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Abstract

Trustworthiness is considered a key element for any successful online retailing initiative. However, extant digital commerce research has displayed mixed and contradictory findings regarding consumer outcomes of trustworthiness beliefs. The current study explains these mixed effects by investigating how and when trustworthiness beliefs can play a significant role in driving digital commerce behaviors. The authors draw on regulatory focus and regulatory fit theory to examine how trustworthiness beliefs—ability, benevolence, and integrity—influence consumers’ evaluations, purchase intentions, and digital commerce behaviors (e.g., rankings and reviews) toward online retailers, based on their regulatory orientations. Using a sequential multistudy design, the authors test the hypotheses through two experiments followed by a survey. The findings demonstrate that the positive consumer outcomes are the highest for (1) prevention-focused individuals with strong beliefs in the online retailer's ability to fulfill its promises and (2) promotion-focused consumers with strong beliefs that the online retailer is benevolent, and comparably strong for (3) all individuals who believe in the online retailer's integrity. Managerial implications from these findings are further discussed in a value-enhancing manner.

Keywords

digital commerce regulatory focus theory trustworthiness beliefs online behavior online retailing

With the growing prevalence of digital channels, retailers have increasingly adopted strategies that facilitate online consumer purchases (Cheng, Chen, and Huang 2023; Shankar et al. 2022). Despite these advancements, there is evidence that consumer trust in online retailers is on the decline. This consumer skepticism is driven by several trustworthiness-eroding factors, including misinformation, such as fake reviews and fraudulent websites (Cheng, Chen, and Huang 2023; Stathis 2025), negative customer feedback (Zhao, Jiang, and Su 2020), and unethical practices like unauthorized data sharing. For instance, Amazon was recently found to have paid employees to post favorable comments and thus misled customers for years (Sucio 2022). Similarly, Shein faced recent public backlash due to its alleged misinformation, unethical practices, and lack of transparency, prompting some consumers to question its trustworthiness and initiate boycotts (Satran 2022). When such issues come to light, consumer reactions can quickly escalate and gain virality across digital platforms.

Fake news surrounding online retailers can have similar detrimental effects, further complicating the challenge of maintaining consumer trust (Berthon et al. 2022; Visentin, Pizzi, and Pichierri 2019). Misinformation about retailers can prompt significant consequences, including consumers shifting their purchases to competitors or a decline in the retailer's market value (Rao 2022). A notable example is U.K.-based Metro Bank, which experienced an 11% drop in market value after being the target of fake news (Binham 2019). This underscores the vulnerability of online retailers to external factors beyond their control, which can erode trustworthiness and impact long-term financial performance. As a result, maintaining consumer trust becomes a critical strategic imperative, requiring proactive efforts to ensure transparency and combat misinformation (Bodó 2021). While the terms “trust” and “trustworthiness” are often used interchangeably, they refer to distinct constructs. Trustworthiness captures consumers’ perceptions of a retailer's ability, benevolence, and integrity (McKnight, Choudhury, and Kacmar 2002; Schlosser, White, and Lloyd 2006). In contrast, trust denotes consumers’ willingness to accept vulnerability grounded in these beliefs (Mayer, Davis, and Schoorman 1995). In this study, we focus on trustworthiness beliefs as key drivers of evaluation, intention, and digital commerce behavior. Given the centrality of trustworthiness to consumer decision-making in digital environments, our study explores (1) how consumers perceive the trustworthiness of online retailers and (2) the factors that may enhance or undermine these perceptions.

As mentioned, an online retailer's trustworthiness is based on consumers’ beliefs about its ability (to deliver products/services appropriately), benevolence (to put its customers’ interests before profits, acting in goodwill), and integrity (adhering to ethical standards and principles) (Jadil, Rana, and Dwivedi 2022; Mayer, Davis, and Schoorman 1995). However, prior research on these trustworthiness beliefs in online retailing has yielded mixed findings: Some studies report no significant effects (e.g., Li, Zhang, and Yang 2018), while others find positive links for certain beliefs but not others (e.g., Hallikainen and Laukkanen 2021). These inconsistencies underscore the need to further examine the theoretical mechanisms through which trustworthiness beliefs influence digital commerce behavior (see Table A.1 in Web Appendix A for a detailed review of empirical work on the topic).

In this study, we argue that a possible reason for the contradictory results is that past studies have failed to include the trustor's personality traits. Studies that have examined individual differences in consumers’ trustworthiness beliefs have done so mainly from a goal motivation perspective—that is, use of the internet for informational versus recreational purposes (Gupta and Kabadayi 2010; Schlosser, White, and Lloyd 2006; see also Table A.1 in Web Appendix A). In fact, the malleability of trustworthiness beliefs as conditions of consumers’ regulatory orientations (promotion vs. prevention) has yet to be examined. Promotion-focused consumers emphasize potential gains and accomplishments, whereas prevention-focused consumers emphasize avoiding losses and fulfilling responsibilities (Avnet and Higgins 2006; Chernev 2004; Haws, Dholakia, and Bearden 2010; Higgins 1998, 2002). The present research provides theoretical reasoning grounded in regulatory focus theory to account for the mixed findings regarding trustworthiness beliefs. Hence, we empirically address the research question: How does regulatory focus influence the impact of ability, benevolence, and integrity beliefs on consumers’ evaluations and behaviors toward online retailers?

We specifically theorize that the ability dimension will have a stronger influence on individuals’ digital commerce shopping behavior among prevention-focused consumers, whereas for those with a promotion focus, benevolence will play a more pivotal role. Our argument draws from regulatory fit theory that explains how individuals evaluate retailers and products more favorably when there is alignment (fit) between their personal orientation and the attributes of the online retailer or product. Importantly, considering the idiosyncrasies of the online environment, which is characterized by heightened consumer privacy and integrity concerns (e.g., Cheng, Chen, and Huang 2023; Stathis 2025; Zhao, Jiang, and Su 2020), we further theorize that the integrity dimension, in contrast to ability and benevolence, will not significantly differ in terms of how it influences consumers’ digital commerce shopping behavior across regulatory orientations.

We provide support for our predictions by adopting a sequential multistudy design, conducting two experiments and two surveys that together cover both hypothetical and behavioral outcomes, as well as potential mechanisms. Notably, as regulatory focus has been shown to function both as a chronic orientation and as a situational state depending on contextual factors (Haws, Dholakia, and Bearden 2010; Higgins et al. 2001; Pham and Avnet 2004), we capture both perspectives by first manipulating regulatory orientations (situational promotion- and prevention-focused) and trustworthiness in Studies 1 and 2. In Study 3, we instead measure individuals’ chronic regulatory orientations (a stable and consistent tendency to be more prevention- or promotion-focused) and trustworthiness beliefs, in pursuit of more robust overall findings.

Our study makes three notable contributions. First, by theorizing and empirically testing the intricate relationships between individuals’ regulatory focus and trustworthiness beliefs dimensions, we offer a theoretically grounded framework, explaining how and when ability, integrity, and benevolence may play a more significant role in influencing consumers’ evaluations, intentions, and purchase behaviors from an online retailer. Our multistudy approach offers robust and compelling evidence that individual trustworthiness beliefs have idiosyncratic effects on consumer outcomes across digital commerce settings depending on the consumer's dominant regulatory focus. Second, we specifically contribute to regulatory focus theory (Higgins 1997) by being among the first to demonstrate that prevention- and promotion-focused consumers differ in their evaluation, intention, and purchase behavior toward online retailers based on trustworthiness beliefs. By drawing on regulatory fit theory as a theoretical lens, we offer a rationale linking users’ regulatory orientations to their trustworthiness beliefs (ability, benevolence, and integrity) toward online retailers. In doing so, we contribute to a better understanding of the contradictory findings observed about the role of trustworthiness belief dimensions in online commerce. Third, the findings of this study can assist online retailers in better predicting the trustworthiness beliefs that their customers are more likely to emphasize during digital commerce interactions depending on their dominant personality orientations. As a result, online retailers can tailor their digital commerce initiatives to enhance trustworthiness and foster positive perceptions and outcomes among their customers.

The remainder of this article is structured as follows. First, we introduce the study's background and hypotheses by referring to the extant literature. Subsequently, we test these hypotheses using a sequential multistudy design and discuss the results. Finally, we elaborate on the theoretical and managerial contributions, limitations, and opportunities for future research.

