry. Int. J. Logist. Manag. 25, 85–108. doi: 10.1108/IJLM-07-2012-0057
Falk, A., and Kosfeld, M. (2006). The hidden costs of control. Am. Econ. Rev. 96, 1611–1630. doi: 10.1257/aer.96.5.1611
Flynn, B. B., Huo, B., and Zhao, X. (2010). The impact of supply chain integration on performance: a contingency and configuration approach. J. Oper. Manag. 28, 58–71. doi: 10.1016/j.jom.2009.06.001
Fornell, C., and Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 18, 39–50. doi: 10.1177/002224378101800104
Fu, H., Zhao, C., Cheng, C., and Ma, H. (2020). Blockchain-based Agri-food supply chain management: case study in China. Int. Food Agribus. Manag. Rev. 23, 667–679. doi: 10.22434/IFAMR2019.0152
Galbraith, J. R. (1974). Organization design: an information processing view. Interfaces 4, 28–36. doi: 10.1287/inte.4.3.28
Gorton, M., Angell, R., Dries, L., Urutyan, V., Jackson, E., and White, J. (2015). Power, buyer trustworthiness and supplier performance: evidence from the Armenian dairy sector. Ind. Mark. Manag. 50, 69–77. doi: 10.1016/j.indmarman.2015.05.024
Guan, Z., Zhang, X., Zhou, M., and Dan, Y. (2020). Demand information sharing in competing supply chains with manufacturer-provided service. Int. J. Prod. Econ. 220:107450. doi: 10.1016/j.ijpe.2019.07.023
Hair, J. F., Hult, G. T. M., and Ringle, C. (2016). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Sage Publications: Thousand Oaks, CA.
Hair, J. F., Risher, J. J., Sarstedt, M., and Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. Eur. Bus. Rev. 31, 2–24. doi: 10.1108/EBR-11-2018-0203
Han, Z., Huo, B., and Zhao, X. (2021). Backward supply chain information sharing: who does it benefit? Supply Chain Manag. Int. J. doi: 10.1108/SCM-03-2019-0098 [Epub ahead of print].
Huang, M. C., Cheng, H. L., and Tseng, C. Y. (2014). Reexamining the direct and interactive effects of governance mechanisms upon buyer–supplier cooperative performance. Ind. Mark. Manag. 43, 704–716. doi: 10.1016/j.indmarman.2014.02.001
Huo, B., Flynn, B. B., and Zhao, X. (2017). Supply chain power configurations and their relationship with performance. J. Supply Chain Manag. 53, 88–111. doi: 10.1111/jscm.12139
Huo, B., Fu, D., Zhao, X., and Zhu, J. (2016a). Curbing opportunism in logistics outsourcing relationships: the role of relational norms and contract. Int. J. Prod. Econ. 182, 293–303. doi: 10.1016/j.ijpe.2016.07.005
Huo, B., Haq, M. Z. U., and Gu, M. (2021). The impact of information sharing on supply chain learning and flexibility performance. Int. J. Prod. Res. 59, 1411–1434. doi: 10.1080/00207543.2020.1824082
Huo, B., Tian, M., Tian, Y., and Zhang, Q. (2019). The dilemma of inter-organizational relationships: dependence, use of power and their impacts on opportunism. Int. J. Oper. Prod. Manag. 39, 2–23. doi: 10.1108/IJOPM-07-2017-0383
Huo, B., Wang, Z., and Tian, Y. (2016b). The impact of justice on collaborative and opportunistic behaviors in supply chain relationships. Int. J. Prod. Econ. 177, 12–23. doi: 10.1016/j.ijpe.2016.04.006
Jia, Y., Wang, T., Xiao, K., and Guo, C. (2020). How to reduce opportunism through contractual governance in the cross-cultural supply chain context: evidence from Chinese exporters. Ind. Mark. Manag. 91, 323–337. doi: 10.1016/j.indmarman.2020.09.014
Kline, R. B. (2011). Principles and practice of structural equation modeling. Guilford Press, New York, NY, 3rd Edn.
