Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080277

Authors: Sayyed Khawar Abbas Hafiz Muhammad Junaid Aseel Smerat

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.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080276

Authors: Sultan Bader Aljehani

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.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080275

Authors: Zhangyi Qin Pei Li Charles Spence

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.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080274

Authors: Lei Liu Xiaojun Zhan Zhaoqi Li

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.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080273

Authors: Xiaochen Liu Qiang Yang Yushi Jiang

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.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080272

Authors: Yohanes Tesemie Gishen Ping Yin

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.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080271

Authors: Maidul Islam Shabnam Abdulkasem Sheikh Ankita Pathak Debarshi Mukherjee

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.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080270

Authors: Yang Zhang Xinjie Huang Dongdong Jia Rongrong Cui

Generative artificial intelligence (GenAI) is reshaping fashion design by expanding creative possibilities and accelerating innovation. For independent designer brands, which rely on originality, distinctive aesthetics, and creative identity, GenAI offers opportunities to sustain innovation while raising concerns about authenticity and consumer trust. However, consumer responses toward AI-generated clothing (AGC) among AGC-interested young Chinese consumers remain underexplored. This study explores factors influencing their attitudes and purchase intention toward AGC. Drawing on a TPB–TCV-based integrated framework, this study develops an integrated model incorporating consumption value dimensions, perceived effort of the brand in design (PEBD), generative quality (GQ), and AI creativity (AIC). Based on 568 valid questionnaires, PLS-SEM was employed, with multi-group analysis as a supplementary analysis. The results show that attitude plays a key mediating role in explaining purchase intention. Specifically, PEBD and subjective norm significantly predict purchase intention, while PEBD also indirectly influences purchase intention through attitude. Emotional, epistemic, and social values, PEBD, GQ, and AIC significantly contribute to attitude formation, whereas only PEBD and subjective norm demonstrate significant direct effects on purchase intention. These findings provide managerial insights for independent designer brands seeking to apply GenAI while considering consumer perceptions of creativity, originality, and value in shaping AGC purchase intention.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080269

Authors: Xiaochen Liu Qing Gu Ding Yuan Qiang Yang

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.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080268

Authors: Manuel Jesús Sánchez González Ana Leal-Solís Rafael Robina-Ramírez

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.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080267

Authors: Avisek Kundu Priyakrushna Mohanty Sanjay Tiwari Seeboli Ghosh Kundu Saurabh Bharti

Influencer marketing has emerged as a dominant communication strategy in India’s rapidly expanding digital marketplace, reshaping how brands engage with consumers across social media platforms. Despite growing scholarly attention, existing research remains fragmented across themes such as celebrity endorsements, influencer credibility, consumer engagement, and technology-driven communication practices. This study synthesizes the evolving body of literature on influencer marketing in India using bibliometric mapping, thematic analysis, and Latent Dirichlet Allocation (LDA) topic modeling. The analysis reveals that the field’s intellectual structure is defined by four major research themes and latent topic patterns: celebrity endorsements, influencer credibility, consumer engagement, and technology-driven communication practices. A central outcome of the study is the development of a conceptual framework grounded in Stimulus–Organism–Response (S–O–R) theory that explicitly demonstrates how influencer attributes and digital communication stimuli trigger consumer cognition and behavioral outcomes. The study’s primary theoretical contribution is to integrate the previously disconnected research streams into a cohesive framework, providing a foundation for future theoretical development and scholarly inquiry. This systematic synthesis offers a structured understanding of the specific mechanisms driving influencer marketing in the Indian context.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080266

Authors: Mengshi Pan Minggui Sun

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.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080265

Authors: Lijing Wang Zelin Zhang

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.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080264

Authors: Subhankar Das Subhra Mondal

With the widespread adoption of conversational artificial intelligence (Conv-AI), consumers have recently reshaped how they search, evaluate, and build relationships with brands. Accessibility and bidirectional communication have created a dynamic interface for the digital ecosystem. Yet despite a growing body of research, the field remains focused on productivity gains and the convenience of AI in digital commerce. But there is very little about how this Conv-AI intermediation affects the emotional aspect of consumer–brand love. This study draws on parasocial relationship theory and relationship marketing to advance the construct of parasocial displacement: the proposition that conversational AI agents do more than help consumers reach brands. They compete with brands for the consumer’s limited emotional attention. The authors test the proposition in three preregistered experiments (n = 791) set in realistic digital commerce scenarios. Study 1 (n = 212) shows that AI-mediated brand interactions reduce brand love compared with direct brand exposure, with the effect amplified when the AI agent presents a richer package of anthropomorphic social cues. Study 2 (n = 322) also finds that the effect passes through AI-induced parasocial attachment by careful attention and distraction. Study 3 (n = 257) demonstrates the moderation displacement of brand heritage, in which brands are largely protected, whereas emerging brands become vulnerable. This study extends parasocial theory from media personalities to algorithmic agents and proposes a triadic consumer–AI–brand framework. This triadic framework replaces the traditional dyadic models in AI-mediated branding activities. In practice, this framework identifies heritage brand equity as a relational buffer within the digital market ecosystem and proposes digital governance, disclosure for AI use, and agentic brand strategy.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080263

Authors: Ying Zhao Xiurui Yang Tian Qiang Luoming Liang

Social Q&A platforms produce short, noisy, and highly diverse user questions, making coarse topic labels insufficient for accurate information organization. This study proposes a dynamic seed topic construction and multi-demand topic identification framework for health science popularization questions on the Zhihu platform. Using 3529 cleaned questions derived from 2011 to 2024, the framework combines BERTopic clustering, LLM-based topic naming, and a TOP-K cross-filtering update strategy that integrates semantic similarity and frequency. The LLM then performs topic identification, optimization, and assignment, and a co-occurrence network analyzes topic associations. The method generates 26 initial seed topics, which expand to 51 topic instances and are subsequently optimized to 36 topics. Compared with LDA, BERTopic, and LLM-based baselines, our framework achieved the best topic coherence and the lowest topic similarity while maintaining good topic diversity. Human evaluation by 17 volunteers on 30 questions showed that the model performed at least as well as user-assigned tags across relevance, comprehensiveness, clarity, accuracy, and readability, with clearer advantages in relevance and accuracy. These results suggest that the proposed framework can identify fine-grained, interpretable, and strongly associated information-demand topics for social Q&A platforms.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080262

Authors: Eunji Choi Qinglin Li

Generative AI is reshaping how workers form digital commerce entrepreneurial intentions, yet how AI-related fear of missing out (AI-FoMO) converts into proactive entrepreneurial behavior remains unclear. Drawing on social cognitive theory, this study tests a model in which AI-FoMO drives AI reliance, which in turn shapes two distinct cognitive routes: a tool-referenced route through perceived task automation (PTA) and an agent-referenced route through borrowed entrepreneurial competence (BEC), a newly proposed construct capturing how human–AI coupling reshapes workers’ self-perceived capability for solo digital entrepreneurship. We further test whether digital self-efficacy (DSE) moderates the conversion of these perceptions into intention. Survey data from 328 Korean workers were analyzed using PLS-SEM. AI-FoMO predicted AI reliance (β = 0.391), which shaped both PTA (β = 0.531) and BEC (β = 0.291); both routes predicted entrepreneurial intention, and both serial mediation paths were significant, with the PTA route approximately 2.6 times stronger than the BEC route. DSE moderated only the BEC route (β = 0.105, p = 0.025), not the PTA route (β = −0.077, p = 0.109), an asymmetry confirmed by a formal coefficient-difference test. The findings reframe AI-FoMO as a potential driver of approach-oriented entrepreneurial behavior, introduce BEC as an extension of vicarious experience to the human–AI context, and show that digital self-efficacy selectively regulates how AI-induced perceptions convert into entrepreneurial intention.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080261

Authors: Kalina Grzesiuk Marcin Lipowski Grzegorz Wesołowski

This paper investigates the determinants of consumer autonomy, such as trust, prior experience, and information richness, within the context of online tourist package purchases. The theoretical considerations presented in this study are grounded in an integrated framework that synthesises Self-Determination Theory (SDT), Cognitive Load Theory (CLT), and the Paradox of Choice (PoC), further enhanced by the principles of Prospect Theory to elucidate the complex dynamics of consumer decision-making. Framing the research within the Stimulus-Organism-Response (SOR) model, the study conceptualises e-purchase characteristics as external stimuli that shape the organism’s (consumer’s) perceived autonomy, subsequently influencing decision-making outcomes. Applying a quantitative Computer-Assisted Web Interview (CAWI) methodology with a sample of 581 Polish consumers, the study demonstrates that trust, prior experience, and information accessibility significantly augment perceived consumer autonomy; notably, perceived risk has no statistically significant impact. A particularly salient and unanticipated finding is that enhanced consumer autonomy significantly reduces decision-making difficulty, which in turn correlates with higher levels of post-purchase satisfaction. By addressing the scarcity of empirical research regarding autonomy in digital environments, this study unpacks the intricate decision-making processes inherent in e-tourism. Ultimately, these findings offer critical managerial implications: tourism operators can effectively mitigate cognitive load for prospective tourists and enhance satisfaction by prioritising transparency and implementing effective trust-building mechanisms, thereby fostering consumer empowerment throughout the digital purchasing journey.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080260

Authors: Nhut Thi Minh Vo Tien Van Thanh Nguyen

The embedded finance paradigm is fundamentally restructuring digital economies by seamlessly integrating financial services into non-financial digital infrastructures. This study dynamically evaluates the productivity frontiers of Sea Limited’s embedded fintech operations (SeaMoney) across six core geographic markets (Indonesia, Thailand, Vietnam, the Philippines, Malaysia, and Brazil) over the 2023–2026 temporal horizon. Employing a rigorous Panel Data Envelopment Analysis (DEA) Malmquist Productivity Index framework, the research measures systemic performance by analyzing Sales & Marketing (S & M) Expenses and the undesirable Non-Performing Loan (NPL) rate as inputs, against Gross Loan Outstanding as the primary credit output. Before model execution, robust isotonicity was empirically validated using Pearson correlation matrices. The empirical findings reveal profoundly robust systemic performance across the global ecosystem, driven primarily by overarching algorithmic innovations captured by the Technical Change (TC) index. However, this technological boundary exhibits a stabilizing deceleration over time, indicative of a maturing ecosystem transitioning from explosive, frontier-shifting innovation to optimized refinement. Furthermore, localized managerial optimization, measured by the Efficiency Change (EC) index, displays significant regional heterogeneity. While markets like Brazil demonstrated aggressive late-stage efficiency spikes, and core Southeast Asian markets (such as Vietnam and the Philippines) maintained highly stable, competitive trajectories, other regions, such as Thailand, experienced notable managerial regression. This regression signals severe internal frictions in optimizing local marketing budgets against rising credit defaults. Managerial Implications: These findings provide critical strategic insights for orchestrators of the multinational e-commerce ecosystem. The empirical evidence suggests that relying exclusively on centralized technological scaling, such as unified platform infrastructure and global AI architectures, is insufficient for sustained operational growth. To maintain a competitive advantage, operations executives must deploy hyper-localized resource-allocation and customer-acquisition frameworks tailored to specific regional market dynamics and consumer behavior. Sustainable scaling in cross-border digital commerce requires a precise dynamic equilibrium: leveraging robust global technological infrastructure while executing highly adaptive, market-specific operational and marketing optimizations to maximize customer lifetime value (CLV), eliminate customer acquisition waste, and streamline localized transaction and engagement cycles.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080259