Literature Review

Trustworthiness Beliefs (Ability, Integrity, and Benevolence)

Trustworthiness plays a central role in shaping consumer–retailer relationships, particularly in digital environments where direct cues for evaluation are limited (Cachero-Martínez and Vázquez-Casielles 2021). In digital commerce contexts, trustworthiness is typically conceptualized along three dimensions: ability, benevolence, and integrity (Gefen and Straub 2004; McKnight, Choudhury, and Kacmar 2002; Nadeem et al. 2015). Ability refers to consumers’ confidence in a firm's competence to effectively deliver on its promises, benevolence reflects the belief that a firm genuinely prioritizes customer interests over self-serving motives, and integrity captures the belief that a firm consistently adheres to ethical standards and principles in its interactions (Mayer, Davis, and Schoorman 1995; Schlosser, White, and Lloyd 2006). An extensive review of literature by McKnight and Chervany (2001) shows these beliefs to be foundational across trust research in management and the social sciences.

While all three beliefs are conceptually related and contribute to trust formation, they are theoretically distinct and should be examined independently (Colquitt, Scott, and LePine 2007; Mayer, Davis, and Schoorman 1995; Schlosser, White, and Lloyd 2006). For instance, while both ability and integrity are cognitively processed, ability provides instrumental assurance by signaling that the retailer has the technical and operational skills needed to fulfill its promises and meet performance expectations (Schlosser, White, and Lloyd 2006). In contrast, integrity offers moral assurance, rather than performance-based assurance (Colquitt, Scott, and LePine 2007; Kharouf, Lund, and Sekhon 2014). It is grounded in ethical expectations and speaks to consumers’ broader evaluations of right and wrong, especially salient in today's digital marketplace. While ability belief helps consumers determine if the retailer can deliver on its promises, integrity addresses whether the brand can be trusted to act fairly and ethically.

Furthermore, prior research has shown that individual consumers may attribute these dimensions unevenly based on their prior experiences and perceptions (Kim and Peterson 2017; Schoorman, Mayer, and Davis 1996). For instance, a consumer may view an online retailer as highly competent (i.e., high in ability) but simultaneously doubt its ethical conduct (i.e., low in integrity), or believe a retailer is well-intentioned (benevolence) but question its operational competence. Despite widespread agreement on the conceptual importance and distinctiveness of trustworthiness beliefs, findings regarding their effects on digital commerce outcomes remain mixed and, at times, contradictory. Some studies report limited or nonsignificant effects for benevolence and integrity. For example, Lu, Zhao, and Wang (2010) find no significant influence of either benevolence or integrity on consumers’ intention to purchase, while Park, Gunn, and Han (2012) find that integrity reduced perceived risk, yet benevolence had no effect. In contrast, other studies have demonstrated more positive effects. Pavlou and Dimoka (2006) identify a combined influence of integrity and benevolence on price premiums, while Benedicktus et al. (2010) find that ability and benevolence jointly increased purchase intentions in hybrid retail settings. Complicating matters further, Hallikainen and Laukkanen's (2021) findings show that the impact of different beliefs is also somewhat dependent on the cultural context. These divergent findings suggest that the effects of trustworthiness beliefs are likely contingent on contextual or individual-level factors that have been insufficiently theorized to date (see Table A.1 in Web Appendix A for a summary of prior work).

A possible explanation for these mixed results lies in how prior studies treat trustworthiness beliefs either as a unified construct or assess their direct effects without considering individual-level psychological moderators. Some work has introduced goal-related variables such as utilitarian versus hedonic motivations (Gupta and Kabadayi 2010; Schlosser, White, and Lloyd 2006), but these studies often treat consumers as homogeneous in how they interpret trust signals. Moreover, relatively few studies examine behavioral outcomes such as usage and willingness to write a favorable review, while most research focuses primarily on attitudes and intentions (Nadeem et al. 2015; Zhou 2013).

Given this backdrop, we argue that the individual's regulatory orientation, based on their motivational focus on either gains (promotion) or security (prevention), offers a powerful explanatory lens. By incorporating regulatory focus as a moderator, the present research seeks to offer a more nuanced and theoretically grounded explanation for the inconsistent findings in the trust literature, while advancing a more personalized view of how trustworthiness operates in digital retail environments.

Regulatory Focus Theory

Regulatory focus theory argues that people are guided by two distinct types of approach–avoidance goals: promotion-focused goals (i.e., approach gains) and prevention-focused goals (i.e., avoid losses) (Higgins 1997). A promotion focus pertains to growth and achievement, while a prevention focus pertains to security and responsibility (Avnet and Higgins 2006). Regulatory orientations impact decision-making by influencing how individuals seek information and the nature of the information they depend on when making decisions. For example, promotion-focused individuals tend to strive to maximize opportunities and exhibit a greater inclination toward risk-taking (Chitturi, Raghunathan, and Mahajan 2007), while prevention-focused individuals are sensitive to losses or failures and, therefore, more risk-averse (Zhou and Pham 2004).

A handful of prior digital commerce studies have used regulatory focus theory. For instance, Thongpapanl et al. (2018) find that consumer regulatory focus moderates the relationship between consumer motivation to use mobile commerce and perceived value. Chang and Cheng (2021) find that online purchase intentions are encouraged when social word-of-mouth communications align with consumers’ regulatory orientations. Khajehzadeh, Oppewal, and Tojib (2014) use the regulatory focus lens to examine how the type of product offered and its congruency with consumers’ needs influence how mobile coupons are redeemed; they find that promotion-focused consumers attain fit when the offer aligns with their shopping goals and require more personalization to redeem coupons. Conversely, prevention-focused consumers redeemed myriad offers and had no difference in their regulatory fit when an offer was diverted from their shopping goals. Research to date has commonly used regulatory focus theory as a vehicle for testing regulatory fit effects (for details, see Motyka et al. [2013]).

Regulatory Fit Theory

Regulatory fit theory suggests that individuals experience a sense of alignment when their goal pursuit strategies match their goal orientation (Aaker and Lee 2006). Regulatory focus theory and regulatory fit theory complement each other while maintaining distinct characteristics (Ashraf and Thongpapanl 2015; Avnet and Higgins 2006). Regulatory focus theory helps explain what motivates individuals when they try to achieve their goals, whereas regulatory fit theory highlights the importance of using the right strategies that match these motivations (Higgins et al. 2001; Mosteller and Poddar 2017). Specifically, promotion-focused individuals feel a sense of fit when they use eagerness strategies, emphasizing advancement, to achieve their goals, whereas prevention-focused individuals experience fit when they employ vigilance strategies, which emphasize caution, to reach their objectives (Avnet and Higgins 2006).

Research in this area demonstrates that attitudes and choices regarding products and information are more favorable when their attributes align with individuals’ regulatory goals (Chernev 2004; Wang and Lee 2006). Furthermore, past research has shown that regulatory fit affects a wide range of outcomes, such as confidence in judgment (Chitturi, Raghunathan, and Mahajan 2007; Lee, Keller, and Sternthal 2010), product evaluation (Ashraf et al. 2025; Wang and Lee 2006), behavioral intention (Ashraf, Razzaque, and Thongpapanl 2016; Avnet and Higgins 2006), and actual behavior (Hong and Lee 2008; Thongpapanl et al. 2018). Considering the preceding contributions and the explanatory power of regulatory focus and regulatory fit theory, it is surprising that, to the best of our knowledge, no study has yet incorporated these theories to explain how trustworthiness beliefs may impact digital commerce outcomes.

Hypothesis Development

Past research has shown that a retailer's trustworthiness positively impacts consumers’ evaluations and purchase intentions (Fang et al. 2014; Fuller, Serva, and Benamati 2007; Schlosser, White, and Lloyd 2006; Xin, Techatassanasoontorn, and Tan 2015), particularly in the online retailing context (Fang et al. 2014; Lee and Lee 2005; Thongpapanl et al. 2018). Trustworthiness beliefs can be analyzed as either more cognitive (i.e., reliability and competence/ability) or more affective (i.e., demonstrated care and concern/benevolence) (Colquitt, Scott, and LePine 2007; Kim and Sundar 2016; Schlosser, White, and Lloyd 2006).

Regulatory focus theory posits that individuals engage in cognitive or affective processes that align with their regulatory orientations during shopping (Barari, Ross, and Surachartkumtonkun 2020; Haws, Dholakia, and Bearden 2010). Prevention-focused consumers emphasize fulfilling their utilitarian needs, while promotion-focused consumers take utility for granted and expect firms to delight them by fulfilling their hedonic needs (Ashraf, Razzaque, and Thongpapanl 2016; Chitturi, Raghunathan, and Mahajan 2007). Prevention-focused individuals process information based on cognition (Barari, Ross, and Surachartkumtonkun 2020; Pham and Avnet 2004), are more pragmatic about their shopping goals (Gupta and Kabadayi 2010), and base their judgments on cognitive reasoning (Avnet and Higgins 2006); they know what they want and prefer efficient retailers that can deliver on their promises (Förster and Higgins 2005; Gupta and Kabadayi 2010; Lapidot, Kark, and Shamir 2007). Hence, we posit that prevention-focused individuals are likely to assign more importance to the rational trustworthiness belief of ability when engaging in digital commerce.