Kumar, P., and Singh, R. K. (2022). Strategic framework for developing resilience in Agri-food supply chains during COVID 19 pandemic. Int J Log Res Appl 25, 1401–1424. doi: 10.1080/13675567.2021.1908524
Li, S., Cui, X., Huo, B., and Zhao, X. (2019). Information sharing, coordination and supply chain performance: the moderating effect of demand uncertainty. Ind. Manag. Data Syst. 119, 1046–1071. doi: 10.1108/IMDS-10-2018-0453
Li, Y., Xie, E., Teo, H.-H., and Peng, M. W. (2010). Formal control and social control in domestic and international buyer–supplier relationships. J. Oper. Manag. 28, 333–344. doi: 10.1016/j.jom.2009.11.008
Liu, G. (2018). The impact of supply chain relationship on food quality. Proc. Comput. Sci. 131, 860–865. doi: 10.1016/j.procs.2018.04.286
Liu, M., Dan, B., Zhang, S., and Ma, S. (2021). Information sharing in an E-tailing supply chain for fresh produce with freshness-keeping effort and value-added service. Eur. J. Oper. Res. 290, 572–584. doi: 10.1016/j.ejor.2020.08.026
Luckstead, J., Nayga, R. M. Jr., and Snell, H. A. (2021). Labor issues in the food supply chain amid the COVID-19 pandemic. Appl. Econ. Perspect. Policy 43, 382–400. doi: 10.1002/aepp.13090
Luo, Y., Liu, Y., Yang, Q., Maksimov, V., and Hou, J. (2015). Improving performance and reducing cost in buyer–supplier relationships: the role of justice in curtailing opportunism. J. Bus. Res. 68, 607–615. doi: 10.1016/j.jbusres.2014.08.011
Lyu, T., Geng, Q., and Zhao, Q. (2023). Understanding the efforts of cross-border search and knowledge co-creation on manufacturing enterprises’ service innovation performance. System 11:4. doi: 10.3390/systems11010004
Lyu, T., Guo, Y., and Lin, H. (2022). Understanding green supply chain information integration on supply chain process ambidexterity: the mediator of dynamic ability and the moderator of leaders’ networking ability. Front. Psychol. 13:1088077. doi: 10.3389/fpsyg.2022.1088077
Mesic, Ž., Molnar, A., and Cerjak, M. (2018). Assessment of traditional food supply chain performance using triadic approach: the role of relationships quality. Supply Chain Manag. Int. J. 23, 396–411. doi: 10.1108/SCM-10-2017-0336
Nakandala, D., Smith, M., and Lau, H. (2020). Shared power and fairness in trust-based supply chain relationships in an urban local food system. Br. Food J. 122, 870–883. doi: 10.1108/BFJ-05-2019-0309
Odongo, W., Dora, M., Molnar, A., Ongeng, D., and Gellynck, X. (2016). Performance perceptions among food supply chain members a triadic assessment of the influence of supply chain relationship quality on supply chain performance. Br. Food J. 118, 1783–1799. doi: 10.1108/BFJ-10-2015-0357
Paswan, A. K., Hirunyawipada, T., and Iyer, P. (2017). Opportunism, governance structure and relational norms: an interactive perspective. J. Bus. Res. 77, 131–139. doi: 10.1016/j.jbusres.2017.04.012
Prajogo, D., and Olhager, J. (2012). Supply chain integration and performance: the effects of long-term relationships, information technology and sharing, and logistics integration. Int. J. Prod. Econ. 135, 514–522. doi: 10.1016/j.ijpe.2011.09.001
Ramirez, M. J., Roman, I. E., Ramos, E., and Patrucco, A. S. (2021). The value of supply chain integration in the Latin American Agri-food industry: trust, commitment and performance outcomes. Int. J. Logist. Manag. 32, 281–301. doi: 10.1108/IJLM-02-2020-0097
Rasool, S., Cerchione, R., Salo, J., Ferraris, A., and Abbate, S. (2021). Measurement of consumer awareness of food waste: construct development with a confirmatory factor analysis. Br. Food J. 123, 337–361. doi: 10.1108/BFJ-02-2021-0160
Reklitis, P., Sakas, D. P., Trivellas, P., and Tsoulfas, G. T. (2021). Performance implications of aligning supply chain practices with competitive advantage: empirical evidence from the Agri-food sector. Sustainability 13:8734. doi: 10.3390/su13168734
Rhee, J. H., Kim, J. W., and Lee, J. H. (2014). Interaction effects of formal and social controls on business-to-business performance. J. Bus. Res. 67, 2123–2131. doi: 10.1016/j.jbusres.2014.04.021
Rizou, M., Galanakis, I. M., Aldawoud, T. M. S., and Galanakis, C. M. (2020). Safety of foods, food supply chain and environment within the COVID-19 pandemic. Trends Food Sci. Technol. 102, 293–299. doi: 10.1016/j.tifs.2020.06.008