Authors: Xinyu Sun Weijun Xu

In the burgeoning live stream landscape, manufacturers face critical dilemmas regarding strategic channel governance and how intermediaries’ bargaining ability dictates sustainable cooperation under Stackelberg leadership change. Addressing these issues is vital, as misaligned governance risks severe profit losses, yet standard heuristics fail to capture how leadership shifts disrupt channel coordination and model selection. To fill this gap, we model a manufacturer’s self-operated (Model SO) live stream channel and an intermediary-operated (Model IO) live stream channel to evaluate trade-offs among bargaining ability, operational costs, and spillover effects. Key findings show that in Model SO, spillover and price adjustments transform the live stream channel into a traffic-channeling gateway in which rising operational costs paradoxically boost total profits. In Model IO, bargaining ability serves as a key determinant, as high pit fees weaponize this ability for predatory commission-squeezing, while low pit fees redirect it toward volume expansion, transforming the intermediary into a synergistic partner. Furthermore, bargaining ability shifts pricing from intermediary-introduction to profit-recapture strategies while exerting cost-magnification and revenue-magnification effects under varying pit fees. Crucially, we uncover two Pareto-optimal cooperation zones alongside a non-cooperation zone caused by incentive incompatibility under mid-tier bargaining ability. Extensions show that intensified price competition turns dominant intermediaries into welfare killers and high service sensitivity induces an over-service trap, though popularity cost-sharing contracts restore coordination. Overall, this study fills a crucial analytical gap by establishing precise theoretical boundaries for channel governance, bargaining ability dynamics, and cost-driven functional transformations in live stream supply chains.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080258

Authors: Ziqing Du Ruiyang Lao Ao Jiao Jiadong Wu Ziqi Wei

This study examines a counterintuitive possibility: the digital divide may generate a short-term and conditional “digital-divide dividend” when intra-provincial digital inequality is accompanied by digital agglomeration, platform externalities, and spatial diffusion. Using provincial panel data from China during 2015–2024, this paper investigates the relationship between the digital divide and digital consumption within a spatial econometric framework. Based on an improved ICT Development Index and a two-way fixed-effects Spatial Durbin Model, this study examines the direct effects, spatial spillover effects, transmission channels, and heterogeneity characteristics of the digital divide. The results show that digital consumption exhibits significant positive spatial dependence. The digital divide is positively associated with digital consumption in the short term, and its spatial spillover effect is substantially stronger than its local effect. Mechanism analysis further indicates that this relationship operates through platform externalities, spatial spillovers, financial development, and R&D expenditure. Heterogeneity analysis shows that the positive association is more pronounced during the mature stage of digital development and in regions with relatively higher levels of digital development. However, this finding should not be interpreted as implying that digital inequality itself is desirable. Rather, it suggests that when digital resources are concentrated in leading regions, their agglomeration advantages may temporarily promote digital consumption through diffusion effects. Policy efforts should therefore focus on transforming digital agglomeration advantages into more inclusive digital diffusion, so that less-developed regions can better share the benefits of digital transformation.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080257

Authors: Buthainah Luqman Ahmad Muneer Alrwashdeh

Digital Content Marketing (DCM) has become a key tool for brands to influence Brand Attitude on social commerce platforms, but its impact on consumer perception remains poorly understood in the emerging market context of the hospitality industry. Based on the theory of Uses and Gratifications (U&G), this study aims to explore the direct and indirect impact of DCM on Brand Attitude, with Consumer Trust and Digital Engagement playing mediating roles among social commerce consumers in Iraq. A quantitative cross-sectional design was employed, with 386 respondents obtained using the social media distribution of a self-administered online questionnaire. Partial Least Squares Structural Equation Modelling (PLS-SEM) was used in Smart PLS 4.0 to analyse the proposed model. All eight hypotheses were supported. Consumer Trust (β = 0.487) and Digital Engagement (β = 0.361) were both positively influenced by DCM, and both mediators had significant positive effects on Brand Attitude (β = 0.394 and β = 0.286, respectively). The total indirect effect of DCM on Brand Attitude (β = 0.351) was significantly larger than the direct effect (β = 0.187), supporting the strength of mediated effects. Serial mediation through Trust and then Engagement was also confirmed. These findings extend the application of U&G theory within an Iraqi social commerce context and offer evidence-based implications for hospitality brands designing content strategies on digital platforms. The findings are only applicable within the context of Iraq and the non-probability sample studied.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080256

Authors: Mengqian Wu Xingbao Hu

Although esports-themed hotels have rapidly emerged as a technology-driven segment of smart hospitality, empirical research on their customer experience and satisfaction formation remains limited. Drawing on schema theory and expectancy violations theory (EVT), this study adopts a mixed-methods approach to examine the thematic attributes of esports-themed hotel experiences and their effects on customer satisfaction. Study 1 applied BERTopic and aspect-based sentiment analysis (ABSA) to 225,318 online reviews of 1601 esports-themed hotels. The results show that customer experience encompasses not only conventional hotel attributes, such as service, environment, and cleanliness, but also esports-specific attributes centered on gaming facilities, technological configurations, and esports-related social activities. ABSA results further reveal generally positive sentiment across most attributes, with service attitude and cleanliness receiving the strongest approval, whereas room facilities and comfort were the main sources of dissatisfaction. Study 2 employed hierarchical linear modeling to further examine the effects of six experience-related sentiments on customer satisfaction. The results indicate that all six sentiment dimensions significantly influence satisfaction and that hotel type moderates these relationships. Compared with professional esports-themed hotels (PETHs), the effects of experience-related sentiments on satisfaction are stronger in non-professional esports-themed hotels (NPETHs). This study extends schema theory and EVT to the esports-themed hotel context and offers practical implications for customer experience design and service management in this emerging hospitality segment.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080255

Authors: Thanaporn Asawanuwat Somchai Lekcharoen Sumaman Pankham

Online furniture commerce has expanded, yet sustaining continuance purchase intention (CPI) remains challenging because furniture is a high-involvement product that consumers prefer to inspect physically. Drawing on the Stimulus–Organism–Response (SOR) framework, this study examines how social media marketing activity, product quality, and customer experience are associated with CPI through transactional appraisals (perceived value and customer satisfaction) and relational appraisals (customer trust and customer loyalty). An exploratory sequential mixed-methods design combined a three-round e-Delphi study with 19 experts and a survey of 1351 Thai consumers who had purchased furniture online. Data were analyzed using covariance-based structural equation modeling, bootstrapped indirect-effect analysis, and fuzzy-set qualitative comparative analysis (fsQCA). Customer loyalty and customer trust showed the strongest positive associations with CPI. Customer satisfaction showed a negative direct but significant positive indirect association through customer loyalty. Constraining the SAT → CPI path to zero significantly worsened model fit; however, the negative coefficient did not support the hypothesized positive association and should be interpreted cautiously as a model-conditional association. The fsQCA identified multiple sufficient configurations associated with high CPI, illustrating equifinality. The findings extend SOR to high-involvement commerce by integrating transactional and relational appraisals and complementary symmetric and configurational evidence.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080254

Authors: Jinsong Chen Yang Wang Feng Chen Qiang Yang

Livestream commerce has become a major retail interface, yet limited attention has been paid to how streamers’ language shapes consumer responses. This research examines the effect of streamer sensory language on consumer purchase in livestream commerce. Drawing on one field study using Douyin livestream data (Study 1: 31,254 min-level observations from 526 livestream sessions involving 136 streamers) and two experiments (Study 2: N = 200; Study 3: N = 320), we show that sensory language increases consumer purchase responses. Study 1 demonstrates a positive association between streamer sensory language and real-time purchase behavior and provides initial evidence for the moderating role of product type. Study 2 establishes the causal effect of sensory language on purchase intention and tests the serial mediation of mental imagery vividness and perceived authenticity. Study 3 further shows that the effect is stronger for experience goods than for search goods.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080253

Authors: Tianyu Bi Zhaohui Zhang

In electronic markets, consumers often form purchase intentions for unfamiliar products under conditions of information asymmetry. How firms portray the origin of a product idea may therefore shape new-product purchase intention. Serendipitous innovation narratives describe how creators recognize the value of an unexpected discovery and subsequently develop it into a new product. This study examines whether, how, and when serendipitous innovation narratives influence new-product purchase intention relative to planned innovation narratives. Three between-subjects, scenario-based experiments using self-reported measures tested the proposed model across three new-product contexts. Study 1 showed that serendipitous innovation narratives elicited higher new-product purchase intention than planned innovation narratives. Study 2 showed a significant indirect effect through customer inspiration. Study 3 showed that this indirect effect became stronger as construal level increased. These findings suggest that serendipitous innovation narratives can function as signals about innovation origin that evoke customer inspiration, particularly among consumers with higher construal levels. This research extends the literature on innovation communication and digital storytelling and offers practical guidance for new-product communication in electronic markets.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080252

Authors: Yiran Li Wanyu Zhu Quan Cheng

In recent years, numerous mobile platforms have introduced product ranking systems to guide consumer choices. Yet, how such rankings shape purchasing decisions remains insufficiently understood. This paper employed an event-related potential (ERP) experiment to capture the neurophysiological responses of consumers during mobile shopping, thereby uncovering the psychological processes and neural mechanisms underlying the influence of online rankings. Our findings reveal that consumers in mobile shopping environments are equally susceptible to information cascades. When product rankings were presented as decision aids, despite limited cognitive resources, low-ranking cues elicited high attentional engagement, as indicated by a higher P2 amplitude. Subsequently, ranking cues appeared to be associated with evaluative processing, as reflected in P3 amplitude differences. These neural and behavioral patterns reflect the avoidance tendencies toward low-ranking products and relatively greater trust-related evaluation of higher-ranked products, ultimately shaping purchase intentions. This study provides cognitive neuroscience evidence for how online rankings modulate mobile consumers’ decision-making.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080251

Authors: Jungyoon Jang Jin-Woo Park

As airlines increasingly deploy an AI-Driven Chatbot (ADC) to enhance service efficiency, this study aims to investigate how the agility capability of ADC drives perceived usefulness and satisfaction, ultimately enhancing customer loyalty in a competitive aviation industry. Drawing on a survey of 303 respondents in South Korea, Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) were employed for empirical analysis. The structural model examines how the agility capability of airline ADC—modeled as a second-order construct comprising competence, responsiveness, speed, and flexibility—shapes the consumer experience. The findings reveal that agility capability positively influences perceived usefulness, which in turn enhances satisfaction and ultimately fosters customer loyalty. Notably, neither agility capability nor perceived usefulness exerts a direct effect on loyalty; their influence is fully channeled through satisfaction, highlighting satisfaction as the pivotal mechanism that transforms agility capability into customer loyalty. These results offer meaningful insights into customer behavior, contributing to the strategic utilization of ADC for better customer experience.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080250

Authors: Xiumei Ma Junxi Lin

The widespread application of artificial intelligence (AI) in electronic commerce platforms has profoundly reshaped frontline employees’ service patterns, psychological experiences, and innovation behaviors. This is especially salient in electronic commerce, where AI systems have made human–AI collaboration a defining feature of frontline service work on digital platforms. Existing research has predominantly focused on either the positive or negative effects of AI, failing to fully explain how employees’ cognitive appraisals of AI technology influence their service innovation behavior through distinct psychological pathways. To address this research gap, this study integrates the transactional theory of stress and the conservation of resources theory into a dual-path model, investigating how challenge appraisal and hindrance appraisal of AI-related work uncertainty respectively are associated with service innovation behavior through two mediating pathways: work engagement and emotional exhaustion. Using partial least squares structural equation modeling (PLS-SEM) to analyze 317 valid responses collected from frontline employees on electronic commerce platforms in China, the results indicate that challenge appraisal is positively associated with service innovation behavior, with work engagement serving as a significant mediator; conversely, hindrance appraisal is negatively associated with service innovation behavior, with emotional exhaustion acting as a significant mediator. This study provides an integrated perspective on the differentiated pathways through which employees’ cognitive appraisals of AI-related work uncertainty are linked to innovation behavior, articulating a complete explanatory chain from cognitive appraisal to psychological resources to behavioral outcomes. The findings offer practical implications for employees, organizations, and governments to foster service innovation in human–AI collaborative environments in electronic commerce platforms.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080249