In contrast, promotion-focused individuals are known to be eager instead of vigilant and seek more experiential and joyful consumption experiences (Higgins 2001). They are more willing to pay a higher price for products when their judgments are driven by affective rather than cognitive processes (Pham and Avnet 2004). One such emotional need is belief in the retailer's goodwill (Hwang and Kim 2007). Benevolent retailers are genuinely interested in consumers’ welfare and are encouraged to seek mutual benefit (Doney and Cannon 1997). Therefore, a retailer perceived as benevolent is likely to be more appealing to promotion-focused individuals when shopping online.

Based on these findings, we expect that prevention-focused individuals, who are cognitively driven, are more likely to rely on the cognitive trustworthiness belief of ability, whereas promotion-focused individuals, who are affectively driven, are more likely to rely on the affective trustworthiness belief of benevolence when engaging in online commerce. The fit experience, in turn, creates a more engaging, satisfying, and trust-enhancing encounter (Ashraf and Thongpapanl 2015). For instance, although prevention-focused individuals may value warmth, kindness, and friendliness, these qualities are secondary to the retailer's ability to fulfil promises and provide dependable services. Therefore, the mechanism through which regulatory orientation impacts the relationship between trustworthiness beliefs and their outcomes is the consumer's sense of fit when a retailer's strengths align with their motivational style:
H₁: Consumers’ regulatory orientations moderate the relationship of trustworthiness beliefs with (a) evaluation, (b) intention to purchase, and (c) digital commerce behavior such that ability has a more pronounced positive effect for prevention-focused individuals than for promotion-focused individuals.

H₂: Consumers’ regulatory orientations moderate the relationship of trustworthiness beliefs with (a) evaluation, (b) intention to purchase, and (c) digital commerce behavior such that benevolence has a more pronounced positive effect for promotion-focused individuals than for prevention-focused individuals.

In contrast to ability and benevolence, we propose that integrity serves as a foundational dimension of trustworthiness that is universally valued, irrespective of individuals’ regulatory orientation. While regulatory fit theory explains how prevention- and promotion-focused consumers prioritize competence and warmth, respectively, integrity reflects a broader judgment of moral and ethical standards that cut across motivational orientations (Kharouf, Lund, and Sekhon 2014; Schlosser, White, and Lloyd 2006). Regardless of the context (e.g., industry, culture, or individual motivations), consumers expect organizations to uphold fundamental ethical principles, including honesty, fairness, and transparency. Whether considering a tech firm handling user data, a fast-fashion brand managing its supply chain, or a bank providing financial advice, consumers tend to evaluate integrity of the retailer through the lens of universally accepted moral standards (Li, Zhang, and Yang 2018; Mayer, Davis, and Schoorman 1995). These expectations are not influenced by situational factors or personal goals but rather reflect stable and universal beliefs about ethical corporate conduct. Indeed, prior research suggests that integrity is often seen as a baseline criterion for trustworthiness, particularly in high-stakes, risk-laden digital contexts where consumers are concerned about issues such as data privacy, surveillance, and online manipulation (Casado-Aranda, Liébana-Cabanillas, and Sánchez-Fernández 2018; Urban, Amyx, and Lorenzon 2009).

Furthermore, recent research suggests that both prevention- and promotion-focused individuals express concerns over integrity in online retail settings, particularly when trust violations, such as data misuse or unethical practices, can have immediate and reputational consequences (Morosan and DeFranco 2015). Thus, while ability and benevolence align with cognitive or affective processing styles, integrity represents a fundamental ethical standard and nonnegotiable expectation—a moral compass by which consumers judge whether an online retailer is fundamentally trustworthy (Kacmar and Tucker 2016; Mayer, Davis, and Schoorman 1995). We therefore expect that integrity beliefs will exert undifferentiated positive effects across both prevention- and promotion-focused consumers.
H₃: The positive effect of integrity on (a) evaluation, (b) intention to purchase, and (c) digital commerce behavior does not significantly differ between promotion- and prevention-focused individuals.

Consistent with the theory of planned behavior (Ajzen 1991), past research has shown that digital consumers’ evaluations and intentions have a significant impact on their decision-making (Ashraf and Thongpapanl 2015; De Guinea and Markus 2009; Giboney et al. 2015; Lütjens et al. 2022; Maseeh et al. 2021). The influence of evaluation and intention on actual usage behavior has also been well explained in past digital commerce research (Nadeem et al. 2015; Wang, Harris, and Patterson 2013). More importantly, evaluation and intention have been identified as key drivers of actual usage behavior (Ashraf et al. 2021; Kim and Peterson 2017; Lütjens et al. 2022; Maseeh et al. 2021; Venkatesh, Thong, and Xu 2012). Hence, we hypothesize the following:
H₄: For both promotion- and prevention-focused individuals, (a) evaluation and (b) intention to purchase have a positive effect on digital commerce behavior.

Overview of Studies

We tested our proposed model (see Figure 1) using a sequential multistudy design. In Study 1, we experimentally manipulated participants’ regulatory focus and retailers’ trustworthiness and examined the moderating role of regulatory focus in the relationship between trustworthiness beliefs and evaluation and intention to purchase from the online retailer, thus establishing a causal relationship. Study 2 builds on the findings of Study 1 and provides external validity by examining how regulatory focus and trustworthiness beliefs influence consumer choices (i.e., ranking and incentivized voluntary reviews). In Study 3, we further establish the external validity of results by using a survey design that also tests the robustness of the theory used by capturing chronic rather than situational regulatory orientations, while also exploring the potential behavioral outcomes of evaluation and intention (i.e., usage).

Figure 1.
Conceptual Framework.

Study 1

Procedure

Study 1 used a 2 (regulatory focus: prevention vs. promotion) × 3 (trustworthiness beliefs: ability, benevolence, integrity) between-subjects design. We recruited 350 participants from Amazon Mechanical Turk (MTurk) in exchange for $.75. Only individuals residing in the United States participated in this study. In line with previous research (Hauser and Schwarz 2018; Puzakova and Aggarwal 2018), we ensured data quality by establishing three criteria for participant selection: have a minimum HIT (Human Intelligence Task) approval rate of 98%, be located in the United States, and have at least 100 approved HITs. We excluded 23 participants from the analysis due to missing key variables or failure to correctly complete the regulatory focus manipulation, such as providing fewer than three strategies.¹ The final sample thus included 327 participants (44.6% female, 55.4% male; M_age = 32.90 years, SD = 9.98).

Study 1 consisted of two ostensibly unrelated tasks. In the first task, we asked participants in the prevention-focused (promotion-focused) condition to take a few minutes to think about and list one current “duty or obligation” (“dream or aspiration”). Next, those in the prevention-focused condition were asked to list three strategies that would help them avoid anything that could go wrong and stop them from realizing their duty or obligation. In contrast, participants in the promotion-focused condition listed three strategies to ensure the successful realization of their dream or aspiration (Avnet, Laufer, and Higgins 2013). We then administered regulatory focus manipulation checks (1 = “do what is right,” and 7 = “do whatever I want”; 1 = “pay back my loans,” and 7 = “take a trip around the world”; Pham and Avnet 2004).