Sener, A., Barut, M., Oztekin, A., Avcilar, M. Y., and Yildirim, M. B. (2019). The role of information usage in a retail supply chain: a causal data mining and analytical modeling approach. J. Bus. Res. 99, 87–104. doi: 10.1016/j.jbusres.2019.01.070
Shan, H., Li, Y., and Shi, J. (2020). Influence of supply chain collaborative innovation on sustainable development of supply chain: a study on Chinese enterprises. Sustainability 12:2978. doi: 10.3390/su12072978
Shepherd, C., and Günter, H. (2010). Measuring supply chain performance: current research and future directions. Behav. Oper. Plan. Scheduling, 105–121. doi: 10.1007/978-3-642-13382-4_6
Shibin, K. T., Dubey, R., Gunasekaran, A., Hazen, B., Roubaud, D., Gupta, S., et al. (2020). Examining sustainable supply chain management of SMEs using resource based view and institutional theory. Ann. Oper. Res. 290, 301–326. doi: 10.1007/s10479-017-2706-x
Son, B.-G., Kim, H., Hur, D., and Subramanian, N. (2021). The dark side of supply chain digitalisation: supplier-perceived digital capability asymmetry, buyer opportunism and governance. Int. J. Oper. Prod. Manag. 41, 1220–1247. doi: 10.1108/IJOPM-10-2020-0711
Srinivasan, R., and Swink, M. (2018). An investigation of visibility and flexibility as complements to supply chain analytics: an organizational information processing theory perspective. Prod. Oper. Manag. 27, 1849–1867. doi: 10.1111/poms.12746
Sundram, V. P. K., Chhetri, P., and Bahrin, A. S. (2020). The consequences of information technology, information sharing and supply chain integration, towards supply chain performance and firm performance. J. Int. Logist. Trade 18, 15–31. doi: 10.24006/jilt.2020.18.1.015
Tajvidi, M., Richard, M. O., Wang, Y., and Hajli, N. (2020). Brand co-creation through social commerce information sharing: the role of social media. J. Bus. Res. 121, 476–486. doi: 10.1016/j.jbusres.2018.06.008
Teller, C., Kotzab, H., Grant, D. B., and Holweg, C. (2016). The importance of key supplier relationship management in supply chains. Int. J. Retail Distrib. Manag. 44, 109–123. doi: 10.1108/IJRDM-05-2015-0072
Tran, P. N. T., Gorton, M., and Lemke, F. (2021). When supplier development initiatives fail: identifying the causes of opportunism and unexpected outcomes. J. Bus. Res. 127, 277–289. doi: 10.1016/j.jbusres.2021.01.009
Wang, Z., Huo, B., Tian, Y., and Hua, Z. (2015). Effects of external uncertainties and power on opportunism in supply chains: evidence from China. Int. J. Prod. Res. 53, 6294–6307. doi: 10.1080/00207543.2015.1053578
Wang, Z., Ye, F., and Tan, K. H. (2014). Effects of managerial ties and trust on supply chain information sharing and supplier opportunism. Int. J. Prod. Res. 52, 7046–7061. doi: 10.1080/00207543.2014.932931
Wen, X., Hussain, R. Y., Salameh, A. A., Hussain, H., Khan, A. B., and Fareed, M. (2022). Does firm growth impede or expedite insolvency risk? A mediated moderation model of leverage maturity and potential fixed collaterals. Front. Environ. Sci. 10:841380. doi: 10.3389/fenvs.2022.841380
Williamson, O. E. (1998). Transaction cost economics: how it works; where it is headed. De Economist 146, 23–58. doi: 10.1023/A:1003263908567
Wong, C. W. Y., Lirn, T. C., Yang, C. C., and Shang, K. C. (2020). Supply chain and external conditions under which supply chain resilience pays: an organizational information processing theorization. Int. J. Prod. Econ. 226:107610. doi: 10.1016/j.ijpe.2019.107610
Wong, C. Y., Wong, C. W. Y., and Boon-itt, S. (2020). Effects of green supply chain integration and green innovation on environmental and cost performance. Int. J. Prod. Econ. 58, 4589–4609. doi: 10.1080/00207543.2020.1756510
Wu, L., Chuang, C. H., and Hsu, C. H. (2014). Information sharing and collaborative behaviors in enabling supply chain performance: a social exchange perspective. Int. J. Prod. Econ. 148, 122–132. doi: 10.1016/j.ijpe.2013.09.016
Wu, R., Huo, B., Yu, Y., and Zhang, Z. (2022). Quality and green management for operational and environmental performance: relational capital in supply chain management. Int J Log Res Appl 25, 471–492. doi: 10.1080/13675567.2020.1836138
Xu, S., Zhao, X., and Liu, Z. (2020). The impact of blockchain technology on the cost of food traceability supply chain. IOP Conf. Ser. Earth Environ. Sci. 615:012003. doi: 10.1088/1755-1315/615/1/012003
