The digital traceability technology acts as a quality signal and has proved useful in reducing the asymmetry of information in agricultural product markets. According to the signaling theory, since product quality and other credence qualities can neither be observed nor verified directly by the buyers of the product, companies that enjoy information advantages can communicate product quality information to buyers through the sending of signals (Connelly et al., 2011; Sigurdsson et al., 2020). Liu et al. (2023) defined environmental information transparency as ‘the level of availability and accessibility of market information to participants,’ demonstrating that blockchain technology enhances both digital trust and swift trust, which subsequently influence purchase behavior. The digital traceability technology enables the buyers to view information related to the origin of the product, the production processes involved, and the quality tests conducted.

Recent empirical studies have found sufficient evidence for the positive effect of digital traceability technology on the perceptions of customer values. Using a sample of 415 Chinese consumers, Tao and Chao (2024) proved that the implementation of blockchain traceability systems can significantly improve the perceptions of product quality, product trust, and the transparency of information related to the environment for organic agricultural products. A massive survey conducted among 5,326 Vietnamese consumers revealed that the implementation of blockchain traceability systems has a significant effect on consumption behavior as it can improve product trust. By incorporating the acceptance model and focusing specifically on the influence of the perceived usefulness of the traceability systems based on the blockchain technology (β = 0.34), the findings of Li et al. (2025) revealed that adopting attitudes toward the implementation can be significantly influenced. A comparative analysis among the OECD member countries revealed that the implementation of digital traceability systems can improve efficiency in operation, ensure food safety, and increase transparency as the perceptions of the customers toward the aforementioned factors can improve due to the implementation of the digital traceability systems. They proved that when customers’ perceptions toward the digital traceability systems increase, customers can extract more information related to the product’s quality, safety, and authenticity from the traceability systems. Henceforth, the present study proposes:

Perceived value as the consumers’ general appraisal of the relative benefits and costs of products has been considered a vital psychological mediator between cognition and behavior. Research on food choice motivations has identified multiple dimensions underlying consumer evaluations, including health, natural content, and ethical concern (Eertmans et al., 2006). Notably, cross-cultural studies found that consumers cognitively link ‘natural’ with ‘healthy,’ which is particularly relevant to green agricultural product positioning. Scholars have conceptualized perceived value from multidimensional perspectives, distinguishing between functional value (quality and utility), emotional value (affective responses), social value (social approval), and economic value (monetary considerations; Sweeney and Soutar, 2001). In the green consumption context, Chen and Chang (2012) proposed that green perceived value encompasses both utilitarian benefits (environmental performance) and the costs (price premium) associated with green products. While multidimensional approaches offer granular insights into value components, the overall perceived value construct remains widely adopted in consumer behavior research as it captures the holistic trade-off assessment that directly precedes purchase decisions (Zeithaml, 1988). This study adopts the overall perceived value approach to examine the aggregate mediating mechanism between technology perception and purchase intention, while acknowledging that future research could benefit from disaggregating value dimensions. In the context of green agricultural products and sustainable consumption, the positive role of perceived value in purchase intention has been thoroughly ascertained. On the basis of their empirical study among 470 young Chinese consumers, Hu et al. (2024) confirmed that value perception has a significant influence on organic food consumption attitudes (β = 0.188) and consumption intentions (β = 0.223), and further affects the willingness to pay the premium price (β = 0.309). Using the moderated mediation analysis among 518 Chinese consumers, Wang et al. (2020) confirmed the mediation role of perceived food quality between the relationship of environmental consciousness and organic food purchase intention. This indicates the vital role of value perception in the role of attitudes toward behavior.

Yang et al. (2023) studied 341 Chinese consumers and showed that the overall agricultural image, product image, social image, and consumer image of green agricultural products all positively influence consumption intention through the mediating role of perceived value. Bazhan et al. (2024) found in their Iranian study that attitude, as an embodiment of value assessment, directly influences organic food purchase intention, with health consciousness generating the strongest effect. Based on the Stimulus-Organism-Behavior-Consequences paradigm, Talwar et al. (2021) confirmed that health consciousness and food safety concerns, as stimulus factors, influence purchase intention and purchase behavior through openness and ethical self-identity (value dimensions). Nguyen et al. (2021) studied 402 Vietnamese consumers and showed that environmentally concerned consumers form more positive attitudes, thereby enhancing organic meat purchase intentions. These cross-cultural, cross-product category studies consistently indicate that when consumers perceive green agricultural products as having high quality, safety, health value, and environmental value, their purchase propensity increases accordingly. Therefore, this study proposes:

In addition to the indirect pathway through enhancing perceived value, perceived digital traceability technology may also directly influence consumers’ purchase intention. From the perspective of cognitive process, the traceability technology can play the role of a decision-making heuristic cue when present. This can increase purchase confidence as well as reduce the purchase risk even before the value for the purchase is actually appraised. This reflects the dual-pathway behavior of consumers when making decisions (Tao and Chao, 2024).

Empirical studies have confirmed the existence of the direct effect. Even though the main goal of the Tao and Chao (2024) study concerned mediation effects, their model also included the traceability system’s potential for a direct effect on purchase intention. In the moderated mediation model proposed by Duong et al. (2024), the results revealed that the functions of the blockchain traceability system have both indirect influences on purchase intention as a mediator through trust and a direct effect. The findings obtained from the study conducted by Li et al. (2025) revealed that the adoption attitudes of consumers toward blockchain traceability systems (technology perception outcome) were very strong in determining their adoption intentions (β = 0.88), determining that the perceptions of technology can quickly translate to behavioristic tendencies following the straightforward attitudinal appraisal. Hence, the following proposals are made for the present study based on the above-mentioned:

Integrating the above analysis, perceived value may play an important mediating role in the relationship between perceived digital traceability technology and purchase intention. Digital traceability technology first influences consumers’ cognitive assessment of product value by transmitting quality signals, and this value cognition subsequently converts into purchase behavioral tendencies. This mediating mechanism aligns with the value-attitude-behavior theoretical framework, reflecting the cognitive processing of consumer decision-making (Shen et al., 2022).

Multiple studies have provided empirical support for the mediating role of perceived value. Wang et al. (2020) moderated mediation model explicitly confirmed that perceived food quality (one value dimension) partially mediates the relationship between environmental consciousness and organic food purchase intention. Yang et al. (2023) study on green agricultural products showed that various dimensions of brand image influence consumption intention through the full mediating role of perceived value, with non-significant direct effects but significant indirect effects. Hu et al. (2024) research revealed a serial mediation pathway: value perception → consumption attitude → consumption intention → willingness to pay a premium, indicating that value cognition is a key mechanism initiating the subsequent decision-making chain. Based on the SOBC paradigm, Talwar et al. (2021) study showed that organism variables (including openness and ethical self-identity, both value dimensions) mediate the relationship between stimuli and behavior. Considering that this study also hypothesizes the existence of a direct effect (H3), perceived value is more likely to exert partial rather than full mediation. Therefore, this study proposes:

Based on the above hypotheses, this study constructs a theoretical conceptual model, as shown in Figure 1. The model integrates signaling theory, technology acceptance theory, and the value-attitude-behavior theoretical framework, encompassing three core variables and their relational pathways, systematically depicting the mechanism through which digital traceability technology influences green agricultural product purchase intention. The model hypothesizes that perceived digital traceability technology influences purchase intention through dual pathways: first, through the cognitive evaluation pathway of enhancing perceived value (indirect effect), and second, through the direct pathway as a decision-making heuristic cue (direct effect), with perceived value exerting partial mediation.

In order to gain the required information, we used a cross-sectional questionnaire survey method (Dillman et al., 2014). The questionnaire used in our study can be divided into three parts. In the first part of the questionnaire, we included screening questions to check whether the respondents had purchase experience of green agricultural products over the Internet in the last 6 months. In the second part of the questionnaire, we gave a brief introduction related to digital traceability techniques. This introduction included explanations of the functions of various technologies such as blockchain traceability, QR code tracking, and Internet of Things (IoT) sensors, emphasizing their common function of enabling consumers to verify product origin, production processes, and quality certifications. Rather than evaluating specific technologies separately, respondents were asked to assess their overall perception of digital traceability technologies as an integrated system for product information verification. This approach reflects the reality that consumers typically encounter these technologies as complementary components of traceability systems rather than as isolated solutions (Charlebois et al., 2024). While this design captures the aggregate effect of technology perception, future research could benefit from comparing consumer responses to different traceability technologies.