For the second task, participants were asked to imagine they needed to buy a new laptop, and when looking around on the internet, they came across an industry report published in an e-magazine. Participants were exposed to an excerpt from the industry report fabricated by the authors solely for this study. The report appeared to be sourced from a fictional e-magazine called “E-Technology Magazine.” The report talked about a rapidly growing online retailer, TrueBee, that offers a wide range of electronic products across many countries. Participants were randomly assigned to either the ability, benevolence, or integrity condition. The excerpt had identical information, except for the content highlighting the e-retailer's ability, benevolence, or integrity aspect. For instance, the ability manipulation emphasized that “the reason behind TrueBee's success is that it is a competent retailer with sufficient expertise and resources to do business online. Experts further believe that TrueBee has the capacity and capability to effectively and efficiently fulfill its commitments and the ability to meet and exceed customer expectations, as demonstrated by its high customer loyalty scores.” The benevolence condition instead focused on how the reason behind TrueBee's success is that “it acts in the customers’ best interests,” and the integrity condition emphasized that the success was due to the fact that “it values a strong sense of justice.” The full manipulations are available in Web Appendix B. We measured manipulation checks by asking respondents in the ability condition, for example, to indicate whether the report highlights the retailer's ability to do online business, using a seven-point Likert scale (1 = “strongly disagree,” and 7 = “strongly agree”).^2,3

As for the dependent variables, participants were asked to evaluate the e-retailer using a four-item, seven-point scale (1 = “very bad,” and 7 = “very good”; 1 = “very unfavorable,” and 7 = “very favorable”; 1 = “very undesirable,” and 7 = “very desirable”; 1 = “dislike very much,” and 7 = “like very much”). The participants then indicated their intentions to purchase using a four-item, seven-point scale (1 = “very unlikely,” and 7 = “very likely”; 1 = “very improbable,” and 7 = “very probable”; 1 = “very impossible,” and 7 = “very possible”; 1 = “definitely not use,” and 7 = “definitely use”). Next, respondents indicated the extent to which they believed in the scenario (1 = “not at all believable,” and 7 = “very believable”), how reliable they found the report to be (1 = “not at all,” and 7 = “very much”), their familiarity with online shopping (1 = “not very familiar,” and 7 = “very familiar”), and how likely they were to buy a laptop in the next three months (1 = “very unlikely,” and 7 = “very likely”). Finally, participants reported their demographics.

Results

Manipulation checks

As expected, participants’ preferences in the prevention focus condition were more skewed toward the prevention option (M = 2.54) than in the promotion focus condition (M = 4.98; t(325) = 20.32, p < .01). Similarly, the average scores for ability (M = 5.67), benevolence (M = 5.47), and integrity (M = 5.52) in their respective conditions were significantly higher than the midpoint (4) (t_ability(112) = 16.45, p < .001; t_benevolence(105) = 16.15, p < .001; t_integrity(108) = 14.72, p < .001). No differences between the three conditions emerged in respondents’ believability of the scenario, reliability of the report, familiarity with online shopping, or likelihood of buying a laptop in the next three months.

Evaluation

A 2 (prevention vs. promotion) × 3 (ability, benevolence, integrity) ANOVA with retailer evaluation (α = .93) as the dependent variable revealed a significant interaction between regulatory focus and trustworthiness beliefs (F(2, 321) = 6.41, p < .01, $η_{p}^{2}$ = .04).⁴ Follow-up planned contrasts reveal that prevention-focused participants evaluated the online retailer more favorably when its ability was highlighted (M_prevention = 5.76 vs. M_promotion = 5.26; t(111) = 2.16, p < .05; d = .41), compared with promotion-focused participants, supporting H_1a. In contrast, in line with H_2a, promotion-focused participants evaluated the online retailer more favorably when its benevolence was highlighted (M_prevention = 5.32 vs. M_promotion = 5.99; t(104) = 3.05, p < .01; d = .59), compared with prevention-focused participants (see Figure 2). In the case of integrity, and as predicted in H_3a, we did not find a significant difference between prevention- and promotion-focused participants’ evaluations of the online retailer (M_prevention = 5.19 vs. M_promotion = 5.35; t(106) = .68, p > .10; d = .13).

Figure 2.
Evaluations by Regulatory Orientation and Trustworthiness Conditions (Study 1).

Intention to purchase

A 2 × 3 ANOVA with intention to purchase (α = .95; r = .90) as the dependent variable revealed a significant interaction between regulatory focus and trustworthiness beliefs (F(2, 321) = 5.15, p < .01, $η_{p}^{2}$ = .03). Follow-up planned contrasts reveal that prevention-focused participants were more likely to purchase from the online retailer when its ability was highlighted (M_prevention = 4.88 vs. M_promotion = 4.31; t(111) = 2.03, p < .05; d = .38), compared with promotion-focused participants. In contrast, promotion-focused participants were more likely to purchase from the online retailer when its benevolence was highlighted (M_prevention = 4.52 vs. M_promotion = 5.17; t(104) = 2.43, p < .05; d = .47), compared with prevention-focused participants (see Figure 3). In the case of integrity, we did not find a significant difference between prevention- and promotion-focused participants’ intentions to purchase from the online retailer (M_prevention = 4.23 vs. M_promotion = 4.52; t(106) = 1.02, p > .10; d = .19). These results support H_1b, H_2b, and H_3b, respectively.

Figure 3.
Purchase Intentions by Regulatory Orientation and Trustworthiness Conditions (Study 1).

Study 2

Procedure

In Study 2, we aimed to replicate our findings using actual behavior with a choice experiment. We recruited 100 U.S. participants via MTurk for an online survey, offering $1 compensation. To ensure data quality, we followed the same steps as in Study 1, leading to exclusion of 12 participants from the analysis due to missing key variables or failing to correctly complete the regulatory focus manipulation. The final sample thus included 88 participants (36.4% female, 63.6% male; M_age = 33.09 years, SD = 7.03).

To increase the external validity of the study, we first asked participants if they were planning to buy an electronic product online in the near future. Those who confirmed their intention were included in the study. We used the same regulatory focus priming and manipulation checks as those used in Study 1. We employed a two-cell mixed design, with regulatory focus as a between-subjects factor (prevention vs. promotion) and the trustworthiness (ability, benevolence, integrity) of the retailers as a within-subjects factor. Participants in the two regulatory focus conditions were asked to imagine that they were searching online for a retailer from which they could purchase the electronic product that they were planning to purchase in the near future. They were then presented with three fictitious online retailers that were high on either ability, benevolence, or integrity.⁵ The full manipulations are available in Web Appendix C. The retailers were presented in a vertically ordered list (top to bottom), with the order randomized for each participant. We then asked participants to rank the three retailers, with the first being the one from which they were most likely to purchase. They were informed that the price and delivery time of the product were the same for all three retailers. According to regulatory focus theory, and in line with our hypotheses, we expected prevention-focused participants to rank the retailer high on ability as their most preferred among the three, whereas we expected promotion-focused participants to rank the retailer high on benevolence as most preferred.

After completing the ranking task, participants were informed that they could also enter a lottery to win a $5 Amazon gift card if they were willing to write a favorable review regarding the preferred retailer among the three presented. For practical and logistical reasons, the randomly selected winner was later notified and instead received a $5 MTurk bonus payment (Papadopoulou, Hultman, and Oghazi 2025). Participants who agreed to write a review (N = 59; 67%) were shown the three retailers displayed side by side in random order and were asked to select their preferred one to review. Participants then indicated their familiarity with the retailers, reported their demographics, and answered some miscellaneous questions.

Results

For the manipulation check, participants’ preferences in the prevention focus condition were more skewed toward the prevention option (M = 2.75) than in the promotion focus condition (M = 5.03; t(86) = 8.59, p < .01). All participants indicated that they were not familiar with the online retailers.

Overall, 34.1% (N = 30) of participants ranked the retailer high in ability as their top choice, 35.2% (N = 31) ranked the retailer high in benevolence as most favorable, and 30.7% (N = 27) ranked the retailer high in integrity as their first choice. Further, our results reveal that regulatory focus significantly influenced consumer rankings for retailers based on ability (χ² = 9.30, p < .01) and benevolence (χ² = 12.73, p < .01), but not integrity (χ² = .45, p > .10). Specifically, among those who ranked ability first, 77% were prevention-focused, while 23% were promotion-focused (p < .01). Among those who ranked benevolence first, 71.0% were promotion-focused, and 29.0% were prevention-focused (p < .01). Integrity was selected at comparable rates across groups (59% among prevention-focused vs. 41% among promotion-focused, p > .10).

Participants who agreed to write a review (N = 59; 67%) in exchange for entering a lottery were thereafter asked to select their preferred retailer from the three options. We excluded three participants from the analysis because they either did not choose a retailer or did not write the actual review, resulting in 56 participants for the final analysis. We subsequently analyzed the influence of regulatory focus on consumer behavior (i.e., writing a positive review) and found significant results (χ² = 9.32, p < .05). Follow-up comparisons using z-tests of column proportions with Bonferroni adjustment revealed that prevention-focused individuals were more likely provide a review for the ability condition (76%), compared with promotion-focused ones (24%, p < .01). In contrast, among those who chose to review the retailer high in benevolence, 72% were promotion-focused, and only 28% were prevention-focused (p < .01). As in the case of rankings, no discernable differences could be established between the prevention-focused (59%) and promotion-focused (41%) groups (p > .10).

