While Augmented Reality (AR) places a product visually and spatially inside the consumer’s own environment, it cannot deliver touch. The more faithfully AR renders an object, the more conspicuous its missing tactile channel becomes. We term this the reality–virtuality paradox, and it sits outside the prevailing account in interactive marketing, which has largely equated richer immersion with more favorable consumer response. In this study, we develop and test a chain mediation model explaining how AR, relative to 2D, 3D, or VR interfaces, elicits compensatory behaviors sequentially through perceived haptic gap and haptic craving. Study 1 (N = 80, AR vs. 2D), together with a same-device replication (Study 1b, N = 60), confirms the main effect of AR on compensatory behaviors. Study 2 (N = 228, AR vs. 3D) documents the chain mediation and the moderating effect of need for touch (NFT). Study 3 (N = 160, AR vs. VR × low vs. high haptic accessibility) establishes the moderating effect of haptic accessibility and extends the effect to a cross-context tactile-relevant choice. AR elicits stronger compensatory behaviors than 2D, 3D, and VR; the effect is sequentially mediated by perceived haptic gap and haptic craving; and it is stronger for high-NFT consumers and under low-haptic-accessibility conditions. We identify a perceptual cost that rises with visual fidelity, extend compensatory consumption from resource scarcity to sensory scarcity, and specify the individual and situational conditions under which the paradox binds most tightly.
Research on generative AI acceptance increasingly identifies new moderators, notably subscription tier, alongside the classic moderators of gender, age, and prior usage experience. Prior studies rarely ask whether these factors condition acceptance once their mutual confounding is addressed, and comparisons of paid and free users routinely contrast groups that also differ in prior usage experience. This study examines which factors moderate users’ acceptance and use of generative AI. Drawing on an extended UTAUT2 model with personal innovativeness and trust, and a use construct capturing reported duration, frequency, and intensity of use, survey data from 765 Croatian users were analyzed using PLS-SEM. Subscription tier, gender, age, and prior usage experience were examined as candidate moderators, with subscription tier and prior usage experience entered jointly as interaction terms on the full sample. Habit showed the strongest association with both behavioral intention and use behavior. The subscription tier initially appeared to moderate the effect of behavioral intention on use behavior, but that difference did not persist once prior usage experience was modeled alongside it. Gender and age produced no robust differences. Prior usage experience was the only candidate whose moderation survived joint estimation, control variables, and adjustment for multiple testing: trust was strongly associated with behavioral intention among newer users and much less so among experienced ones. Trustworthiness signals therefore serve newer users, while sustained use among experienced users rests on habit.
International trade has become increasingly digital, yet cross-border transactions remain document-heavy and fragmented across organizations. Blockchain offers a possible response, but firm-level implementation in Chinese cross-border digital trade remains uneven. Prior research often treats blockchain adoption as a binary decision, leaving limited understanding of how implementation varies among adopters and relates to performance. This study examines the extent of blockchain technology adoption (BTA) among adopting firms, jointly analyzing its antecedents and performance outcomes. Adoption extent refers to the degree to which a firm commits resources, integrates blockchain into operations, and applies it across key cross-border trade functions. Drawing on the Technology–Organization–Environment framework, Diffusion of Innovation theory and the Resource-Based View, the study links eight antecedents to adoption extent and adoption extent to firm performance. Survey data from 438 blockchain-adopting import and export firms across nine Chinese provincial-level regions were analyzed using partial least squares structural equation modeling. Trading partner pressure shows the strongest positive association with BTA, whereas insecurity shows the strongest negative association. Indirect associations run through BTA between five antecedents and both performance dimensions, with a stronger association for operational than financial performance. The study shows how ecosystem pressure and governance risk relate to blockchain implementation and performance among adopters.