All the variables were measured utilizing standardized scales and were modified suitably based on the context of the studies. Perception of digital traceability technology was measured utilizing a set of adapted items (three) from the standardized scale given by Garaus and Treiblmaier (2021), estimating the degree to which customers were aware and familiarized with the traceability technology. Value-perception was measured utilizing a set of adapted items (five), combining the concepts of the standardized measures given by Dodds et al. (1991), Zhou et al. (2018), and Liu et al. (2023), estimating customers’ perceptions related to product quality and trust. The purchase intention of customers toward the product was measured utilizing a set of adapted items (three) from the standardized measures given by Everard and Galletta (2005) and Zhou et al. (2018), estimating customers’ purchase intentions. All the measures used a 7-point Likert-type scale (1 = strongly disagree to 7 = strongly agree), following established scale development guidelines (DeVellis and Thorpe, 2021). The demographic factors included the customers’ gender, age group, educational attainment, income level, and residing location.

Complete measurement items for all constructs are provided in Supplementary Table S5. For the purpose of content validity and clarity of the questionnaire, a pre-test (n = 100) was carried out following established pretesting procedures (Presser et al., 2004). On the basis of the pre-test results, we modified the items and removed the items whose factor loading was less than 0.6.

The target population for this study was consumers aged 18 and above in mainland China who had online green agricultural product purchase experience within the past 6 months. This research protocol was approved by the Ethics Committee of Dongshin University (Ethics Approval Number: DU-ETH-2025-025). We employed convenience sampling and collected data through the “Wenjuanxing” online survey platform. Data collection was conducted in two phases: the pretest phase (April 23–28, 2024) collected 99 valid questionnaires for preliminary scale reliability and validity testing and item optimization; the formal survey phase (May 3–28, 2024) distributed 418 questionnaires. After excluding 3 invalid questionnaires with excessively short completion times (<120 s), patterned responses, or excessive missing values, we obtained 415 valid questionnaires, yielding an effective response rate of 99.28%. To ensure data quality, the questionnaire included attention check items and logical consistency checks. All data collection was conducted with respondents’ informed consent. Respondents were informed that the survey was completely anonymous, data would be used solely for academic research, and there were no correct answers. Personal sensitive information was protected through the anonymization processing of the Wenjuanxing platform. The sample covered 10 provinces and 3 municipalities in China, with relatively broad geographic distribution.

For the analysis of the obtained data, we used the following software: SPSS 26.0 and AMOS 24.0. In the first stage of our analysis, we conducted exploratory factor analysis. In this analysis, we conducted Principal Component Analysis together with varimax rotation. Moreover, we conducted the KMO test and Bartlett’s sphericity test for determining whether the data can be used for factor analysis. For the second stage of our analysis, we conducted Confirmatory factor analysis. In this stage, we compared the fit of the one-factor solution model, two-factor solution model, and the three-factor solution model. In the process of determining the fit indices of the model, we used the following fit indices: χ²/df, RMSEA, CFI, TLI, SRMR. For the determination of the fit indices, we referred to the following guidelines: Hu and Bentler (1999), Malhotra et al. (2014).

In the reliability and validity assessment of the measurement model, the following tests were performed: (1) Internal consistency reliability, whereby the values were compared against the 0.7 cut-off point through the use of Cronbach’s alpha and composite reliability (CR); (2) Convergent validity whereby the results were compared against the 0.6 and 0.5 cut-off points through factor loadings and Average Variance Extracted (AVE), respectively; and (3) Discriminant validity whereby the measures were compared against the 0.85 cut-off point through the HTMT statistic (Henseler et al., 2015). In the assessment of common method bias, Harman’s Single-factor Test was used. Here, if the unrotated first factor accounted for less than 40% of the total variance, the common method bias can be deemed as not severe (Cote and Buckley, 1987).

In the analysis of the hypotheses, we used the structural equation model (SEM) for the analysis of the paths. For the mediation effect test, we used the Bootstrap method. In the Bootstrap method, we set the resamples at 5,000 to generate the confidence interval at 95% bias-corrected. When the confidence interval does not contain zero in the confidence interval, the mediation effect is significant (Hayes, 2009). In the Bootstrap method, we were able to calculate the total effect, the proportion of the mediation effect to the total effect, as well as the other effects.