Discussion of Studies 1 and 2

Study 2 builds on Study 1's findings by replicating them on participants’ actual behavior in the form of ranking their choices and writing a favorable review about their preferred choice. More specifically, we show that prevention-focused individuals emphasized online retailers’ ability to fulfill their promises, while promotion-focused individuals put more emphasis on retailers’ benevolence, in support of H_1c and H_2c respectively. Our results further reveal that prevention- and promotion-focused individuals emphasized integrity to a statistically similar extent when considering online retailers, in support of H_3c. Hence, Studies 1 and 2 enable us to establish causal relationships.

Study 3

Study 3 had several objectives. First, we sought to replicate the findings of Studies 1 and 2. By further recruiting participants from a different country using a different panel data provider, we also aimed to demonstrate the robustness and generalizability of the results. Second, rather than manipulating participants’ regulatory focus (i.e., situationally induced), we independently measured individuals’ chronic prevention focus and chronic promotion focus—that is, a dominant or stable personality disposition (Haws, Dholakia, and Bearden 2010; Higgins 1997)—using established measures from the literature. Third, in addition to measuring evaluation and intention, we also measured individuals’ online retailing usage behavior as a dependent variable.

Procedure

We recruited 440 participants from Prolific Academic Platform in exchange for £2. The survey was made accessible only to participants residing in the United Kingdom. We excluded 18 participants from the analysis because they had missing key variables or did not correctly respond to two of the alertness-testing questions. The final sample thus included 422 participants (40.3% female, 59.7% male; M_age = 31.12 years, SD = 11.13). Study 3 comprised two ostensibly unrelated sections. In the first section, participants were asked to imagine that they needed to buy a new laptop, and when looking around on the internet, they came across an industry report, published by a magazine, about a rapidly growing online retailer that offers a wide range of electronic products across many countries. The report also highlighted the attributes of the retailer that had led to its success (see Web Appendix D). The attributes were presented in a questionnaire format, and participants were asked to indicate which attributes they would value more when evaluating the online retailer (1 = “not very important for me,” and 7 = “very important for me”). Next, participants indicated their evaluation and intention to purchase from the retailer on a seven-point Likert-type scale (1 = “strongly disagree,” and 7 = “strongly agree”). We also measured participants’ digital commerce usage on a two-item, seven-point scale (see Table 1 for the measurement items).

Table 1.
Measurement Model with Factor Loadings (Study 3).

Indicators Loadings

Ability ( Schlosser, White, and Lloyd 2006 )

… is successful at the things it tries to do. .90

… is well-qualified in digital commerce. .93

… has much knowledge about fulfilling digital commerce. .88

… is highly capable of performing digital commerce. .84

Benevolence ( Schlosser, White, and Lloyd 2006 )

… is concerned about the welfare of its customers. .83

… values customers’ needs and desires. .89

… looks out for what is important to its customers. .89

… tries to help its customers. .86

… keeps its customers’ best interests in mind. .79

Integrity ( Schlosser, White, and Lloyd 2006 )

… has a strong sense of justice. .88

… is fair in dealing with its customers. .81

… is fair in its use of private user data collected. .87

… is sincere and honest in performing digital commerce. .74

Evaluation

1 = “very bad,” and 7 “very good” .87

1 = “very unfavorable,” and 7 = “very favorable” .90

1 = “very undesirable,” and 7 = “very desirable” .86

1 = “dislike very much,” and 7 = “like very much” .71

Intention ( Kleijnen, De Ruyter, and Wetzels 2007 )

1 = “very unlikely,” and 7 = “very likely” .92

1 = “very improbable,” and 7 = “very probable” .90

1 = “very impossible,” and 7 = “very possible” .86

1 = “definitely not use,” and 7 = “definitely use” .90

Digital Commerce Usage ( Limayem, Hirt, and Cheung 2007 )

How many times you have engaged in digital commerce during a week for the last month? (1 = “Not at all,” and 7 = “Several times a day”) .90

How many times you have used digital commerce during a week for the last month? (1 = “Not at all,” and 7 = “Several times a day”) .91

Promotion Focus ( Haws, Dholakia, and Bearden 2010 )

I feel like I have made progress toward being successful in my life. .61

When I see an opportunity for something I like, I get excited right away. .65

I frequently imagine how I will achieve my hopes and aspirations. .89

I see myself as someone who is primarily striving to reach my “ideal self” to fulfill my hopes, wishes, and aspirations. .83

Prevention Focus ( Haws, Dholakia, and Bearden 2010 )

I worry about making mistakes. .75

I frequently think about how I can prevent failures in my life. .79

I see myself as someone who is primarily striving to become the self I “ought” to be to fulfill my duties, responsibilities, and obligations. .71

I usually obeyed rules and regulations that were established by my parents. .72

In the second section, participants completed a ten-item regulatory focus scale that measures individuals’ chronic regulatory orientations (Haws, Dholakia, and Bearden 2010) using a seven-point scale (1 = “strongly disagree,” and 7 = “strongly agree”). Similar to Study 1, participants rated the extent to which they believed in the scenario, how reliable they found the report to be, their familiarity with online shopping, the type of internet plan they had (fixed and/or variable internet plan), and how likely they were to buy a laptop in the next three months. Finally, participants reported their demographics.

Results

Measurement model

We assessed the factor structure of our measured variables (ability, benevolence, integrity, evaluation, intention to purchase, prevention focus, and promotion focus) using AMOS 27. Our results indicate that the measurement model fit the data well (χ²/d.f. = 2.15, CFI = .96, TLI = .95; RMSEA = .05; RMR = .04). Table 1 shows that the indicator loadings for most of the constructs were well above the common .70 heuristic (Chin 1998), confirming the reliability of our indicators. We had to drop one item each from the promotion-focus and prevention-focus scales as the item loadings were well below the recommended value. Table 2 shows that both the Cronbach's alpha and composite reliability scores exceeded the recommended value of .70 (Chin 1998). Similarly, all average variance extracted (AVE) scores exceeded the recommended value of .50 (Fornell and Larcker 1981). Finally, we checked heterotrait–monotrait (HTMT) values to establish discriminant validity. All HTMT values were below .85 (the strict standard) (Henseler, Ringle, and Sarstedt 2015).

Table 2.
Descriptive Statistics, Discriminant Validity, and Correlations (Study 3).

1 2 3 4 5 6 7 8

1. Ability .89 .35 .48 .41 .32 .36 .01 .12

2. Benevolence .35* .85 .63 .58 .54 .44 .34 .05

3. Integrity .47* .63* .83 .58 .45 .48 .29 .00

4. Evaluation .41* .60* .58* .84 .54 .69 .29 .13

5. Intention .33* .54* .46* .57* .89 .62 .30 .24

6. Use .37* .44* .48* .68* .62* .91 .14 .13

7. Promotion .03 .33* .27* .31* .32* .16 .75 .15

8. Prevention .18* .09 .06 .18 .25* .17 .16 .74

Mean 5.93 5.98 5.87 5.27 4.62 4.03 4.63 4.20

SD 1.11 .94 1.01 1.42 1.30 1.86 1.24 1.24

Composite reliability .94 .93 .89 .90 .94 .90 .84 .83

AVE .79 .72 .68 .70 .80 .82 .57 .55

α .93 .92 .89 .9 .94 .90 .84 .83

p < .10, p < .05, p < .01.

Notes: The diagonal values (bold) represent the square roots of AVE values. Below the diagonal are correlations between the constructs; above-diagonal elements are the HTMT ratio.

Structural model

We first estimated an overall model of the direct effects of trustworthiness beliefs on evaluation and intention to purchase using the full dataset (n = 422). Next, we estimated the direct effects separately for the prevention-focused (n = 183) and promotion-focused (n = 239) groups. See Table 3 for the model fit and direct effect results.

Table 3.
Structural Model Path Estimates and Multigroup Analysis (Study 3).

Constructs Pooled Sample Prevention Focus Promotion Focus Multigroup (Prevention vs. Promotion)

N = 422 N = 183 N = 239

β CI β CI Β CI Δχ² Δd.f.

