Dayu Cao
Qiang Xie1
Yan Zheng- 1School of Economics and Management, Jiangxi Agricultural University, Nanchang, Jiangxi, China
- 2Jiangxi Rural Revitalization Strategy Research Institute, Jiangxi Agricultural University, Nanchang, Jiangxi, China
- 3School of Vocational Normal, Jiangxi Agricultural University, Nanchang, Jiangxi, China
Introduction: In tandem with economic growth and enhanced individual income levels, the demand for superior food quality has seen a significant uptick, leading to increased consumer interest in organic food products. However, studies focused on organic food consumption reveal a strikingly low conversion rate of this interest into actual purchasing behavior, particularly in the context of China. It is, therefore, crucial to implement effective strategies to bridge this gap, thereby fostering the growth of China's organic food sector.
Methods: This research introduces the theory of perceived values and innovation resistance into the stimulus-organism-response (SOR) theoretical model, exploring the interrelationships among various facets of food safety concerns, perceived values, perceived risks, and organic purchasing behavior. Furthermore, the moderating influence of trust in these relationships is taken into account. Employing structural equation modeling, data from 352 organic food consumers in China's premier cities were analyzed.
Results and discussion: Findings substantiated the significant interplay between perceived values and risks with food safety concerns. It was also observed that perceived values had a positive and significant impact on purchasing behavior, while perceived risks exerted a negative and significant influence. Importantly, the relationship between nutritional value and risk barrier with purchase behavior was found to be moderated by the level of trust. This study may help organic food producers, retailers, and policymakers bridge the consumers' intention-behavior gap.
1 Introduction
Despite the remarkable achievements of modern agriculture in eliminating agricultural poverty and bolstering the food supply, its unintended repercussions on food quality and safety cannot be overlooked. Issues stemming from overexploitation of agricultural resources, excessive pesticide and fertilizer use, heavy metal contamination, and the application of novel technologies (including hormones, ripening agents, and antibiotics) have raised significant food safety and environmental protection concerns. As a result, consumers are increasingly prioritizing high-quality, safer, and more environmentally-friendly green food (Rana and Paul, 2020; Mai et al., 2023).
Organic food, as a typical representative of green food (Rana and Paul, 2020), is attracting more and more consumers' attention and also propelling the global growth of the organic food industry (Le-Anh and Nguyen-To, 2020). The global market value of organic food in 2020 was estimated at 120.6 billion euros, marking a substantial increase from the 15.1 billion euros reported in 2000 (Willer and Lernoud, 2022). However, previous studies have highlighted a paradox: although consumers exhibit strong intentions to buy organic food, the actual conversion of these intentions into purchasing behavior is disappointingly low (Rana and Paul, 2017; Kushwah et al., 2019; Tandon et al., 2020). This gap is particularly pronounced in China, where the intention-to-purchase conversion rate is even lower (Liu et al., 2021a). As reported by Willer and Lernoud (2022), China's total organic food market value in 2020 was ~10.2 billion euros (the fourth highest globally). Despite this, the per capita consumption of organic food in China was a mere seven euros, less than half of the global per capita consumption (15.8 euros). Therefore, this study aims to investigate the discrepancy between consumer purchasing intentions and actual purchasing behavior in relation to organic food, with the goal of proposing viable solutions, thereby offering practical implications for research.
The importance of food safety in promoting organic consumption has been highlighted in various studies (Pham et al., 2018; Saraiva et al., 2021; Chu et al., 2023). Previous research has primarily explored the link between consumers' concerns about the safety of organic food production processes and their purchasing decisions (e.g., Rana and Paul, 2017; Le-Anh and Nguyen-To, 2020). In addition, some scholars have emphasized that food safety regulatory authorities must implement strict and transparent regulatory measures to ensure the compliance of organic food production and sales enterprises, thereby allowing consumers to purchase and consume organic food with confidence (Rana and Paul, 2020). Moreover, advancements in science, technology, and the food industry have led to the emergence of various technologies, such as food preservatives, anti-staling agents, and mold inhibitors. However, the addition of these substances during food storage, packaging, and transportation has resulted in numerous food safety issues throughout the supply chain. For instance, the “stinky overnight meat washed and resold” incident at RT-Mart, a major supermarket chain (www.315djjd.com) in 2021, drew significant attention to food safety concerns at different stages of the food supply chain. Despite this, previous research has paid limited attention to the impact of various dimensions of food safety concerns on purchasing decisions in the organic food supply chain. Examining the effects of different dimensions of food safety concerns on organic purchasing decisions could provide valuable insights to enrich existing research on organic consumption and address the inconsistency between consumers' intentions and behaviors regarding organic food.
In addition to food safety factors, psychological factors have also been recognized as influential in consumer decision-making (Teng and Lu, 2016; Khan et al., 2022). Scholars have employed the stimulus-organism-response (SOR) theoretical framework to elucidate the relationship between psychological factors and organic purchasing behavior. Especially, perceived values have been recognized as one of the most important psychological factors driving consumers to purchase organic food (Tandon et al., 2020). Furthermore, individuals' inner perceptions can have both positive and negative impacts on consumer decision-making (Verhagen and van Dolen, 2011). Additionally, Kushwah et al. (2019) applied the innovation resistance theory to investigate the barrier effect of psychological factors on organic purchasing behavior. Despite the extensive research on various psychological factors influencing consumers, few studies have examined the influence of both positive and negative psychological factors on organic purchasing decisions. By focusing on the impact of both positive and negative psychological factors on consumers' decisions to buy organic food, valuable insights can be gained to address the inconsistencies between consumers' intentions and behaviors regarding organic food purchases.
Furthermore, scholars have identified consumers' lack of trust in organic food as a key factor contributing to the gap between their intention to buy organic products and their actual purchasing behavior (Gracia and De-Magistris, 2016; Nguyen and Dang, 2022). Vega-Zamora et al. (2019) posited that consumers' trust in the authenticity of organic labels, as well as the standardization and rigor of organic food certification procedures, play a crucial role in their decision-making when choosing organic food. Additionally, Sultan et al. (2020) revealed that trust could moderate the relationship between behavioral motivation and organic procurement behavior. So, can trust increase consistency between buyers' perceived values and procurement behavior or improve inconsistency between perceived risks and procurement behavior? Clarifying these issues is extremely helpful in addressing the discrepancy between buyer motivation and the behavior of organic purchasing.
