Consumer behavioral outcomes in agrifood traceability: a systematic review and a proposed definition of consumer habit

Technological change in the agrifood sector is transforming consumers' expectations around food safety, sustainability and information transparency. Traceability systems have become crucial for ensuring food safety, quality and sustainability (Aung and Chang, 2014; Anastasiadis et al., 2022). They are increasingly seen as a way to improve transparency (Pant et al., 2015), ensure product integrity (Marozzo et al., 2022) and build consumer trust (Hobbs, 2006; Meijer et al., 2021). Traceability systems also improve value chain efficiency by providing real-time product tracing and tracking, enabling better decision-making and facilitating coordination among actors (Feng, 2017).

Several existing studies have focused on understanding the factors that influence consumer willingness to pay (WTP) (Dickinson and Von Bailey, 2005; Hobbs et al., 2005) and purchase intention (Ding et al., 2022; Buaprommee and Polyorat, 2016) for traceable agrifood products. These methods reflect the various theoretical frameworks and methodological approaches employed in consumer studies to acknowledge the complexity of understanding consumer behavior. The technology acceptance model (TAM) explore adoption (Bandinelli et al., 2023), the theory of planned behavior (TPB) examines behavioral intentions (Spence et al., 2018) and choice experiments and auction methods assess WTP by integrating attitudinal and utility-based perspectives (Loureiro and Umberger, 2007; Bai et al., 2013).

Consumer behavior toward traceable food is shaped by a nexus of factors. The determinants of consumer responses to food traceability include internal factors like habit and behavior frequency, external perception and contextual factors (Chaudhary et al., 2024). Sociodemographic factors and certifications are the most frequently evaluated factors (Vriezen et al., 2023). Product type is also a factor, with consumers more interested in traceability for higher-risk products such as meat (Kehagia et al., 2007a). Psychological factors, such as price consciousness, food safety concerns, health consciousness and environmental concerns shape consumer segments with distinct preferences (Wu et al., 2016). Cultural differences further affect perceptions of traceability (Kehagia et al., 2007a) while consumers' understanding of traceability, trust in the information collected and perceived benefits and risks of traceability technologies affect their attitudes and behaviors (Chaudhary et al., 2024; Mora and Menozzi, 2008). This suggests that behavioral determinants operate differently, therefore needing a more nuanced understanding of the underlying mechanisms.

Although consumer behavior regarding traceable agrifood products has been well studied, insufficient attention has been paid to how existing consumer habits shape consumer behavioral outcomes. Existing methods for understanding consumer behavior in traceable agrifood assume that behavior is deliberate, conscious and intentional. However, consumption choices are often driven by certain habits (Fürtjes et al., 2020; Kaushal and Rhodes, 2015). Habits can either strengthen continued purchase or non-purchase of a product (Wood and Neal, 2009). Habit strength has been shown to predict food choice and purchase behavior and it often outperforms intention-based predictors when contexts are stable (Honkanen et al., 2005; de Bruijn, 2010). Habits operate with minimal conscious effort once formed as opposed to consumer attitudes or perceived risks, which are formed through conscious deliberation (Ouellette and Wood, 1998). Habits tend to be overlooked as a determinant of consumer behavior toward traceable agrifood products, compared to more commonly studied factors such as trust and perceived value (Chaudhary et al., 2024). Habits are therefore important to understand how preferences and purchase intentions are shaped by existing consumer routines and automatic processes, not only by deliberate reasoning. This aspect is not extensively explored, hindering a holistic understanding of consumer behavior toward traceable agrifood products, although attempts have been made to identify behavioral patterns, such as frequency.

There remains a huge gap in our understanding of how consumers value traceable agrifood products. The use of diverse theoretical foundations and elicitation techniques in different contextual settings makes it challenging to synthesize findings and develop comprehensive behavioral models. Furthermore, despite the increasing number of consumer studies, limited attention has been paid to how habits influence purchase behavior. Therefore, this systematic literature review aims to fill these gaps by providing a comprehensive overview of current knowledge on methods for capturing behavioral outcomes in traceable agrifood products. The review seeks to address two research questions.

In addressing the research questions, the review operates on two levels. First, it provides a broad methodological mapping of the behavioral outcomes and elicitation techniques used across the traceable agrifood space (Section 3.2). Second, it examines how the literature defines, conceptualizes and operationalizes habit (Section 3.3).

Prior reviews have already examined antecedents of consumers' decision-making for food traceability (Chaudhary et al., 2024), synthesized the WTP literature for food traceability (Tran et al., 2024; Vriezen et al., 2023) and categorized its determinants (Chaudhary et al., 2024), including habit and behavior frequency as internal determinants, without examining habit in depth. This review extends that line of inquiry and broadens the scope beyond WTP to include traceability-attribute valuation and intention studies and assesses how habit has been conceptualized and measured across the behavioral-outcomes literature.

