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Article

Reexamining the Determinants of Organic Food Purchases in Online Contexts: The Dual-Factor Model Perspective

by
Ching-Hsuan Yeh
and
Min-Hsien Yang
*
Department of International Business, Feng Chia University, No.100 Wenhwa Road, Seatwen, Taichung 407802, Taiwan
*
Author to whom correspondence should be addressed.
Agriculture 2025, 15(8), 883; https://doi.org/10.3390/agriculture15080883
Submission received: 28 February 2025 / Revised: 26 March 2025 / Accepted: 16 April 2025 / Published: 18 April 2025
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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Abstract

:
The ever-expanding market has made organic food a popular research topic, with the primary question being what factors facilitate or hinder consumers in making an organic purchase. The most relevant studies have been conducted in an offline context. As selling organic food online has become a common practice and is underresearched, this study aims to (1) explore the drivers and barriers of online organic food shopping and (2) investigate the shopping behavior of organic food from an omnichannel perspective. The results of partial least square structural equation modeling (PLS-SEM), with 278 valid samples, indicate that trust in organic labels and positive review sentiment significantly contribute to the intention to purchase organic food online, which in turn influences online purchase behaviors. For online shopping behavior, the investigation shows that Taiwanese consumers, on a monthly basis, make an average of 3.22 organic food purchases and spend US$156.44 through offline channels, whereas they make 2.34 purchases of organic food and spend US$114.71 via online channels. Organic vegetables and fruits are the most frequently purchased organic foods. Among online channels, consumers prefer visiting the websites of general grocery stores and specialty stores over social media platforms. Our findings suggest that the determinants of organic food shopping differ between offline and online contexts and reveal interesting behavioral patterns of online organic shopping.

1. Introduction

Organic food has been growing in popularity globally in recent decades. Its production and process requirements enable organic food to present unique product value, making it attractive to people who are concerned with human health and environmental sustainability and to ordinary people as well. While enjoying increasing sales growth, the ratio of organic food sales to total food sales remains limited. In the US, the global market leader, organic food made up 6% of total food sales in 2022, with its sales share increasing only 3% from 2008 to 2022 [1]. The low sales share of organic food and slow increase in sales share suggest that there is a great opportunity for market expansion. It is, thus, imperative to have a clear understanding of consumers’ perceptions of and behaviors toward organic food.
Numerous studies have explored the drivers of and barriers to organic food consumption [2,3,4,5]. Environmental concerns, health attitudes, familiarity and product knowledge, organic labeling and certification, and product quality are widely recognized as determining consumer intention to buy organic food. These findings are typically derived from offline shopping contexts. Furthermore, consumer- and product-related factors have received greater attention from scholars than venue/channel-related factors. As e-commerce infrastructure and business matures, selling organic food online is a common practice. Interestingly, only a few studies have examined consumer motivation to purchase organic food in online contexts [6,7,8]. Since previous studies pay less attention to marketing channels in general and online channels in particular, more investigation of online organic food purchases is required.
The main objective of this study is to explore the effects of determinants on consumers’ organic food shopping online. Based on the dual-factor model, trust theory and risk theory are used to identify drivers and barriers. As organic food is considered a credence product whose quality consumers have difficulty evaluating [9], trust and risk perceptions will play salient roles in consumers’ decision-making processes. The combination of these two theories could help us understand how consumers evaluate and behave in the face of this uncertainty.
Our findings could expand scholarly knowledge of organic food shopping. First, several studies of online organic food shopping focus on the factors resulting in consumers’ intention to buy organic food over the Internet [6,7,8]. This study specifies positive and negative determinants jointly in the model from a dual-factor perspective. The inclusion of drivers and barriers more truly resembles a genuine shopping scenario, improving external validity. Next, we inquire into consumers’ behavioral patterns in organic food shopping, such as shopping frequency and shopping expenditure, across online and offline channels, in addition to hypothesis validation. This information is rarely examined and may help us understand organic food consumption from an omnichannel perspective.
Third, this study investigates both purchase intention and regular purchase behavior. A body of studies has observed that a significant gap exists in the intention–behavior relationship [10,11], a discrepancy also found in the context of organic food [12]. By linking consumers’ purchase intention to regular shopping, this study provides more evidence of the intention–behavior arguments about organic food purchasing in online contexts. Finally, many interesting findings on organic food are generated with Western samples [13]. The Taiwanese samples utilized in this study could provide fruitful market insights from an Eastern Asia perspective.