The current investigation aims to answer the following questions: (i) Can different dimensions of food safety concerns ameliorate the inconsistency between organic purchase intention and consumer purchase behavior? (ii) How do different dimensions of perceived values and perceived risks influence consumers' organic purchase behavior? (iii) Can trust improve consistency between consumers' perceived values and purchase behavior? and (iv) Can trust improve inconsistency between consumers' perceived risks and purchasing behavior?
This study makes several key contributions. While previous research has discussed the relationship between consumers' concerns about the safety of organic food production processes and their purchasing decisions, limited attention has been given to the impact of different dimensions of food safety concerns along the organic food supply chain on organic purchasing behavior. Thus, this paper investigates the effects of consumers' food safety concerns in three distinct dimensions of the organic food supply chain (i.e., concerns toward organic producers, retailers, and public departments) on their organic purchasing behavior. This not only enriches the existing research on organic consumption but also provides new insights into addressing the inconsistency between consumers' intention to purchase organic food and their actual purchasing behavior. Moreover, previous studies have rarely focused on the influence of both positive and negative psychological factors on organic purchasing decisions. To contribute to the existing research, this paper introduces the theory of perceived values and innovation resistance into the SOR theoretical framework to comprehensively examine the impact of consumers' internally perceived positive psychological factors (perceived values) and negative psychological factors (perceived risks) on organic purchasing behavior. This may not only provide a new perspective for existing organic consumption research, but also offer important insights for finding solutions to the discrepancy between consumers' intention to buy organic food and their actual purchasing behavior. Additionally, the findings and insights from this study can provide valuable recommendations for organic producers, retailers, and policymakers.
2 Theoretical background
2.1 Perceived values
Values refer to the beliefs and concepts that govern specific ideal states, which in turn influences behavior (Schwartz and Bilsky, 1987). Besides influencing individuals' attitudes and behaviors, values also aid in differentiating between objects, scenarios, and events (Long and Schiffman, 2000). Values have been recognized as a significant predictor of consumer decision-making (Sheth et al., 1991), and perceived values theory has been extensively utilized for this purpose. Perceived values encompass the comprehensive evaluation made by consumers regarding the worth of products (Zeithaml, 1988), involving a balance between what they receive and what they give in exchange.
Sheth et al. (1991) introduced the theoretical framework of perceived values and suggested that dividing perceived values into dimensions could enhance the prediction of consumer decision-making. Khan and Mohsin (2017) classified perceived values into various dimensions, such as environmental value, functional value, and emotional value, to predict consumers' organic purchasing behavior. Their findings indicated that environmental value and functional value had significant positive impacts on consumers' organic purchase behavior. Additionally, nutritional value was identified as a vital factor influencing organic purchasing behavior (Tandon et al., 2020). Building upon the aforementioned studies and considering the focus of this research, our study aims to investigate organic consumption behavior by examining two dimensions of perceived values: environmental value and nutritional value.
2.2 Innovation resistance theory
The theory of innovation resistance identifies two types of barriers—functional barriers and psychological barriers—that reflect consumer resistance (Kaur et al., 2020; Talwar et al., 2020). Functional barriers arise from changes in consumption patterns that significantly impact consumers' perceptions, encompassing usage, risk, and value barriers (Ram and Sheth, 1989). On the other hand, psychological barriers stem from conflicts between consumers' pre-existing beliefs and specific products, including tradition and image barriers (Ram and Sheth, 1989). The theory of innovation resistance has been widely applied across various research domains, such as social media (Lian and Yen, 2013; Chen and Kuo, 2017), online purchasing (Molesworth and Suortti, 2002), smart products and services (Chaouali and Souiden, 2019; Juric and Lindenmeier, 2019), and organic consumption (Kushwah et al., 2019; Tandon et al., 2020).
The choice of the innovation resistance theory for this study is based on the observation that while there is increasing acceptance of the benefits of organic food, such as environmental protection, nutrition, and safety (De-Magistris and Gracia, 2016; Nguyen and Dang, 2022), some consumers still harbor doubts regarding these benefits (Kushwah et al., 2019). This skepticism may arise from the obstacles faced by consumers during the process of purchasing organic food. As previous studies suggested, consumers may be suspicious or distrustful of organic food available in the market (image barrier), leading to perceived risks associated with buying and using organic food (Misra and Singh, 2016; Kushwah et al., 2019). Furthermore, convenience issues and difficulties in finding organic food and relevant information have been highlighted as concerns (Smith and Paladino, 2010), compounded by limited availability in organic food specialty shops and supermarkets (Pham et al., 2018). Additionally, consumers perceive the high price of organic food and express uncertainty regarding labeling and certification procedures, contributing to risk barriers in the purchase process (Tandon et al., 2020). Consequently, this study will investigate organic consumption behavior by examining three factors from the innovation resistance theory: image, usage, and risk barriers.
2.3 The stimuli-organism-response theoretical model
The SOR theoretical model, rooted in environmental psychology, posits that various aspects of the environment play a stimulating role (S), influencing individuals' internal state (O), and subsequently prompting behavioral responses (R) (Mehrabian and Russell, 1974). According to this model, the psychological changes in organisms are influenced by external environmental factors and settings, which, in turn, elicit behavioral responses. Furthermore, the model explains how external stimuli can affect individuals' internal states (Eroglu et al., 2001). Previous research suggested that the impacts of individuals' internal state can be both detrimental and favorable (Verhagen and van Dolen, 2011). Ultimately, individuals make choices based on their internal state and then respond behaviorally (Mehrabian and Russell, 1974).
The SOR theoretical model is relevant to this study for two reasons. Firstly, it has been widely employed in previous research on consumer behavior (Konuk, 2019; Tandon et al., 2020; Liu et al., 2021b). For example, Konuk (2019) used the SOR model to investigate consumer behavior related to social media, specifically word-of-mouth, and revisiting. Tandon et al. (2020) explored whether environmental stimuli can promote consumers' organic purchasing behavior using the SOR model as a basis. Secondly, considering the significant influence of environmental factors on consumer behavior, the SOR model offers a concise and structured approach to evaluate how environmental stimuli impact the psychological parameters (e.g., emotion, cognition, perception) of consumers. It further examines the effects of consumers' psychological parameters on their organic purchasing behavior. Thus, in this study, we apply the proposed SOR model to examine organic consumption behavior.