This systematic literature review is guided by the preferred reporting items for systematic reviews and meta-analyses (PRISMA) 2020 framework (Page et al., 2021), the current standard reporting guideline for systematic reviews. Considering the limited number of studies that explicitly integrate consumer habits into consumer behavior frameworks for agrifood traceability, this review strategically included articles in the consumer domain of traceable food and agricultural products that examine consumer adoption of traceability technologies and consumer valuation of traceability attributes and information. While the PRISMA framework is commonly associated with meta-analyses, its application here is strictly as a reporting guideline for a systematic literature review. A meta-analysis was not performed. The review only employs narrative and thematic synthesis to map behavioral outcomes and elicitation methods in the consumer behavior toward traceable agrifood products literature. A three-sequence search syntax was used in the Web of Science (WoS) and Scopus databases. The first sequence encompasses all possible research in consumer studies. The second sequence includes studies that involve traceability-related research, which may use terms such as “track” or “trace”. The final sequence applies to all products of interest in the food and agricultural domain. The search terms employed were “consumer” AND “trac*” AND (“food*” OR “agri*” OR “crops” OR “livestock” OR “poultry” OR “fruit” OR “aquaculture” OR “dairy” OR “fishery”), utilizing Boolean operators and a wildcard to capture variations of the terms trac, food and agri. The search string was further refined to include only peer-reviewed journal articles written in English and in the final publication stage. The syntax was applied to the “Topic” field in WoS and the “Article Title, Abstract and Keywords” field in Scopus, which are among the largest databases of scientific journals. The search date is January 10, 2025. Hence, the study included only records before that period. The initial search yielded 10,161 records (Scopus: 4,878; WoS: 5,283), which were exported to EndNote for deduplication. After removing the duplicates, the records were reduced to 6,672 for further screening.

ASReview LAB (ASReview LAB developers, 2024), an open-source machine-learning tool, was used to enhance the efficiency and accuracy of the initial screening phase. To expedite the screening process, the tool uses an algorithm to predict article rankings based on the author's initial labels (Quan et al., 2024). Once most irrelevant records are excluded, manually screening all the remaining results may no longer be deemed necessary, as the relevant records have already been considered. For this review, the first author screened the records presented by ASReview for relevance. Before starting the review process, the said author decided to stop screening when ASReview had presented 200 consecutive irrelevant records. This stopping criterion of 200 consecutive irrelevant records was adopted following the data-driven heuristic evaluated by Callaghan and Müller-Hansen (2020), specifically using this threshold in their assessment of stopping criteria among others (50 and 100) for active-learning-assisted screening. The remaining records were automatically classified as irrelevant by the program. The ASReview process was conducted in two iterations to ensure the reliability and consistency of the screening outcomes. The results from both iterations were compared and showed high similarity. Ultimately, ASReview yielded 254 relevant records, 808 labeled records and 6,418 automatically excluded records. Of the 254 relevant records from ASReview Lab, the articles were further screened against the inclusion and exclusion criteria, yielding 145 articles (Figure 1). Of the 145 articles deemed eligible after the full-text screening, additional four articles were identified through reference list checking. This yielded a total of 149 articles used for data extraction.

Only studies focusing on traceability in the context of agrifood products were considered. Studies were included if they evaluated consumer decision-making, such as actual purchase behavior, purchase intention, choice or preference in relation to traceable agrifood products whose traceability systems document key supply chain attributes, including but not limited to product origin, processing details, safety and quality assurance, ethical practice and sustainability information. Studies that focused on consumer attitudes, perceived risks or costs associated with traceability but did not directly link these to consumer decision-making were excluded. Studies that suggested traceability as a potential future solution for acquiring product information, but did not explicitly investigate its use or impact on consumer decisions, were also excluded.

Each eligible study was classified by its geographic location, agrifood product category, measured outcome and whether it measured consumer habit. Studies were coded as measuring habit (Yes) if they included an explicit behavioral frequency, repetition or routine-based variable (e.g. frequency of purchase, frequency of consumption, habitual label-reading) as a measured construct, regardless of whether the original authors explicitly labeled this construct as “habit”. Studies that measured purchase intention, preferences or WTP without any frequency or similar to repetition were coded as “No”. For studies that were coded as measuring habit, the specific habit-related variable was extracted using terms as close as possible to the original author's construct labels so that the diversity of operationalizations found in all the studies is preserved rather than forcing all into standardized categories. Studies that covered multiple products and countries were coded as “multiple food categories” and “multiple countries”, respectively.