Virtual influencers have emerged as an important marketing tool in social commerce and digital consumer engagement. However, limited research has examined how virtual influencer characteristics shape consumers’ green purchase intention through underlying emotional and relational mechanisms. Drawing upon Parasocial Interaction Theory and Affect-as-Information Theory, this study investigates the effects of perceived authenticity, social presence, and interactivity on consumers’ green purchase intention, with parasocial intimacy and emotional arousal serving as mediating variables. A mixed-method approach integrating Partial Least Squares Structural Equation Modeling (PLS-SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA) was employed. Data were collected from 346 consumers who had prior experience interacting with virtual influencers and purchasing green products. The PLS-SEM results indicate that perceived authenticity, social presence, and interactivity significantly enhance parasocial intimacy and emotional arousal, which subsequently increase green purchase intention. Furthermore, the fsQCA findings reveal multiple configurational pathways leading to high green purchase intention, suggesting that different combinations of virtual influencer characteristics can generate similar consumer outcomes. This study contributes to the electronic commerce and influencer marketing literature by extending the understanding of virtual influencer effectiveness in sustainable consumption contexts. Methodologically, the integration of symmetrical and asymmetrical analytical approaches provides a more comprehensive explanation of consumer decision-making in virtual influencer marketing environments. The findings also offer practical implications for marketers seeking to leverage virtual influencers to promote green consumption in digital commerce settings.
Although AI streamers are increasingly used in live commerce, it remains unclear when they are more persuasive than human streamers for green products and through which psychological mechanisms their influence operates. Using a fictitious plant-based hand wash, we conducted a 2 × 2 between-subjects experiment with 408 Chinese consumers; confirmatory factor analysis was performed using IBM SPSS Amos 26.0, whereas two-way ANOVA and moderated parallel mediation analysis were conducted using IBM SPSS Statistics 26.0. Human streamers increased green purchase intention through greater perceived authenticity, whereas AI streamers did so through weaker inferences of personal selling motive. The authenticity pathway was stronger under experiential positioning, whereas the selling-motive pathway was stronger under utilitarian positioning; these opposing indirect effects partially offset one another, resulting in a nonsignificant total effect of streamer type. By identifying these competitive dual pathways and their positioning-dependent boundary condition, this study extends research on AI-mediated persuasion and provides e-commerce platforms and green-product brands with guidance for aligning streamer type with product-positioning strategy.
Algorithmic personalization is the flagship mechanism of interactive marketing: platforms learn from user behavior and respond with individually curated value propositions, and industry practice treats the quality of that loop as a lever for customer retention. Whether personalization actually converts into consumer commitment—operationalized here as continuance intention—has been tested almost exclusively in affluent, algorithm-saturated markets. This study tests the conversion where the assumption should be most fragile: subscription video-on-demand in Albania, a price-sensitive transitional European market. Drawing on an extended value-based expectation-confirmation model within a stimulus–organism–response structure, algorithmic personalization, perceived value, and cross-device compatibility are modeled as antecedents of customer satisfaction and continuance intention—the attitudinal antecedent of subscription renewal—among 284 Albanian subscribers, using partial least squares structural equation modeling with 5000 bias-corrected bootstrap resamples. Personalization did not predict continuance intention directly (β = −0.060, ns), and its total effect was effectively zero (−0.004): the significant but weak indirect pathway through satisfaction was offset by the small negative direct path. Perceived value dominated—associated with continuance intention directly (β = 0.337) and through satisfaction (total effect = 0.609)—and emerged in an importance–performance map analysis as the highest-importance, lowest-performing construct. Robustness analyses show the null is not a measurement artifact and sharpens among personal payers; within the sample, it decomposes into a modestly positive personalization effect among lower-income subscribers and a negative effect among higher-income respondents (difference p = 0.003), contradicting a simple income-constraint account and leaving the moderating mechanism open. The findings provide evidence for the market contingency of personalization’s retention promise: in this market, the algorithm does not earn the renewal; the value proposition does.
The growing importance of sustainable digital commerce has increased the need to understand how Environmental, Social, and Governance (ESG)-related information communicated through digitally mediated environments influences consumer decision-making and value creation. This study investigates the effects of corporate ESG information and public ESG information on consumer awareness and subsequent behavioral responses, including purchase intention, investment intention, advocacy, and positive perceptions toward ESG-oriented companies. Drawing on Stakeholder Theory and the theory of planned behavior, the study examines how different sources of ESG information shape consumer evaluations and intentions in an increasingly information-driven marketplace. Using partial least squares structural equation modeling (PLS-SEM), data from 1836 respondents obtained from the Korea Consumer Agency’s national consumer survey were analyzed. The results indicate that corporate ESG information significantly enhanced consumer awareness, whereas public ESG information did not have a significant effect on consumer awareness. Public ESG information significantly strengthened positive perceptions but did not significantly influence advocacy. Consumer awareness emerged as the strongest predictor of purchase intention, investment intention, advocacy, and positive perception. It significantly mediated the relationships between corporate ESG information and consumer responses, whereas no significant indirect effects were observed for public ESG information. However, corporate ESG information did not directly increase purchase intention or investment intention, suggesting that awareness represents the primary mechanism through which ESG communication influences consumer responses. These findings contribute to the literature on sustainable digital commerce by demonstrating that ESG information functions as a strategic market signal that promotes consumer engagement and sustainable value creation through awareness. The study further provides practical implications for firms and policymakers seeking to develop credible ESG communication strategies that support the green transition and foster sustainable consumer decision-making.