The formal survey collected 418 questionnaires. After excluding 3 invalid questionnaires with excessively short completion times, patterned responses, or excessive missing values, we obtained 415 valid questionnaires, yielding an effective response rate of 99.28%. Table 1 presents the demographic characteristics of the sample.

The sample has a fairly equal sex distribution, with female and male respondents at 51.08 and 48.92% respectively, which closely mirrors the national sex distribution of 48.76% female and 51.24% male according to the Seventh National Population Census of China (National Bureau of Statistics of China, 2021). In regards to the age distribution, the highest percentage belonged to the 25–44 age group at 62.17% breakdown of 37.59% for the 25–34 age group and 24.58% for the 35–44 age group. This represents the nature of the main consumption group for the online green agricultural products. Educationally speaking, the structure broke down as follows: 42.89% earned a bachelor’s degree; 23.61% earned associate degrees; and 13.01% held master’s degrees or above. In total, 79.52% of respondents held college degrees or above, substantially higher than the national rate of 15.47% according to the census. This overrepresentation of highly educated individuals reflects the characteristics of online green agricultural product consumers. Monthly income broke down as follows: 40.48% of the total belonged to the 4,000–6,999 RMB income bracket; 23.61% were in the 7,000–9,999 RMB income bracket. This reflects the income stratification of the middle income bracket found in the general population of the people of the great Republic of China. In regards to the residential structure: 37.59% belonged to the second-tier category; 28.43% belonged to the first-tier category; and 33.98% belonged to the third-tier category and below.

The surveyed sample included 10 provinces and 3 municipalities in mainland China: Hebei, Liaoning, Anhui, Fujian, Hunan, Guangdong, Jiangsu, Zhejiang, Shandong, and Henan. In general, while the sample closely approximates the national gender distribution, it overrepresents younger, more educated, and urban consumers compared to the general population. This composition is consistent with the characteristics of online green agricultural product consumers, though it may limit generalizability to the broader Chinese population, as further discussed in the limitations section.

The measurement model was evaluated through a systematic process of exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and reliability and validity assessment. Detailed results are presented in Supplementary Tables S1–S4.

Exploratory factor analysis was conducted using SPSS 26.0 to examine the factor structure of the measures. The Kaiser-Meyer-Olkin (KMO) value of 0.873 exceeded the recommended threshold of 0.7, and Bartlett’s test of sphericity was significant (χ² = 2856.42, df = 55, p < 0.001), confirming the appropriateness of the data for factor analysis. Principal component analysis with varimax rotation extracted three factors with eigenvalues greater than 1, accounting for 72.64% of the cumulative variance, which exceeded the required 60% level. All items loaded above 0.6 on their intended factors with no significant cross-loadings, supporting a clear three-factor structure corresponding to perceived digital traceability technology, perceived value, and purchase intention (Supplementary Table S1). The Cronbach’s α coefficients for the three variables were 0.798 (perceived digital traceability technology), 0.834 (perceived value), and 0.861 (purchase intention), all exceeding the 0.7 threshold, indicating good internal consistency reliability.

Confirmatory factor analysis was performed using AMOS 24.0 to further test the structural validity of the scales by comparing four alternative models (Supplementary Table S2). The single-factor model, which assumed all items loaded on one factor, demonstrated poor fit (χ²/df = 23.12, RMSEA = 0.232, CFI = 0.621), confirming structural differences among the constructs. Two-factor models showed marginal improvement but still failed to meet acceptable thresholds. In contrast, the hypothesized three-factor model, treating perceived digital traceability technology, perceived value, and purchase intention as distinct constructs, demonstrated excellent fit (χ²/df = 1.926, RMSEA = 0.047, CFI = 0.978, TLI = 0.971, SRMR = 0.038), with all indices exceeding recommended cutoffs. These results confirmed that the three constructs are empirically distinct.