Traceability attributes were classified into nine themes (Figure 6). The categorization was based on the attribute(s) examined in each study. Studies that examined multiple traceability attributes were coded under each applicable theme. As a result, the total number of theme classifications (n = 347) exceeds the number of included studies (n = 149). Because there were variations in how the terms associated with traceability were used across all the included articles, the classification was based on what dimension of traceability was evaluated. For example, if the study used “traceable” or “traceable product” alone, it was treated as a product description, which is inherent to the study context since all included studies involve traceable agrifood products by design. On the other hand, if the study used terms like “traceability information”, “traceable information”, “traced information” and “level of traceability information”, they were coded under traceability information and technology because these refer to information disclosed by traceability systems. Terms such as “traceability to the farm” and “farm origin traceability” were coded under origin and provenance, because these describe provenance rather than a specific traceability system or technology. The first author conducted the data extraction and classification. The full details of the attribute classification for all 149 included studies are presented in Appendix 2. A formal risk-of-bias assessment was not conducted, as this review intends to characterize how behavioral outcomes and habit have been studied across the literature.

This section discusses the descriptive characteristics of the 149 included studies. This covers publication trends, geographic distribution, commodity focus and traceability attributes examined.

Interest in consumer behavioral outcomes related to traceable agrifood products has increased over time (Figure 2). Between 2002 and 2015, the field had limited studies, reflecting minimal but gradually increasing academic attention. However, a notable shift occurred around 2018, after which the number of studies increased rapidly, peaking in 2022 at 2021. While 2025 shows a marked drop, this reflects the search cutoff date of January 10, 2025. Hence, only studies published in the first ten days of 2025 were eligible. This is not thus a reflection of a decline in scholarly interest.

As shown in Figure 3, studies are distributed across a range of journals, reflecting both specialization and cross-disciplinary interest. The journals Food Control and British Food Journal are prominent. Articles published in Foods, Canadian Journal of Agricultural Economics and Food Policy also imply a multidisciplinary interest encompassing food sciences, policy implications and economic perspectives. Representations from other journals (1–4) emphasize the cross-disciplinary nature of this research area, which spans environmental impacts, the psychological underpinnings of consumer choices, sustainability and business strategies.

The geographic distribution of studies shows the regional disparities in research attention on consumer behavior toward traceable agricultural products. China leads in the number of published studies in this field, with 58 articles (Figure 4), likely reflecting the country's heightened policy focus on food safety and traceability following food safety incidents, particularly in pork products (Wu et al., 2016). This might have prompted significant government investment in traceability systems and increased academic interest in Chinese consumer responses to traceable food. The country with the fewest dedicated studies is the Philippines, with one study, reflecting the infant stage of traceability consumer research in many Southeast Asian contexts.

The general food category and pork separately have the most articles, followed by beef, suggesting that meat products, especially pork and beef, have received the most research attention. This concentration on meat products shows heightened consumer concern about food safety risks associated with animal products, such as disease outbreaks and lack of transparency across the supply chain, particularly regarding farm-source, handling and logistics information. This has motivated major meat-producing and consuming countries to push for traceability policies and research. Several studies have also covered multiple agrifood products. Similarly, the prominence of apples and dairy products emphasizes the importance of traceability for perishable, commonly consumed foods. Other products, such as milk, fish, rice and meat in general, also appear, but with fewer studies (4–7 each). There is a strong emphasis on fresh agricultural and food products, particularly meat (Figure 5).

As seen in Figure 6, the included studies have a wide range of traceability attributes. A total of 27% examined how the product's traceability system, including technologies such as blockchain, and QR codes, influences consumer behavior. Certification and labeling schemes constituted 22%, highlighting the importance of trust in verifying certain claims through standard seals, and government, private or third-party certification. Some of the certifications examined include organic, Hazard Analysis and Critical Control Points (HACCP) and other inspection certifications. Although food safety accounts for only 9%, it is closely related to certification and labeling schemes, as some food safety claims are also certified. Origin and provenance information accounts for 14%. Consumers may associate geographical origin with product quality and strict safety standards, making it a key credence attribute in traceable food purchasing decisions.

This section addresses RQ1, mapping the behavioral outcomes and elicitation methods that were used across all 149 included studies to categorize the methodological landscape of consumer behavior research on traceable agrifood products.