2. Literature Review and Hypotheses Development

2.1. Organic Food Shopping in Offline and Online Contexts

The expanding market makes organic food a popular research issue. What factors facilitate and frustrate consumers in making organic purchases is the major concern. Some studies performed systematic reviews and meta-analyses to extract insights from the abundant findings (see Table 1). These studies not only recognized the drivers and barriers comprehensively but also classified these factors to advance this research stream.
Kushwah et al. [4] suggested two classifications based on consumption value theory and innovation resistance theory and grouped 24 drivers into five consumption values and 16 barriers into five types. Similar to Rana and Paul [14], Kushwah et al. [4] reported that the effects of drivers/barriers may vary in terms of the level of country development and consumer involvement. Also, Katt and Meixner [3] clustered 31 drivers into three main types: consumer-related, product-related, and venue-related. Specifically, they summarized how often a driver was examined in the previous studies and how often its significance was confirmed. For example, more than 30 organic food studies investigated the relationship between health attitude and willingness to pay, and over 75% of them had positive and significant results. Finally, Massey et al. [5] categorized 12 motivating factors with a credence-experience-search typology. All three attribute types were critical to consumers’ shopping for organic food, but the effects of credence attributes were superior to those of experience and search attributes.
In sum, these systematically reviewed studies provided valuable insights by assigning the overarching factors into meaningful types and synthesizing the diverse findings.
As existing knowledge of organic food consumption has largely been validated in physical store environments, several researchers have explored drivers of and barriers to organic food purchasing in online organic shopping contexts. Danner and Menapace [15] identified the predominant role of consumer beliefs in organic food purchases and contended that online comments are surrogates for consumer beliefs. Their content analysis of over 1000 online comments showed that user-generated content can be grouped into four main themes: product (e.g., food safety), food system (e.g., system integrity), authenticity (e.g., organic labels), and production (e.g., environment). Bryła [16] compared online and offline consumers’ organic food consumption in terms of price, retailer trust, quality signs, area of origin, brand, and product appearance.
Unlike previous studies, Lin et al. [6] integrated online and organic drivers in a research model and suggested that both social commerce characteristics and product characteristics lead to functional and emotional value and then to purchase intention. Similar findings were observed in Lin et al.’s [7] study, in which the effects of platform characteristics were three times greater than those of product characteristics. By contrast, Robina-Ramírez et al. [8] reported that attitude towards online organic shopping was the antecedent of purchase intention, and two organic drivers (ethics and fair trade, environmental protection and health) had greater effects on attitude than an online driver (ease of use). As researchers expand the scope of drivers and barriers and include both online and organic factors, current findings remain inconclusive. Understanding the factors that impact consumers’ online organic shopping requires more empirical evidence.
Cenfetelli [17] stated that barriers are not the opposite of drivers and have distinct definitions and psychological characteristics. As with previous studies, we believe the drivers and barriers coexist in the consumers’ decision-making process. We then synthesize these factors into our research model based on the dual perspective [18]. Trust theory and risk theory, commonly used in organic food studies [19], and e-commerce studies [20] are used as the theoretical bases.

2.2. Trust Theory

Trust refers to when one party believes that the other parties will act in a predictable and beneficial manner, meeting the trusting party’s expectations [21]. Trust beliefs enable consumers to feel safe and comfortable in a transaction, making them more willing to purchase. Studies have typically viewed organic food as a product with credence attributes [5], and trust is essential in handling the uncertainty in organic shopping [22]. Trust may be acquired directly and indirectly. Direct trust indicates the consumers form a trust perception directly by evaluating the trustee’s (e.g., producers and retailers) competence, benevolence, and integrity, while indirect trust comes from third parties’ certification and positive WOM. This study considers two forms of direct trust (trust in producers and trust in e-tailers) and two indirect trusts (trust in organic labels and positive review sentiment) in the research model.
Trust in producers and trust in e-tailers indicate that consumers have confidence in producers and e-tailers and believe they can safely benefit from transactions. Based on information-processing models, Vega-Zamora et al. [23] suggested abundant and transparent information could lead consumers to build authentic trust and functional trust by helping them to understand how the product is produced. Ladwein and Sánchez Romero [24] found that ensuring consistent product quality signals the reliability and integrity of organic producers and retailers, and can earn consumers’ confidence. Given the foregoing, the following hypotheses were constructed:
H1. 
Trust in producers positively relates to the intention to purchase organic food online.
H2. 
Trust in e-tailers positively relates to the intention to purchase organic food online.
Consumers may also acquire product information indirectly from parties outside the transaction. Organic labels are issued by third parties, such as government agencies and private organizations (e.g., farmers’ associations), and are a mandatory certification that notifies consumers that the products satisfy the regulations of organic production and distribution. Trust in organic labels denotes that consumers have confidence in the organic certification labels and systems, and believe they can benefit from the certifications by using them as proxy information. Janssen and Hamm [25] found that well-known organic certification labels can earn consumers’ trust and willingness to pay. Ha et al. [26] showed that trust in organic labels significantly predicts consumer’s purchase intention. Based on the above discussion, we developed hypothesis H3:
H3. 
Trust in organic labels positively relates to the intention to purchase organic food online.
Another source of indirect information on organic products is review sentiment. This is textual content provided by consumers to express their opinions and feelings about a product/service. Review sentiment allows consumers to share context-specific product information and usage experience, and significantly impacts purchase intention. Compared with information from producers and retailers, review sentiment provided by ordinary consumers has higher trustworthiness and reduces information search costs. Moreover, Massey et al. [5] argued that regulations on organic labeling and advertising generally constrain speculative information (e.g., health benefits) and subjective information (e.g., quality and safety). Consumers, thus, can learn more information about organic foods from review sentiment. Therefore, we hypothesize the following:
H4. 
Positive review sentiment positively relates to the intention to purchase organic food online.