2.3.1 Stimuli (S)
Stimulus refers to various environmental factors that individuals encounter (Jacoby, 2002). In recent times, China has experienced several food safety incidents, such as the “earth pit” pickled cabbage incident involving suppliers of Master Kang and Uni-President in 2022, as well as the “lean” events of Shuanghui (Hsu and Chen, 2014). These food safety issues, originating from different nodes of the food supply chain, can influence consumers' internal perception. Previous studies have also demonstrated the significant impact of food safety concerns on consumer perception (Pham et al., 2018; Liu et al., 2022). Therefore, in this study, we examine organic consumption by focusing on the entire supply chain of organic food and constructing three dimensions of food safety concerns: safety concerns toward organic producers, safety concerns toward organic retailers, and safety concerns toward public departments, as the “stimulus.”
2.3.2 Organism (O)
Organism refers to the internal perception of each individual (Eroglu et al., 2001), which encompasses both detrimental and favorable factors (Verhagen and van Dolen, 2011). Previous studies have highlighted the importance of perceived values and risks as key components of consumers' internal perception. These factors not only drive consumers to select or avoid specific products but also serve as primary predictors of consumer purchasing behavior (Tandon et al., 2020). Therefore, in this study, we consider the positive and negative aspects of consumers' internal perception, specifically perceived values and perceived risks, as the “organism” in order to investigate organic consumption.
2.3.3 Response (R)
Response refers to the ultimate outcome and decision made by consumers based on their internal perception, which may involve either approach or avoidance behavior (Sherman et al., 1997). Verhagen and van Dolen (2011) emphasized that consumers' internal perception has both positive and negative effects on purchasing behavior. Therefore, this study examines the influence of positive factors (perceived values) and negative factors (perceived risks) of consumers' internal perception on their organic purchasing behavior.
3 Hypothesis development
3.1 Food safety concerns, perceived risks, and perceived values (S-O)
Food safety concerns refer to consumers' apprehensions regarding pesticide residues, chemical fertilizers, veterinary drug residues, heavy metals, pollutants, and the use of agricultural biotechnology in food production practices (Teng and Lu, 2016). Previous studies on organic food consumption have indicated that food safety concerns are linked to consumers' perceived values (Pino et al., 2012; Kareklas et al., 2014; Teng and Lu, 2016; Liu et al., 2022). For example, Pino et al. (2012) noted that the production process of organic food often leads people to believe that it has higher nutritional and environmental protection value compared to traditional food, as organic food production avoids the use of harmful pesticides or chemicals (Kareklas et al., 2014). Moreover, consumers' concerns about food safety have increased due to frequent food safety issues (Çabuk et al., 2014), leading individuals to seek safer food options to avoid consuming substances that are detrimental to human health (Hsu and Chen, 2014). Consequently, consumers who prioritize food safety may perceive lower risks associated with organic food. Liu and Zheng (2019) found that consumers' safety concerns toward organic producers can enhance their understanding of organic food. Based on these findings, the following hypothesis can be proposed:
H1a−1b: Safety concerns toward organic producers (SCOP) positively influence environmental value (EV) and nutritional value (NV).
H1c−1e: Safety concerns toward organic producers (SCOP) negatively affect image barrier (IB) as well as usage barrier (UB) and risk barrier (RB).
Furthermore, Pham et al. (2018) highlighted that food safety concerns also encompass worries about the addition of preservatives and enzymes during storage, packaging, transportation, and other stages of the food supply chain. Additionally, scholars have pointed out that consumers are also concerned about the regulatory measures implemented by authorities governing food production and sales enterprises (Kushwah et al., 2019; Li et al., 2021). Therefore, this study not only examines the effects of safety concerns toward organic producers on perceived values and perceived risks but also considers the impacts of the other two dimensions of food safety concerns, namely safety concerns toward organic retailers and safety concerns toward public departments, on perceived values and perceived risks. Based on this, the following hypothesis can be proposed:
H2a−2b: Safety concerns toward organic retailers (SCOR) positively influence environmental value (EV) and nutritional value (NV).
H2c−2e: Safety concerns toward organic retailers (SCOR) negatively affect image barrier (IB) as well as usage barrier (UB) and risk barrier (RB).
H3a−3b: Safety concerns toward public departments (SCPD) positively influence environmental value (EV) and nutritional value (NV).
H3c−3e: Safety concerns toward public departments (SCPD) negatively affect image barrier (IB) as well as usage barrier (UB) and risk barrier (RB).
3.2 Perceived values, perceived risks and purchase behavior (O-R)
Perceived values represent the comprehensive evaluation made by purchasers regarding the worth of related products (Zeithaml, 1988) and are considered crucial predictors of consumer purchasing behavior (Sheth et al., 1991). Furthermore, Sheth et al. (1991) highlighted that perceived values intrinsically explain why consumers choose specific products. In previous studies, perceived values have been categorized into various dimensions based on the research focus. For instance, Khan and Mohsin (2017) classified perceived values into six dimensions, including functional, environmental, social, emotional, conditional, and epistemic values. In alignment with the theme of our current research, we categorize perceived values into two dimensions: nutritional and environmental values. Based on this, we propose the following hypothesis:
H4: EV has a positive influence on purchase behavior (PB).
H5: NV has a positive influence on PB.
Previous research has indicated that consumers harbor doubts about the benefits of organic food, leading them to perceive certain risks when making organic food purchases (Bryła, 2016; Kushwah et al., 2019). To mitigate these perceived risks and encourage organic food purchases, scholars have employed the innovation resistance theory to explore the internal factors that discourage consumers from buying organic food (Kushwah et al., 2019; Tandon et al., 2020). For example, Kushwah et al. (2019) examined the impact of risk barriers and image barriers on organic purchase intentions using the innovation resistance theory and found that these barriers significantly and negatively affect organic purchase intentions. Additionally, the usage barrier has been identified as a crucial factor that impedes consumers from buying organic food (Bryła, 2016; Pham et al., 2018). Consequently, in this study, we adopt the innovation resistance theory to investigate three dimensions of perceived risks (i.e., image, usage, and risk barriers) in relation to organic purchasing behavior. Based on this, the following hypotheses are proposed:
H6: IB has a negative influence on PB.
H7: UB has a negative influence on PB.
H8: RB has a negative influence on PB.