Authors: Minjung Roh

Chinese cross-border e-commerce (CBEC) platforms have rapidly reshaped contemporary digital retail, achieving substantial adoption. However, prior research on consumer heterogeneity has yet to sufficiently examine how this fast-growing consumer base is segmented across these platforms’ broad category spectrum. The present study accordingly applies latent class analysis to 16 product purchase indicators from 600 Korean AliExpress or Temu users, and conducts bias-adjusted three-step analyses that relate segment membership to demographic and platform covariates and to distal outcomes in shopping behavior and platform awareness channels. Five qualitatively distinct segments emerge: Minimal, Fashion, Household, Auto & Home, and Omnivore shoppers. Omnivore shoppers, the smallest segment, register the highest per-transaction expenditure, satisfaction, and active search. Auto & Home shoppers form a second active-search tier for product search and deal seeking. Household shoppers, the lowest-income tier, show greater receptiveness to offline broadcast advertising. Fashion shoppers have the highest share of consumers aged younger than 20 years and the lowest satisfaction. Minimal shoppers, the largest segment, have the lowest per-transaction expenditure and item count. Platform affiliation crosscuts these segments, with Minimal and Auto & Home shoppers favoring AliExpress and the remaining three Temu. Consumer heterogeneity on CBEC platforms thus forms a clearly differentiated subpopulation structure.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080248

Authors: Yi Yang Jiao Liu Ruoxuan Huang Qinghong Shuai

Artificial intelligence-generated content (AIGC) is reshaping content production on e-commerce platforms, while also facilitating AIGC misuse, including fake reviews and low-quality marketing. These behaviors undermine the platform’s content quality, consumer trust, and transaction order. To address governance issues in platform content ecosystems, this study investigated the application of an established tripartite evolutionary game framework to the governance of AIGC misuse involving e-commerce platforms, content creators, and users. The model characterizes the strategic interactions among strict platform governance, creators’ deep creation, and users’ active supervision, and reveals the evolutionary path of the system through a stability analysis and numerical simulations. The results show that the comparison between governance benefits and governance costs is the basic condition affecting platforms’ choice of strict governance. The content identification capability, creator support, and penalties for violations jointly reshape the expected payoff structure of AIGC misuse and are key factors driving creators toward deep creation. Effective user reporting and supervision rewards can enhance users’ willingness to supervise and compensate for insufficient platform governance by increasing the probability of violation exposure. When the net benefit of strict platform governance, the relative benefit of creators’ deep creation, and the net benefit of users’ active supervision are simultaneously ensured, the system evolves toward a collaborative state of strict governance–deep creation–active supervision. This study extends the application of the tripartite evolutionary game framework to AIGC misuse governance and clarifies, within a unified analytical framework, the strategic interdependence among platform governance, creators’ strategy choices, and user supervision in e-commerce content ecosystems.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080247

Authors: Aniela Bălăcescu Marian Zaharia Olivia-Roxana Alecsoiu Andreea Anamaria Panagoreț Dragoș Mihai Panagoreț

The digitalisation of small and medium-sized enterprises (SMEs) is not just a technological trend, but also a strategic path towards sustainable economic development. This study provides a comprehensive comparative analysis of SMEs digitalisation across the EU-27 during the post-pandemic period (2019–2025). The originality of this study stems from its comprehensive comparative analysis by integrating dynamic, cluster, concentration, and regression analyses, it provides updated evidence on the relationship between e-commerce adoption, enterprise turnover, and economic development, while highlighting cross-country disparities and convergence trends within the European Union. The results confirm a positive association between the adoption of e-commerce practices and enterprise economic performance, underlining the contribution of digitalisation to broader development objectives. The study provides relevant insights for policymakers seeking to strengthen digital ecosystems and support SMEs competitiveness, as well as for business managers aiming to leverage digital technologies as a source of growth and resilience. As the analysis is based on aggregated country-level data and the correlational methods, the findings should be interpreted as evidence of statistical associations rather than causal relationships and should not be directly extrapolated to individual SMEs.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080246

Authors: Rasha Medhat Hussein Yasser Ahmed Elkassrawy Reham Shawky Ebrahim

This study examines the complex dynamics of advertising avoidance in the social media context, with particular emphasis on the roles of psychological reactance perceived surveillance, privacy concerns, and algorithmic fatigue. It delineates the intricate interplay between personalization technologies and users’ surveillance, privacy awareness, and exhaustion from continuous media consumption, which may contribute to the rising tendency of users to avoid digital advertisements. Drawing on the psychological reactance theory (PRT), the study proposes that perceived algorithmic personalization increases perceptions of surveillance, privacy concerns, and algorithmic fatigue, which trigger defensive responses—advertising avoidance. Furthermore, the study examines the moderating role of perceived privacy control in the relationships between reactance-related antecedents and ad avoidance. An online survey of Instagram users was conducted over a one-month period starting from the 15th of December to empirically test the hypotheses. The findings reveal that perceived algorithmic personalization has a significant positive effect on algorithmic fatigue, privacy concerns, and surveillance. Moreover, perceived control weakens the impact of algorithmic fatigue on ad avoidance, highlighting its role as a critical boundary condition. This study contributes to the literature by integrating technological and psychological determinants of ad avoidance into a unified framework, offering a more comprehensive understanding of user resistance in algorithm-driven environments.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080245

Authors: Hongze Hao Chien-Chih Wang

Multimodal online reviews, which integrate textual content with user-uploaded images, have emerged as a significant information source for consumer decision-making and enterprise service enhancement in electronic commerce. Sarcastic reviews, characterized by positive textual statements that are contradicted by negative visual evidence, systematically distort sentiment analysis and subsequent evaluations. Most existing research on multimodal sarcasm detection is limited to binary classification. In contrast, many conventional review-based decision models permit cross-criterion trade-offs, allowing favorable performance on secondary attributes to obscure deficiencies in tolerance-sensitive attributes. To bridge the gap between misleading review content and reliable decision support, this study introduces an integrated three-module framework, the RDSF-MSD-SC. The first module, the Deep Feature Fusion Sarcasm Detection Model (DFF-SDM), combines RoBERTa-based contextual text analysis with CLIP-based cross-modal alignment to detect reviews in which linguistic and visual cues diverge. The second module, the Large Language Model-Driven Structured Semantic Correction Mechanism (LLM-SSCM), reinterprets identified sarcastic reviews and translates their implicit negative sentiment into structured evaluation units (problem dimension, problem severity, and correction confidence) that can be directly utilized by decision models. The third module, Non-Compensatory Decision-Making based on Contextual Tolerance and Mismatch Rate (NCDM-CTMR), aggregates the resulting severity matrix using context-specific tolerance vectors, penalizing breaches of tolerance rather than averaging them out. An empirical analysis of 8623 multimodal reviews from eight major U.S. passenger airlines demonstrates that DFF-SDM achieves an F1-score of 0.86, LLM-SSCM attains an expert-assessed correction quality score of 0.92, and NCDM-CTMR identifies Delta as the most context-appropriate airline with zero mismatch in a time-sensitive business travel scenario. Delta maintained the top ranking in 99.82% of the 20,000 Monte Carlo simulations conducted under a simplified mismatch formulation. Ablation analysis showed that the full pipeline recovered negative evidence from approximately 87% of the 2847 expert-labeled sarcastic reviews, a rate bounded by DFF-SDM’s recall of 0.87; the no-correction and no-detection variants recovered approximately 0%. Compared to the WSM, TOPSIS, and VIKOR baselines, the proposed framework yields a more threshold-sensitive differentiation among mid-tier alternatives.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080244

Authors: Ahmed Abdulaziz Alshiha

This study investigates the impact of generative AI personalization on brand loyalty within smart tourism platforms and examines the mediating roles of perceived transparency and consumer trust. As generative AI technologies become increasingly integrated into tourism services, understanding the mechanisms through which personalized AI-driven interactions influence tourists’ loyalty has become an important research priority. Drawing upon the Stimulus–Organism–Response (S-O-R) framework, the study proposes that generative AI personalization is associated with brand loyalty both directly and indirectly through tourists’ evaluations of transparency and trust. A quantitative research design was adopted using a structured questionnaire administered to tourists with prior experience using generative AI-enabled smart tourism platforms. A total of 688 valid responses were collected and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that generative AI personalization is positively associated with brand loyalty, perceived transparency, and consumer trust. The proposed model explained 76.5% of the variance in brand loyalty (R2 = 0.765), with consumer trust emerging as the strongest direct predictor of loyalty (β = 0.379). Furthermore, both perceived transparency and consumer trust significantly mediated the relationship between generative AI personalization and brand loyalty, indicating that tourists’ evaluations of platform transparency and trustworthiness play important roles in linking personalized AI experiences with long-term loyalty intentions. The study contributes theoretically by extending the application of the S-O-R framework to generative AI-enabled tourism environments. Specifically, it conceptualizes generative AI personalization as an adaptive technological stimulus and demonstrates how perceived transparency and consumer trust function as key cognitive mechanisms linking AI-driven personalization to brand loyalty. The findings also provide practical insights for tourism organizations seeking to leverage generative AI technologies to strengthen customer relationships and foster long-term loyalty within smart tourism platforms.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080243

Authors: Ru Wang Shugang Li Hongyu Liu

In a highly homogeneous market environment, consumers’ cognitive inertia towards standardized products continues to strengthen, and traditional functional attributes significantly weaken the driving force of impulse buying. This study is based on the elaboration likelihood model (ELM). It constructs a dual-path theoretical framework of “cognitive inertia cue arousal”, aiming to reveal a new triggering mechanism for impulse buying behavior (IBB) in homogeneous competition. This study proposes a hybrid structural equation model (SEM) and artificial neural network (ANN) approach to evaluate the driving factors behind consumers’ maintenance of IBB in the context of homogeneous competition. Specifically, the first stage involves identifying key variables that significantly affect IBB using SEM. The second stage consists of predicting the accuracy of the outcome variables and ranking their importance using an ANN. This study confirms two pathways: product cognitive inertia (customized information and promotional discounts) and peripheral cue arousal (anchor novelty display strategy and observation learning). Research has found that observational learning has a positive impact on perceived attention. Interestingly, perceived attention does not have a significant mediating effect between promotional discounts and IBB. This discovery provides a theoretical basis and practical paradigm for cracking the involution competition.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080242

Authors: Xuna Wang Zhiqian Lei

Real-time interaction and immersive experience in livestream e-commerce greatly boost consumers’ impulse buying, yet few studies explore how streamer interaction quality affects impulse buying via emotional bonds. Drawing on the SOR framework, this study constructs a moderated mediation model with parasocial interaction as the mediator and self-construal as the moderator. Combining structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA), we empirically analyze 406 valid questionnaires. The results show that: (1) four dimensions of streamer interaction quality (expertise, empathy, entertainment, reliability) positively predict impulse buying intention, with partial mediation of parasocial interaction; (2) the moderation effect is asymmetric: interdependent self-construal strengthens the positive link between parasocial interaction and impulse buying intention, while independent self-construal exerts no significant moderation; (3) fsQCA detects six equivalent configurations triggering high impulse buying intention, uncovering complementarity and substitution among interaction dimensions. This study clarifies the emotional transmission mechanism in livestream scenarios and offers tiered implications: streamers should uphold credibility as the baseline and adopt differentiated interaction strategies based on individual strengths; platforms should quantify interaction quality and implement targeted training and precision matching based on configurational paths; and merchants should match streamer characteristics to product types, strengthen group belonging for interdependent consumers, and reduce decision-making barriers for independent consumers.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080241