Economic valuation methods dominate the literature, with WTP being the most frequently studied behavioral outcome (see Appendix 1). This implies recognition that food traceability often entails additional costs, making consumers' willingness to bear these costs an important determinant of market feasibility. The studies of Boncinelli et al. (2018), examining fish catch zone labeling in Italy, and Dickinson and Bailey (2002) examining US meat traceability, showed how WTP measures could inform policy decisions regarding mandatory versus voluntary traceability implementation. Most WTP studies elicit responses using stated-preference methods, such as choice experiments, conjoint analysis, contingent valuation and experimental auctions (Appendix 1).

Experimental auction methods create real market conditions where participants bid real money for products. They generate more realistic estimates. Some of the included studies used real-money experimental auctions that employ various auction formats, including Becker–DeGroot–Marschak mechanisms (My et al., 2018; Hou et al., 2019) and nth-price sealed-bid auctions (Umberger et al., 2009; Wu et al., 2016).

Purchase intention is the second most studied behavioral outcome, which is commonly elicited using self-reported Likert and semantic differential intention scales. The included studies employed established measurement approaches to understand purchase intention mechanisms. Studies that employed self-reported measures examined various constructs such as attitudes, trust, perceived value and their influence on purchase intention. This methodological approach aligns with the theoretical emphasis on the TPB, TAM and related frameworks that examine the antecedents of specific consumer intention. On the other hand, a few studies examine actual purchase behavior rather than intentions alone (Brunoro et al., 2020; Liu et al., 2024; Duong et al., 2025b).

Intention to adopt studies represent technology-focused measures that acknowledge traceability systems as technological innovations requiring user adoption decisions (Bandinelli et al., 2023; Karyani et al., 2024; Kim and Woo, 2016; Kumar et al., 2022; Lin et al., 2021; Martinelli and De Canio, 2024) and intention to switch (Lin et al., 2021; Nguyen et al., 2022). These are primarily used in studies examining blockchain-based and quick response-supported traceability systems, thereby making consumer acceptance of the technology a distinct research question, separate from conventional factors that drive food choice. Repurchase intention as a behavioral outcome suggests that traceability systems can influence consumer loyalty and repeat-purchase patterns (Wang et al., 2021).

On the other hand, studies that focused on evaluating specific product attributes using conjoint analysis and best-to-worst scaling methods focus on how consumers value and prioritize various traceability attributes, such as certification, origin, safety information and production, rather than solely measuring monetary valuation (Castro et al., 2021; Marques et al., 2022; Saidi et al., 2024; Zhai et al., 2023; Bai et al., 2013).

Mapping the theoretical frameworks utilized across the included studies directly addresses the RQ1 by characterizing the conceptual foundations used to analyze consumer behavioral responses toward traceable agrifood products. Lancaster theory (Lancaster, 1966) and random utility theory (McFadden, 1972) were the most represented among the included studies. Lancaster's theory, which emphasizes consumers' valuation of product attributes rather than products themselves, provides a framework for understanding how traceability attributes contribute to total product utility. The Lancaster theory is often complemented by the random utility theory to analyze consumer choice processes under uncertainty.

The TPB (Ajzen, 1985) and its extensions are well-established frameworks that have been applied among the included studies. This theory examines attitudes, subjective norms and perceived behavioral control and provides a framework for understanding how these factors ultimately affect purchase intentions and behavior. Studies using TPB and its extended variants on traceable agrifood products examine how factors such as trust (Lin et al., 2021; Carfora et al., 2021), habits (Spence et al., 2018; Dang and Tran, 2020), perceived risk (Dang and Tran, 2020, 2021) and information system quality (Lin et al., 2021) affect consumer intentions toward traceable products.

Technology-focused theoretical frameworks, including TAM (Davis, 1989) and its variants, show that modern traceability systems increasingly rely on digital technologies and require user acceptance. This framework is used to examine the influence of perceived usefulness, ease of use and related constructs on consumer adoption of technology-mediated traceability systems, such as QR codes and blockchain-supported systems. Stimulus-organism-response theory (Duong et al., 2025a), stimulus-organism-behavior-consequence (SOBC) framework (Duong et al., 2025b), information systems success models (Ho et al., 2025) and the push-pull-mooring frameworks (Jin et al., 2023) apply theories from related disciplines such as psychology and technology diffusion to understand consumer engagement toward traceable agrifood products.

The predominance of TPB and TAM clearly shows that the included studies rely on deliberative intention-based frameworks that assume conscious purchasing decision-making among consumers. Hence, the automaticity and cue-dependence of consumer behaviors seem not to have been paid attention to.