2.3. Risk Theory

Risk indicates the perceived likelihood of suffering losses due to transaction uncertainty [21]. Risk coexists with trust in the consumers’ decision-making process, and is negatively related to purchase intention [27]. As with trust, the importance of risk perception is greater for credence products than search and experience products.
Previous studies have acknowledged that various risks occur in the retail context [28] and e-tail context [21,27]. This study follows Hong’s [27] classification and specifies financial risk, product performance risk, and delivery risk in the research model. Buying organic food online is typically an independent decision based on consumer beliefs, and psychological risk and social risk are not considered [29].
Financial risk refers to monetary loss deriving from overpriced products, unexpected expenses, and online fraud. Product performance risk is associated with defective products and below-expected performance. Delivery risk involves delivery problems, such as late delivery, wrong address/product, and poor fresh-keeping facilities [30]. While consumers have a high-risk perception in terms of monetary loss, unexpected product performance, and delivery failure, they are less likely to purchase organic food online [19]. Thus, we hypothesize:
H5. 
Financial risk negatively relates to the intention to purchase organic food online.
H6. 
Product performance risk negatively relates to the intention to purchase organic food online.
H7. 
Delivery risk negatively relates to the intention to purchase organic food online.
Behavioral intention is defined as an individual’s propensity to engage in a specific behavior [12]. The reasoning behind the intention–behavior connection is a self-fulfilling mechanism [31], indicating that intention situates an individual in a “must do” or “will do” status. Individuals with high behavioral intentions are motivated to perform the relevant behavior [32,33].
The relationship between purchase intention and actual purchase has been widely examined in the organic food context [10,11,12]. Studies typically found the relationship is not as strong as expected, but purchase intention is still a substantial antecedent of actual purchase. Consumers will turn their intention to buy organic food into a real purchase action. Therefore, we constructed hypothesis H8 as follows:
H8. 
The intention to purchase organic food online positively relates to the purchase of organic food online.
To sum up, this study specified four trust factors and three risk factors as drivers and barriers of the intention to purchase organic food online, respectively. Purchase intention is then modeled as the determinant of purchase behavior. The effects of online shopping experience on purchase behavior were controlled as well. Our research model is shown in Figure 1.

3. Method

3.1. Measured Items

This study performed an online survey method to collect data for analyses. The measurement items were developed based on previous studies, as shown in Table 1. Most were formatted using a 5-point Likert scale, except for purchase of organic food online, which used respondents’ monthly shopping frequency and average expenditure per purchase [34]. The questionnaire contained 3 sections. The first section investigated respondents’ shopping behavior of organic food, such as are you the main food purchaser of your family and have you ever bought organic food. The second section presented the measurement items. The last section collected respondents’ demographic information. Based on a pre-test of a small convenient sample, item wording and questionnaire format were modified to facilitate respondents’ comprehension and response time.

3.2. Survey

We hosted the questionnaire on a professional survey website (Surveycake) and worked with a survey service provider (Gosurvey). The survey service provider is initially a bonus points platform and has a database of 8.92 million authentic members, covering 46% of the adult population (aged 20 and up) in Taiwan. Per the population distribution in Taiwan by gender (51% male and 49% female) and region (45% north, 25% center, 26% south, and 4% east), a total of 7700 email invitations were sent to targeted respondents who may have an interest in buying organic food. Within four days 972 targeted respondents read the email, with 492 of them answering the questionnaire. Thus, the opening rate was 12.62%, and the response rate was 50.62%. No duplicate responses were received.
We then eliminated invalid data, including no organic food shopping experience (n = 6), not the main food purchaser (n = 20), missed data on income, expenditure, and shopping frequency (n = 123), contradictory data (n = 6), and irrational expenditure/income ratio (n = 59). A total of 278 responses were tested in the research model. The sample size satisfies the 10 times rule used in partial least squares structural equation modeling (PLS-SEM) studies, which is the main analytic method in this study. It also meets the minimum sample requirements for successfully examining R2 values of 0.10 at the 0.01 significance level, under 80% statistical power [35]. The average response time was about 7 min.

3.3. Sample Characteristics and Shopping Characteristics of Organic Food

Our sample was 48.92% male and 51.08% female, with respondents from north (51.08%), central (23.38%), south (24.82%), and east (0.72%) Taiwan. The age distribution was 21–30 (9.35%), 31–40 (27.34%), 41–50 (30.94%), 51–60 (24.10%), and above 60 (8.27%), and the average age was 44.46. Regarding education, 91.73% were well educated and had a college degree and above. Three-fifths of the respondents were married (64.75%), with a family size of 3.26. Personal monthly income and family monthly income were US$2174.91 and US$3915.62, respectively.
Our respondents generally bought organic food 5.56 times a month, and spent US$48.31 on each purchase. The monthly expenditure on organic food was US$271.16. Vegetables (87.77%) were the most popular organic food respondents, followed by fruits (63.31%), grains (46.40%), processed products (32.73%), meats (23.38%) and dairy products (23.38%), and fishery products (15.83%). The respondents further reported that 43.83% of their monthly purchases were online (i.e., approximately 2.34 times a month) and that they spent US$114.71.
For online channels, consumers tend to purchase organic food on websites more than on social media platforms (SMPs), such as Facebook, Instagram, and/or Line. Websites of general grocery stores were the most visited (75.18%), followed by websites of specialty stores (69.42%), SMPs of general grocery stores (50.00%), SMPs of specialty stores (47.84%), and SMPs of individual farmers and farmers’ associations (46.40%).