Promoting consumers’ green consumption is an important way to drive the low-carbon transition on the demand side and achieve sustainable development. Existing research mainly compares the persuasive effects of AI and human recommenders, with limited attention to how different types of AI recommenders and algorithmic matching frames can collaboratively promote online green purchasing. Through two studies, this work examines the impact of AI recommender types (thinking vs. feeling) and algorithmic matching frames (item-oriented vs. user-oriented) on online green purchasing intentions and their psychological mechanisms. The results indicate that there is a significant interaction effect; combining thinking AI with an item-oriented frame is more effective in enhancing purchasing intentions, while combining feeling AI with a user-oriented frame is even more effective. Furthermore, in the thinking AI context, the item-oriented frame enhances purchasing intentions by increasing perceived product efficacy; in the feeling AI context, the user-oriented frame functions by enhancing perceived social identification. This work reveals the interaction between AI recommender types and algorithmic matching frames, as well as their differentiated psychological mechanisms. It expands the application of Schema Congruity Theory in AI-driven green consumption contexts and provides practical insights for digital platforms to develop AI recommendation and information intervention strategies.
User-generated videos (UGVs) and professional-generated videos (PGVs) have become important information sources in movie marketing, yet prior research has largely examined user-generated content and professional-generated content as independent drivers of market performance. This study investigates how UGV influences movie box office revenue through PGV and identifies the boundary conditions of this process. Drawing on signaling theory and the elaboration likelihood model, we propose that UGV volume generates social attention and stimulates PGV production, while PGV serves as a more credible quality signal that affects consumers’ viewing decisions. Using panel data on 226 movies released in China from 2024 to 2025 and 245,890 videos collected from Weibo, we test the proposed framework with fixed-effects models, endogeneity tests, and robustness checks. The results show that UGV volume indirectly increases box office revenue through PGV volume. Moreover, UGV creator reputation strengthens the positive relationship between UGV volume and PGV volume, and PGV perceived usefulness strengthens the positive effect of PGV volume on box office revenue. These findings reveal a sequential pathway through which user-generated social attention is transformed into professional market persuasion, offering theoretical and managerial implications for video-based interactive marketing.
Generative AI shopping assistants are becoming a primary touchpoint for online consumers, yet they often produce convincing but inaccurate content, a phenomenon known as AI hallucination that poses an under-studied risk to consumer trust in e-commerce. This study explains that phenomenon by building and testing a moderated mediation model grounded in Expectation Violation Theory, Epistemic Vigilance Theory, and Algorithmic Trust Repair Theory. A sequential, exploratory mixed-methods design was used: a qualitative phase identified the dimensions and configurational pathways of consumer trust withdrawal using the Gioia methodology and fuzzy-set Qualitative Comparative Analysis, and a subsequent large-scale quantitative phase tested and refined the resulting model across a multi-country European sample using partial least squares structural equation modeling and Necessary Condition Analysis. The results show that exposure to hallucinations triggers expectation violation, activating epistemic vigilance and reducing perceived AI competence; this sequence drives trust recalibration, reflected in lower continued-use and purchase intentions and greater negative word-of-mouth. AI literacy, prior trust, and transparency cues significantly moderate these relationships, and structural trust repair mechanisms, namely retrieval-augmented generation and uncertainty disclosure, prove more effective than purely communicative repair strategies. Theoretically, this study advances a dynamic account of trust recalibration in AI-mediated commerce; practically, it offers concrete guidance for platform design and regulatory policy under the EU AI Act.