Paths

Ability → Evaluation .16* (.06, .25) .32* (.13, .49) .11 (.01, .21) 8.29* 1

Benevolence → Evaluation .38* (.24, .51) .25 (.02, .48) .43* (.25, .58) 7.23* 1

Integrity → Evaluation .28* (.13, .42) .10 (−.07, .33) .24 (.18, .51) 2.48 1

Ability → Intention .11 (.01, .21) .39* (.24, .57) .03 (−.08, .13) 4.32 1

Benevolence → Intention .40* (.28, .52) .11 (−.10, .30) .57* (.44, .71) 10.98* 1

Integrity → Intention .15** (.01, .30) .12 (−.07, .30) .14* (.01, .30) 1.21 1

Evaluation → Usage .53*** (.39, .65) .21* (.03, .47) .62* (.49, .73)

Intention → Usage .22* (.09, .35) .51* (.25, .75) .15 (.01,.27)

Covariates

Age → Evaluation .05 .87 −.01 −.14 .05 1.40

Education Evaluation −.01 −.31 −.09 −1.28 .07* 1.67

Gender → Evaluation .04 .92 .07 1.04 .03 .59

Internet → Evaluation −.03 −.67 −.01 −.04 −.06 −1.38

Familiarity → Evaluation .12* 3.02 .14 2.05 .06 1.45

Age → Intention .03 .74 −.04 −.59 .15 2.71

Education → Intention .01 .24 −.10 −1.45 .07 1.24

Gender → Intention .03 .74 .04 .22 .06 1.13

Internet → Intention .05 1.21 .03 .47 .05 .98

Familiarity → Intention .16 3.74 .15** 2.27 .02 .26

Age → Usage .02 .33 −.06 −.96 .04 .76

Education → Usage .05 1.26 .05 .74 .03 .58

Gender → Usage .04 .99 .02 .42 .06 1.09

Internet → Usage .02 .58 −.03 −.45 .05 1.12

Familiarity → Usage −.06 −1.35 .11* 1.78 −.03 −.60

Fit statistics χ²/d.f. = 2.58, CFI = .95, TLI = .93, RMSEA = .06, SRMR = .04 χ²/d.f. = 1.73, CFI = .95, TLI = .94, RMSEA = .06, SRMR = .05 χ²/d.f. = 2.19, CFI = .92, TLI = .91, RMSEA = .07, SRMR = .05

Significant at .10, significant at .05, significant at .01.

Moderating role of regulatory focus

Multigroup analysis using a chi-square (χ²) difference test was used to categorize the differences in individual paths and test the moderating role of regulatory focus (Byrne 2010). In line with previous research (Haws, Dholakia, and Bearden 2010; Higgins et al. 2001), we created a measure of dominant regulatory focus for participants by subtracting the prevention scores from the promotion scores. Positive scores on this measure indicate a predominant promotion focus; negative scores reflect a predominant prevention focus. We separated the individuals into promotion- and prevention-focused groups (coded as 1 for prevention-focused and 2 for promotion-focused) based on their dominant regulatory focus scores.

Table 3 shows that the effects of ability on evaluation and intention for the prevention-focused participants (evaluation: β = .32, p < .01; intention: β = .39, p < .01) were numerically greater and significantly different from those of the promotion-focused participants (evaluation: β = .11, p < .05; Δχ² (d.f.) = 8.29 (1), p < .01; intention: β = .03, p > .10; Δχ² (d.f.) = 4.32 (1), p < .05), thus supporting H_1a and H_1b. The R² values for evaluation and intention were .36 and .35, respectively. Similarly, the effects of benevolence on evaluation and intention for the promotion-focused participants (evaluation: β = .43, p < .01; intention: β = .57, p < .01) were numerically greater and significantly different from those of the prevention-focused participants (evaluation: β = .25, p < .05; Δχ² (d.f.) = 7.23 (1), p < .01; intention: β = .11, p > .10; Δχ² (d.f.) = 10.98 (1), p < .01), in support of H_2a and H_2b (see Figures 4 and 5). The R² values for evaluation and intention were .65 and .47, respectively. As we hypothesized, the effects of integrity on evaluation and intention were not significantly different for prevention-focused participants (evaluation: β = .10, p > .10; intention: β = .12, p > .10) and promotion-focused participants (evaluation: β = .24, p < .05; Δχ² (d.f.) = 2.48 (1), p > .10; intention: β = .14, p < .10; Δχ² (d.f.) = 1.21 (1), p > .10), thus supporting H_3a and H_3b.

Figure 4.
Path Coefficients for Evaluations Obtained from the Structural Model (Study 3).

Figure 5.
Path Coefficients for Intentions to Purchase Obtained from the Structural Model (Study 3).

Post Hoc Analysis to Further Validate Our Findings

We performed a post hoc mediation analysis to test whether evaluation and intention mediate the relationships between trustworthiness beliefs and usage behavior in line with our predictions. As suggested by Hair et al. (2017) and Hayes (2022), we used a bootstrapping technique (PROCESS Model 7) with 5,000 subsamples to estimate the 95% bias-corrected confidence interval of the indirect effect. Mediation analysis reveals that evaluation (β = .07, p < .10; 95% CI: [.01, .27]) and intention (β = .20, p < .01; 95% CI: [.12, .49]) only mediated the ability–usage relationship for prevention-focused individuals. In contrast, evaluation (β = .27, p < .01; 95% CI: [.23, .63]) and intention (β = .09, p < .10; 95% CI: [.02, .27]) mediated the benevolence–usage relationship for promotion-focused individuals. Our results further reveal that evaluation (β = .15, p < .05; 95% CI: [.13, .47]) mediated the integrity–usage relationship for promotion-focused participants only. These findings provide support for H_4a and H_4b; for prevention-focused (promotion-focused) individuals, online retailers’ ability to fulfill their promises (online retailers’ benevolence) plays a more significant role in influencing evaluation and intention to purchase from an online retailer, which in turn influences digital commerce usage.

General Discussion

Digital transformation of traditional customer experiences is of great interest to online retailers (O’Connor et al. 2021). However, online retailers are challenged with defending their trustworthiness among consumers. Our study sought to investigate whether consumers use their beliefs about online retailers’ trustworthiness (ability, benevolence, and integrity) when evaluating and acting toward them. Drawing on regulatory focus theory, we show that for prevention-focused (promotion-focused) consumers, an online retailer's ability to fulfill its promises (act benevolently) plays a more significant role in influencing the consumers’ evaluation and intention to purchase from the online retailer. This, in turn, influences digital commerce behavior via a mechanism of experienced fit. Furthermore, both prevention- and promotion-focused consumers positively evaluate, purchase from, and are willing to write positive reviews for online retailers that behave justly, ethically, and with integrity. Against this backdrop, we contribute theoretically and managerially in the following ways.

Theoretical Implications

From a theoretical perspective, the online retailing literature's excessive focus on a unidimensional trustworthiness construct and lack of proper contextualization may have led to a limited understanding of digital commerce usage. As mentioned previously, past studies examining trustworthiness beliefs were conducted over a decade ago, report mixed findings (Fuller, Serva, and Benamati 2007; Gefen 2002; Hong and Cho 2011; Lee and Turban 2001; Liu et al. 2014; Pan and Chiou 2011; Thatcher et al. 2013), or overlook how trustworthiness beliefs may change depending on individual consumers’ characteristics such as their regulatory orientation (Kim and Peterson 2017). While some scholars acknowledge the importance of individual trustworthiness beliefs (Nadeem et al. 2015; Zhou 2013), contextual factors such as the individual's unique beliefs about the likelihood to gain or lose should also be considered (Hubert et al. 2018; Mayer, Davis, and Schoorman 1995; McAllister 1995). This is relevant to online retailers because consumers’ individual characteristics undoubtedly impact their digital commerce usage (Hubert et al. 2018; Kim and Peterson 2017). Thus, our study first contributes by offering a more detailed and granular understanding of how trustworthiness beliefs can affect individual consumers’ digital commerce usage behavior (e.g., Hillman and Neustaedter 2017).

Second, we draw on regulatory focus theory, which corresponds to online retailing researchers’ calls for using theories that are not commonly used in the field (Ashraf, Razzaque, and Thongpapanl 2016; Huang and Rust 2013). Regulatory focus theory is a motivation-based theory that distinguishes between individuals who are motivated to approach gains (i.e., promotion-focused) or avoid losses (i.e., prevention-focused) (Higgins 1997). Regulatory focus research has shown that promotion-focused (vs. prevention-focused) individuals differ in their perceptions of online privacy (Mosteller and Poddar 2017), product and website attributes (Ashraf and Thongpapanl 2015; Chernev 2004), advertisement messages (Tran et al. 2020; Wang and Lee 2006), and construal level (Hong and Lee 2008; Yang et al. 2021). However, prior to this study, there was little to no significant understanding regarding how individuals’ regulatory orientations can influence the relationship between trustworthiness beliefs and digital commerce usage. Thus, we contribute theoretically by examining the intricate relationship between consumers’ individual regulatory orientation and trustworthiness beliefs. What is more, by investigating the effects of consumers’ regulatory focus both as a situational orientation in Studies 1 and 2 and as a chronic orientation in Study 3, we provide additional evidence of the explanatory power and stability of the theory when it comes to predicting how different trustworthiness beliefs impact various consumer outcomes in digital commerce settings. This is an important extension of the regulatory focus literature.