This section addresses RQ2 by examining how the 43 studies that incorporated habit measures operationalized and measured consumer habit. Only 43 of the 149 articles integrated habit measures into assessments of consumer behavioral outcomes for traceable agrifood. Frequency of consumption and the most frequently-used purchase channel are commonly employed as proxies for habitual behavior. They provide quantitative measures of the consumer's established consumption patterns. Studies measuring behavioral frequency specify a specific time frame (e.g. weekly, monthly or yearly). Another way behavioral frequency is measured across the included studies is through ordinal frequency-response scales or dummy variables to answer the question, “Do you usually buy this product?” (Bandinelli et al., 2023). Questions regarding the most frequently-used purchasing channel present respondents with predetermined options, like supermarkets, wet markets, convenience stores and online markets.

Some studies also examined the frequency of certain behaviors, such as reading labels, which is measured as a dummy variable indicating whether consumers typically seek product information through product labels rather than scanning. Boncinelli et al. (2018) used a dichotomous response to characterize whether a respondent frequently reads information on product origin (1 = frequent reader; 0 = otherwise). On the other hand, My et al. (2018) transformed a Likert scale into a dummy variable where 1 is when the respondent answered often or always, and 0 otherwise. Meléndez et al. (2025) used a frequency scale to measure reading product labels of consumers, where they could respond always, most of the time, about half the time, sometimes or never. Similarly, Menozzi et al. (2015) assessed habits through agreement with statements regarding the frequency and automaticity of searching for information about producers, production processes and certificates when buying chicken or honey. Tran et al. (2022) used a 10-point scale to capture how often the respondent buys water spinach with either VietGAP or organic label, yet it does not reflect consumer habits.

It is also noteworthy that a few studies have adapted and modified established measures of habit and automaticity. Spence et al. (2018), Dionysis et al. (2022), and Dang and Tran (2020) employed a four-item self-behavioral automaticity index to assess information-seeking habits regarding the country or region of origin, production processes and assurance schemes when purchasing beef products. Respondents rated statements such as “I do automatically,” “I do without having to consciously remember,” “I do without thinking” and “I start doing before I realize I'm doing it” on a 7-point scale (Spence et al., 2018). The items employed by Spence et al. (2018), Dionysis et al. (2022), and Dang and Tran (2020) are drawn from the canonical self-report habit instrument developed in behavioral psychology, which is the self-report behavioral habit index (SRHI: Verplanken and Orbell, 2003), that distinguishes habit strength from past behavioral frequency, and the self-report behavioral automaticity index (SRBAI: Gardner et al., 2012), which separates the automaticity part of habit. The study highlighted the significant positive effects of the habit of seeking information about the production process and origin on buying traceable beef steak, whereas the frequency of this behavior showed no significant correlation with purchase intention.

Validated habit measures, such as the SRHI (Verplanken and Orbell, 2003) and SRBAI (Gardner et al., 2012), have not been widely adopted in the traceable agrifood literature, despite being widely used in health and consumer research. This emphasizes the field's reliance on frequency-based proxies and motivates the definition proposed in this review.

The result of this review concurs with the earlier systematic reviews of Vriezen et al. (2023), Chaudhary et al. (2024) and Tran et al. (2024) that the literature on consumer valuation to assess consumer WTP for traceability is concentrated on the use of economic valuation methods, particularly stated-preference and hypothetical methods. Stated-preference methods are widely used because they are more efficient for collecting large-scale datasets (Liu et al., 2023; Solomon, 2020) to support generalizability within the sampled population. Among stated-preference methods, choice experiments are the most widely employed. For instance, Ward et al. (2005) and Umberger et al. (2009) used real money to elicit willingness to accept (WTA) and WTP through an experimental auction format. Umberger et al. (2009) further cautioned that even though auctions are less hypothetical, the bids consumers made during the experiment may not reflect the actual price they are willing to pay, but the study gives a benchmark of what matters most to buyers.

Stated-preference methods are vulnerable to hypothetical bias because respondents might overstate their WTP compared to what they would actually pay in real purchasing situations (Murphy et al., 2005; Fifer et al., 2014). Although hypothetical bias is a significant issue across consumer research, it tends to affect stated-choice methods more (Fifer et al., 2014). Biases are more pronounced in studies involving public goods or socially desirable attributes compared to private goods (Gschwandtner and Burton, 2020; Murphy et al., 2005). Hence, when designing consumer surveys for agrifood traceability, it is important to clarify how traceability is conceptualized, particularly because traceability systems may provide both private and public goods. One way of mitigating hypothetical bias is by introducing calibration techniques like “cheap talk” or budget reminders (Fifer et al., 2014; Murphy et al., 2005). Aside from cheap talk, using certainty scales at the end of the survey to gauge participants' confidence in their answers has also been shown to reduce hypothetical bias in stated-choice models (Fifer et al., 2014). This approach encourages respondents to think more carefully about their responses, which reduces the gap between what they say and what they would actually do.