3.3 The moderating effect of trust
Trust is widely recognized as a crucial factor influencing consumers' decision to purchase organic food (Vega-Zamora et al., 2019; Nguyen and Dang, 2022). Previous studies have highlighted that consumer suspicion or lack of trust in organic food hinders the growth of the organic food industry (Gracia and De-Magistris, 2016; Nuttavuthisit and Thogersen, 2017; Carfora et al., 2019). Additionally, Tung et al. (2012) suggested that trust can bridge the gap between intention and actual behavior in organic food purchases. Furthermore, Sultan et al. (2020) revealed that trust could moderate the relationship between behavioral intention and organic purchase behavior. Therefore, in this study, we examined the moderating effect of trust on the relationship between perceived values (environmental value and nutritional value), perceived risks (image barrier, usage barrier, and risk barrier), and purchase behavior. Based on this, the following hypotheses are proposed:
H9a−9e: Trust moderates the effects of environmental value as well as nutritional value, image barrier, usage barrier and risk barrier on purchase behavior.
The comprehensive model incorporating all the aforementioned hypotheses is illustrated in Figure 1.
Figure 1. The hypothesized model.
4 Methodology
4.1 Data collection
The present study conducted data collection from May to June 2022 using an online survey administered to participants residing in China's first-tier cities, including Beijing, Shanghai, Shenzhen, and Guangzhou. The data collection process was facilitated by Wenjuanxing (https://www.wjx.cn), a professional online questionnaire service company. The main reason for choosing these cities for the survey is that per capita organic food consumption in China lags behind the global average (Willer and Lernoud, 2022), with a majority of organic food consumers located in China's first-tier cities (Liu et al., 2021a). Therefore, collecting data online from these cities provided a more representative sample for the study. Prior to the formal survey, a pilot survey was conducted where 30 questionnaires were distributed online to ensure the clarity of the survey items and the appropriateness of data collection procedures. Based on the feedback received during the pilot survey, appropriate modifications were made to the questionnaire. In accordance with the relevant institutional and national guidelines and regulations in China, ethical approval was not required, and informed consent was obtained during the survey submission.
In addition, the questionnaire included a question asking participants if they had ever purchased organic food before. Twenty-eight participants responded “No” to this question and were subsequently excluded from the analysis since the focus of the present research was on organic purchasing behavior. Therefore, a total of 352 valid responses were obtained out of the 380 initial responses. As shown in Table 1, among the respondents, 32.1% were aged between 18 and 30 years, while 26.1% were aged above 40 years. The sample consisted of 60.8% female and 39.2% male respondents. Furthermore, 23.9% of the participants reported a monthly income per capita between RMB 5,001 and 8,000 yuan. Additionally, 82.4% of the respondents had received a junior college or undergraduate education.
Table 1. Demographic data of the survey respondents (N = 352).
4.2 Evaluation measures
The previously validated multiple-item scales were utilized for measuring the constructs in the proposed model. Minor modifications were made to ensure the face validity of these measures in the context of the current research. Supplementary Appendix A presents the construct items, which were assessed using a 7-point Likert scale, where 1 indicated “strongly disagree” and 7 indicated “strongly agree.”
4.3 Analytical method
To assess the proposed model, this study utilized the two-stage method of structural equation modeling (SEM) as outlined by Anderson and Gerbing (1988). Model fit evaluation and hypothesis testing were conducted using AMOS 24.0. Additionally, hierarchical regression analysis was performed using SPSS 23.0 to examine the moderating effects of trust on the relationships between perceived values, perceived risks, and purchase behavior.
5 Results
5.1 Common method variance
To assess the potential influence of common method variance on the study, we conducted Harman's single-factor test as proposed by Podsakoff et al. (2003). The results of the test revealed that a single factor could only account for 35.1% of the total variance, indicating that the majority of the variance was not attributable to a single factor. Thus, it is unlikely that common method bias poses a significant concern in the present research.
5.2 Validity of measurement model
Confirmatory factor analysis (CFA) was conducted using AMOS 24.0 to assess the measurement model of the study. Model fit was evaluated based on various criteria, including degrees of freedom (df), chi-square (χ2) value, χ2/df ratio, root mean square error of approximation (RMSEA), goodness-of-fit index (GFI), comparative fit index (CFI), and Tucker-Lewis index (TLI) following the protocols suggested by Jackson et al. (2009). However, due to non-multivariate normality, several fit statistics of the model did not meet their minimum acceptable levels. To address this issue, bias correction in the model fit statistic was performed using the Bollen-Stine bootstrap method (Bollen and Stine, 1992; Fisher and King, 2010). After a 2,000-times bootstrap correction, the resulting fit statistics (df = 327, χ2 = 396.712; χ2/df = 1.21; RMSEA = 0.02; GFI = 0.93; CFI = 0.99; TLI = 0.99) met the acceptable criteria (Hu and Bentler, 1999).
To assess the internal consistency reliability, convergent validity, and discriminant validity of the constructs in the proposed model, CFA was performed for all nine constructs (SCOP, SCOR, SCPD, EV, NV, IB, UB, RB, and PB). As shown in Table 2, the findings indicated that both Cronbach's alpha and composite reliability (CR) values exceeded 0.7, indicating acceptable internal consistency reliability (Nunnally, 1978). Moreover, the average variance extracted (AVE) values for all constructs were above the threshold of 0.5 (Fornell and Larcker, 1981). Convergent validity was supported by standardized factor loadings of all items exceeding the threshold of 0.6 (Hair et al., 2009). Additionally, the intercorrelation estimates between constructs were all below the square roots of their respective AVE, providing evidence for discriminant validity (Table 3) (Fornell and Larcker, 1981).
Table 2. The coefficients determined for the measurement model.
Table 3. Discriminant validity.
5.3 Hypothesis testing
Correlations among factors containing control variables, including age, gender, and education, were examined using SEM. Bias correction in the model fit statistic was achieved through the Bollen-Stine bootstrap method, resulting in fit indicators of χ2 = 520.692, df = 441, χ2/df = 1.18, CFI = 0.99, TLI = 0.98, GFI = 0.92, and RMSEA = 0.02. These indices indicate a well-fitting model that explains a significant 64.5% of the variation in purchase behavior.