With the rapid development of artificial intelligence, AI-driven algorithmic goal setting has become an important mechanism of digital platform management. In the livestream e-commerce industry, platforms increasingly use algorithmic systems to assign tasks, monitor performance, and regulate streamers’ work. Although this intensive and dynamic form of algorithmic management can improve operational efficiency, it may also be associated with potential ethical risks. Drawing on Conservation of Resources Theory, Emotional Labor Theory, and Sociotechnical Systems Theory, this study examines whether AI-driven algorithmic goal setting is associated with streamers’ unethical selling behavior through emotional dissonance and whether AI transparency moderates this relationship. Using a three-wave time-lagged survey design, data were collected from 427 livestream e-commerce streamers in China. SPSS-based hierarchical regression analysis and bootstrapping were employed to test the proposed moderated mediation model. The results showed that AI-driven algorithmic goal setting was significantly and positively associated with streamers’ unethical selling behavior and that emotional dissonance partially mediated this relationship. Furthermore, the positive relationship between AI-driven algorithmic goal setting and emotional dissonance, as well as the corresponding indirect relationship with unethical selling behavior, was weaker at higher levels of AI transparency. These findings identify emotional dissonance as an important psychological mechanism linking algorithmic performance demands with unethical selling behavior and indicate the conditional buffering role of AI transparency. This study extends algorithmic management research to the livestream e-commerce context and provides practical implications for enhancing algorithmic transparency and reducing ethical risks in platform governance.
Artificial intelligence (AI)-powered online shopping is profoundly reshaping the way in which consumers engage with sustainable clothing. However, the relationships between digital multisensory cues and perceived quality with sustainable consumption intention still need to be explored. Based on the Stimulus–Organism–Response (S-O-R) framework, the present study explores the relationships of digital multisensory cues and perceived quality with consumers’ flow experience, pleasure, and responses (e.g., engagement, loyalty, and sustainable consumption intention) in the context of AI-powered online shopping using a cross-sectional, scenario-based, assisted recall survey. A total of 571 valid responses were retained for analysis. Partial least squares structural equation modelling (PLS-SEM) was adopted for data analysis. The findings indicate that digital multisensory cues showed a positive relationship with pleasure, whereas perceived quality was positively associated with both flow experience and pleasure. Flow experience was positively linked to pleasure, which was further associated with engagement and sustainable consumption intention. Statistically significant indirect effects on sustainable consumption intention through pleasure were observed for flow experience, digital multisensory cues, and perceived quality. The results of this study offer practical implications for retailers seeking to design emotionally engaging AI-powered shopping experiences that may support sustainable consumption intention.
Open banking enables customers to provide third parties with financial information, yet this will be successful only when customers are ready to share it. Previous research concentrates on security, trust, and usefulness as direct motivators without paying attention to mechanisms. This research fills this gap by evaluating the effect of Perceived Security Assurance, Trust in Banks and FinTech Providers, and Perceived Usefulness on Willingness to Share Financial Data based on Perceived Data Control, which is based on Privacy Calculus Theory. A combination of cluster and purposive sampling was used to collect data from digital banking users in five regions in Saudi Arabia. There were a total of 384 valid responses that were analysed. SmartPLS 4 was used to run Partial Least Squares Structural Equation Modelling. The results reveal that PSA, TBFP, PUOB, and PDC directly impact WSFD and that PSA, TBFP, and PUOB also have a considerable impact on PDC. The outcomes of the mediation confirm that PDC mediates these relationships partially. Multi-group analysis also shows that these effects are greater amongst users with less experience in FinTech and with younger customers, especially in the pathways that involve perceived data control. The work adds to the theory in two ways. The extension of the Privacy Calculus Theory by adding the perceived control and pointing out the heterogeneity of users in data-sharing behaviour. It also changes the attention from the general adoption intention to the actual data-sharing behaviour in open banking. Practically, the results suggest that to foster customer engagement in open banking ecosystems, it is necessary to improve security, develop trust, prove value, provide user control, and use segment-specific actions.