Economic valuation methods are restricted to observable choices and stated preferences. Economic methods quantify what is valued but not why those values are formed. They tend to overlook the intricate psychological and behavioral factors that underlie consumer decision-making. To better understand consumer behavior related to agrifood traceability, economic models are often complemented by psychological and behavioral theories.

Chaudhary et al. (2024) adapted the framework of Lavidge and Steiner (1961) describing the stages through which consumers undergo exposure and awareness, leading to cognitive and emotional responses, shaping attitudes and preferences that drive purchase intentions and ultimately result in actual buying behavior. These are all influenced by various internal and external factors. It opened the discussion of how consumer behavior regarding food traceability increasingly draws on established psychological and behavioral theories to explain why and how consumers value traceability.

The findings of this research are consistent with a prior systematic review (Chaudhary et al., 2024), which states that TPB and TAM are the most widely adopted theories in the traceable agrifood domain. These methods often rely on self-reported intentions instead of actual behaviors. Mental biases and heuristics significantly influence consumer responses (Oxoby and Finnigan, 2007). Transparency, safety and ethical assurance drive consumer intentions (He, 2024). However, these expectations are often shaped by marketing experience, prior experience and broader cultural or societal values (Kehagia et al., 2007b), which introduce heuristics and context-dependent influences that intention-based frameworks usually do not fully account for.

Among the included studies that used theory-based approaches, they are predominantly analyzed using structural equation models (Bandinelli et al., 2023; Ding et al., 2022; Buaprommee and Polyorat, 2016). Structural equation models are used to test the relationships among variables, including direct, mediating and moderating effects. However, these theory-based frameworks provide little insight into long-term consumer engagement with traceability systems. While the literature yields generalizable findings on trust, risk and social influence, it lacks substantial evidence of behavioral patterns, particularly those driven by habit.

This section addresses RQ2, which critiques how habit is interpreted in the included articles and proposes a definition of consumer habit for traceable agrifood.

Mazar and Wood (2018) emphasized the need to start with a definition, as it will affect how the habit will be measured. Habit is conceptualized as cue–response associations in memory, acquired through repetition of an action in a stable context (Wood and Rünger, 2016). On the other hand, Orbell and Verplanken (2010) characterize habit as a form of automaticity involving the association of a cue and a response. Gardner (2015) defines it as a process by which a stimulus generates an impulse to act resulting from learned stimulus-response associations. Once established, these behaviors are triggered by contextual cues and operate independently of intentions or active goals (Wood and Neal, 2009; Orbell and Verplanken, 2010). This means that habits are distinct from goal-directed behaviors, emphasizing the automaticity and context dependence (Wood and Rünger, 2016). Habits are not just repeated behaviors. They are automatic and performed with minimal conscious deliberation in response to cues rather than through intentional decision-making (Ouellette and Wood, 1998).

Fleetwood (2021) argues that defining habit as mere behavior, repetition or a tendency is insufficient. Instead, Fleetwood emphasizes that habit should be understood as a cognitive representation of a cue-action response. This means that the core of a habit is the mental link formed between a specific cue, such as a traceability label or QR code associated with certain traceability attributes, and the corresponding action (purchasing the product), rather than just the behavior's repeated or automatic nature. Fleetwood's definition highlights that context provides the stable environment in which cues are experienced repeatedly, enabling the cue-action association to form and strengthen in procedural memory. Hence, even if the habit is not always enacted, the habit will persist because the cue-action association remains in memory. Fleetwood (2021) also mentions that habitual engagement is demi-regular in nature, which means that it tends to occur when context is stable, but may be interrupted by competing influences.

Among the included articles that incorporated habits, behavioral frequency was used as a proxy for habit to implicitly assume that repeated purchasing reflects habitual behavior. There is not much meaningful interpretation of the frequency of purchase or consumption frequency. For example, the variable “frequency of purchase or consumption of the product” (not necessarily the traceable agrifood) can be interpreted in two ways: First, if it is significantly negative toward intention/choice for agrifood traceability, it would mean that the more frequently consumers purchase a “product” that may not be traceable, the lower the likelihood that the consumer will choose traceable agrifood. This could be because they have already established patterns of choice. These repetitive behaviors or patterns may motivate them to consistently purchase the same products (Ji and Wood, 2007; Wood and Neal, 2009). Second, if it is positive (i.e. those who tend to purchase the product more frequently are more likely to choose agrifood), it would indicate that, despite already established patterns, they are more likely to prefer traceable food. However, the reason they would do that depends on the other variables studied in the model. Yet, neither interpretation gives direct evidence of habit. Because the interpretations are ambiguous, behavioral frequency is an insufficient proxy for habits and is not a meaningful measure of consumer engagement with traceable agrifood products.