3.4. Analytic Strategies

The analytic strategies of this study followed two steps. First, we performed a nonresponse bias test using SPSS 20 software to assess sample representativeness [36]. Next, the PLS-SEM analyses were conducted with SmartPLS 4 software to validate the research model [37]. PLS-SEM is a multivariate method that examines the psychometric properties of the constructs (measurement model) and the relationships between the constructs (structural model) simultaneously. Only when the measurement model achieves the requirements of reliability and validity can the quality of the results of the structural model be established. The path coefficients, which were estimated with a path weighing scheme, indicate the strength and direction of the relationship between the two constructs. Lastly, the results derived from SmartPLS 4 include variance inflation factors (VIF), enabling us to evaluate common method biases and then proceed to hypotheses validation, effect size, and direct/indirect effects.

4. Results

4.1. Nonresponsive Bias Test

To ensure the sampling procedure generates representative data, a nonresponse bias test is suggested to compare returned and non-returned data. For online sampling, whether the difference lies between the first and last quarter of the returned data in terms of demographics and main constructs is considered to detect nonresponse bias [36]. The results of a test of homogeneity and independent samples t-test indicated that the two groups were indifferent across all the demographical, psychological, and behavioral characteristics (see Table 2). Respondents were reached based on the sampling frame during the whole sampling process, and the sample representativeness was, thus, acceptable. Nonresponsive bias was not a significant threat, and the results of this study show good generalizability.

4.2. PLS-SEM: Measurement Model

The construct–item relationships in this study were specified reflectively, and the measurement model was evaluated using reliability, convergent validity, and discriminant validity. The results of the measurement model satisfied all reliability and validity tests after excluding three items (1 for financial risk and 2 for delivery risk).
For reliability, Cronbach’s α ranged from 0.75 to 0.95, and the CR values (i.e., composite reliability) were from 0.88 to 0.97. Both indices exceeded the minimum value of 0.7. For convergent validity, the indicator reliability values and the AVE values were greater than the threshold values of 0.7 and 0.5, respectively. For discriminant validity, the results of cross-loadings showed that all the items were strongly connected with their corresponding constructs instead of other constructs. The HTMT results were below the critical value of 0.9. The Fornell–Larcker results reported that the constructs shared variance with their associated items more strongly than with other constructs. The constructs were significantly distinct from each other. The reliability, convergent validity, and discriminant validity were, thus, ensured (see Table 3, Table 4 and Table 5).

4.3. PLS-SEM: Structural Model

As the data were self-reported, we used the VIF to detect common method biases prior to hypotheses validation [42]. Our VIF values ranged from 1.40 to 3.38, showing that collinearity is not a serious problem in this study. Common method biases were not problems in this study, and the path coefficients were appropriately estimated.
Next, the structural relationships were examined in terms of path coefficients significance and effect sizes. As Table 6 shows, the online shopping experience (β = 0.15, p < 0.05) was controlled in the analytic model. Of the four drivers, trust in organic labels (β = 0.24, p < 0.05) and positive review sentiment (β = 0.32, p < 0.001) were significantly related to consumers’ intention to purchase organic food online. H3 and H4 were supported. However, trust in producers (β = 0.14, p > 0.05) and trust in e-tailers (β = −0.08, p > 0.05) were found to be irrelevant to purchase intention; thus, H1 and H2 were rejected. The three barriers of financial risk (β = −0.04, p > 0.05), product performance risk (β = 0.07, p > 0.05), and delivery risk (β = −0.11, p > 0.05) were all not significant. All three risks failed to predict the intention to purchase organic food online, meaning that H5, H6, and H7 were not supported. Finally, consumers’ intention to purchase organic food (β = 0.17, p < 0.05) was associated with their organic food consumption significantly and positively. H8 was, thus, consistent with our expectation.
This study further examined effect sizes using the in-sample f2 value and out-of-sample q2 value. Effect size f2 denotes the changes in variance of an endogenous construct while a specific exogenous construct is removed from the model. Similarly, effect size q2 indicates the changes in the predictability of an exogenous construct on its endogenous construct, based on the blindfolding procedure, which is a sample reuse technique. The higher the effect size f2 and q2 is, the stronger an exogenous construct contributes to its endogenous construct [35]. The values of effect size for the three significant exogenous constructs were trust in organic labels (f2: 0.03; q2: 0.03), positive review sentiment (f2: 0.08; q2: 0.07), and intention to purchase organic food online (f2: 0.02; q2: 0.01). The three exogenous constructs had significant roles in their corresponding endogenous constructs with small effects.
Finally, mediation analyses were conducted as well. All the mediating effects of purchase intention were not significant. Consumers’ organic food purchase online is determined by purchase intention but not by drivers/barriers.

