Online review platforms are core infrastructure of digital commerce, yet the business-level determinants of review content remain poorly understood. We examine how service participation intensity, the degree to which a business format requires active customer involvement, relates to the affective–experiential language of reviews and to rating extremity. We analyze 6,151,996 Yelp reviews (2010–2022) with two independently validated instruments: a Business Participation Index, whose 39 attribute and 1263 category weights derive from an independent coding procedure (two large-language-model raters from different model families applying written, hypothesis-blind codebooks; Krippendorff’s α=0.88–0.96) and a 35-indicator text framework validated against a review sample dual-coded under the same procedure. Participation and hedonic–utilitarian positioning, measured independently for the first time, are negatively entangled across the platform (r=−0.27). Decomposing the two shows that positioning absorbs roughly thirty percent of the raw participation–hedonic association. The remainder survives category, price, geography, and year fixed effects with business-clustered errors (b=−0.029, standardized β=−0.084), inverse-probability weighting, and Oster bounds (δ=13.7). Extremity analysis reveals an asymmetry: higher participation predicts one-star reviews (odds ratio of 1.13) but not five-star reviews. The hedonic–analytical co-occurrence index, the joint elevation of the two language modes, adds no information beyond its components (incremental R2=0.00006) and is not advanced as a mechanism. Format-based comparisons in review analytics must condition on positioning.
This study examines digital readiness and e-commerce participation across the EU-27 during 2020–2025, combining common-sample two-way fixed-effects models for 2020–2024, one-year-lagged specifications, first-difference and post-break sensitivity analyses, and convergence tests. Household internet access shows the most stable contemporaneous within-country association: in the preferred specification, a one-percentage-point increase in the national access rate is associated with approximately 0.82 percentage points higher online-purchasing participation. The result remains significant under country-specific trends, restricted wild-cluster bootstrap inference, fractional-response models, and first differences, but not in the one-year-lagged models. ICT-specialist employment, treated as an exploratory indicator of national digital-ecosystem capacity, is not statistically significant, including in the post-2021 sensitivity sample; the GDP relationship is also not robust. Sigma-convergence evidence is stronger: in a balanced 26-country sample, the cross-country standard deviation falls from 15.39 percentage points in 2020 to 11.64 in 2025, and the coefficient of variation falls from 0.244 to 0.159, with country-trajectory bootstrap intervals for both the change and the trend remaining below zero. The conventional beta-convergence estimate is negative and significant (β = −0.0998, p < 0.001) but becomes insignificant in ceiling-sensitive logit and headroom-adjusted specifications. The findings therefore support a robust contemporaneous association between basic digital access and e-commerce participation and statistically validated short-run sigma convergence, while beta catch-up remains specification-sensitive.
Amid the integration of the digital and platform economies, rural e-commerce traffic resource integration involves complex multi-factor interactions and hidden causal pathways, making key mechanism identification challenging. To overcome the limitations of conventional DEMATEL–ISM studies, which often rely on manually defined factors and lack integration between factor discovery and structural analysis, this study proposes an integrated LLM–DEMATEL–ISM workflow. Rather than a simple application of LLMs, the proposed framework combines LLM-assisted factor identification, structured validation, expert evaluation, and DEMATEL–ISM modeling into a unified analytical process. LLMs support multi-source factor discovery, while expert validation ensures conceptual reliability; DEMATEL and ISM further quantify causal relationships and reveal hierarchical transmission structures. The results identify market entity scale, organizational coordination capability, and digital traffic mechanisms as key hub factors. The main methodological contribution lies in establishing a reproducible human–machine–structure collaborative framework that bridges data-driven factor discovery and expert-validated structural modeling, providing a new analytical paradigm for complex-systems research in the digital economy.
This study examines recurrent pet care spending, its perceived economic incidence on household budgets, and barriers to e-commerce adoption in Yopal, Colombia. Using a quantitative, cross-sectional, non-experimental design, data were collected from a non-probability convenience sample of 619 pet owners. Descriptive statistics, chi-square and Fisher’s exact tests, and binary logistic regression models were applied; online-channel adoption was analyzed exploratorily. Higher pet food and annual healthcare expenditures and ownership of two pets were associated with greater odds of perceiving moderate-to-high economic incidence, while partial affective justification was also associated with greater perceived economic incidence. Online purchasing remained marginal, with only 31 respondents identifying it as their primary purchasing channel. Among non-adopters, distrust of digital platforms and limited internet access were the most frequent barriers, with distributions differing significantly across age and income groups. Overall, the findings are consistent with an economically active but digitally incomplete market configuration, where recurrent demand coexists with limited digital transactional development.