Authors: Ali Mahdavian Hadi Moradi Behnam Bahrak

The growing convergence of personalized recommendations and pricing strategies has created new opportunities for retailers to support more targeted promotional decisions and potentially improve revenue-related outcomes, provided that such systems are implemented with appropriate managerial and fairness safeguards. This study introduces a Transformer-driven modeling approach that jointly performs product recommendation and personalized price-level assignment by modeling observed discount-level acceptance as an operational proxy for consumers’ willingness to pay (WTP). Instead of handling price as an auxiliary or fixed attribute, the proposed model learns patterns of observed price responsiveness from sequential purchase histories that integrate user actions, item attributes, and discount exposure. The learning task is cast as a multi-class classification problem with five outcomes—non-purchase and purchases occurring at 0%, 10%, 20%, and above 30% discount levels—enabling the system to recommend not only relevant products but also economically appropriate pricing options. Empirical evaluation on the Dunnhumby retail dataset, which provides rich information on transactions and promotions, shows consistent performance across multiple evaluation criteria, including macro- and micro-level classification metrics, ranking-based measures such as NDCG@5, and Macro averaged AUC. Across the reported metrics, the Transformer-based framework performs better than our earlier deep reinforcement learning approach in this five-class setting, suggesting that sequence-based self-attention can better capture observed behavioral patterns related to discount-level acceptance. By producing integrated product–price recommendations, the proposed framework contributes toward closing the methodological gap between recommendation systems and dynamic pricing, and offers a scalable decision-support framework for data-driven, value-sensitive promotion design in retail and e-commerce contexts, subject to future validation in real-world and fairness-aware implementation settings.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080240

Authors: Yu Yan Shuang Qiu Sangbum Son Xuyang Duan Jian Li

Targeted coupons are widely used in e-commerce, yet campaign evaluation often emphasizes conversion or gross merchandise value without distinguishing realized coupon cost from the monetary exposure created by coupon allocation. This study presents an applied policy-evaluation framework that combines established propensity-adjustment, uplift-ranking, and offline evaluation tools to compare coupon-allocation policies under recipient-coverage and full-face-value budget constraints. The analysis uses 8414 anonymized assignment records representing 8402 users within a focal campaign window. Because the platform’s accounting margin is unavailable, a 20% margin is used as the baseline scenario for an incremental net-contribution proxy. Statistical uncertainty is assessed through 2000 user-cluster bootstrap replications and five fixed user-level data partitions. At 20% coverage, PA-RF produced a less negative proxy value than response-oriented policies, although its absolute confidence interval included zero and its ordering relative to PO-RF varied across data partitions. At 30% coverage, PA-RF produced a smaller net-contribution loss than PO-RF in all five partitions, while its absolute value and ROI proxy remained uncertain. The complete monetary-budget grid further shows that value-density allocation can differ materially from score-based allocation, that economically unattractive recipients may leave part of the available budget unused, and that expanding expenditure can reduce marginal subsidy efficiency. The contribution lies in a transparent and decision-oriented evaluation structure for interpreting coupon-allocation performance under explicit margin, cost, overlap, and model assumptions.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21080239

Authors: Vasile Paul Bresfelean Calin-Adrian Comes Paula Pop-Nistor

Cross-border e-commerce in the European Union remains operationally heterogeneous despite Digital Single Market integration, which complicates first-stage market comparison. This study develops a portfolio-first public-data framework for EU-27 cross-border e-commerce market-entry screening using 2023 as the common reference year. The framework derives PC1_EF, a PCA-derived execution-condition screening axis, from enterprise e-sales penetration, a digital financial participation proxy and the World Bank Logistics Performance Index. Market potential is calculated by multiplying the population aged 16–74 by online-shopping incidence, while cross-border buying openness remains a separate demand-side overlay. The first component explains 67.84% of backbone variance, with all loadings being positive. The portfolio distinguishes country positions across execution conditions, market scale and cross-border openness. Auxiliary rule-based screening bands serve as a compact summary. PC1_EF is positively associated with enterprise-side e-commerce turnover intensity (Spearman ρ = 0.486, p = 0.014, N = 25), providing partial criterion-consistency evidence. GDP_PPS rank differences provide interpretive context. Equal-weight and leave-one-variable-out checks assess the sensitivity of the continuous ordering, while LPI gate-family and no-LPI/no-gate checks assess the sensitivity of the band summaries. The framework provides a transparent and reproducible basis for comparing EU-27 cross-border e-commerce markets.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070238

Authors: Muhammad Ziad Soliman Abdullah Sarwar Ng Kok Wah

Despite the rapid proliferation of social commerce, limited empirical attention has been directed towards understanding how marketer-generated content (MGC) shapes consumer digital engagement through consumer perceived behaviour—particularly in conflict-affected emerging economies characterised by constrained digital infrastructure. This study addresses this gap by developing and testing an integrated mediation model in which MGC influences consumer digital engagement (CDE) both directly and indirectly through consumer perceived behaviour (CPB) as a mediating mechanism. Grounded in the consumer engagement theory, signalling theory, and social influence theory, the study employs a quantitative, cross-sectional design using Partial Least Squares Structural Equation Modelling (PLS-SEM) via Smart-PLS 4.0, drawing on data collected from 234 Palestinian SMEs actively engaged in social commerce activities. Empirical findings confirm that MGC is positively associated with both consumer perceived behaviour (H1: β = 0.440, p < 0.001) and consumer digital engagement (H2: β = 0.293, p < 0.001), while consumer perceived behaviour shows a significant positive association with digital engagement (H3: β = 0.429, p < 0.001). Critically, consumer perceived behaviour emerged as a significant partial mediator of the MGC–digital engagement relationship (H4), with the model explaining 38.0% of the variance in digital engagement. Theoretically, this study advances the consumer engagement literature by uncovering the pathway through which MGC translates into sustained digital engagement on social commerce platforms—a pathway previously unexamined in conflict-affected emerging market contexts. Empirically, the study contributes rare evidence from Palestine, positioning this digitally constrained economy as a meaningful frontier for social commerce and digital inclusion scholarship. Practically, the findings offer actionable guidance for resource-constrained SMEs seeking to optimise MGC strategies to enhance social commerce engagement and commercial conversion.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070237

Authors: Yang Li Zhengqi Deng Yuxin Sun Yu Li

Focusing on the dual pathways of emotional contagion and cognitive reinforcement, this study explores consumer interaction mechanisms in e-commerce live streaming and investigates the moderating role of live streaming assistants. Using valid real-time panel data spanning 4671 min across 40 Taobao Live sessions, this research adopts the LIWC-based text mining approach to extract indicators of user emotional expression and cognitive expression from bullet comments and matches minute-level multi-source data for empirical analysis. The empirical results indicate that early emotion expression and cognitive expression are significantly associated with intertemporal reinforcement patterns, with emotional contagion being more closely linked to instant purchase decisions and cognitive reinforcement being more closely linked to user follow behavior. While assistant intervention is associated with a more active overall interaction atmosphere across the two psychological pathways, it is negatively correlated with product sales, suggesting a dual-edged association. By introducing human assistants into the analytical framework and empirically distinguishing differentiated outcome associations related to emotional and cognitive mechanisms, this study extends the application of consumer-behavior theories to real-time livestream interaction contexts and offers cautious practical implications for livestream operation and assistant deployment.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070236

Authors: Abdulrahman Abdullah Alhelal

This study aims to examine how celebrity-endorsed travel livestreaming, as a digital marketing innovation, influences booking intention through the mediating roles of perceived authenticity and emotional engagement. Drawing on the Stimulus–Organism–Response (SOR) framework, the study conceptualizes livestreaming as a stimulus that activates both cognitive and affective responses, which are subsequently associated with consumer behavioral intentions. A quantitative approach was adopted using a structured questionnaire distributed online to a diverse sample of 609 respondents. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the proposed relationships and mediation effects. The findings reveal that celebrity-endorsed travel livestreaming has a significant positive effect on booking intention, perceived authenticity, and emotional engagement. In addition, both perceived authenticity and emotional engagement positively influence booking intention, with emotional engagement demonstrating a stronger effect. The mediation analysis further confirms that both perceived authenticity and emotional engagement partially mediate the relationship between livestreaming and booking intention, indicating that the impact of livestreaming is largely transmitted through cognitive and affective mechanisms. The study makes several key contributions by extending the SOR framework to digitally immersive marketing contexts, reconceptualizing authenticity as an interaction-driven construct within celebrity-endorsed environments, and highlighting the prominent role of emotional engagement in consumer decision-making. These findings provide a more nuanced understanding of how innovative digital marketing formats are associated with consumer behavior and offer practical insights for designing more effective livestreaming strategies in the tourism industry.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070235

Authors: Hsiang-Fei Luoh Pei-Chun Lo Min-Yen Lu Wen-Hwa Ko Yi-Xuan Xu

Online food delivery services (OFDSs) have become an integral component of consumers’ daily dining habits, making it important to understand consumers’ continuance tipping intention toward OFDS couriers. On the basis of stimulus–organism–response theory and social exchange theory, the present study examined how service quality and psychosocial motivations influence consumers’ continuance tipping intention toward OFDS couriers in Taiwan. Purposive sampling was adopted, and an online questionnaire survey was administered to OFDS consumers who had recently tipped delivery couriers. In total, 845 valid questionnaires were collected, and structural equation modeling was employed to test several proposed hypotheses. The results indicated that past tipping behavior was significantly and positively associated with continuance tipping intention. Perceived value partially mediated the relationships between perceived service quality and tipping behavior, as well as between perceived service quality and continuance tipping intention. In addition, psychosocial motivations, including gratitude, social obligation, and helping couriers, were significantly and positively associated with continuance tipping intention. Finally, the findings highlight the roles of service evaluations and psychosocial motivations in explaining consumers’ continuance tipping intention toward OFDS couriers.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070234

Authors: Qian Hu Zhao Pan

As large language models (LLMs) advance the machine mind of artificial intelligence (AI) to be more human-like, the dynamic impact of users’ perceptions of this mind on their engagement remains underexplored. Specifically, little is known about the “tipping point” where appreciation shifts to aversion. Grounded in mind perception theory, this research investigates the dynamic relationship between the perceived machine mind of AI voice assistants and user engagement using data from online reviews and experiments. Study 1 reveals a significant inverted U-shaped relationship, indicating that a moderate level of human-like machine mind maximizes user engagement, while excessive anthropomorphism triggers aversion—a form of the “uncanny valley” effect. Study 2 further dissects the differential impacts of the two core dimensions of the mind: the agentic mind (i.e., the capacity for thinking and planning) and the experiential mind (i.e., the capacity for feeling and emotion). Our findings reveal the comparative effects of AI agentic and experiential minds on the inverted U-shaped relationship, showing that a high experiential mind attenuates the positive effect of the agentic mind on user engagement. These findings deepen our understanding of mind perception effects in human–AI interaction and offer critical practical insights for AI designers on optimizing mind simulation to avoid user alienation.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070233

Authors: Yuancheng Liu Hengyu Liu Keun-Soo Park

Although social media influencers (SMIs) play a pivotal role in destination marketing, the underlying factors through which they shape tourists’ interest and travel intentions remain underexplored. Based on the modified attention interest desire action (AIDA) model, this study addresses this gap by investigating how the characteristics of SMIs influence travel intentions through destination perceived trust and destination perceived attractiveness. A total of 416 valid questionnaires were analyzed using partial least squares structural equation modeling (PLS-SEM) and an artificial neural network (ANN). The results revealed that SMIs’ similarity, expertise, physical attractiveness, social attractiveness, sincerity, and visibility enhanced tourists’ destination perceived trust and destination perceived attractiveness, thereby influencing travel intentions. Additionally, the ANN analysis complements the PLS-SEM results by comparing predictive performance and identifying the relative importance of SMI characteristics. These findings provide several suggestions for destination management organizations to improve influencer marketing.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070232