Reliability and validity assessments further confirmed strong psychometric properties (Supplementary Tables S3, S4). Internal consistency was established with Cronbach’s α coefficients ranging from 0.798 to 0.861 and composite reliability (CR) values ranging from 0.802 to 0.865, all exceeding the 0.7 threshold. Convergent validity was supported by average variance extracted (AVE) values ranging from 0.508 to 0.681, all above the 0.5 cutoff, with standardized factor loadings exceeding 0.6. Discriminant validity was confirmed using the heterotrait-monotrait ratio (HTMT), with all values below the 0.85 threshold; the HTMT value between perceived digital traceability technology and purchase intention was the lowest (0.326), while that between perceived value and purchase intention was the highest (0.681). In summary, the measurement model demonstrated good reliability, convergent validity, and discriminant validity, making it suitable for subsequent structural model testing.

Because this study collected data using self-report questionnaires, with all variables completed by the same respondents at the same time, common method bias may exist. In order to evaluate the degree of common method bias, we used Harman’s Single-factor test. In this regard, we included all the items in the exploratory factor analysis and determined the percentage of the variance accounted for by the first factor in the unrotated solution. According to the pre-specified criterion recommended by Cote and Buckley (1987), if the first factor accounted for less than 40% of the total variance, the common method bias can be ignored. However, the test revealed that the first factor accounted for 36.82% of the total variance in the unrotated solution. This indicates that this study does not have severe common method bias. The quality of the data was very good.

This study systematically examined the relationships among perceived digital traceability technology, perceived value, and purchase intention through structural equation modeling. The final validated theoretical model is shown in Figure 2. The model demonstrated good fit (χ²/df = 2.104, RMSEA = 0.052, CFI = 0.972, TLI = 0.965, SRMR = 0.041), and all hypotheses received data support.

The perceived digital traceability technology had a strong positive effect on the perceived value (β = 0.468, p < 0.001), accounting for 21.9% of the variation in the perceived value (R² = 0.219), supporting H1. The effect of the perceived value on the purchase intention was strong and significant (β = 0.512, p < 0.001), supporting H2. The direct effect of the perceived digital traceability technology on the purchase intention was strong and significant (β = 0.178, p < 0.01), supporting H3. The total model accounted for 37.6% of the variation in the purchase intention (R-squared = 0.376), representing strong explanatory power.

From the mediation analysis, the following results were obtained: the model involving the relationship between the perceived digital traceability technology and purchase intention through the mediator of the product’s perceived value confirmed H4. In specific terms, the total effect of the perceived digital traceability technology on the purchase intention was 0.418, wherein the direct effect equaled 0.178 or 42.6% and the indirect effect equaled 0.240 or 57.4% due to the mediator. Moreover, as per the Bootstrap Test employed for the entire analysis at the 95% confidence level through 5,000 resamples used to test the results for relevance, the 95% confidence interval failed to contain zero.

This empirical analysis identified that the perceived digital traceability technology positively affects the perceived value (β = 0.468, p < 0.001), and the finding perfectly aligns with the numerous studies conducted. Pan et al. (2025) found the same standardized regression coefficient (β = 0.468) among 501 consumers. On the other hand, Yuan et al. (2020) and Tao and Chao (2024) found that the traceability system attribute has a positive effect on consumers’ perceptions of value and product quality cognitions. From the signaling theory angle, the digital traceability technology effectively solves the information asymmetry issue as the product information can be verified, thereby sending signals of quality reliability to the consumers (Treiblmaier and Garaus, 2023). Yet, the meta-analysis carried out by Tran et al., 2024 confirmed that the effect of traceability information among consumers’ willingness to pay emerged differently across the studies. Some studies found insignificant effects of traceability information on purchase intentions among consumers. Moreover, some traceability codes included in the product reduced the purchase intentions. This indicates that the signaling effectiveness of the traceability technology can differ based on the implementation procedures, information formats, as well as the understanding capacity of the customers.

The relationship between perceived value and purchase intention (β = 0.512, p < 0.001) has long been proven. A meta-analysis among 54 studies conducted by Zhuang et al. (2021) revealed the average effect size of r = 0.596 for green perceived value and purchase intention. This relationship has been confirmed in the original theory of the field written by Chen and Chang in 2012. The moderation coefficient used in the current study is reasonable compared to the existing literature (β: 0.188–0.90), representing the general trend toward the conversion of value cognitions toward the formulation of intentions. However, as observed in the previous studies conducted by Liu et al. (2024), a large discrepancy has been identified between the purchase intentions and actual purchase behavior of consumers. This phenomenon has been identified as the “intention-behavior gap.” This can be attributed to the limitations set by the context-related factors of price-sensitivity and purchase convenience as observed in the studies published in 2020.