Yadav, S., Luthra, S., and Garg, S. (2021). Modelling internet of things (IoT)-driven global sustainability in multi-tier Agri-food supply chain under natural epidemic outbreaks. Environ. Sci. Pollut. Res. 28, 16633–16654. doi: 10.1007/s11356-020-11676-1
You, J., Chen, Y., Wang, W., and Shi, C. (2018). Uncertainty, opportunistic behavior, and governance in construction projects: the efficacy of contracts. Int. J. Proj. Manag. 36, 795–807. doi: 10.1016/j.ijproman.2018.03.002
Zhang, J., and Chen, J. (2013). Coordination of information sharing in a supply chain. Int. J. Prod. Econ. 143, 178–187. doi: 10.1016/j.ijpe.2013.01.005
Zhang, M., Tse, Y. K., Dai, J., and Chan, H. K. (2019). Examining green supply chain management and financial performance: roles of social control and environmental dynamism. IEEE Trans. Eng. Manag. 66, 20–34. doi: 10.1109/TEM.2017.2752006
Zhou, H., and Benton, W. C. Jr. (2007). Supply chain practice and information sharing. J. Oper. Manag. 25, 1348–1365. doi: 10.1016/j.jom.2007.01.009
Appendix A. Measurement scales
Social control adapted from Claro et al. (2003) and Cai et al. (2009).
Joint planning
JP1 Our company plans food demands for the next seasons together with supply chain partners.
JP2 Our company plans the food types demands for the next seasons together with supply chain partners.
JP3 Our company tests market acceptance of new foods for the next seasons together with supply chain partners.
JP4 Our company shares long-term plans of food development for the next seasons together with supply chain partners.
Joint problem solving
JPS1 Our company and supply chain partners deal with problems that arise in the course of the relationship together.
JPS2 Our company and supply chain partners do not mind owing each other favors.
JPS3 Our company and supply chain partners share the responsibility for ensuring food quality and supply.
Trust
TR1 We expect supply chain partners to work with us for a long time.
TR2 Based on experience, we have complete confidence in relying on the supply chain partners to keep promises to us.
TR3 The selected supply chain partners are trustworthy.
Information Sharing adapted from Flynn et al. (2010) and Prajogo and Olhager (2012).
Information sharing with customer
CIS1 Our company shares marketing information with our primary customers.
CIS2 Our company keeps good communication with our primary customers.
CIS3 Our company keeps regular contact with major customers.
Information sharing with supplier
SIS1 Our major supplier participates in the food design and processing process.
SIS2 Our major supplier shares their production schedule with our company.
SIS3 We share our demand forecasts with our primary suppliers.
SIS4 We share our inventory levels with our primary suppliers.
Supply Chain performance adapted from Huo et al. (2017) and Prajogo and Olhager (2012).
SCP1 Our company can realize lower production costs than the competitors.
SCP2 Our company can achieve better food quality and stability than the competitors.
SCP3 Our company has an outstanding on-time delivery record with our customers.
SCP4 Our lead time for fulling customers’ personalized food needs is short.
SCP5 Our company can achieve a better food supply than the competitors*. (self-developed).
Opportunistic behavior adapted from Luo et al. (2015) and Huo et al. (2016a).
OB1 Our partners sometimes violate contractual terms and conditions.
OB2 Our partners increase their own gain by evading contractual obligations.
OB3 Our partners take advantage of contract leaks to expand their interests.
OB4 Our partners sometimes take advantage of contingencies to stop us from pursuing their responsibilities.
Keywords: social control, information sharing, opportunistic behavior, supply chain performance, joint planning, joint problem solving
Citation: Lyu T, Guo Y and Geng Q (2023) Research on paths of opportunistic behavior avoidance and performance improvement in food supply chain from the perspective of social control. Front. Psychol. 13:1101543. doi: 10.3389/fpsyg.2022.1101543
Edited by:
Mirela Panait, Petroleum and Gas University of Ploieşti, RomaniaReviewed by:
Stefano Abbate, University of Naples Federico II, ItalyRana Yassir Hussain, University of Education Lahore, Pakistan
Copyright © 2023 Lyu, Guo and Geng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Tu Lyu, ✉ cGlrbzEyMTBAMTI2LmNvbQ==