Authors: Tiannv Ma Yuqi Du Yong Wang Liying Zhou

Algorithm-aversion research predicts that consumers prefer human curators to algorithmic ones in subjective decision domains, including taste-based hedonic recommendation. Drawing on the algorithmic-symbiosis paradigm and on assortment-perception theory, this paper identifies a boundary condition on that prediction: disclosing that recommendations are AI-curated rather than human-curated lifts purchase intention in hedonic e-commerce but not in utilitarian e-commerce. The mechanism is a search-side option-breadth inference—the consumer’s attribution about the size of the option pool the curator considered upstream—which is diagnostic in preference-formative consumption categories where consumers build, rather than match, a preference. Three online experiments deployed through a Chinese consumer panel test the framework. Study 1 (N=228) finds the predicted Disclosure × Product-type interaction (ηp2=0.025) with the AI-versus-human lift confined to the hedonic cell (d=0.65). Study 2 (N=257) isolates the option-breadth pathway against trust and competence as competing mediators. Study 3 (N=519) extends the design to a second hedonic category, decomposes option breadth into search-side and display-side subdimensions through an eight-item bi-factor scale, tests mentalizing alongside option breadth as a competing mediator, and brings the moderated-mediation test by consumer AI familiarity to conventional statistical power (index of moderated mediation =+0.065, 95% CI [+0.014,+0.118]). Implications for interactive-marketing practice and algorithmic-disclosure regulation are discussed.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070231

Authors: Mei Huang Qiulin Sun Xiao Yu Fang Wan Danping Liu

Traditional celebrity endorser models conceptualize expertise as a static trait focused on the possession of product knowledge. However, the synchronous and highly interactive nature of live commerce challenges these static models in explaining consumer engagement. Grounded in Social Presence Theory and a contextual competence perspective, the present study reconceptualizes live-streamer professionalism as a set of dynamic competencies enacted through real-time interaction. Using a sequential mixed-methods design, Study 1 employs a grounded theory analysis of qualitative data collected from consumers, streamers, and platform practitioners to identify core dimensions of streamer professionalism. Study 2 develops and validates a multidimensional measurement scale. Study 3 leverages a comprehensive engagement model to benchmark the proposed framework against competing, expertise-based explanations. The results reveal five distinct dimensions of streamer professionalism (Business Knowledge Reserve, Expressive Ability, Professional Quality, Interactive Ability, External Visible Traits) and a dual-pathway mechanism: Business Knowledge Reserve primarily enhances cognitive product involvement, whereas Interactive Ability strengthens context attachment by amplifying perceived social presence. The proposed model demonstrates greater explanatory power than traditional expertise frameworks, particularly in explaining affective attachment. Collectively, these findings shift the analytical focus from who the source is to how professionalism is performed within interactive service encounters, thereby advancing a performance-oriented view of professionalism in live commerce and informing platform governance and influencer management.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070230

Authors: Ovidiu-Aurel Ghiuță

Online shopping is expanding rapidly in emerging European markets, yet many consumers still combine digital channels with offline verification. This study asks whether the resulting heterogeneity among young consumers is genuinely complex or reducible to a simpler structure. Drawing on technology-acceptance and perceived-risk perspectives, it analyses survey data from 457 Romanian university students, of whom 269 were routed to eight attitudinal items on online smartphone purchasing. The questionnaire was distributed to students at two Romanian universities through institutional email and faculty social media accounts, yielding a non-probability convenience sample; no probability-based selection procedure was applied. After examining the dimensionality and reliability of the items through exploratory factor analysis, K-means clustering was used to segment respondents, and the solution was checked against hierarchical clustering. Two segments emerged: cautious, lower-online-orientation consumers, who report lower perceived convenience, speed, price advantage, and overall use of online purchasing, and online-oriented adopters, who view digital channels as efficient and convenient. Rather than many fine-grained segments, the data point to a dominant behavioural axis, namely overall orientation towards online purchasing. The findings provide evidence consistent with a parsimonious two-segment account of consumer heterogeneity in a rapidly developing digital market, and they suggest differentiated omnichannel strategies: confidence-building and risk-reduction measures for cautious consumers and experience optimisation for digital adopters.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070229

Authors: Reema Nofal

The convergence of artificial intelligence (AI) and augmented reality (AR) is reshaping digital retail, yet how algorithmic transparency, explainable AI, and AR-based immersion jointly shape consumer responses remains theoretically underdeveloped. Drawing on the Stimulus–Organism–Response (S–O–R) model, this study examines the influence of cognitive stimuli (algorithmic transparency and explainable AI) and experiential cues (AR immersion) on trust, perceived usefulness, immersion, purchase decisions, and continued usage intention. Using survey data from 420 online shoppers in the West Bank, Palestine, and analyzed via structural equation modeling, the findings indicate that cognitive evaluations—particularly perceived usefulness and trust—exert stronger effects on purchase decisions than immersion. Specifically, algorithmic transparency enhances trust, while explainable AI strengthens perceived usefulness; AR immersion contributes to engagement, but its behavioral effects remain comparatively weaker. The results suggest that experiential engagement serves as a complementary enhancer, operating most effectively when supported by transparent and useful AI systems. The study extends AI–AR commerce literature by proposing a multi-stage S–O–R framework and demonstrating the conditional role of immersion in emerging digital markets.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070228

Authors: Israt Jahan Shithii Afrosa Al-Jahan Md Abdul Hannan Mia

Framing the study within the Unified Theory of Acceptance and Use of Technology (UTAUT) and the extended online privacy concern model, this study investigates the effects of effort expectancy, social influence, privacy concerns, and perceived risk on e-commerce consumer behavior, while positioning trust in AI as a key mediating factor. The data were collected from 250 active e-commerce users in Bangladesh, and the analysis was done with a hybrid approach that integrates partial least squares structural equation modeling (PLS-SEM) with artificial neural network (ANN) analysis. The SEM results indicate that effort expectancy and social influence have significant positive effects on consumer behavior. Social influence also shows a significant positive effect on trust in AI. Trust in AI exhibits a significant positive effect on consumer behavior. However, perceived risk does not show a significant effect on either trust in AI or consumer behavior. Privacy concerns demonstrate a significant positive relationship with both trust in AI and consumer behavior, contrary to the hypothesized negative relationships. The mediation analysis shows that trust in AI significantly mediates the relationship between social influence and consumer behavior, while no significant mediation effects are observed for privacy concerns or perceived risk. The ANN results further confirm the dominance of social influence as the most important predictor of both trust in AI and consumer behavior, followed by effort expectancy and trust in AI, while perceived risk shows minimal predictive relevance. Overall, the findings suggest that consumer adoption of AI-enabled e-commerce is primarily driven by benefit-oriented factors rather than risk-based considerations in the present context. The study contributes to the literature by extending UTAUT and privacy calculus theory to AI-mediated commerce and by demonstrating the value of combining SEM and ANN to capture both explanatory relationships and predictive importance. From a managerial perspective, the results highlight the importance of strengthening social influence mechanisms, improving system usability, and building trust in AI systems to enhance consumer engagement in AI-driven e-commerce environments.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070227

Authors: Osama Wagdi Walid Abouzeid Heba Farid Sharihan M. Aly

Artificial intelligence-integrated ‘buy now, pay later’ (BNPL) platforms are diffusing rapidly across the Middle East and North Africa (MENA), raising concerns about consumer financial vulnerability. Drawing on choice architecture, payment decoupling, and financial literacy literatures, this study examines how three platform-level features—algorithmic nudging, AI personalization intensity, and perceived ease of credit—are associated with impulsive buying tendency and downstream financial outcomes, and whether BNPL-specific financial literacy attenuates these associations. A multi-method design combined cross-sectional partial least squares structural equation modeling (N = 1247 active BNPL users in seven MENA countries) with a six-month longitudinal follow-up (N = 847, 68% retention). Algorithmic nudging was positively associated with impulsive buying tendency, which in turn was associated with elevated financial stress and longitudinal debt accumulation. The ‘loyalty trap’—a paradoxical state in which financially stressed consumers maintain high platform loyalty—is provisionally documented via piecewise longitudinal trajectories. We emphasize that this pattern is consistent with but not causally established by the present design, and we outline specific experimental and quasi-experimental research designs needed for causal identification. BNPL-specific financial literacy moderated the associations between algorithmic nudging, impulsive buying, and adverse financial outcomes, with the highest-literacy quartile exhibiting substantially attenuated debt trajectories. We discuss boundary conditions, alternative explanations, and the limits of causal inference in non-experimental panel data. Findings inform evolving BNPL regulatory frameworks in MENA, with particular relevance to nudge-transparency disclosures, contractual cooling-off periods, and credit-bureau reporting standards.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070226

Authors: Rulan Liu Syuhaily Osman Mohamad Fazli Sabri Nur Aqilah Amalina Jaafar

Beginning with the notion that short-form video sites represent key digital commerce destinations for food influencers in general, there is very little research explaining how specific persuasion cues lead to varying degrees of consumer engagement. Based upon the Elaboration Likelihood Model (ELM), Social Proof theory and the COBRA framework, this study aims to understand whether argument quality as a central-route cue and perceived emotional appeal as a peripheral-route cue affect consumption, contribution, creation engagement behaviors. Additionally, the study will determine if perceived post popularity, a platform-generated social proof signal, affects these relationships. A survey was conducted of 386 users of short food influencer videos. PLS-SEM analysis was performed on the survey data. Results indicated that both argument quality and perceived emotional appeal positively influence all three engagement levels. Perceived emotional appeal exerts a stronger effect, particularly on higher-engagement behaviors. The moderating effects of perceived post popularity were limited and applied solely to contribution, where it weakened the positive relationship between argument quality and contribution but strengthened the positive relationship between perceived emotional appeal and contribution. This study builds upon existing knowledge of how consumers respond to food influencer content by exploring differences between types of engagement and integrating platform-generated signals within an ELM-based theoretical model.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070225

Authors: Lei Wang Jiayi Ren Shiyi Sun Yitao Chen

Intelligent customer service is increasingly used to reduce service costs and improve efficiency, yet users often experience divergent outcomes, ranging from value co-creation to value co-destruction. Using task–technology fit theory as the primary theoretical lens, this study examines how the alignment between users’ service task requirements and intelligent customer service capabilities is associated with divergent value outcomes. Within this framework, cognitive load and perceived control are theorized as two localized psychological mechanisms, human–AI trust as a conversion mechanism, and digital self-efficacy as a boundary condition. Using two scenario-based experiments, we develop and test the proposed model. Study 1 shows that participants in the high-fit condition reported higher value co-creation and lower value co-destruction than those in the low-fit condition. The results further reveal an asymmetric mechanism: cognitive load did not directly explain value co-creation but was more strongly associated with value co-destruction, whereas perceived control showed a broader association with both value outcomes. Study 2 extends Study 1 by examining digital self-efficacy as a boundary condition and shows that the conditional indirect association patterns vary across levels of digital self-efficacy. These findings contribute to extending task–technology fit theory to post-use value formation and provide cautious implications for intelligent customer service design and management.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070224

Authors: Wenhao Bai Zhong Yao Wuhuan Xu Yazhou Luo

Product usage tutorials are central to social commerce, yet little is known about whether the same tutorial information persuades consumers differently when it is generated by brands or by peer users. Drawing on the Persuasion Knowledge Model and cue consistency theory, this study examines how brand-generated tutorials (BGTs) and user-generated tutorials (UGTs) influence purchase intention, and how trust, brand image, and cross-source content similarity shape these effects. Two between-subjects experiments were conducted in the context of cosmetics tutorials on Rednote. Study 1 used a 3 (tutorial source: BGT, UGT, none) × 2 (brand image: high, low) design; Study 2 compared high BGT–UGT similarity, low similarity, and UGT-only conditions. Results show that both BGTs and UGTs increase purchase intention relative to no tutorial, with UGTs producing a stronger effect. Trust mediates the effect of UGTs but not BGTs, while brand image does not moderate source effects. In co-presence conditions, high BGT–UGT similarity strengthens purchase intention, whereas low similarity weakens it. These findings identify source-level commercial intent as a boundary condition for trust formation and show that cross-source cue consistency determines whether brand tutorials validate or undermine peer-generated guidance.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070223