As hospitality e-commerce expands, consumers in emerging markets may face uncertainty about the accuracy and credibility of online hotel information. This study examines resilient online booking intention, defined as consumers’ willingness to continue booking through digital platforms despite uncertainty and perceived risk. Drawing on signaling theory and cognitive appraisal theory, it investigates how digital ESG signaling is associated with resilient booking intention in Ethiopia through perceived transparency, perceived value, and platform trust, while considering the moderating role of AI literacy. Survey data from 457 hotel consumers were analyzed using partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA). The results show that digital ESG signaling is positively associated with perceived transparency, perceived value, and platform trust, while platform trust is strongly associated with resilient booking intention. AI literacy modestly strengthens the associations of digital ESG signaling with perceived transparency and perceived value. The fsQCA results identify alternative configurations for high ROBI, with perceived value and platform trust present in all retained configurations. These findings show how verifiable digital responsibility signals can support platform credibility and resilient online booking decisions in AI-enabled hospitality environments.
As artificial intelligence and digital human technologies become increasingly integrated into livestream commerce, AI anchors are becoming important marketing agents. Yet prior research has focused primarily on their visual characteristics, leaving dynamic vocal cues largely unexplored. Drawing on social response theory and perceived authenticity research, this study examines the nonlinear association between AI anchor pitch variability and consumer engagement, together with a proposed psychological pathway and boundary condition. Study 1 analyzes 4322 product-presentation segments nested within 330 AI-anchored livestreams and 85 independent accounts on Douyin. Negative binomial models, formal boundary-slope tests, and additional specifications using account and livestream-session fixed effects, a correlated-random-effects decomposition, and viewer-minutes exposure provide robust evidence of an inverted U-shaped association between pitch variability and real-time danmaku engagement. Evidence concerning appearance-realism moderation is conditional and specification-sensitive across alternative pitch operationalizations, exposure definitions, and within-account specifications. Study 2 uses a preregistered multi-stimulus mixed design with four AI anchors, four products, and three between-participants pitch-variability conditions. Correctly scaled planned contrasts show that moderate pitch variability produced greater perceived authenticity and engagement intentions than the average of the two endpoint conditions. A 2-1-1 multilevel analysis yielded an indirect-effect pattern consistent with the proposed role of perceived authenticity. Models allowing treatment effects to vary across the 16 included anchor-product combinations showed a positive average moderate-pitch advantage, although its magnitude varied across stimuli. These findings extend livestream-commerce research from human streamers to AI-mediated communication while indicating that appearance-realism moderation, stimulus-level generalization, and causal mediation require further replication.
Purpose: Online customer reviews (OCRs) are an essential informational resource in e-commerce, but little is known about the interaction between review type and reader type. This research explores whether consumer’s hedonic or utilitarian personality orientation moderates the relationship between OCR type (hedonic vs. utilitarian) and perceived OCR helpfulness, and whether this moderation is further conditioned by product type. Design/methodology: A 2 (OCR type: hedonic vs. utilitarian) × 2 (product type: bar soap vs. perfume) × 2 (personality type: hedonic vs. utilitarian) mixed factorial experiment was conducted with 586 usable responses collected in South Korea. Review-type and product-type manipulations were validated, personality orientations were measured with established shopping-value scales, and hypotheses were tested using three-way ANOVA with simple-effects decomposition. Findings: Personality orientation significantly moderated the review type–helpfulness relationship (F(1, 586) = 14.67, p < 0.0001), and this moderation was itself qualified by product type (three-way interaction: F(1, 586) = 24.23, p < 0.0001). When the review type was congruent with the product context, personality-mismatched readers—hedonic consumers reading utilitarian reviews and utilitarian consumers reading hedonic reviews—reported the highest helpfulness, whereas reviews that mismatched both the product and the reader received the lowest helpfulness ratings. Implications and originality: The results indicate that review helpfulness is an emergent property of the review–reader–product configuration rather than of review text alone, providing e-commerce platforms with a basis for orientation-aware review ranking, solicitation, and page design.
As VR live streaming becomes an emerging form of immersive retailing, understanding how consumers respond to streamers’ social cues is increasingly important. Drawing on social presence theory, this research examines how streamer gaze in VR live streaming influences purchase intention and investigates the underlying mechanism and boundary condition. We propose that streamer gaze increases purchase intention by enhancing consumers’ feeling attended to, and in turn, social presence, and that this process is shaped by virtual space scale. Three studies provide convergent support for this framework. Study 1, using a retrospective survey and structural equation modeling with 386 participants, offers initial evidence for the proposed relationships. Study 2, using an immersive VR experiment with 160 participants, provides causal support for the serial mediation mechanism. Study 3, using a 2 × 2 immersive VR experiment with 240 participants, further shows that the positive effect of streamer gaze is stronger in smaller virtual spaces than in larger ones. This research contributes to the literature on VR retailing, live-streaming commerce, and social presence by identifying streamer gaze as a concrete nonverbal cue that shapes consumer responses in immersive selling environments.