As presented in Table 4 and Figure 2, the results of hypothesis testing support 16 hypotheses (H1a–H1e, H2a–H2b, H2d, H3b–H3c, H3e, H4, H5, H6, H7, and H8). Notably, SCOP (H1a: β = 0.234, p < 0.001; H1b: β = 0.456, p < 0.001; H1c: β = −0.346, p < 0.001; H1d: β = −0.363, p < 0.001; H1e: β = −0.252, p < 0.01) shows a significant impact on EV, NV, IB, UB, and RB, respectively, supporting H1a, H1b, H1c, H1d, and H1e. Additionally, SCOR (H2a: β = 0.093, p < 0.05; H2b: β = 0.112, p < 0.05; H2d: β = −0.232, p < 0.01) exhibits a significant effect on EV, NV, and UB, respectively, supporting H2a, H2b, and H2d. Similarly, SCPD (H3b: β = 0.168, p < 0.01; H3c: β = −0.313, p < 0.01; H3e: β = −0.294, p < 0.01) significantly influences NV, IB, and RB, respectively, supporting H3b, H3c, and H3e. Moreover, EV, NV, IB, UB, and RB all have a significant influence on PB at various significant levels (EV: 1%, NV: 0.1%, IB: 0.1%, UB: 0.1%, RB: 5%), supporting H4, H5, H6, H7, and H8. However, SCOR does not have a significant effect on IB and RB, and SCPD does not have a significant effect on EV and UB. Consequently, H2c, H2e, H3a, and H3d are not supported.
Table 4. The results of the hypothesis test.
Figure 2. Tested model. ***p < 0.001; **p < 0.01; *p < 0.05.
To investigate the moderating effects of trust, a hierarchical moderation regression analysis was conducted using SPSS 23.0. The results, as presented in Table 5.1, indicated that trust could moderate the relationship between nutritional value and purchase behavior (H9b: β = 0.071, p < 0.01), as well as between risk barrier and purchase behavior (H9e: β = 0.093, p < 0.05). Thus, H9b and H9e were supported. Regardless of whether trust levels were high (95% confidence interval = 0.181, 0.437) or low (95% confidence interval = 0.037, 0.229), trust played a significant and positive moderating role between nutritional value and purchase behavior. Conversely, its moderating role between risk barrier and purchase behavior was significant and negative when trust levels were low (95% confidence interval = −0.224, −0.059; Table 5.2).
Table 5.1. Moderation analysis for trust.
Table 5.2. The moderating impact of trust.
To further interpret the moderating effect of trust, the interactive effects were illustrated in Figures 3, 4. These figures demonstrate that trust strengthens the positive effect of nutritional value on purchase behavior and weakens the negative impact of the risk barrier on purchase behavior. Moreover, the slope between nutritional value and purchase behavior is notably positive for consumers with low trust (β = 0.133, p < 0.01) and significantly positive for consumers with high trust (β = 0.309, p < 0.001). Additionally, the slope between the risk barrier and purchase behavior is notably negative for consumers with low trust (β = −0.141, p < 0.01).
Figure 3. Moderation of trust on the relationship between NV and PB.
Figure 4. Moderation of trust on the relationship between RB and PB.
6 Discussion and implications
6.1 Discussion
Firstly, the study findings reveal the relationship between “stimulus” (food safety concerns) and “organism” (perceived values and perceived risks). The results indicate that safety concerns toward organic producers have a positive impact on environmental value and nutritional value, while negatively influencing image barrier, usage barrier, and risk barrier. These findings align with previous research (Kareklas et al., 2014). For example, Liu and Zheng (2019) found that consumers' safety concerns toward organic producers can enhance their perception of organic food. Additionally, the study shows that safety concerns toward organic retailers significantly affect environmental value, nutritional value, and usage barrier. However, they have no significant impact on image barrier and risk barrier. One possible explanation is that consumers primarily associate image barrier and risk barrier with producers and regulators in the organic food supply chain. Meanwhile, Li et al. (2021) argued that agricultural product traceability records from producers can greatly reduce purchase barriers, such as the risk barrier. Furthermore, safety concerns toward public departments are found to have a significant effect on nutritional value, image barrier, and risk barrier. This suggests that when public departments prioritize food safety supervision and implement strict regulations on organic food, consumers perceive the nutritional attributes of organic food more strongly and reduce their concerns regarding image barrier and risk barrier.
Secondly, regarding the relationship between “organism” (perceived values and perceived risks) and “response” (purchase behavior), the study results indicate that environmental value and nutritional value have a significantly positive impact on purchase behavior, while image barrier, usage barrier, and risk barrier have a significantly negative effect on purchase behavior. These findings align with prior literature, indicating that perceived values can promote consumers' organic buying behavior (Sheth et al., 1991; Khan and Mohsin, 2017), while perceived risks hinder organic purchasing behavior (Kushwah et al., 2019). This may help explain why consumers' intention to purchase organic products may not always translate into actual purchase behavior.
Finally, trust is found to positively moderate the relationship between nutritional value and purchase behavior, as well as between risk barrier and purchase behavior. However, trust does not moderate other perceived values and perceived risks. This may be because many consumers prioritize personal factors, such as nutritional attributes and health benefits, when making organic food purchases, and the perceived risk associated with the higher price of organic food acts as a significant barrier preventing consumers from making organic purchases. Therefore, trust facilitates the conversion of consumers' perceived value (nutritional value) into organic purchase behavior and reduces the impediment of perceived risk (risk barrier) on organic purchase behavior.
6.2 Theoretical implications
The present study contributes to the existing literature in several ways. Firstly, it addresses a research gap by examining the relationships between different dimensions of food safety concerns and organic consumption. To address this gap, the study takes a comprehensive approach by considering three dimensions of food safety concerns: safety concerns toward organic producers, safety concerns toward organic retailers, and safety concerns toward public departments. Investigating the correlations between these dimensions and purchase behavior enhances our understanding of organic consumption. Secondly, while previous studies have explored the associations between perceived values and organic consumption, there is limited research on the connections between perceived values, perceived risks, and organic purchase behavior. To bridge this gap, this study differentiates “organism” (O) into positive internal perception (perceived values) and negative internal perception (perceived risks) of consumers, drawing on the SOR theoretical model, perceived values theory, and innovation resistance theory. By examining the relationships among perceived values, perceived risks, and organic purchase behavior, this study sheds light on the inconsistent relationship between intention and behavior in organic consumption and offers a fresh perspective for understanding organic consumption. Thirdly, the study investigates the moderating role of trust in the relationships between perceived values, perceived risks, and organic purchase behavior. The findings reveal that trust moderates the relationships between nutritional value and risk barrier with purchase behavior. These findings have important implications for organic sellers, public departments, and even organic producers. Lastly, this study expands the emerging literature on the application of food safety concerns, perceived values, and perceived risks in the context of organic consumption, providing insights into unexplored associations. By examining the interplay among food safety concerns, perceived values, perceived risks, and purchase behavior, this research makes a specific contribution to the marketing literature.