Authors: Junbiao Xu Zhicai Zhang Chong Zhang

Recommendation systems are widely used in e-commerce, social media, and content distribution, yet LLM-based recommendation workflows still face recurring challenges in data completeness, sample balance, and output stability. In addition, the conventional “user-platform” structure leaves limited room for user-side mediation of exposure and preference expression. This paper presents ABP, a workflow-level extension of i2Agent in the User–Agent–Platform setting. ABP contains three modules: Adaptive Description Enrichment (ADE), Batch-balanced Sampling Strategy (BSS), and Prompt-driven Workflow Optimization (PWO). ADE repairs missing or rigid item text with richer natural-language descriptions, BSS builds balanced comparative inputs for user profiling, and PWO strengthens multi-stage reasoning with structured output constraints. Experiments on four real-world datasets show that ABP achieves strong ranking results under the reported protocol. Across the 16 reported dataset–metric pairs, the five-run mean results of ABP show an average relative improvement of 24.62% over i2Agent, with especially large gains on Amazon Book and Amazon Movietv. Under a fixed five-run protocol, ABP shows limited run-to-run dispersion on Amazon Book, Amazon Movietv, and Yelp, while Goodreads exhibits comparatively larger but still bounded variation. Overall, these results suggest that carefully designed workflow improvements can improve LLM-based recommendation quality in the reported setting while maintaining the agent’s role as a user-side mediation layer in the User–Agent–Platform setting.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070221

Authors: Kai Lin Hsu Wei-Hsi Hung

This study examines how key opinion consumers (KOCs) influence consumer behavior in social commerce, particularly under growing distrust toward traditional key opinion leaders (KOLs) and the resulting limitations in their influence. While prior research has primarily focused on trust-building, this study investigates how KOCs create perceived value through a distinct influence mechanism that distinguishes between the roles of trust and distrust. Using an explanatory sequential mixed-methods approach, this research integrates results from a quantitative study with findings from a qualitative study to arrive at robust meta-inferences. Following the PLS-SEM analysis (N = 582), qualitative interviews were conducted to corroborate these inferences and provide complementary insights into the psychological mechanisms underlying trust and distrust, identifying boundary conditions—such as the ineffectiveness of physical attractiveness—that redefine influence models in social commerce. The results indicate that a KOC’s expertise and recommendation cues significantly enhance perceived value, which serves as a key mediator influencing purchase behavior. This study conceptualizes the “invisible influence” effect as a distinct persuasion mechanism. KOCs construct utilitarian value through authentic, experience-based sharing. By identifying the mediating role of perceived value and mapping the evolutionary trajectories of influencer roles, this research extends social commerce strategy.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070222

Authors: George To Sum Ho Valerie Tang Carmen Kar Hang Lee Manviel Man Fei Tam Elle Wing Ho Chow

As e-commerce expands, automated e-fulfilment centers are important for fast and dependable delivery of orders placed online. Robotic equipment performs most of the operational work in these centers, supported by Internet of Things (IoT) that provide real-time operational data. Although these innovations show the principles of Industry 4.0, the transition to Industry 5.0 creates an economic trade-off between resilience and sustainability for the increasing demand for both. The main challenge is to reduce energy use without compromising the ability to manage demand fluctuations. Existing research has typically focused on sustainability or resilience by improving individual processes in isolation, which limits trade-off of resources usage and performance, as well as the interconnected nature of logistic operations. To respond to this challenge, the study proposes a Digital Twin for Joint Sustainability and Resilience Optimization (DT-JSRO) model to help in decision making coupled sustainability-resilience optimization. The DT-JSRO model lets managers run scenarios for performance evaluation in sustainability and resilience while guiding users on the best resource allocation strategies. A simulation experiment proved the feasibility and effectiveness of the proposed approach. The model simulates different operational scenarios to produce the best resource allocation strategies that can assist practitioners based on practical priorities, including solely sustainability, resilience or jointly optimizing the two when needed.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070220

Authors: Ramesh Shahi Tej Bahadur Shahi Bishnu Bahadur Khatri Arjun Neupane

With the rapid growth of social media use in Nepal and their engagement with customers, understanding how these platforms support customer loyalty becomes essential for retail businesses. This study investigates the role of social media marketing in shaping brand loyalty among retail customers in Nepal through an integrated research design that combines systematic bibliometric and survey data analysis. The bibliometric findings suggest a clear progression in the literature, moving from a foundational emphasis on relationship marketing and loyalty theory toward a more integrated framework that incorporates social media engagement and, ultimately, measurable business outcomes. The quantitative results based on survey data conducted with 100 Nepalese consumers active on Facebook, Instagram, and TikTok show that effective social media strategies and the ability to address implementation difficulties significantly enhance brand loyalty. In contrast, the direct influence of customer trust is limited. The findings highlight the importance of tailored and interactive content, supported by appropriate digital practices, particularly in regions with developing infrastructure. This study offers practical recommendations to improve digital engagement, encourage retailers to collaborate with local influencers, respond to customer feedback, maintain transparency in messaging, and enhance digital capabilities to implement effective social media strategies. It also underscores the value of producing culturally relevant content that aligns with local interests and behaviours.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070219

Authors: Wei Song Siyuan Wei Zinuo Li Shengliang Deng Yuqi Du

Virtual influencers in diverse appearances emerged and gained popularity on virtual platforms. However, how the appearances of virtual influencers affect consumers’ attitudes and reactions remained largely unexplored. Through three experimental studies, this paper examines the psychological mechanism and boundary conditions for consumer reactions to virtual influencer transgressions. The results show that consumers are less forgiving and more negative in their reactions to transgressions conducted by human-like virtual influencers compared to anime-like ones, regardless of the type of transgression or the gender of the virtual influencer (Studies 1 and 2). Additionally, the driving mechanism of the virtual influencers has a moderating effect. When consumers are informed that the virtual influencer transgression is driven by a real person rather than AI, the impact of appearance on the reactions to transgressions is aggravated (Study 3). The result shows that appearance and driving mechanism both influence consumer perceptions of the virtual influencers’ agency, thereby determining the degree of reaction to transgressions.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070218

Authors: Matilde Martínez Moriel Guillermo García-Badell Delibes

This article examines how DressGO, a fashion game developed by DRESSX on Roblox, organizes immersive fashion commerce for youth-oriented audiences through persuasive and gamified interface design. Rather than treating purchase as a discrete transactional event, the study examines how monetization cues are embedded in progression loops, cosmetic status, and habitual return within a platform-mediated retail environment. Methodologically, the article adopts a qualitative single-case study and interface analysis based on a primary corpus of 12 screenshots selected from an initial pool of 34 screenshots collected across six gameplay sessions in January 2026, complemented by observational notes and contextual documentary triangulation and supplementary verification material for Robux/payment-route visibility. The analysis identifies four operational mechanisms: staged unboxing as a sensory gateway to acquisition, daily rewards and quantified tasks as retention infrastructure, rankings and rarity displays as social comparison cues, and accelerators, probability boosters, and waiting timers as conversion pressure mechanisms. The findings indicate that the interface integrates virtual fashion consumption into ordinary play and social visibility, while monetization cues operate through playful aesthetics, repetition, scarcity cues, and low-friction prompts embedded in progression systems. The article contributes to immersive commerce research by examining how gamification, interface design, and symbolic fashion value converge in a youth-oriented virtual retail environment. It further argues that randomized access, temporal friction, and comparative visibility should be understood not only as engagement features, but also as matters of digital fairness, platform trust, and responsible interface design.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070217

Authors: Mostafa Aboulnour Salem Zeyad Aly Khalil

Consumer resistance remains a major barrier to the effectiveness of AI-enabled social media marketing despite advances in content personalisation, influencer marketing, and intelligent recommendation systems. This study investigates how content personalisation, influencer trust, and platform interactivity influence consumer resistance, user engagement, and purchase intention by proposing a behaviourally informed Deep Reinforcement Learning (DRL) framework that integrates empirical behavioural modelling with adaptive optimisation. Survey data were collected from 619 higher education students in Saudi Arabia and analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM), Multi-Group Analysis (MGA), and a Deep Q-Network (DQN)-based optimisation framework. The results show that content personalisation, influencer trust, and platform interactivity significantly increase user engagement while reducing consumer resistance. User engagement positively influences purchase intention, whereas consumer resistance negatively affects purchasing behaviour. Multi-Group Analysis revealed that Generation Z responded more strongly to personalisation and platform interactivity, whereas Generation Y showed greater responsiveness to influencer trust. The proposed behaviourally informed DQN framework incorporated latent behavioural constructs and statistically validated structural relationships into the reinforcement learning environment to generate adaptive marketing policies. Compared with conventional static and rule-based strategies, the proposed framework achieved approximately 36% higher optimisation performance across repeated behavioural simulations. The study contributes by positioning consumer resistance as the central behavioural construct, introducing an integrated behavioural–computational framework that embeds empirical behavioural relationships into the DRL state representation, reward mechanism, and policy-learning process, and providing practical guidance for developing transparent, trust-sensitive, and adaptive social media marketing strategies that enhance user engagement, reduce consumer resistance, and improve purchase intention in digital commerce environments.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070216

Authors: Miao Wang Zixuan Zhou Qingjun Chen Delian Xu Shiqun Yuan Guangfan Sun

Against the backdrop of the rapid development of digital marketing in emerging economies, livestreaming e-commerce provides a new pathway for products from resource-constrained regions to overcome geographical limitations and expand market channels. Existing studies have largely focused on the sales performance, platform models, or general determinants of purchase intention in livestreaming e-commerce, while insufficient attention has been paid to the formation mechanism of consumers’ purchase intention within livestreaming interactions. Drawing on the S-O-R model and interaction ritual chain theory, this study constructs a theoretical model of how rural livestreaming e-commerce influences consumer purchase intention. Specifically, live streaming scenario atmosphere, product packaging, anchor interaction, and consumer engagement are identified as key stimulus factors. This study examines how these factors influence purchase intention through affective perception, cultural perception, and consumer trust, and further investigates the moderating role of emotional energy. The results show that: (1) the four livestreaming interaction factors significantly enhance consumers’ affective perception and cultural perception; (2) affective perception and cultural perception each form a chain mediation path with consumer trust, playing an important transmission role between livestreaming stimuli and purchase intention; and (3) emotional energy strengthens the effects of certain livestreaming stimuli on consumers’ psychological perceptions. This study reveals the pathway of “livestreaming stimuli–affective/cultural perception–consumer trust–purchase intention,” and provides strategic implications for livestreaming marketing, local brand communication, and rural economic development.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070215

Authors: Cong Sun Xinyu Li Xing Meng

Unlike B2C service failures, where firms respond to their own failures within a dyadic firm–customer relationship, C2C platform complaints often originate from third-party sellers. In such triadic platform–seller–consumer interactions, AI customer service agents become front-end governance interfaces through which consumers judge platform fairness, rule enforcement, and institutional reliability. This study examines anthropomorphic AI as a task-contingent governance cue in C2C platform complaint handling. Across two scenario-based experiments, we test how AI role framing, anthropomorphism, and complaint severity jointly shape consumer evaluations. Study 1 shows that high anthropomorphism increases service recovery satisfaction when AI is framed as a relational representative, but not when framed as a rule-based arbitrator. Study 2 reveals significant three-way interactions among complaint severity, role framing, and anthropomorphism for satisfaction, platform trust, and continuance intention. Under low severity, high anthropomorphism benefits both roles; under high severity, its benefit remains mainly for the relational representative and weakens for the rule-based arbitrator. Mechanism analyses show that social presence explains responses under low severity, whereas both social presence and procedural justice shape evaluations under high severity. Together, these findings identify governance-task fit as a key condition for the value of anthropomorphic AI in platform complaint handling, showing when human-like AI builds trust and when it may undermine governance credibility.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070214