5. Discussion

Consuming organic food is a typical sustainability behavior. Previous studies have focused on understanding the drivers of and barriers to organic food shopping and have yielded fruitful findings. However, investigation of organic food shopping in the online context remains in the formative stage. E-commerce allows vendors to reach large numbers of potential consumers, and is recognized as an efficient and effective alternative for popularizing organic food [18]. To explore consumers’ online organic food shopping, we investigated actual buying behaviors, such as frequency, expenditure, and highly purchased food, and shopping channels on a monthly basis. Four trust drivers and three risk barriers involved in online organic food shopping were examined [22].
Our findings show that trust in organic labels and positive review sentiment are two significant drivers of intention to purchase organic food online, and that intention is related to behavior positively and significantly. Five of our hypotheses were not supported, and the effects of two trust beliefs and three risk perceptions were against both our expectations and previous findings [24,38]. Producers and e-tailers provide rich attribute information that consumers can use to infer product quality. This information may offset information asymmetry but fails to transform into trust perceptions because of its commercial nature. Grabner-Kräuter and Kaluscha [43] reasoned that the lack of product trials makes it more difficult for online consumers to make product assessments. Thus, forming trust in producers and retailers rarely occurs.
By contrast, third-party certifications and online reviews (i.e., review sentiment) enjoy higher credibility and persuasiveness than traditional one-way marketing messages [44]. As trust in organic labels and positive review sentiment are significant drivers, this study finds indirect trust superior to direct trust and can promote consumers’ intent to purchase organic food online. It is also noteworthy that Qi et al. [45] argued that mistrust of certification and social media reports are the factors that disconnect intention and behavior in the green food purchase context, given intention is attributed to factors such as perceived attributes and environmental consciousness. Different from their qualitative findings, this study revealed these two indirect trusts are able to trigger consumers’ intention to engage in organic shopping online.
The three risk barriers were found to be irrelevant to purchase intention in the online context. Our results were against Brach et al.’s [19] findings, in which financial and performance risk and time risk were negatively associated with purchase intention. It may be that e-commerce institutional mechanisms in Taiwan are mature. Third-party escrow services and vendors’ policies, such as product warranties and money-back guarantees, secure transactions and relieve risk perceptions [19]. Feedback systems empower consumers to make complaints publicly, and may result in compensation from vendors [46]. All these practices create safe e-commerce environments, and consumers have confidence in shopping for organic food online without risk concerns.

5.1. Theoretical Implications

This study extends the studies on organic food shopping from the offline context to the online context. Considering the credence characteristics of organic food, this study developed a research model based on trust theory and risk theory, which are commonly used in organic food studies and e-commerce studies. The model specification also echoes Mayer et al.’s [22] arguments that consumers’ decision making is a holistic assessment of trust and risk (i.e., dual-factor model). Unlike Lin et al. [6] and Lin et al. [7], which explore the relative importance of online and organic facilitators, this study concentrates on the positive and negative determinants. Our findings can genuinely demonstrate consumer evaluations in an online organic food shopping context.
Of the four drivers and three barriers, trust in organic labels and positive review sentiment are found to contribute to the intention to purchase organic food online, with the latter having the greatest effect. Our results are consistent with studies on eWOM [43,47] and certification labeling [26]. Massey et al. [5] reported that the credence attributes impact organic food shopping more strongly than search and experience attributes, and consumers need more reliable and credible information to make purchases. Verma and Yadav [48] suggested that user-generated content (i.e., eWOM) expresses users’ first-hand experience and is disseminated without filters. It may be more trustworthy than marketing messages from producers and retailers. Brach et al. [19] contended that third-party certification labels can serve as product cues and convey unobservable quality to consumers. While interacting with producers and retailers may help consumers to learn organic information directly, build interpersonal trust, and make purchases in offline environments [24], online consumers rely on indirect trust, which is derived from third parties with no commercial intention.
Moreover, studies have divided trust into personal trust and system trust [46]. Personal trust sources from an individual’s localized knowledge and personal relationships, including trust in producers or retailers. System trust is universalistic and institution-related, with trust in a labeling system being a good example. The effects of system trust on green buying behavior were found to be stronger than those of personal trust [46]. Given this, information that comes from third parties that are outside the transaction (e.g., indirect trust or system trust) can earn greater trust and facilitate online organic food purchases.
The relationship between trust and risk is a concern when specifying them in a model. Considering that trust is critical in consumer buying decisions when risk and uncertainty are present, studies typically specify risk as an antecedent of trust [27] or examine whether trust moderates or reduces risk perception [19]. This study suggests that trust and risk affect consumer decision making simultaneously [18], findings that may in part echo previous arguments. As we stated earlier, information and warranty policies by producers and e-tailers may help consumers build direct trust, which mitigates information asymmetry and risk perception. Non-commercial information from certification labels and online reviews may be a powerful source for consumers to gain indirect trust. It, thus, determines online organic food consumption.
Another research focus in this study is the relationship between intention and behavior. Organic food studies have extensively examined the intention–behavior gap using a variety of theoretical grounds, and purchase intention is specified with different antecedents. For example, TPB proposes that attitude, subjective norm, and perceived behavioral control constitute purchase intention [12]. Carfora et al. [38] modeled the three determinants of TPB, fours trust beliefs, past behavior, and self-identity to predict purchase intention. Akehurst et al. [10] specified that ecologically conscious behavior orientation and green purchase intention are predictors of green purchase behavior. Talwar et al. [49] adopted a stimulus–organism–behavior–consequence framework and identified openness to change, self-identity, and ethical self-identity as the three antecedents of intention. As with most previous studies, we recognize that the trust–risk-based intention could be an antecedent of purchase intention, but small in-sample effects (f2 = 0.02) and out-of-sample effects (q2 = 0.01) indicate that the intention–behavior gap may still be an issue in the context of online organic shopping. Our findings also show all the drivers/barriers are not able to influence purchase behavior directly but through purchase intention indirectly, indicating that the intention to purchase organic food online is the main determinant of actual purchase behavior.
In addition to model validation, this study investigates organic consumption patterns with a representative sample. Our results present monthly purchase frequency and expenditure of organic food from an omnichannel perspective (offline: 3.22 times and US$156.44; online: 2.34 times and US$114.71). Popular organic foods are explored. We find that consumers prefer organic vegetables and fruits to other products. As for online shopping channels, store websites enjoy the most popularity and attract higher consumer interest than social media and other platforms. Pham et al. [13] observed that organic food studies are typically conducted in Western countries. This study may, thus, be helpful in understanding organic food consumption patterns in eastern contexts.