6.3 Practical implications
The current study has important implications for practitioners in the field. Firstly, the findings highlight the significance of addressing food safety concerns to influence consumers' organic purchase behavior. Therefore, organic producers, retailers, and public departments should develop appropriate strategies to ensure that consumers feel confident and secure when buying organic food. For instance, they can collaborate to establish a comprehensive organic food traceability system, providing consumers with access to detailed information about the entire production process, including transportation, storage, and packaging. Public departments should rigorously supervise this system and effectively communicate regulatory information to consumers through authoritative media channels, thereby enhancing trust in the traceability system and meeting consumers' food safety concerns.
Secondly, perceived values play a crucial role in shaping consumers' organic purchase behavior. To capitalize on this, organic producers and retailers should devise strategies to enhance the perceived values associated with organic food consumption. For example, organic producers can leverage new media platforms, such as TikTok short videos, to showcase various aspects of organic food production. Furthermore, organic retailers can organize experiential activities linked to organic food, such as advertising during important events and integrating rural tourism, to help consumers better understand the benefits of organic food in terms of safety, environmental sustainability, and nutrition.
Lastly, perceived risks have a significant negative impact on organic purchase behavior. Therefore, organic producers, retailers, and public departments should implement strategies aimed at reducing consumers' perceived risks when purchasing organic food, thereby promoting organic consumption. For instance, organic retailers can conduct market research and strategically increase the availability of organic food sales points based on consumer demand, making it more convenient for consumers to access organic products and reducing usage barriers. Additionally, public departments can adopt a two-pronged approach to supervise third-party organic food certification agencies. This entails strengthening regulatory oversight and management from government agencies while also mobilizing public participation in monitoring these certification agencies, thereby mitigating the risk barrier associated with purchasing organic food.
6.4 Limitations and prospects
Nevertheless, this study has certain limitations. Firstly, it relied on self-reported questionnaires and cross-sectional research data. Consequently, further investigations are required to extend the results to Chinese consumers. Nonetheless, the study took appropriate measures to ensure unbiased responses and included a sufficient sample size, thereby enhancing the robustness of the findings. Additionally, when examining the correlation between perceived values and purchase behavior, the study solely considered two dimensions of perceived values, namely nutritional value and environmental value, without exploring the impact of other dimensions on organic purchase behavior. Biswas and Roy (2015) argued that knowledge plays a crucial role in driving consumers to buy organic food. Moreover, as individuals increasingly prioritize food pleasure, it may also serve as a significant factor in promoting organic food purchases (Hyldelund et al., 2021). Therefore, future research should encompass other dimensions of perceived values, such as knowledge value and pleasure value.
7 Conclusion
Based on the research findings, this study has made significant progress in exploring the interrelationships between food safety concerns, perceived values, perceived risks, and organic purchasing behavior. By introducing the theory of perceived values and innovation resistance into the SOR theoretical model, this research not only enriches existing research in the field of organic consumption, but also provides a new perspective to bridge the gap between consumers' purchase intention and actual behavior. In addition, this study also focused on the impact of positive and negative psychological factors on organic purchasing decisions. Therefore, for researchers, organic producers, retailers, and policymakers, this study has important practical significance and helps to fill the gap between consumer intention and behavior. These findings and insights provide valuable suggestions for the growth of the organic food market.
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Author contributions
DC: Conceptualization, Supervision, Writing—original draft, Writing—review & editing. QX: Data curation, Writing—review & editing. XY: Methodology, Writing—review & editing. YZ: Data curation, Methodology, Writing—original draft, Writing—review & editing.
Funding
The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by Jiangxi Province University Humanities and Social Sciences Research Project (No. JC19119) and Jiangxi Social Science 14th Five Year Plan fund project (No. 21ST03).
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.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fsufs.2023.1319309/full#supplementary-material
References
Anderson, J. C., and Gerbing, D. W. (1988). Structural equation modeling in practice: a review and recommended two-step approach. Psychol. Bull. 103, 411–423. doi: 10.1037/0033-2909.103.3.411
Biswas, A., and Roy, M. (2015). Leveraging factors for sustained green consumption behavior based on consumption value perceptions: testing the structural model. J. Clean. Prod. 95, 332–340. doi: 10.1016/j.jclepro.2015.02.042
Bollen, K. A., and Stine, R. A. (1992). Bootstrapping goodness-of-fit measures in structural equation models. Sociol. Methods Res. 21, 205–229. doi: 10.1177/0049124192021002004
Bryła, P. (2016). Organic food consumption in Poland: motives and barriers. Appetite 105, 737–746. doi: 10.1016/j.appet.2016.07.012
Çabuk, S., Tanrikulu, C., and Gelibolu, L. (2014). Understanding organic food consumption: attitude as a mediator. Int. J. Consum. Stud. 38, 337–345. doi: 10.1111/ijcs.12094
Carfora, V., Cavallo, C., Caso, D., Del Giudice, T., De Devitiis, B., Viscecchia, R., et al. (2019). Explaining consumer purchase behavior for organic milk: including trust and green self-identity within the theory of planned behavior. Food Qual. Prefer. 76, 1–9. doi: 10.1016/j.foodqual.2019.03.006
Chaouali, W., and Souiden, N. (2019). The role of cognitive age in explaining mobile banking resistance among elderly people. J. Retail. Consum. Serv. 50, 342–350. doi: 10.1016/j.jretconser.2018.07.009
Chen, P., and Kuo, S. (2017). Innovation resistance and strategic implications of enterprise social media websites in Taiwan through knowledge sharing perspective. Technol. Forecast. Soc. Change 118, 55–69. doi: 10.1016/j.techfore.2017.02.002
Chu, M., Anders, S., Deng, Q., Contador, C. A., Cisternas, F., Caine, C., et al. (2023). The future of sustainable food consumption in China. Food Energy Secur. 12, e405. doi: 10.1002/fes3.405
De-Magistris, T., and 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, 97–104. doi: 10.1016/j.jclepro.2016.01.050
Eroglu, S. A., Machleit, K. A., and Davis, L. M. (2001). Atmospheric qualities of online retailing. J. Bus. Res. 54, 177–184. doi: 10.1016/S0148-2963(99)00087-9
Fisher, M. J., and King, J. (2010). The self-directed learning readiness scale for nursing education revisited: a confirmatory factor analysis. Nurse Educ. Today 30, 44–48. doi: 10.1016/j.nedt.2009.05.020
Fornell, C., and Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: algebra and statistics. J. Mark. Res. 18, 382–388. doi: 10.1177/002224378101800313
Gracia, A., and De-Magistris, T. (2016). Consumer preferences for food labeling: what ranks first? Food Control 61, 39–46. doi: 10.1016/j.foodcont.2015.09.023
Hair, J., Black, W., Babin, B., and Anderson, R. (2009). Multivariate Data Analysis. Upper Saddle River, NJ: Prentice Hall.