Digital visual marketing increasingly relies on aesthetically modified social media content to attract consumers, yet such practices may also create authenticity risks when online representations are compared with actual experiences. This study examines how four visual modification cues in destination photos—light–shadow atmosphere, tone enhancement, framing angle, and focus prominence—shape post-visit destination trust, value co-destruction, and revisit intention, and whether persuasion knowledge moderates these effects. Survey data from 572 tourists who viewed Chongqing-related social media photos before visiting the city were analyzed using structural equation modeling, bootstrap mediation analysis, and hierarchical regression. Tone enhancement, framing angle, and focus prominence increased destination trust, whereas light–shadow atmosphere did not. Light–shadow atmosphere, tone enhancement, and framing angle increased value co-destruction, whereas focus prominence did not. Destination trust positively affected revisit intention, while value co-destruction had a negative effect. The two mediators operated as largely independent evaluative paths, with the trust pathway exerting the stronger effect. Persuasion knowledge weakened trust gains and amplified value co-destruction. These findings show that digital visual marketing produces asymmetric post-experience outcomes and highlight the need to balance aesthetic optimization with authentic representation when designing social media marketing content.
Temu has become a prominent cross-border e-commerce platform combining low-price positioning, personalized recommendations, and mobile-based shopping, but its platform features may create both perceived value and digital risk concerns. This study examines how perceived price advantage, personalized recommendation quality, and platform usability are associated with Korean Generation Z consumers’ purchase intentions toward Temu through perceived value, trust, platform attitude, perceived manipulation concern, privacy concern, and perceived risk. Survey data were collected from 676 Korean Generation Z consumers between December 2025 and February 2026 and were analyzed using structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA). The SEM results show that perceived price advantage, personalized recommendation quality, and platform usability were positively associated with perceived value. Perceived value was positively associated with trust and platform attitude, and platform attitude was positively associated with purchase intention, whereas perceived risk was negatively associated with purchase intention. However, trust was not directly associated with purchase intention, perceived manipulation concern was not significantly associated with perceived risk, and perceived risk was not significantly associated with platform attitude. The fsQCA identified four configurations for high purchase intention, with overall solution consistency of 0.892 and coverage of 0.642. These configurations particularly involved perceived value, favorable platform attitude, low perceived risk, and recommendation quality. The findings indicate that perceived risk was directly associated with lower purchase intention, whereas manipulation and privacy concerns did not operate through uniformly expected risk mechanisms.
The National E-Commerce Demonstration City (NEDC) program provides a quasi-natural experiment for assessing whether e-commerce-oriented place-based policies improve urban green productivity. Using a balanced panel of 298 Chinese cities from 2007 to 2023, this study measures green total factor productivity (GTFP) with a global frontier SBM-EFF-ML index and exploits the program’s staggered rollout in a difference-in-differences framework. NEDC designation increases GTFP by 0.0293 index units, equivalent to approximately 12.2% of the sample mean. The result is robust to event-study and placebo tests, an alternative GTFP measure, controls for concurrent national pilot policies, entropy balancing, and estimators that accommodate heterogeneous treatment effects. Channel tests provide evidence consistent with information service agglomeration and improved e-commerce logistics; the evidence for digital-payment facilitation is positive but weaker. Pooled interaction models further identify significant differences across comprehensive economic regions and city administrative levels, although the latter result is exploratory. These findings suggest that e-commerce demonstration policies can support greener urban growth when accompanied by specialized digital services, efficient circulation systems, and adequate local administrative capacity.
Digital transformation can help firms put circular-economy practices into operation. Existing studies often discuss technical tools and implementation practices separately. Few studies explain the organizational logic that connects the two. This systematic review identifies 146 peer-reviewed English-language journal articles published from January 2015 to June 2026. The review follows PRISMA 2020. It examines how digital technologies support circular-economy adoption at the firm and organizational levels. Industry and supply-chain levels are also included. The evidence points to three core mechanisms. The first is information transparency. The second is resource-efficiency optimization. The third is circular business-model innovation. Their effects depend on internal green and digital capabilities. Supply-chain coordination and external institutional pressures also change how these mechanisms work. Environmental outcomes receive the most consistent empirical support. Operational and social outcomes depend more strongly on context and innovation and economic outcomes are also context-dependent. This review integrates dynamic capabilities theory with the natural-resource-based view and institutional theory to distinguish internal capability development from external constraints. The resulting framework links mechanisms with boundary conditions and outcomes while identifying priorities for longitudinal research, comparative research and improved outcome measurement.