5.2. Managerial Implications

E-commerce has become a promising solution for expanding the scope of agribusiness [39]. Our results for online organic food shopping offer strategic insights for managers. Two indirect trusts, trust in organic labels and positive review sentiment, are shown to be powerful drivers of intent to purchase organic food online. Marketing managers should, thus, leverage organic labels as a communication strategy. As acquiring and maintaining organic labels is costly, managers have to choose certification systems carefully. Moderate certification adoption could balance consumers’ trust and firm revenues.
Further, marketing managers and website developers should design comment areas and actively manage consumers’ comments. Specifically, managers could use incentives, such as loyalty points (economic value), or develop reviewer levels/badges (i.e., social status) to encourage consumers to share their experiences of organic food. Organic food, which receives rich positive comments, could capture consumer interest. Some studies propose that the negative review sentiments induced by product/service failure will be posted more than positive evaluations [50]. However, if product/service failure can be recovered immediately and sincerely, it could be a great opportunity to turn an angry consumer into a loyal advocate and secure their trust in e-tailers and producers [51].

5.3. Limitations and Future Research

In discussing shopping drivers and barriers, several studies suggest that organic consumption patterns may differ in consumer involvement. For example, Robina-Ramírez et al. [8] found the effects of price barriers are stronger for occasional buyers than for general buyers. Future studies should take consumer involvement into consideration to examine trust and risk perception. This is also the case with age differences (young vs. old consumers) [52], cultural differences (developed vs. developing countries) [7], consumer traits (optimistic vs. pessimistic) [53], and product context (fresh vs. processed food) [24]. Finally, advanced technology enables massive consumer shopping data to be recorded. The application of real shopping data will enable better validity of research on the intention–behavior gap than survey data.

6. Conclusions

Previous studies have paid much attention to the determinants of organic food purchase and collected fruitful findings in the offline context. As modern marketing knowledge suggests vendors establish an omnichannel environment to seamlessly serve consumers, it is surprising that the understanding of consumers’ organic food shopping online is relatively less. Online channels could be a potential alternative for market expansion, and it is imperative to tap into the determinants of organic shopping in an online context.
Considering the product characteristics (i.e., credence attribute) and context characteristics (i.e., e-commerce), this study integrates trust drivers and risk barriers in the research model and finds only indirect trusts are able to influence purchase intention. In a well-infrastructured e-commerce environment, consumers could access information with ease and heavily rely on indirect information, such as online reviews and certification labels, to make online purchases of organic food. Parties outside the transaction generate greater consumer trust than producers and e-tailers, and aid in closing transactions. Producers and e-tailers fail to earn strong consumer trust, but their policies, such as product warranties and money-back guarantees, could mitigate consumers’ risk perception, which may contribute to the purchase intention of organic food online. Interestingly, as consumers are able to access online reviews and certification labels and are provided with friendly vendor policies, whether organic food still holds a credence attribute based on risk perception [19] deserves more academic efforts.

Author Contributions

Conceptualization, C.-H.Y.; Methodology, C.-H.Y.; Investigation, C.-H.Y.; Formal Analysis, C.-H.Y.; Writing—Original Draft, C.-H.Y.; Writing—Review and Editing, M.-H.Y.; Supervision, M.-H.Y.; Resources, C.-H.Y. and M.-H.Y.; Funding acquisition, C.-H.Y. and M.-H.Y. All authors have read and agreed to the published version of the manuscript.

Funding

The research was funded by the Ministry of Science and Technology of Taiwan under Grant No. MOST 110-2410-H-035-030. The APC was funded by the National Science and Technology Council of Taiwan under Grant No. NSTC 113-2410-H-035-021.

Institutional Review Board Statement

Ethical review and approval were waived for this study due to the fact that data were collected anonymously and no personal identifiable information was gathered.