Frequency variables cannot capture the defining mechanisms of habit. Habits are cue–response associations where environmental cues activate automated actions (Orbell and Verplanken, 2010; Wood and Rünger, 2016). Although numerous studies have examined how frequently the habitual act occurs, little research provides the context needed to develop a complete understanding of habit. While habit is highly associated with behavioral frequency, it is not equated with it. Frequency of behavior can only tell how often a behavior occurs but not the cue-action association that initiates and sustains it (Gardner, 2015; Labrecque and Wood, 2015). Some studies attempt to control this by asking about observable context cues, such as the most used purchasing channel, the usual place of purchase and the frequency of reading labels, which precede the behavior and function as habit cues. This context is important, as it not only confirms that a repetitive behavior exists, but also where and when it is likely to be practiced. For example, consumers may routinely purchase certain products in specific settings. Therefore, introducing enhancements, such as product traceability, might be more effective, as these locations may serve as cues for habitual behavior. However, while these contexts are provided, they are not enough to be considered a habit. Moving forward, it is important that, in examining habitual behaviors, the inclusion of contextual cues and cue-action links is established.

Another characteristic of habit is that it is goal-independent and requires minimal cognitive effort to perform. According to Mazar and Wood (2018), once a habit is formed, the behavior is enacted automatically as a response to contextual cues with little need for deliberation. The efficiency of habit ensures that such actions continue and influence regular behavior patterns. For example, some studies have collected data on how often people search for information or read labels, but these measures capture only intentional behaviors and not automatic habitual responses. Habits, on the other hand, continue even when there are no specific goals (Gardner, 2009; Mazar and Wood, 2018). This means that habitual engagement with traceability cues would continue even without requiring consumers to seek traceability information deliberately each time they make a purchase.

Clearly, the traceable agrifood domain lacks a coherent, consistent definition of consumer habits, as evidenced by the various measurement approaches and limited theoretical integration. Current measures in agrifood traceability range from simple frequency counts to more complex scales but rarely capture the automaticity or context dependence central to psychological definitions of habit. This significant gap in consumer studies on agrifood traceability emphasizes that consumer habits remain underexplored, resulting in conceptual ambiguity and inconsistent operationalization across studies. Because of this, it is challenging to compare findings and to understand the role of habit.

This study proposes a definition for consumer habits in the context of traceable agrifood. This contribution aims to standardize future research and enhance the understanding of how habitual behaviors influence consumer engagement with traceable food systems. This study seeks to incorporate the conceptual elements operationalized by Verplanken and Orbell (2003) and Gardner et al. (2012) in relation to the repetition in stable context and automaticity of habit. The proposed definition also incorporates cognitive representation of habit (Fleetwood, 2021) and cue–response associations in memory (Wood and Rünger, 2016). Consumer habits toward traceable agrifood products are learned cognitive associations between marketplace cues that indicate the presence of traceability information and purchasing behaviors, developed through repeated actions in stable contexts and automatically activated with minimal conscious effort upon re-encountering those cues. This definition highlights the following elements of habits:

1. Cue-action mental link – there exists a mental link in memory between a cue and an action such that when consumers see visible product labels or signs (i.e. QR or bar codes or RFID) that signal the presence of traceability information and/or certifications, including but not limited to safety and origin, they mentally connect it to “buy product.”

2. Automaticity – due to cue-action mental association, there is little conscious effort needed to enact an action when the cue is present, wherein buying a product that has traceability cues almost happens automatically.

3. Acquisition through repeated behavior – This association is formed when a consumer, through repeated exposure to traceability cues in stable purchase contexts, gradually develops an automatic purchase response.

In the context of traceable agrifood, having cues that draw visual attention, such as labels, QR or bar codes and RFID, may facilitate habit formation by serving as consistent contextual triggers, especially when consumers encounter them repeatedly in stable purchase environments. Furthermore, it is important to make cues consistent and available across purchase contexts to sustain the learning sequence and reinforce the habit over time.

Several methodological limitations regarding the review process itself should also be noted. Screening was conducted by a single reviewer, introducing a risk of subjectivity in inclusion and exclusion decisions that was not mitigated by a second independent screener. To reduce this risk, the screening decisions were guided by explicit, pre-specified eligibility criteria which applied consistently across all articles. The ASReview process was run in two independent iterations, and the comparison of the results showed that the outcomes were similar, indicating consistency in the screening judgments made.