Practical Implementations

The findings offer several important implications for online retailers seeking to build trust more effectively with consumers. First, our results show that regulatory orientation influences which trustworthiness beliefs consumers prioritize. Specifically, prevention-focused consumers place more weight on a retailer's ability, while promotion-focused consumers rely more heavily on perceptions of benevolence. Integrity, while important across outcomes, does not appear to be weighted differently across regulatory orientations. These differences suggest that trust-building strategies should be adapted to consumers’ motivational styles rather than assuming a one-size-fits-all approach.

First, retailers may wish to tailor the content of their digital communications depending on their target audience. While ability, benevolence, and integrity are all important, attempting to emphasize them simultaneously may dilute message clarity and reduce persuasion. Our results indicate that integrity functions as a universal dimension, crucial for both prevention-focused and promotion-focused individuals. Therefore, while integrity should always be highlighted, the emphasis on ability and benevolence should be strategically adjusted based on the target audience to maximize effectiveness. For example, to appeal to prevention-focused consumers, retailers could emphasize their competence, reliability, and fulfillment guarantees by showcasing certifications, experience, and policy clarity (Colquitt, Scott, and LePine 2007; Schlosser, White, and Lloyd 2006). These cues assure consumers that the retailer can reliably meet expectations, which aligns with prevention-focused consumers’ emphasis on risk avoidance and consistency (Higgins 1997). To appeal to promotion-focused consumers, retailers may instead emphasize their care for customers, values, and relational commitment, such as through emotional language, testimonials, and brand narratives that reflect empathy and long-term engagement (McKnight, Choudhury, and Kacmar 2002; Pham and Avnet 2004). These elements reflect benevolence and are more likely to resonate with promotion-focused consumers’ goal-seeking orientation (Aaker and Lee 2001).

Second, although regulatory focus is not always observable, firms can approximate it through behavioral cues or segmentation indicators. Consumers who regularly engage with content about policies, procedures, and product specifications may reflect a prevention orientation, whereas those drawn to aspirational stories, new arrivals, or social values may reflect a promotion orientation. These patterns can be monitored using CRM (customer relationship management) data, web analytics, or engagement histories, enabling firms to serve communications that emphasize the most relevant trustworthiness cues. In addition, regulatory focus can also be activated situationally through contextual framing (Cesario, Grant, and Higgins 2004). Prevention-focused states can be primed through risk-mitigation language (“avoid delays,” “guaranteed delivery”), while promotion-focused states can be primed through gain-oriented messages (“elevate your experience,” “achieve more”). This distinction between chronic orientation and situational activation enables flexibility in targeting strategies.

Third, these strategies can also extend across multiple communication channels. While trust-building efforts on websites and social media are important, firms can further personalize content through email marketing, push notifications, and targeted ads. For instance, a retargeting email to a prevention-focused consumer might reinforce the retailer's reliability and policies (“We’ve reserved your cart—returns are easy”), while a message to a promotion-focused consumer might highlight opportunity and goals (“Let's complete your journey—your next step awaits”). Aligning trust cues with consumers’ regulatory focus may increase engagement, conversion, and message relevance (Higgins 2005).

Fourth, the findings also have implications for service failure and trust recovery. When trust has been violated, prevention-focused consumers may respond more positively to corrective efforts that clearly demonstrate what the company is doing to ensure the issue will not recur (e.g., process improvements, guarantees). In contrast, promotion-focused consumers may be more receptive to relational repair, including empathetic messaging, apologies, and reassurance of long-term commitment (Kharouf, Lund, and Sekhon 2014). Firms that align recovery strategies with consumers’ trust priorities may be more successful in restoring trust and satisfaction.

Fifth, regulatory focus may serve as a diagnostic lens for optimizing trust-based communication. Firms can employ A/B testing to assess which trust cues (i.e., ability, benevolence, or integrity) perform better across segments or campaigns. For instance, if a competence-heavy message leads to higher engagement, this may suggest a more prevention-focused segment. Conversely, greater response to care-driven messaging may indicate promotion-focused receptivity. This diagnostic approach enables firms to iterate and improve their content strategy based on empirical performance rather than assumption.

Finally, these insights may be further extended to cart abandonment and reengagement strategies in electronic commerce. For prevention-focused consumers, firms might emphasize reminders of security, policy flexibility, or time-sensitive guarantees. For promotion-focused consumers, messaging can highlight aspirational tone, positive outcomes, or personal progress (“You’re almost there,” “Your goals are within reach”). These adjustments are particularly useful in high-drop-off situations where targeted reminders can significantly improve conversion (Kukar-Kinney, Ridgway, and Monroe 2009). Such high-drop-off situations can be during mobile checkout, where consumers may be distracted, hesitant to commit without full information, or unsure about policies. By aligning the trust signal in reengagement messages with the consumer's regulatory orientation, firms can reduce abandonment rates and improve conversion outcomes.

Taken together, the findings suggest that successful trust-building in digital commerce requires more than just communicating credibility; it requires aligning how trustworthiness is communicated with the motivational orientation of the consumer. By tailoring trustworthiness signals to consumers’ regulatory focus, online retailers can improve the clarity, persuasiveness, and effectiveness of their communication strategies.

Limitations and Future Research Directions

As with any other research, this study has limitations that provide opportunities for further research. First, although we managed to establish a causal linkage in Studies 1 and 2, the data from the survey in Study 3 are cross-sectional in nature. We were unable to explore consumers’ changing digital commerce usage. Future research can take a longitudinal approach as time can play an important role in technology-related behaviors (Venkatesh, Thong, and Xu 2012). Second, the combined results of our studies show stable effects across a multitude of attitudinal and behavioral variables such as evaluation, intention, usage, and willingness to write a favorable review, which speaks in favor of the robustness of the findings. Yet, the investigation falls short in capturing actual sales and profit-based metrics. Future researchers are therefore recommended to partner with an actual online retailer to study these effects over time on actual financial outcome variables. Field data like that would add significant value to the collective knowledge about the effects of trustworthiness beliefs.

Third, although our findings point toward a nonsignificant differential effect with regard to the role of regulatory fit on the link between integrity beliefs and outcomes, the results point toward some stable descriptive differences across the sample in that promotion orientation seems to interact more positively than prevention orientation. Such a trend may point toward some potential situations in which the effect of integrity beliefs might be malleable as well. Future research that compares significantly different cultural contexts and non-Western markets might lend some additional insights into this issue since there is evidence that regulatory focus can operate differently in different cultures (Ashraf, Razzaque, and Thongpapanl 2016).

Fourth, we used a black-box approach in capturing how individuals engage in digital commerce. Consumers may perceive websites differently (e.g., Nike vs. Adidas; Walmart vs. Costco), which may merit attention in future research. Finally, we believe that this study is among the first to provide a better understanding regarding how consumers’ chronic regulatory orientations can impact their evaluation of and intention to purchase from an online retailer. Future researchers can advance our findings by examining the competing mediating roles of habit and intention by thoroughly theorizing and empirically testing when one path takes precedence over the other.