Data Availability Statement

The datasets utilized in this study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Agriculture 15 00883 g001
Figure 1. Research model of this study.
Figure 1. Research model of this study.
Agriculture 15 00883 g001
Table 1. Summary of drivers and barriers to organic food purchase.
Table 1. Summary of drivers and barriers to organic food purchase.
Authors (Year)Research CoverageDriversBarriers
Hughner et al. [2]33 studies during 1991–2004(1) is healthier; (2) tastes better; (3) environmental concern; (4) concern over food safety; (5) concern over animal welfare; (6) supports local economy and helps to sustain traditional cooking; (7) is wholesome; (8) reminiscent of the past; (9) fashionable.(1) rejection of high prices; (2) lack of availability; (3) skepticism of certification boards and organic labels; (4) insufficient marketing; (5) satisfaction with current food source; (6) cosmetic defects.
Rana and Paul [14]146 studies during 1985–2015(1) health consciousness and expectations of well being; (2) quality and safety; (3) environmental friendliness and ethical consumerism; (4) willingness to pay; (5) price and certification; (6) fashion trends and unique lifestyle; (7) social consciousness.n.a.
Massey et al. [5]150 studies during 1991–2016
  • Credence attributes: (1) health benefits; (2) safety; (3) environmental impact; (4) animal welfare; (5) production practices; (6) nutritional values; (7) quality.
  • Search attributes: (1) price premium; (2) availability; (3) appearance.
  • Experience attributes: (1) taste; (2) freshness.
n.a.
Kushwah et al. [4]89 studies during 2005–2018
  • Functional value: (1) harmful ingredient-free; (2) sensory aspect; (3) quality; (4) food safety; (5) nutritional value; (6) natural content; (7) freshness; (8) health attribute.
  • Emotional value: mood/emotion.
  • Social value: (1) environment; (2) social approval; (3) reputation concern; (4) social identity; (5) support LFS (fair-trade); (6) animal welfare; (7) regional production.
  • Epistemic value: (1) nostalgia; (2) fashionable; (3) knowledge.
  • Conditional value: (1) convenience; (2) personal health; (3) media exposure to food messages; (4) children at home/household member; (5) local pollution.
  • Usage barrier: (1) limited variety/poor product range; (2) availability; (3) low visibility in the shop; (4) limited information; (5) convenience.
  • Value barrier: (1) higher price; (2) time.
  • Tradition barrier: (1) sensory cues (appearance and olfactory cues); (2) shorter shelf life; (3) habit; (4) satisfaction with conventional product; (5) lack of knowledge.
  • Image barrier: (1) perceived skepticism against organic food; (2) no perceived difference between organic and conventional.
  • Risk barrier: (1) lack of trust in stakeholders; (2) doubt regarding certification/labeling.
Katt and Meixner [3]138 studies during 1995–2015
1.
Consumer related:
(1)
Demographics: (a) age; (b) gender; (c) income; (d) education; (e) children/household size; (f) place of living (urban/rural); (g) employment status; (h) home ownership; (i) marital status.
(2)
Values and attitudes: (a) environmental concern; (b) ethical concern; (c) cultural values; (d) attitude towards CSR; (e) health attitude.
(3)
Consumer behavior: (a) frequency of purchase/consumption; (b) recycling behavior.
2.
Product related:
(1)
Product attributes: (a) price; (b) locality; (c) quality food safety.
(2)
Signaling: (a) promotion; (b) organic label; (c) certification; (d) traceability.
(3)
Consumer relationship: (a) involvement; (b) familiarity; (c) availability.
3.
Purchasing venue related:
(1)
Store: (a) type of store; (b) convenience/proximity to consumer; (c) store design/visual stimulus; (d) retailer brand perception.
n.a.
Table 2. Results of nonresponse bias test.
Table 2. Results of nonresponse bias test.
First Quarter Respondents (n = 70)Last Quarter Respondents (n = 70)Statistics
Demographics
     Gender48.6% vs. 51.4%48.6% vs. 51.4%χ2 = 0.00
     Age44.2943.86t = 0.24
     Education1.4% vs. 5.7% vs. 61.4% vs. 31.4%0.0% vs. 7.1% vs. 68.6% vs. 24.3%χ2 = 2.03
     Marriage62.9% vs. 37.1%68.6% vs. 31.4%χ2 = 0.51
     Region51.4% vs. 24.3% vs. 22.9% vs. 1.4%52.9% vs. 22.9% vs. 24.3% vs. 0.0%χ2 = 1.07
     Family size3.213.40t = −0.83
     Monthly family income4052.873846.35t = 0.79
Main constructs
     Trust in producers3.613.75t = −1.30
     Trust in e-tailers3.413.58t = −1.48
     Trust in organic labels3.673.72t = −0.58
     Positive review sentiment3.503.59t = −0.95
     Financial risk3.453.41t = 0.34
     Product performance risk3.713.83t = −1.02
     Delivery risk3.313.47t = −1.42
     Intention to purchase organic food online3.763.80t = −0.39
     Purchase of organic food online110.15104.70t = 0.33
Table 3. Descriptive statistics, reliability, and convergent validity of constructs.
Table 3. Descriptive statistics, reliability, and convergent validity of constructs.
ReliabilityConvergent Validity
MeanSDCronbach’s AlphaCR ValuesIndicator ReliabilityAVE Values
Trust in producers [38]3.620.610.950.96 0.79
TPRO1 Producers take good care of the safety of organic food. 0.91
TPRO2 Producers give special attention to the safety of organic food. 0.90
TPRO3 Producers have the competence to control the safety of organic food. 0.87
TPRO4 Producers have sufficient knowledge to guarantee the safety of organic food. 0.87
TPRO5 Producers are honest about the safety of organic food. 0.91
TPRO6 Producers are sufficiently open regarding the safety of organic food. 0.88
Trust in e-tailers [38]3.450.640.950.96 0.80
TTAI1 E-tailers take good care of the safety of organic food. 0.92
TTAI2 E-tailers give special attention to the safety of organic food. 0.89
TTAI3 E-tailers have the competence to control the safety of organic food. 0.92