Hsu, C. L., and Chen, M. C. (2014). Explaining consumer attitudes and purchase intentions toward organic food: contributions from regulatory fit and consumer characteristics. Food Qual. Prefer. 35, 6–13. doi: 10.1016/j.foodqual.2014.01.005
Hu, L., and Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct. Equ. Model. 6, 1–55. doi: 10.1080/10705519909540118
Hyldelund, N. B., Byrne, D. V., Raymond, C. K. C., and Andersen, B. V. (2021). Food pleasure across nations: a comparison of the drivers between Chinese and Danish populations. Food Qual. Prefer. 97, 104493. doi: 10.1016/j.foodqual.2021.104493
Jackson, D. L., Gillaspy, J. A., and Purc-Stephenson, R. (2009). Reporting practices in confirmatory factor analysis: an overview and some recommendations. Psychol. Methods 14, 6–23. doi: 10.1037/a0014694
Jacoby, J. (2002). Stimulus-organism-response reconsidered: an evolutionary step in modeling (consumer) behavior. J. Consum. Psychol. 12, 51–57. doi: 10.1207/S15327663JCP1201_05
Juric, J., and Lindenmeier, J. (2019). An empirical analysis of consumer resistance to smart-lighting products. Light. Res. Technol. 51, 489–512. doi: 10.1177/1477153518774080
Kareklas, I., Carlson, J. R., and Muehling, D. D. (2014). I eat organic for my benefit and yours: egoistic and altruistic considerations for purchasing organic food and their implications for advertising strategists. J. Advert. 43, 18–32. doi: 10.1080/00913367.2013.799450
Kaur, P., Dhir, A., Singh, N., Sahu, G., and Almotairi, M. (2020). An innovation resistance theory perspective on mobile payment solutions. J. Retail. Consum. Serv. 55, 102059. doi: 10.1016/j.jretconser.2020.102059
Khan, S. N., and Mohsin, M. (2017). The power of emotional value: exploring the effects of values on green product consumer choice behavior. J. Clean. Prod. 150, 65–74. doi: 10.1016/j.jclepro.2017.02.187
Khan, Y., Hameed, I., and Akram, U. (2022). What drives attitude, purchase intention and consumer buying behavior toward organic food? A self-determination theory and theory of planned behavior perspective. Br. Food J. 125. doi: 10.1108/BFJ-07-2022-0564
Konuk, F. A. (2019). The influence of perceived food quality, price fairness, perceived value and satisfaction on customers' revisit and word-of-mouth intentions towards organic food restaurants. J. Retail. Consum. Serv. 50, 103–110. doi: 10.1016/j.jretconser.2019.05.005
Kushwah, S., Dhir, A., and Sagar, M. (2019). Understanding consumer resistance to the consumption of organic food. A study of ethical consumption, purchasing, and choice behaviour. Food Qual. Prefer. 77, 1–14. doi: 10.1016/j.foodqual.2019.04.003
Laukkanen, T. (2016). Consumer adoption versus rejection decisions in seemingly similar service innovations: the case of the Internet and mobile banking. J. Bus. Res. 69, 2432–2439. doi: 10.1016/j.jbusres.2016.01.013
Le-Anh, T., and Nguyen-To, T. (2020). Consumer purchasing behaviour of organic food in an emerging market. Int. J. Consum. Stud. 44, 563–573. doi: 10.1111/ijcs.12588
Li, L., Paudel, K. P., and Guo, J. (2021). Understanding chinese farmers' participation behavior regarding vegetable traceability systems. Food Control 130, 108325. doi: 10.1016/j.foodcont.2021.108325
Lian, J., and Yen, D. C. (2013). To buy or not to buy experience goods online: Perspective of innovation adoption barriers. Comput. Human Behav. 29, 665–672. doi: 10.1016/j.chb.2012.10.009
Liu, C., Yao, X., Zheng, Y., Zhu, Y., and Cao, D. (2022). Organic foods purchase intention, food safety information, and information on organic foods: value orientations as a mediator. Soc. Behav. Pers. 50, 1–13. doi: 10.2224/sbp.11404
Liu, C., and Zheng, Y. (2019). The predictors of consumer behavior in relation to organic food in the context of food safety incidents: advancing hyper attention theory within an stimulus-organism-response model. Front. Psychol. 10, 2512. doi: 10.3389/fpsyg.2019.02512
Liu, C., Zheng, Y., and Cao, D. (2021a). An analysis of factors affecting selection of organic food: perception of consumers in China regarding weak signals. Appetite 161, 105145. doi: 10.1016/j.appet.2021.105145
Liu, C., Zheng, Y., and Cao, D. (2021b). Similarity effect and purchase behavior of organic food under the mediating role of perceived values in the context of COVID-19. Front. Psychol. 12, 628342. doi: 10.3389/fpsyg.2021.628342
Long, M. M., and Schiffman, L. G. (2000). Consumption values and relationships: segmenting the market for frequency programs. J. Consum. Mark. 17, 214–232. doi: 10.1108/07363760010329201
Mai, N. T., Phuong, T. T. L., Dat, T. T., and Truong, D. D. (2023). Determinants of organic food purchasing intention: an empirical study of local consumers in Da Nang city, Central Vietnam. Front. Sustain. Food Syst. 7, 1180612. doi: 10.3389/fsufs.2023.1180612
Mehrabian, A., and Russell, J. A. (1974). An Approach to Environmental Psychology. Cambridge, MA: MIT.