Supplemental Material

sj-pdf-1-jnm-10.1177_10949968251365181 - Supplemental material for How and When Trustworthiness Beliefs Influence Digital Commerce Behavior: A Regulatory Focus Perspective

Supplemental material, sj-pdf-1-jnm-10.1177_10949968251365181 for How and When Trustworthiness Beliefs Influence Digital Commerce Behavior: A Regulatory Focus Perspective by Narongsak Thongpapan, Abdul Ashraf, Magnus Hultman, and Raeesah Chohan in Journal of Interactive Marketing

Indicators	Loadings
Ability ( Schlosser, White, and Lloyd 2006 )
… is successful at the things it tries to do.	.90
… is well-qualified in digital commerce.	.93
… has much knowledge about fulfilling digital commerce.	.88
… is highly capable of performing digital commerce.	.84
Benevolence ( Schlosser, White, and Lloyd 2006 )
… is concerned about the welfare of its customers.	.83
… values customers’ needs and desires.	.89
… looks out for what is important to its customers.	.89
… tries to help its customers.	.86
… keeps its customers’ best interests in mind.	.79
Integrity ( Schlosser, White, and Lloyd 2006 )
… has a strong sense of justice.	.88
… is fair in dealing with its customers.	.81
… is fair in its use of private user data collected.	.87
… is sincere and honest in performing digital commerce.	.74
Evaluation
1 = “very bad,” and 7 “very good”	.87
1 = “very unfavorable,” and 7 = “very favorable”	.90
1 = “very undesirable,” and 7 = “very desirable”	.86
1 = “dislike very much,” and 7 = “like very much”	.71
Intention ( Kleijnen, De Ruyter, and Wetzels 2007 )
1 = “very unlikely,” and 7 = “very likely”	.92
1 = “very improbable,” and 7 = “very probable”	.90
1 = “very impossible,” and 7 = “very possible”	.86
1 = “definitely not use,” and 7 = “definitely use”	.90
Digital Commerce Usage ( Limayem, Hirt, and Cheung 2007 )
How many times you have engaged in digital commerce during a week for the last month? (1 = “Not at all,” and 7 = “Several times a day”)	.90
How many times you have used digital commerce during a week for the last month? (1 = “Not at all,” and 7 = “Several times a day”)	.91
Promotion Focus ( Haws, Dholakia, and Bearden 2010 )
I feel like I have made progress toward being successful in my life.	.61
When I see an opportunity for something I like, I get excited right away.	.65
I frequently imagine how I will achieve my hopes and aspirations.	.89
I see myself as someone who is primarily striving to reach my “ideal self” to fulfill my hopes, wishes, and aspirations.	.83
Prevention Focus ( Haws, Dholakia, and Bearden 2010 )
I worry about making mistakes.	.75
I frequently think about how I can prevent failures in my life.	.79
I see myself as someone who is primarily striving to become the self I “ought” to be to fulfill my duties, responsibilities, and obligations.	.71
I usually obeyed rules and regulations that were established by my parents.	.72

	1	2	3	4	5	6	7	8
1. Ability	.89	.35	.48	.41	.32	.36	.01	.12
2. Benevolence	.35***	.85	.63	.58	.54	.44	.34	.05
3. Integrity	.47***	.63***	.83	.58	.45	.48	.29	.00
4. Evaluation	.41***	.60***	.58***	.84	.54	.69	.29	.13
5. Intention	.33***	.54***	.46***	.57***	.89	.62	.30	.24
6. Use	.37***	.44***	.48***	.68***	.62***	.91	.14	.13
7. Promotion	.03	.33***	.27***	.31***	.32***	.16**	.75	.15
8. Prevention	.18*	.09	.06	.18**	.25***	.17**	.16**	.74
Mean	5.93	5.98	5.87	5.27	4.62	4.03	4.63	4.20
SD	1.11	.94	1.01	1.42	1.30	1.86	1.24	1.24
Composite reliability	.94	.93	.89	.90	.94	.90	.84	.83
AVE	.79	.72	.68	.70	.80	.82	.57	.55
α	.93	.92	.89	.9	.94	.90	.84	.83

Constructs	Pooled Sample	Prevention Focus	Promotion Focus	Multigroup (Prevention vs. Promotion)
Paths
Ability → Evaluation	.16***	(.06, .25)	.32***	(.13, .49)	.11**	(.01, .21)	8.29***	1
Benevolence → Evaluation	.38***	(.24, .51)	.25**	(.02, .48)	.43***	(.25, .58)	7.23***	1
Integrity → Evaluation	.28***	(.13, .42)	.10	(−.07, .33)	.24**	(.18, .51)	2.48	1
Ability → Intention	.11**	(.01, .21)	.39***	(.24, .57)	.03	(−.08, .13)	4.32**	1
Benevolence → Intention	.40***	(.28, .52)	.11	(−.10, .30)	.57***	(.44, .71)	10.98***	1
Integrity → Intention	.15**	(.01, .30)	.12	(−.07, .30)	.14*	(.01, .30)	1.21	1
Evaluation → Usage	.53***	(.39, .65)	.21*	(.03, .47)	.62***	(.49, .73)
Intention → Usage	.22***	(.09, .35)	.51***	(.25, .75)	.15**	(.01,.27)
Covariates
Age → Evaluation	.05	.87	−.01	−.14	.05	1.40
Education Evaluation	−.01	−.31	−.09	−1.28	.07*	1.67
Gender → Evaluation	.04	.92	.07	1.04	.03	.59
Internet → Evaluation	−.03	−.67	−.01	−.04	−.06	−1.38
Familiarity → Evaluation	.12***	3.02	.14**	2.05	.06	1.45
Age → Intention	.03	.74	−.04	−.59	.15**	2.71
Education → Intention	.01	.24	−.10	−1.45	.07	1.24
Gender → Intention	.03	.74	.04	.22	.06	1.13
Internet → Intention	.05	1.21	.03	.47	.05	.98
Familiarity → Intention	.16**	3.74	.15**	2.27	.02	.26
Age → Usage	.02	.33	−.06	−.96	.04	.76
Education → Usage	.05	1.26	.05	.74	.03	.58
Gender → Usage	.04	.99	.02	.42	.06	1.09
Internet → Usage	.02	.58	−.03	−.45	.05	1.12
Familiarity → Usage	−.06	−1.35	.11*	1.78	−.03	−.60
Fit statistics	χ²/d.f. = 2.58, CFI = .95, TLI = .93, RMSEA = .06, SRMR = .04	χ²/d.f. = 1.73, CFI = .95, TLI = .94, RMSEA = .06, SRMR = .05	χ²/d.f. = 2.19, CFI = .92, TLI = .91, RMSEA = .07, SRMR = .05

Footnotes

Coeditor

Peeter Verlegh

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported in part by the University of Cape Town's University Research Committee.

ORCID iDs

Narongsak Thongpapanl

Abdul Ashraf

Magnus Hultman

Notes

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Constructs	Pooled Sample		Prevention Focus		Promotion Focus		Multigroup (Prevention vs. Promotion)
	N = 422		N = 183		N = 239		Multigroup (Prevention vs. Promotion)
	β	CI	β	CI	Β	CI	Δχ²	Δd.f.
Paths
Ability → Evaluation	.16***	(.06, .25)	.32***	(.13, .49)	.11**	(.01, .21)	8.29***	1
Benevolence → Evaluation	.38***	(.24, .51)	.25**	(.02, .48)	.43***	(.25, .58)	7.23***	1
Integrity → Evaluation	.28***	(.13, .42)	.10	(−.07, .33)	.24**	(.18, .51)	2.48	1
Ability → Intention	.11**	(.01, .21)	.39***	(.24, .57)	.03	(−.08, .13)	4.32**	1
Benevolence → Intention	.40***	(.28, .52)	.11	(−.10, .30)	.57***	(.44, .71)	10.98***	1
Integrity → Intention	.15**	(.01, .30)	.12	(−.07, .30)	.14*	(.01, .30)	1.21	1
Evaluation → Usage	.53***	(.39, .65)	.21*	(.03, .47)	.62***	(.49, .73)
Intention → Usage	.22***	(.09, .35)	.51***	(.25, .75)	.15**	(.01,.27)
Covariates
Age → Evaluation	.05	.87	−.01	−.14	.05	1.40
Education Evaluation	−.01	−.31	−.09	−1.28	.07*	1.67
Gender → Evaluation	.04	.92	.07	1.04	.03	.59
Internet → Evaluation	−.03	−.67	−.01	−.04	−.06	−1.38
Familiarity → Evaluation	.12***	3.02	.14**	2.05	.06	1.45
Age → Intention	.03	.74	−.04	−.59	.15**	2.71
Education → Intention	.01	.24	−.10	−1.45	.07	1.24
Gender → Intention	.03	.74	.04	.22	.06	1.13
Internet → Intention	.05	1.21	.03	.47	.05	.98
Familiarity → Intention	.16**	3.74	.15**	2.27	.02	.26
Age → Usage	.02	.33	−.06	−.96	.04	.76
Education → Usage	.05	1.26	.05	.74	.03	.58
Gender → Usage	.04	.99	.02	.42	.06	1.09
Internet → Usage	.02	.58	−.03	−.45	.05	1.12
Familiarity → Usage	−.06	−1.35	.11*	1.78	−.03	−.60
Fit statistics	χ²/d.f. = 2.58, CFI = .95, TLI = .93, RMSEA = .06, SRMR = .04		χ²/d.f. = 1.73, CFI = .95, TLI = .94, RMSEA = .06, SRMR = .05		χ²/d.f. = 2.19, CFI = .92, TLI = .91, RMSEA = .07, SRMR = .05