TTAI4 E-tailers have sufficient knowledge to guarantee the safety of organic food. 0.87
TTAI5 E-tailers are honest about the safety of organic food. 0.88
TTAI6 E-tailers are sufficiently open regarding the safety of organic food. 0.88
Trust in organic labels [39]3.640.580.880.93 0.81
TLAB1 I believe the organic food label information is true and reliable. 0.88
TLAB2 I believe that the organic food label information can reflect the production and distribution process of the product. 0.93
TLAB3 I think the organic food label information is consistent with the production and distribution process of the product. 0.90
Positive review sentiment [40]3.490.550.900.93 0.78
PREW1 Online comments on the organic food are excellent. 0.85
PREW2 Online comments on the organic food are good. 0.92
PREW3 Online comments on the organic food are positive. 0.88
PREW4 Online comments on the organic food are pleasant. 0.87
Financial risk [27]3.340.780.750.89 0.80
RFIN1 I am concerned that the online payment methods may not be safe. 0.90
RFIN2 I am concerned that the online price of the organic food may be too high.** --
RFIN3 I am concerned that I may suffer from monetary loss due to the e-tailer’s fraudulent acts. 0.89
Product performance risk [27]3.740.690.860.88 0.79
RPER1 I am concerned that the organic food delivered may not perform to my expectations. 1.00
RPER2 I am concerned that the organic food delivered may not match the descriptions, including the pictures, given on the website. 0.76
Delivery risk [27]3.010.89---- --
RDEL1 I am concerned that the organic food may be delivered without good fresh-keeping facilities. **
RDEL2 I am concerned that the organic food may be delivered to a wrong address. 1.00
RDEL3 I am concerned that the organic food may not be delivered in time. **
Intention to purchase organic food online [38]3.710.640.950.97 0.91
INT1 I intend to purchase organic food online. 0.95
INT2 I plan to purchase organic food online. 0.95
INT3 I want to purchase organic food online. 0.97
Purchase of organic food online [34]114.71114.19---- --
PUR1 Monthly organic food expenditure online (monthly shopping frequency X average expenditure per purchase) 1.00
Online shopping experience [41] 0.850.91 0.76
EXP1 I have shopped online extensively. 0.91
EXP2 I have used the Internet to shop for a long time. 0.85
EXP3 I shop online frequently. 0.86
** Items dropped. The Mean and SD values of PUR are presented in US$.
Table 4. Results of the heterotrait–monotrait ratio (HTMT) to assess discriminant validity.
Table 4. Results of the heterotrait–monotrait ratio (HTMT) to assess discriminant validity.
Construct(1)(2)(3)(4)(5)(6)(7)(8)
(1) Trust in producers
(2) Trust in e-tailers0.80
(3) Trust in organic labels0.830.80
(4) Positive review sentiment0.690.630.66
(5) Financial risk0.060.090.120.08
(6) Product performance risk0.130.180.120.070.67
(7) Delivery risk0.080.080.070.040.510.44
(8) Intention to purchase organic food online0.480.390.510.540.090.010.12
(9) Purchase of organic food online0.220.240.250.210.120.150.060.26
Table 5. Results of Fornell–Larcker for discriminant validity.
Table 5. Results of Fornell–Larcker for discriminant validity.
Construct(1)(2)(3)(4)(5)(6)(7)(8)(9)
(1) Trust in producers0.89
(2) Trust in e-tailers0.760.89
(3) Trust in organic labels0.760.730.89
(4) Positive review sentiment0.640.580.590.88
(5) Financial risk0.00−0.04−0.10−0.030.90
(6) Product performance risk−0.10−0.14−0.09−0.010.540.89
(7) Delivery risk−0.010.08−0.02−0.040.450.421.00
(8) Intention to purchase organic food online0.450.370.470.50−0.08−0.02−0.120.95
(9) Purchase of organic food online0.220.230.240.20−0.10−0.150.060.251.00
Note. Diagonals are the value of square root of average variance extracted, and off-diagonals are the correlations.
Table 6. Results of the structural model (n = 278).
Table 6. Results of the structural model (n = 278).
Hypothesized PathCoefficientt-Value
C.V. Online shopping experience → Purchase of organic food online0.15 *2.29
H1. Trust in producers → Intention to purchase organic food online0.141.17
H2. Trust in e-tailers → Intention to purchase organic food online−0.080.85
H3. Trust in organic labels → Intention to purchase organic food online0.24 *2.30
H4. Positive review sentiment → Intention to purchase organic food online0.32 ***4.55
H5. Financial risk → Intention to purchase organic food online−0.040.50
H6. Product performance risk → Intention to purchase organic food online0.070.98
H7. Delivery risk → Intention to purchase organic food online−0.110.61
H8. Intention to purchase organic food online → Purchase of organic food online0.18 *2.14
Note: * < 0.05, *** < 0.001.
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Yeh, C.-H.; Yang, M.-H. Reexamining the Determinants of Organic Food Purchases in Online Contexts: The Dual-Factor Model Perspective. Agriculture 2025, 15, 883. https://doi.org/10.3390/agriculture15080883

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Yeh C-H, Yang M-H. Reexamining the Determinants of Organic Food Purchases in Online Contexts: The Dual-Factor Model Perspective. Agriculture. 2025; 15(8):883. https://doi.org/10.3390/agriculture15080883

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Yeh, Ching-Hsuan, and Min-Hsien Yang. 2025. "Reexamining the Determinants of Organic Food Purchases in Online Contexts: The Dual-Factor Model Perspective" Agriculture 15, no. 8: 883. https://doi.org/10.3390/agriculture15080883

APA Style

Yeh, C.-H., & Yang, M.-H. (2025). Reexamining the Determinants of Organic Food Purchases in Online Contexts: The Dual-Factor Model Perspective. Agriculture, 15(8), 883. https://doi.org/10.3390/agriculture15080883

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