Misra, R., and Singh, D. (2016). An analysis of factors affecting growth of organic food. Br. Food J. 118, 2308–2325. doi: 10.1108/BFJ-02-2016-0080
Molesworth, M., and Suortti, J. (2002). Buying cars online: the adoption of the web for high-involvement, high-cost purchases. J. Consum. Behav. 2, 155–168. doi: 10.1002/cb.97
Nguyen, N. P. T., and Dang, H. D. (2022). Organic food purchase decisions from a context-based behavioral reasoning approach. Appetite 173, 105975. doi: 10.1016/j.appet.2022.105975
Nuttavuthisit, K., and Thogersen, 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, 1–15. doi: 10.1007/s10551-015-2690-5
Pham, T. H., Nguyen, T. N., Phan, T. T. H., and Nguyen, N. T. (2018). Evaluating the purchase behaviour of organic food by young consumers in an emerging market economy. J. Strateg. Mark. 27, 540–556. doi: 10.1080/0965254X.2018.1447984
Pino, G., Peluso, A. M., and Guido, G. (2012). Determinants of regular and occasional consumers' intentions to buy organic food. J. Consum. Affairs 46, 157–169. doi: 10.1111/j.1745-6606.2012.01223.x
Podsakoff, P. M., MacKenzie, S. B., Lee, J., and Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. J. Appl. Psychol. 88, 879–903. doi: 10.1037/0021-9010.88.5.879
Ram, S., and Sheth, J. N. (1989). Consumer resistance to innovations: the marketing problem and its solutions. J. Consum. Mark. 6, 5–14. doi: 10.1108/EUM0000000002542
Rana, J., and Paul, J. (2017). Consumer behavior and purchase intention for organic food: a review and research agenda. J. Retail. Consum. Serv. 38, 157–165. doi: 10.1016/j.jretconser.2017.06.004
Rahnama, H. (2017). Effect of consumption values on women's choice behavior toward organic foods: the case of organic yogurt in Iran. J. Food Prod. Mark. 23, 144–166. doi: 10.1080/10454446.2017.1244790
Rana, J., and Paul, J. (2020). Health motive and the purchase of organic food: a meta-analytic review. Int. J. Consum. Stud. 44, 162–171. doi: 10.1111/ijcs.12556
Saraiva, A., Fernandes, E., and von Schwedler, M. (2021). The pro-environmental consumer discourse: a political perspective on organic food consumption. Int. J. Consum. Stud. 45, 188–204. doi: 10.1111/ijcs.12611
Schwartz, S. H., and Bilsky, W. (1987). Toward a universal psychological structure of human values. J. Pers. Soc. Psychol. 53, 550–562. doi: 10.1037/0022-3514.53.3.550
Sherman, E., Mathur, A., and Smith, R. B. (1997). Store environment and consumer purchase behavior: mediating role of consumer emotions. Psychol. Mark. 14, 361–378. doi: 10.1002/(SICI)1520-6793(199707)14:4<361::AID-MAR4>3.0.CO;2-7
Sheth, J. N., Newman, B. I., and Gross, B. L. (1991). Why we buy what we buy: a theory of consumption values. J. Bus. Res. 22, 159–170. doi: 10.1016/0148-2963(91)90050-8
Singh, A., and Verma, P. (2017). Factors influencing Indian consumers' actual buying behaviour towards organic food products. J. Clean. Prod. 167, 473–483. doi: 10.1016/j.jclepro.2017.08.106
Smith, S., and Paladino, A. (2010). Eating clean and green? Investigating consumer motivations towards the purchase of organic food. Australas. Mark. J. 18, 93–104. doi: 10.1016/j.ausmj.2010.01.001
Sultan, P., Tarafder, T., Pearson, D., and Henryks, J. (2020). Intention-behaviour gap and perceived behavioural control-behaviour gap in theory of planned behaviour: moderating roles of communication, satisfaction and trust in organic food consumption. Food Qual. Prefer. 81, 103838. doi: 10.1016/j.foodqual.2019.103838
Talwar, S., Talwar, M., Kaur, P., and Dhir, A. (2020). Consumers' resistance to digital innovations: a systematic review and framework development. Australas. Mark. J. 28, 286–299. doi: 10.1016/j.ausmj.2020.06.014
Tandon, A., Jabeen, F., Talwar, S., Sakashita, M., and Dhir, A. (2020). Facilitators and inhibitors of organic food buying behavior. Food Qual. Prefer. 88, 104077. doi: 10.1016/j.foodqual.2020.104077
Teng, C., and Lu, C. (2016). Organic food consumption in Taiwan: motives, involvement, and purchase intention under the moderating role of uncertainty. Appetite 105, 95–105. doi: 10.1016/j.appet.2016.05.006
Tung, S. J., Shih, C. C., Wei, S., and Chen, Y. H. (2012). Attitudinal inconsistency toward organic food in relation to purchasing intention and behavior. Br. Food J. 114, 997–1015. doi: 10.1108/00070701211241581
Vega-Zamora, M., Torres-Ruiz, F. J., and Parras-Rosa, M. (2019). Towards sustainable consumption: keys to communication for improving trust in organic foods. J. Clean. Prod. 216, 511–519. doi: 10.1016/j.jclepro.2018.12.129
Verhagen, T., and van Dolen, W. (2011). The influence of online store beliefs on consumer online impulse buying: a model and empirical application. Inf. Manag. 48, 320–327. doi: 10.1016/j.im.2011.08.001
Willer, H., and Lernoud, J. (2022). The World of Organic Agriculture Statistics and Emerging Trends 2022. Available online at: http://www.organic-world.net/yearbook/yearbook-2022.html (accessed March 12, 2023).
Keywords: food safety concerns, perceived values, perceived risks, organic food, SOR theoretical model
Citation: Cao D, Xie Q, Yao X and Zheng Y (2023) Organic food consumption in China: food safety concerns, perceptions, and purchase behavior under the moderating role of trust. Front. Sustain. Food Syst. 7:1319309. doi: 10.3389/fsufs.2023.1319309
Received: 11 October 2023; Accepted: 20 November 2023;
Published: 11 December 2023.
Edited by:
Fatima Zahra Jawhari, Sidi Mohamed Ben Abdellah University, MoroccoReviewed by:
Ian Jenson, University of Tasmania, AustraliaSaloua Biyada, Sidi Mohamed Ben Abdellah University, Morocco
Hajar Belhassan, Sidi Mohamed Ben Abdellah University, Morocco
Copyright © 2023 Cao, Xie, Yao and Zheng. 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: Xiaoying Yao, MTEyNTc0NzE5JiN4MDAwNDA7cXEuY29t; Yan Zheng, ODUyNTUzNTAzJiN4MDAwNDA7cXEuY29t