The AI-customer relationship management (AI-CRM) presents opportunities to reshape how firms engage, enhance their analysis of large volumes of customer data, and develop processes that improve their resilience. To bridge this gap, the present study investigates the relationship between AI-CRM and Artificial Intelligence-Driven Decision Making (AIDDM), with digital platform capabilities as a mediating factor. Furthermore, it examines whether AIDDM contributes to transformative resilience within firms by mediating the association between AI-CRM and firm resilience. A conceptual model was developed and empirically tested using survey data collected from 262 experts working in Saudi Arabian enterprises. The Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to assess the hypothesized relationships and mediation effects. The results confirmed the influence of AI-CRM on AIDDM and digital platform capabilities, which significantly enhance firm resilience. Furthermore, the analysis demonstrates that AIDDM and digital platform capabilities mediate the effects of AI-CRM on resilience. This underscores the potential of AI-CRM adoption to support adaptive capabilities within organizational systems. This research advances the theories of RBV and DCV by highlighting how AI-CRM advances capabilities that support resilience in dynamic environments. It positions AI-CRM and AIDDM as capabilities associated with organizational adaptation and innovation in dynamic environments. Additionally, it offers managers theoretically grounded guidance on aligning their AI-CRM and AIDDM to enhance overall firm resilience and support socio-economic objectives, thereby enabling firms to make meaningful contributions to digital innovation.
In the contemporary AI-mediated digital landscape, digital literacy has emerged as a cornerstone competency for students and is closely associated with institutional perception. This study examines the relationship between students’ digital literacy—within the context of AI-integrated learning environments—on Higher Education Online Brand Equity (HEOBE), brand trust, brand affect, and electronic word of mouth (e-WOM). Utilizing a Structural Equation Modeling (SEM) framework, data were collected from university students in Taiwan (N = 645) between January and February 2024. Statistical validation and hypothesis testing were performed using SPSS and AMOS. The results demonstrate that digital literacy in the AI era exerts a significant positive association with HEOBE, indicating that technologically proficient students within AI-integrated educational environments perceive higher value in a university’s digital brand. Furthermore, the four dimensions of HEOBE—institutional reputation, brand identification, brand loyalty, and campus perception—were found to exhibit significant positive associations with both brand trust and brand affect. Intriguingly, the study uncovers a behavioral dichotomy in word-of-mouth intentions: while brand affect is positively associated with e-WOM, brand trust shows an unexpected negative structural association with e-WOM. This suggests that while emotional attachment spurs advocacy, high cognitive trust may lead to “passive loyalty” in AI-driven digital spaces. These findings provide critical theoretical insights and practical benchmarks for higher education institutions seeking to optimize AI-augmented brand management and student engagement strategies.
Travel blogs and vlogs increasingly influence destination choices, yet limited research explains how their informational characteristics and planning functions help prospective tourists evaluate less familiar destinations. Drawing on the Stimulus–Organism–Response framework, this study examined how seven travel-content stimuli influence intention to visit Khon Kaen through perceived destination diagnosticity. Survey data from 403 respondents were analyzed using partial least squares structural equation modeling, bias-corrected and accelerated bootstrapping with 5000 resamples, and permutation-based multigroup analysis. Content novelty, content reliability, itinerary information, and attraction information significantly enhanced perceived destination diagnosticity, whereas content understandability, content interestingness, and accommodation information had nonsignificant effects. Perceived destination diagnosticity strongly increased visit intention and transmitted the effects of novelty, reliability, itinerary information, and attraction information. Attraction information produced the strongest direct and indirect effects. Although some relationships displayed different significance patterns across the female and male subsamples, permutation testing identified no statistically significant between-group differences. The findings position perceived destination diagnosticity as a cognitive mechanism through which digital travel content supports destination evaluation and behavioral intention. They also provide practical guidance for destination marketers, tourism businesses, and content creators seeking to develop credible, distinctive, and decision-relevant content for digital tourism platforms.
Consumer self-disclosure remains a central question in digital markets, as e-commerce participation increasingly depends on voluntary personal information sharing. Despite extensive application of privacy calculus theory, empirical findings regarding privacy concerns, trust, and perceived risk remain inconsistent, suggesting that the structural relationships underlying privacy trade-offs may vary across consumers and contexts. This study examines how privacy concerns, perceived benefits, perceived risk, and trust relate to perceived risk and personal information disclosure intention in e-commerce, and whether these structural relationships differ between consumers with higher and lower perceived privacy control (PPC). Survey data from 743 online consumers were analyzed using partial least squares structural equation modeling and multigroup comparisons between consumers with high and low perceived privacy control. Findings show privacy concerns do not directly inhibit disclosure intention; instead, they are strongly associated with perceived risk, which is negatively associated with disclosure intention. The results reveal a separation between benefit and risk appraisal: perceived benefits motivate disclosure intention but do not significantly reduce perceived risk. The multigroup results reveal significant structural differences between the relatively high- and low-PPC groups. In the relatively high-PPC group, the relationship between perceived benefits and disclosure intention is stronger, as is the negative relationship between trust and perceived risk. In the relatively low-PPC group, perceived risk exerts a stronger inhibiting effect, while trust assumes a compensatory role, helping consumers navigate limited agency. By identifying structural heterogeneity in the privacy calculus across PPC groups, this study explains variations in disclosure decisions and clarifies inconsistent findings in prior research.
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.