Authors: Ming-Hsuan Wu

Influencer-mediated social commerce has become a prominent environment in which consumers encounter brands through influencer recommendations, livestreaming, short videos, sponsored posts, and socially embedded interactions. Although influencer campaigns often generate visible engagement metrics, such as views, likes, comments, saves, and livestream participation, these engagement responses do not necessarily translate into public brand advocacy. Drawing on the stimulus–organism–response framework, balance theory, and the social risk perspective, this study examines how parasocial relationships with influencers are associated with eWOM advocacy intention through customer engagement and how perceived brand transparency conditions this conversion process. Data were collected through an online self-administered questionnaire targeting consumers in Taiwan with recent influencer-mediated social commerce experience, and 372 valid responses were retained for analysis. The results show that parasocial relationship is positively associated with customer engagement, and customer engagement is positively associated with eWOM advocacy intention. Customer engagement partially mediates the relationship between parasocial relationship and eWOM advocacy intention. In addition, perceived brand transparency strengthens the relationship between customer engagement and eWOM advocacy intention, and the moderated mediation results indicate that the indirect effect of parasocial relationship on eWOM advocacy intention through customer engagement is stronger when perceived brand transparency is higher. These findings clarify the relationship–engagement–advocacy conversion process and identify perceived brand transparency as an assurance condition under which influencer-driven engagement is more likely to develop into public eWOM advocacy.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070213

Authors: Sicheng Liao Qingmin Kong

Considering the high user engagement and collaborative content creation in the digital cultural industry, we explore how digital platform capabilities (DPCs) enable value co-creation in corporate innovation ecosystems (VCC-CIE). Drawing on dynamic capability theory and survey data from 504 employees of Chinese digital cultural enterprises, we analyze the impact of DPCs on VCC-CIE. DPCs were found to positively affect VCC-CIE, with organizational structural agility (OSA) and business model innovation (BMI) serving as mediators. Furthermore, digital business intensity (DBI) not only positively moderates the direct relationship between DPCs and VCC-CIE but also significantly moderates the mediating role of OSA, creating a moderated mediation model. This research advances our understanding of value co-creation in the digital age. Practically, it reveals that digital cultural enterprises can boost VCC-CIE by enhancing their DPCs, improving OSA, and adopting BMI.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070212

Authors: Bo Yang Xinmeng Wang

As video-based influencer marketing becomes central to digital brand communication, understanding what drives consumer engagement on interactive platforms is increasingly critical. This study examines how content and contextual features influence engagement behavior in influencer-sponsored videos on Bilibili. Drawing on the theory of conversation and the customer-perceived-value framework, we propose a four-part framework—Who (influencer characteristics), What (message content), How (presentation strategy), and When (timin g)—to explain engagement variation. Using a manually coded dataset of 457 sponsored videos, we find that hedonic appeal, comparative messaging, and message sidedness significantly enhance engagement, while signals of brand control, promotional incentives, and technical features (e.g., plug-ins, progress bars) have no significant effect. Notably, perceived expertise and posting during the platform’s “golden period” also do not affect engagement, underscoring the importance of relatability and content salience over authority or timing. By integrating message strategy with interactive design, this study advances marketing communication theory in digital contexts and offers practical insights for optimizing content strategy on social video platforms.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070211

Authors: Juan Li Siti Ngayesah Ab Hamid

Live-streaming commerce increasingly relies on live streamers as frontline retail actors, yet the identity structures through which streamers consistently define and enact their personal brand remain theoretically underdeveloped. Drawing on semi-structured in-depth interviews with 13 live streamers across diverse content categories on Chinese platforms (Douyin and Kuaishou), selected through purposive and referral sampling, this qualitative study adopts a theory-guided thematic analysis informed by Kapferer’s Brand Identity Prism. The analysis identifies six interrelated personal brand identity dimensions: Personality-Based Identity, Symbolic and Aesthetic Identity, Relational Orientation Identity, Value-Based Identity, Audience-Aligned Identity, and Content-Oriented Identity. Three theoretical contributions are advanced. First, four of Kapferer’s original facets require contextual deepening in live-streaming commerce, where real-time interaction, continuous audience feedback, and platform affordances substantively reshape how identity operates. Second, Audience-Aligned Identity reconceptualizes Kapferer’s reflection and self-image facets as a unified dimension, grounded in the inseparability of these processes in synchronous interactive retail. Third, Content-Oriented Identity extends existing personal branding frameworks by theorizing content production as a key mechanism of brand legitimation in real-time digital retail environments. The findings offer a conceptual foundation for future scale development and empirical investigation, and provide practical guidance for streamers, platform developers, and brand managers in digital retail contexts.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070210

Authors: Maryam Asgharinajib Davood Feiz Shahryar Sorooshian

The internationalization of business-to-business (B2B) professional services is being reshaped by generative artificial intelligence (GenAI). Despite its potential to enhance productivity and reduce export uncertainty, existing research has focused on B2C contexts, leaving a gap in understanding how B2B experts perceive and exploit this technology. This research, using a Q methodology, seeks to explore the discursive framework of experts’ mindsets regarding exploitation of GenAI to develop B2B professional services exports. Using 32 experts from five countries (Iran, United States, United Kingdom, Germany, and India), four mindsets were identified: (1) Human–GenAI Synergy, (2) Export Innovation Catalyst, (3) Facilitator of Managerial Mindset, and (4) Moral Hazard Paradox. By conceptualizing mindsets as intangible resources within the Resource-Based View (RBV) and interpreting their role through the Uppsala model, this study makes three contributions: enriching theory through discourse-based analysis of expert mindsets, extending Q methodology to B2B export research, and providing practical insights for human–GenAI collaboration, export innovation, and ethical governance. The findings indicate that successful GenAI-enabled export development depends not only on technological capabilities but also on how experts interpret, adopt, and utilize the technology. The results highlight the need to balance innovation with ethical risks to achieve export growth.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070209

Authors: Qin Zhang Firdaus Abdullah

Virtual influencers are increasingly used in influencer marketing, yet it remains unclear whether trust generated by an artificial persona can be transferred to endorsed brands in the same way as trust generated by human influencers. This study examines artificial intelligence (AI)-mediated trust transfer among female consumers by comparing virtual and human influencers across four social media platforms. Drawing on source credibility, parasocial interaction, social presence, trust transfer, and brand attachment perspectives, we propose that influencer type is associated with brand outcomes through three observable social-cue pathways: perceived authenticity, parasocial interaction, and social presence. These cues are expected to be associated with influencer trust, which is then associated with brand trust, brand attachment, purchase intention, and recommendation intention. Using 78,432 female consumer comments from Xiaohongshu, Instagram, Weibo, and Douyin, matched across 12 virtual–human-influencer pairs, we construct text-derived linguistic indicators—proxies rather than validated psychometric constructs—through keyword dictionaries, sentiment classification, and standardized composite scoring. The results show that human influencers are associated with higher perceived authenticity, parasocial interaction, and social presence than virtual influencers. The strongest association in the model is the trust-transfer path from influencer trust to brand trust, and the indirect path is associated with a substantial share of the observed covariation between influencer type and brand attachment. Product type further qualifies these patterns: the human-influencer advantage is stronger for hedonic products than for utilitarian products. These findings suggest that virtual influencers should not be understood as universal substitutes for human influencers. Instead, their effectiveness depends on whether AI-mediated personas can generate the social and authenticity cues that the literature associates with trust transfer in a given product context.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070208

Authors: Ru Guo Bolu Wei Xuemeng Guo

E-commerce platforms increasingly rely on recommendation systems whose internal logic is often opaque, making explanation design important for consumer evaluation. Drawing on procedural justice theory, this study examines whether process-based explanations function as procedural justice cues in e-commerce recommendations and how they relate to algorithmic trust and continuance intention. In a between-subjects online experiment with 394 Chinese consumers (197 per condition), participants received either an outcome-based recommendation or a process-disclosure package that disclosed data inputs and reasoning and therefore bundled procedural content with greater specificity and informational richness. Relative to outcome-based explanations, this package increased perceived procedural justice and was associated with higher trust in the algorithm and greater continuance intention. Perceived procedural justice and trust formed a theoretically ordered indirect pathway, but this ordering should be read as theory-grounded rather than causally established because the mediators and outcome were measured contemporaneously. Exploratory moderation analyses suggested that responsiveness to process-based explanations reflected broader self-reported digital interpretive capacity rather than algorithm-specific literacy alone. Robustness checks further indicated that the procedural justice pathway was not eliminated by explanation clarity, cognitive load, scenario realism, product attractiveness, or privacy intrusiveness. The findings position process-disclosure packages as practical transparency tools while cautioning that their benefits depend on consumers’ interpretive capacity and processing costs.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070207

Authors: Noramira Anis Shukor Hanis Diyana Kamarudin Norizan Anwar Aniza Jamaluddin

Social commerce has become a significant strategy for organisations intending to strengthen their competitive advantage in the digital economy. Despite this, prior research primarily addressed the consumer perspective, resulting in limited insight into organisational perceptions and utilisation of social commerce. This study seeks to identify research trends and examine factors influencing social commerce adoption from an organisational standpoint. A systematic literature review (SLR) was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, analysing 3793 articles sourced from Scopus, Web of Science, and ScienceDirect, published between 2017 and 2025. The findings indicate a consistent increase in social commerce research over the past decade, with a minor decline in the most recent year. The majority of studies employ quantitative methodologies and utilise theoretical frameworks such as the Technology–Organisation–Environment (TOE) framework, Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), and UTAUT2. Key determinants of social commerce utilisation identified include relative advantage, compatibility, complexity, top management support, organisational readiness, organisational structure, customer pressure, bandwagon effect, and competitive pressure. These results show the technology, organisation and environment elements shaping organisational utilisation on social commerce and provide guidance for policymakers as well as organisations in formulating effective implementation strategies.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070206

Authors: Tingting Liu Joon Koh

With the rapid advancement of digital technology, the self-service model has emerged, introducing a new work model known as digital shadow work (DSW). In this model, consumers perform tasks traditionally performed by employees, such as item scanning and self-checkout, without compensation. While this optimizes service processes and reduces business costs, it raises concerns about consumer rights, work value, and business sustainability. This study explores the psychological factors that affect consumer participation in DSW within self-service environments. Using a grounded theory approach and semi-structured interviews, the study reveals key psychological drivers under the dual-system framework. This study’s findings indicate that habitual behavior, impulsivity, time pressure, technological dependence, social identification, and delayed gratification significantly affect participation in DSW. Notably, the intuitive system (System 1) plays a dominant role in decision-making, leading consumers to make quick, automatic choices, often leaving them unaware of the work involved. By identifying these psychological factors, this research increases consumer awareness of DSW, promoting self-protection in self-service contexts. Additionally, understanding decision-making psychology provides essential insights for companies in non-face-to-face self-service technologies, supporting sustainable business practices.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070205