This study confirmed the partial mediating role of perceived value (with the indirect effect accounting for 57.4%), consistent with the partial mediation pattern reported by Ge (2022). Ge’s study showed that traceability knowledge both directly influences purchase intention (β = 0.193) and produces an indirect effect through perceived value (β = 0.120). The experimental study by Garaus and Treiblmaier (2021) similarly found dual pathways through which blockchain traceability influences retailer selection. This finding aligns with the dual-pathway theoretical framework: digital traceability technology indirectly influences decision-making by enhancing consumers’ cognitive assessment of product value, while also directly triggering purchase intention as a heuristic cue. Notably, Zanoli et al. (2015) found a full mediating effect of trust (100%) in their study of organic labels, contrasting with the partial mediation results in this study. This difference may stem from differences in research objects—organic certification labels, as mature quality signals, exert their effects entirely through trust transmission; whereas digital traceability technology, as an emerging technology, also directly influences decisions through consumers’ perceptions of the technology itself.

Moreover, the results obtained from the study conducted by Liu et al. (2019) revealed that the role of traceability technology can be moderated by the level of trust among consumers toward government regulations and food labels. The results of the study conducted by Duong et al. (2024) revealed that the adverse effect of technology anxiety can moderate the positive role of blockchain traceability.

While the directional relationships among digital traceability technology perception, perceived value, and purchase intention align with theoretical expectations, the contributions of this study extend beyond hypothesis confirmation. Scientific progress often relies on empirical validation of theoretically grounded predictions, and the value of this research lies in several distinct contributions that advance both theory and practice.

This empirical study offers both confirming and quantitative evidence in support of the theoretical integration between digital traceability technology and the behavior of green consumption. Current studies tend to concentrate more on traditional traceability systems or general food safety. This empirical study combines the latest technological advancements in digital traceability techniques and green agricultural products. The empirical verification has clearly shown the mediating role of perceived value in the relationship between the constructs. A Bootstrap test determined that the post-structural effect can be explained at the level of 57.4% total effect. This explains the verification process for the belief-attitude-behavior framework indicating the premise role of the mentioned framework in the development between perceptions and intention behavior in two pathways: the cognitive assessment route (via the role of the actual value at 57.4%), as well as the heuristic route (direct effect at 42.6%).

First, this study provides precise quantification of effect sizes in the digital traceability context. The path coefficient from perceived digital traceability technology to perceived value (β = 0.468) and to purchase intention (β = 0.178), along with the explained variance (R² = 0.219 for perceived value; R² = 0.376 for purchase intention), offer benchmark parameters for future research and practical decision-making. Second, the decomposition of total effects into direct (42.6%) and indirect (57.4%) pathways reveals the dual-mechanism framework through which technology perception converts into behavioral intention—a finding that has not been empirically documented in the green agricultural product context. This quantified pathway analysis provides actionable insights: it demonstrates that while enhancing perceived value is the primary route (cognitive evaluation pathway), direct technology cues also trigger purchase decisions through heuristic processing. Third, this study attempts to integrate signaling theory, technology acceptance theory, and the value-attitude-behavior framework within a unified model for examining digital traceability in green consumption. This theoretical integration extends the explanatory power of each individual framework. Fourth, the empirical validation in the Chinese green agricultural product market, which is characterized by unique food safety concerns and rapid digital technology adoption, provides valuable context-specific evidence for this emerging research domain.

This work offers additional proof for the relevance of signaling theory in the context of the digital age. This experiment has confirmed that the quality signal produced by traceability technology in the digital domain has a positive effect on the perceptions of customers’ values as the asymmetry of information is diminished (β = 0.468). This finding perfectly matches the results obtained by Pan et al. in 2025. The findings of the experiment also highlight the complicated process of signal delivery since the signal produced by the technological factor does not translate wholly to behavior based solely on the cognitive aspect of values but has a heuristic influence at the decision-making stage. This experiment has also contributed to the development of signaling theory since the methodologies used incorporate exploratory factor analysis, the verification of the factors through confirmatory factor analysis, and the structural equation modeling.