Artificial intelligence (AI) is reshaping the financial sector by redefining how customers access, interpret, and experience banking services. However, financial inclusion is still frequently analyzed in terms of access, use, or availability of digital channels, with less attention paid to the relational quality of interactions between customers and institutions. This study introduces Relational Inclusion in Banking (RIB) as a specific dimension of financial inclusion in AI-mediated contexts, referring to the degree to which customers continue to feel understood, treated fairly, heard, and connected to their institution. Drawing on Social Exchange Theory and Sociotechnical Systems Theory, a PLS-SEM model was tested using data from 770 banking customers in Extremadura, Spain. The results show that institutional trust was the strongest antecedent of RIB, followed by relational mediation capacity, perceived fairness of AI, and AI transparency. In addition, the rural-urban context influenced digital literacy level, although it did not directly explain institutional trust. At the theoretical level, this study contributes to existing knowledge by shifting the analysis of financial inclusion from digital access toward the relational position customers retain within automated banking services. It also identifies human mediation as a sociotechnical mechanism that connects AI-assisted outcomes with customers’ specific circumstances. At the practical level, the managerial implications direct financial institutions toward clear AI governance, better-designed AI-assisted interactions, and effective procedures for explanation and human review. The policy implications highlight the need to strengthen the traceability of automated decisions, ensure effective human review mechanisms, and monitor potential inequalities associated with territory or digital vulnerability. They also point to the value of maintaining hybrid support mechanisms that enable customers with greater digital difficulties to understand and challenge AI-assisted decisions.
Despite the considerable attention given to virtual streamers, their effectiveness in live streaming warrants further investigation. Drawing on the stimulus-organism-response (SOR) framework, social role theory and social cognitive theory, this study examines the influence mechanism of virtual streamer roles on consumers’ virtual place attachment. An online scenario experiment employing a 2 (assistant-role virtual streamers vs. friend-role virtual streamers) and 2 (search products vs. experience products) between-subjects design (N = 328) was conducted to verify research hypotheses. The results demonstrate that, compared with the assistant-role virtual streamer, the friend-role virtual streamer elicits a higher level of virtual place attachment among consumers. This effect is mediated through three distinct pathways: the positive mediation of social self-efficacy, the negative mediation of task self-efficacy, and the positive serial mediation involving both. Moreover, product type exerts a significant moderation effect on the association between social self-efficacy and task self-efficacy. Specifically, this relationship is stronger under search (vs. experience) product conditions. Such a moderation effect, however, is conditional upon consumer expertise and is only significant for consumers with low expertise. Additionally, consumer expertise moderates the influence of task self-efficacy on virtual place attachment, and this influence is stronger for high-expertise consumers than for low-expertise consumers. These findings shed empirical light on the functioning and influence of virtual streamers in e-commerce live streaming and extend the theoretical literature on virtual place attachment. They also offer practical insights for companies to optimize virtual streamer design, implement differentiated interaction strategies, and configure personalized features.
Shopping cart abandonment remains a persistent challenge in e-commerce, yet evidence regarding the role of perceived risk remains fragmented across shopping contexts. This study aims to clarify when and under what conditions perceived risk is most strongly associated with shopping cart abandonment by quantitatively synthesizing 153 effect sizes from 60 independent studies reported in 49 empirical articles published between 2004 and 2026. A multilevel random-effects meta-regression approach was employed to account for dependent effect sizes and to test risk source, product category, shopping purpose, sample location, and publication year as moderators, together with two theoretically specified interactions. The results show that perceived risk is positively associated with shopping cart abandonment (r = 0.244, 95% CI [0.191, 0.296]). The association was stronger for transaction than product-related risk, for fast-moving consumer goods rather than durable goods, and in hedonic rather than utilitarian shopping contexts. Sample location and publication year were not significant moderators. Risk source also interacted with shopping purpose and product category, indicating that risk effects depend on configurations of contextual conditions. Sensitivity analyses and publication-bias diagnostics did not materially alter the pooled estimate. The findings shift attention from whether perceived risk matters to the contexts in which it is most likely to interrupt purchase completion.