The stopping rule of 200 consecutive irrelevant records is consistent with the thresholds used in comparable machine-learning-assisted reviews (Callaghan and Müller-Hansen, 2020). However, this was not validated through a post hoc sensitivity check that extended screening beyond the stopping point. To reduce the risk of stopping too early, the two iterations served as an internal consistency check. For each iteration, the stopping rule threshold was set at the upper range reported in other reviews that used machine-learning tools in screening (König et al., 2024). The heuristic nature of the stopping rule approach is acknowledged. Future reviews using active-learning-assisted screening would benefit from dual independent screening of at least a subsample, alongside more rigorous, multi-criteria stopping heuristics (e.g. Boetje and van de Schoot, 2024).

This review limits the conceptualization of habit to consumer habit psychology (Verplanken and Orbell, 2003; Gardner et al., 2012; Wood and Rünger, 2016) and habits as used in socio-economic studies (Fleetwood, 2021). As such, the range of interpretations may not fully account for alternative views of definitions that arise from other fields, such as neuroscience or broader psychological theories of habit. As a result, the proposed definition may not have incorporated other relevant constructs emphasized in other disciplines. Furthermore, this review focused solely on consumer behavior toward traceable agrifood products, and the findings and conclusions may not be directly applicable to non-consumer contexts.

The review shows the predominance of economic valuation methods, particularly WTP studies, to evaluate behavioral outcomes. There is also an increasing use of psychological and technology acceptance frameworks to capture the intricacies of behavioral intentions and actual purchasing behaviors. This study shows a gap in the conceptualization and measurement of habit in the traceable agrifood product literature. Habit is often equated with behavioral frequency, which neglects the automaticity, cue-dependence and cognitive representation that define habitual actions according to psychological theory.

This review proposes a definition of consumer habit specific to agrifood traceability, emphasizing the learned, cue-driven cognitive associations that enable consumers to engage automatically with traceable agrifood products that are signaled using labels or visible indicators containing traceability information. This definition clarifies that habits should not just be treated as repeated behaviors, but as cue-action, context-sensitive and automatic responses triggered by stable cues, which require minimal conscious deliberation. This definition seeks to standardize future research and encourage a more nuanced understanding of how traceability systems can become embedded in everyday consumer routines to foster automatic and sustained consumer engagement with traceable agrifood products.

The geographical concentration in East Asia and China warrants caution in generalizing this review's findings to developing and emerging economies more broadly. China's food and retail regulatory environment is in rapid transition, as recent governance research highlights the food safety oversight within China. This is due to the absence of a certification standard and fragmented enforcement of traceability mechanisms in loosely regulated segments of the food industry (Tang et al., 2025). The studies reviewed in this manuscript thus may not accurately represent the retail and institutional conditions found in other developing and emerging economies, where traditional and informal retail channels are still considerably more predominant. This distinction matters because context stability is needed for habit to form, as conceptualized in this review.

The differences mentioned have implications for how traceability systems should be designed. Habitual behavior depends on repeated performance of an action within a stable context, such that a cue can reliably and automatically trigger an associated response (Wood and Neal, 2009). Yet, in many developing economies such as in Asia and Africa, wet markets, small independent retailers and other informal market outlets dominate the fresh food purchasing sector even as modern retail continues to expand in these regions (Oparebea Boateng et al., 2023). When traceability cues such as QR codes or standardized labels are inconsistently present or absent, the contextual stability required for habit formation is undermined. Consumers in this type of setting may be more deliberate with traceability information rather than progressing toward the automatic, minimal-effort habitual engagement that is assumed by the literature. This deliberate behavior may be more disproportionately set in higher-income or already modernized retail settings. Furthermore, in developing countries, consumers tend to trust government authorities and third-party institutions more than producers or retailers directly when verifying food safety and traceability claims, a pattern that contradicts consumer behavior in developed countries, which more often place greater trust in local farmers and retailers (Wu et al., 2021). Hence, habit formation around traceable agrifood is likely to be slower and more fragile in fragmented retail settings, which may need sustained repeated exposure strategies instead of one-time interventions.

This implies that traceability systems designed for deliberate consumers might fail in markets where purchasing is habitual and cue-driven. When consumers rely on automatic responses rather than conscious deliberation, traceability information that lacks consistent, visible cues will not enter the decision process and will not change purchasing behavior. It might not generate the consumer demand signals that justify farmer-level investment in traceability compliance. This proposed definition implies that systems built around repeated exposure to consistent traceability cues, until engagement becomes automatic, are most likely to generate sustainable behavior change, consequently making traceability adoption viable for smallholder farmers.

The supplementary material for this article can be found online.