This study provides specific advice for the digital transformation of green agricultural product enterprises and e-commerce platforms. In addition, the results obtained for the total effect of digital traceability technology revealed the following: purchase intention β = 0.418. This shows the investment in traceability technology infrastructure has strong business value. In regard to the construction of traceability technology infrastructure among enterprises, priority should focus on the perceptible traceability functions for customers among the mentioned factors: traceability query functions like the query of origin information through the QR code. In view of the important role of the mediating factor of perceived value at 57.4 percent, the announcement of traceability technology should come from the perspective of the enhancement of product quality assurance.

E-commerce platforms should improve the format for traceability information presentation and user experience. They should set up traceability query access points in prominent locations on product pages and use visualization techniques to present supply chain procedures. They should also offer brief summaries of quality certifications. Platforms should improve their role in consumers’ traceability technology awareness through the creation of traceability product zones and traceability quality recognition badges. Policymakers should encourage the development of traceability technology standardization and interoperability development. They should use policy tools like the development of standardized information disclosure formats and the construction of certification systems for traceability platforms. They should also use subsidies to support the cost of applying the traceability technology. In the marketing of traceability technology, the strategy “technology empowerment + value communication” should be employed.

This study has some limitations. In matters pertaining to the design of the research, the fact that the data used were cross-sectional casts some doubt as to the drawing of causal conclusions. While the theoretical rationale and the antecedent literature validate the supposed causal links, future studies should use longer-term data or even experimental designs to validate the supposed causal links. In addition, the fact that the data used in the research were derived from self-administered questionnaires can pose other biases. Even if the results were accepted as free from common method bias (36.82%), the biases of social desirability and recall bias should not be ruled out.

Some limitations related to the sample and context should also be considered. The sample only included mainland Chinese consumers. The specific nature of the cultural and context factors might restrict the generalizability of the empirical results to other cultural contexts. Chinese consumers’ greater concern about food safety and their greater willingness toward new technologies might boost the effectiveness of traceability technology. Cross-national studies among diverse cultural contexts can be considered in future studies to analyze the role of cultural values as a moderation factor for the proposed research model. Furthermore, as noted in the sample description, the respondents were predominantly young, highly educated, and urban-based, with 79.52% holding college degrees or above compared to the national rate of 15.47%. This sample composition, while representative of online green agricultural product consumers, may have led to an overestimation of the effects of digital traceability technology, as these consumers are likely more familiar with and receptive to such technologies, potentially having already internalized the value perceptions associated with these technologies. Future research should include more diverse samples encompassing rural consumers, older age groups, and individuals with varying levels of technology familiarity to examine whether the observed relationships hold across different consumer segments. In the current study, the findings were not presented based on the variety of digital traceability technologies. Various technologies have different levels of reliability, complexities, and expenses. Future studies can analyze the relative impacts of diverse traceability technologies to assist in selecting the appropriate technology for the business.

Regarding measurement approach, this study measured perceived value as a unidimensional construct capturing consumers’ overall assessment of benefits relative to costs. While this approach is consistent with established consumer behavior research (Zeithaml, 1988) and allowed for parsimonious model testing, it does not capture the potentially differential effects of distinct value dimensions. Perceived value can be conceptualized as comprising multiple dimensions, including functional value (quality and utility), emotional value (affective responses), social value (social approval), and economic value (price considerations; Sweeney and Soutar, 2001). In the context of green agricultural products, consumers may weigh health benefits, environmental benefits, safety assurance, and price premiums differently. Future research adopting a multidimensional perceived value framework could reveal which specific value dimensions are most strongly influenced by digital traceability technology and which dimensions most powerfully drive purchase intention. Such fine-grained analysis would provide more targeted guidance for practitioners seeking to optimize their traceability system design and marketing communications.

As regards the specification of the model, this empirical study failed to include the potential role of moderating factors. Variables such as the level of trust, the role of technology anxiety, and the role of customer or product knowledge can play important mediation factors in the efficiency of traceability technology. In the future, the development of more complex conditional models can assist in understanding the factors under which traceability technology can be more effective. As regards the outcome factor involved in the purchase intention level instead of actual purchasing behavior, Liu et al. (2024) observed the lack of equivalence between the outcome factors involved. Future research can also explore the long-term effects and dynamic evolution processes of traceability technology, as well as extend the research to other green product categories or service domains to test the theoretical boundaries of the research framework.