Authors: Kai Liu Zhibin Xing Haizhang Chen

Digital game consumption increasingly unfolds across video platforms, comment sections, and community discussions, where platform visibility, creator-mediated information, interaction metrics, and commercialization signals shape users’ expectations. In platform-mediated digital commerce, visible interaction may indicate information use, cultural resonance, payment concern, or consumption-related complaint rather than uniformly positive engagement. Using self-determination theory as a motivational lens within a platform-mediated consumer-behavior framework, this study examines whether platform content cues, public comment responses, and user perceptions provide convergent evidence on differentiated engagement meanings. The empirical setting is Bilibili content related to the Chinese wuxia role-playing game Where Winds Meet. The analysis combines 1164 public videos, 19,919 hot comments, and a content-exposure-anchored survey of 564 valid respondents. The results show differentiated patterns: functional information cues correspond to saving-oriented engagement and useful responses; cultural-aesthetic cues correspond to supportive interaction and cultural responses; and payment-mechanism and experience-problem cues correspond to payment concerns and complaints. The survey further shows that perceived information value, cultural/experiential connection, perceived monetization fairness, consumer autonomy in spending decisions, and perceived monetization risk are associated with continued engagement intention. These findings suggest that engagement quality should be interpreted through platform-mediated consumer relationships rather than interaction volume alone, while recognizing that hot-comment evidence reflects a platform-visible layer of user response rather than the full distribution of comments or player attitudes.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070204

Authors: Ming-Hsuan Wu

Drawing on signaling theory and a relational perspective, this study examines how perceived influencer credibility is associated with e-loyalty intentions in influencer-mediated social commerce. Rather than claiming that each individual path is theoretically unexpected, this study positions its contribution in specifying a conditional signal-to-relationship process. Specifically, perceived influencer credibility is conceptualized as a credibility-based digital promotional signal, brand trust and customer engagement are examined as sequential relational mechanisms, and perceived brand authenticity is positioned as an identity-consistency boundary condition in the customer engagement–e-loyalty intention relationship. Using survey data from 372 consumers with influencer-mediated social commerce experience, the data were analyzed using partial least squares structural equation modeling (PLS-SEM). The results show that perceived influencer credibility is positively associated with brand trust, brand trust is positively associated with customer engagement, and customer engagement is positively associated with e-loyalty intentions. The bootstrapped serial indirect association through brand trust and customer engagement is significant, indicating a trust–engagement pathway linking influencer-mediated promotional credibility to e-loyalty intentions. Perceived brand authenticity also positively moderates the customer engagement–e-loyalty intention relationship. Descriptive conditional indirect estimates further suggest that the indirect association through brand trust and customer engagement is stronger at higher levels of perceived brand authenticity; however, this conditional indirect pattern is interpreted conservatively because bootstrapped confidence intervals for the low and high conditional estimates were not directly available. These findings extend influencer marketing research by shifting attention from isolated credibility effects to a theory-driven process explaining how influencer credibility is associated with e-loyalty intentions through trust, engagement, and authenticity-based boundary conditions.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070203

Authors: Mengqi Sun Haitao Chen Hao Chen

Social interaction plays an increasingly important role in facilitating consumption, and many e-commerce platforms have incorporated peer information into product interface design. Given the limited attention paid to action-based information cues, this study proposes repeat purchase action as an information cue and examines its impact on user purchase intention. Drawing on social learning theory, we developed a research model and conducted two online experiments on Credamo, a professional online survey platform in China. The research model was empirically tested using bootstrapping-based mediation analysis. The results showed that both purchase action and repeat purchase action significantly increased user purchase intention. In addition, the impact of repeat purchase action on purchase intention was mediated by trust expectation, whereas purchase action did not elicit users’ trust expectation. These findings add to the literature on information cues in e-commerce and extend the theoretical explanatory power of social learning theory. This study also provides managerial implications for e-commerce platforms and sellers in implementing effective product interface information design and selection strategies.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070202

Authors: Jinjiang Yan Usama Khaliq Nosherwan Khaliq Anita Tangl

Purpose: This study explores the nonlinear influences of three organizational capabilities, including Cultural Adaptability (CA), Service Efficiency (SE), and Brand Commitment (BC), on Perceived Fairness (PF) and their subsequent effects on Customer Retention (CR) and Purchase Decision (PD). It also analyses the moderating effect of Perceived Social Norms (PSN) in this context. Design/Methodology/Approach: A two-wave time-lagged design was used to increase temporal precedence and reduce common-method bias, and 500 consumers in China and Pakistan were sampled. The hypothesized curvilinear associations were tested using Structural Equation Modeling (SEM). Findings: CA, SE, and BC have a positive influence on PF, though their relationships follow an inverted U-shaped pattern. PF shows positive correlations with CR and PD, and PSN enhances the relationship between PF and customer outcomes. Originality: The research adds value to Social Exchange Theory and Commitment–Trust Theory by demonstrating that organizational capabilities may yield diminishing returns of fairness and thus disproves the more-is-better linear relationships in customer relationship management.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070201

Authors: Jiaxiang Hu Caoyu Fan Lukai Zhang

This study examines how live-streaming cues influence impulsive purchase intention in fresh-fruit e-commerce, where consumers face substantial quality uncertainty and limited opportunities for pre-purchase inspection. Drawing on the Stimulus–Organism–Response (S-O-R) framework, we examine five stimuli—anchor professionalism, anchor interactivity, visual attractiveness, price discount, and scarcity—and test whether perceived value (cognitive) and positive emotions (affective) operate as parallel mediators. Based on survey data from 353 Chinese consumers, the results show that anchor professionalism, anchor interactivity, price discount, and scarcity are positively associated with impulsive purchase intention both directly and indirectly through perceived value and positive emotions, whereas visual presentation follows a different pattern. Contrary to the common assumption that vividness primarily triggers emotional impulse, visual attractiveness does not exhibit a robust direct effect on purchase intention; instead, its influence is transmitted dominantly through cognitive perceived value rather than affective positive emotions. This finding suggests that, in high-uncertainty perishable categories, vivid presentation is more consequential when it helps consumers evaluate product value than when it merely stimulates affective reactions. The study offers targeted implications for S-O-R theory and provides practical guidance for platform design and promotional disclosure in real-time e-commerce.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070200

Authors: Xue Pan Xin Xia Lei Hou

Short videos have become a dominant format in digital commerce, enabling brands to engage consumers and drive purchases through dynamic and visually rich content. This highlights the need for a more nuanced understanding of visual framing strategies, that is, what elements are shown and how they are presented. Drawing on Cognitive Load Theory, this study explores the impact of visual compositional framing strategies and their dynamics on consumer engagement and sales. Applying a CNN-based deep learning model, 249,043 images (video frames) extracted from 3426 book-related short sales videos on Douyin are classified into one of three categories: functional, contextual, or social, according to the visual composition of the frame. Further econometric modeling reveals distinct effects of such framing categories: functional framing is positively associated with both engagement and sales, contextual framing relates to higher sales only, while social framing relates positively to engagement but negatively to sales. From a dynamic perspective, frequent transitions between framing types within a short video increase visual complexity, which reduces both engagement and sales and moderates the effects of specific framing strategies. These findings advance theoretical understanding of visual framing in dynamic media environments and offer practical insights for designing more effective short video content.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070199

Authors: Montchai Pinitjitsamut

This study examines motivational and capability barriers to internet non-adoption in Thailand’s near-saturated digital economy. Using the 2025 Q4 ICT Household Survey conducted by Thailand’s National Statistical Office, the analysis focuses on 20,633 adult non-adopters who report either motivational or capability-related barriers. The dependent variable distinguishes capability non-adoption, defined as lack of skill or awareness, from motivational non-adoption, defined as lack of perceived need or privacy/security concerns. Weighted logistic regression with normalised population weights, PSU-clustered robust standard errors, and average marginal effects is used to estimate associations between household ICT access, age, education, employment, smartphone access, and barrier type. Motivational barriers account for 56.2% of the two-category non-adopter population and capability barriers for 43.8%. Although motivational reasons are the more common, household ICT access is positively—if modestly—associated with capability rather than motivational barriers (average marginal effect +1.7 percentage points): capability-constrained non-adopters are concentrated in connected households, the compositional signature predicted by the second-level digital divide. Age does not significantly moderate this association. Among older non-adopters, education, employment, and smartphone access are negatively associated with capability barriers, while household ICT access is not. The findings suggest that in post-access digital economies, household connectivity is insufficient for digital market inclusion; individual-level skills and device access become central to expanding effective digital market reach.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070198

Authors: Yiming Li Mingyao Sun Fang Wang Giri Kumar Tayi

Global retail e-commerce sales have surged, yet product fit uncertainty remains a significant challenge, leading to rising product return rates. To address consumer concerns about return shipping costs, major Chinese online retail platforms have introduced return shipping insurance (RSI). Retailers can choose between Retailer-RSI (RRSI), which is provided by the retailer, and Customer-RSI (CRSI), which is purchased by consumers. Despite these options, information asymmetry causes insurers to assess return rates with bias—referred to as managerial confidence bias. Consequently, platforms are increasingly partnering with insurers to enhance their RSI offerings. This study develops a game-theoretical model to examine the dynamics between a platform and an insurer, as well as the impact of managerial confidence bias on RSI strategies. Our analysis reveals that the platform–insurer relationship is crucial in determining the optimal RSI strategy. Under an independent insurer, RSI is viable only if the insurer underestimates product return rates (i.e., exhibits overconfidence bias); RRSI is preferred if the bias is sufficiently strong, whereas CRSI is chosen otherwise. In contrast, under a dependent insurer, CRSI is favored by the retailer only when its return handling costs are substantially high; otherwise, RRSI is preferred. Furthermore, RSI consistently increases consumer surplus by reducing return hassle costs while only mildly raising the product price. However, the independent insurer’s bias leads to its own profit loss, resulting in a “loss–win–win–win” scenario across stakeholders. In contrast, the dependent insurer, supported by platform subsidies, can yield a “win–win–win–win” outcome that aligns stakeholder interests and enhances long-term platform benefits.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21070197

Authors: Omar Munther Nusir Che Aniza Che Wel Siti Ngayesah Ab Hamid

This study examines how consumers respond to personalized advertisements that appear to be derived from prior conversations with generative AI assistants in social commerce settings. Drawing on the Privacy Calculus Theory, Trust Theory, and the Stimulus–Organism–Response framework, the study investigates whether perceived GenAI-based advertising personalization simultaneously creates perceived personalization value and privacy concerns, and how these evaluations shape algorithmic trust and social commerce purchase intention. A scenario-based survey was conducted with 435 social commerce users in Jordan. Respondents evaluated a situation in which a product advertisement appeared to reflect a previous conversation with a generative AI assistant. The data were analyzed using partial least squares structural equation modeling with SmartPLS 4. The findings show that perceived GenAI-based advertising personalization increases both perceived personalization value and privacy concerns. Personalization value strengthens algorithmic trust, whereas privacy concerns weaken it. Algorithmic trust, in turn, strongly enhances social commerce purchase intention. The mediation results show that personalization value and privacy concerns transmit the dual effect of perceived GenAI-based advertising personalization to algorithmic trust. In contrast, algorithmic trust transmits these effects to purchase intention. Perceived transparency disclosure does not significantly reduce privacy concerns, but it strengthens the positive relationship between personalization value and algorithmic trust. This study contributes to digital marketing and social commerce research by showing that GenAI-personalized advertising can be perceived as both useful and intrusive and that perceived transparency disclosure may support trust formation without necessarily eliminating privacy concerns.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21060196

Authors: Thabit Atobishi Saeed Nosratabadi

While the operational benefits of Artificial Intelligence (AI) are well-documented, the mechanisms through which firms leverage AI for strategic exploration and radical innovation remain under-theorized. This study addresses the “black box” of AI value creation by integrating the Technology–Organization–Environment (TOE) framework with the Dynamic Capabilities View (DCV). We propose that AI adoption is not a direct antecedent to performance but a multi-stage process wherein technological, organizational, and environmental factors enable the development of sensing capability, which in turn fosters a novel capability we term “AI-Enabled Exploration.” Analyzing survey data from 245 senior executives in Saudi Arabia, a high-growth economy undergoing state-led digital transformation, we employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the model. The results confirm a serial mediation chain: organizational readiness and technology compatibility drive sensing capability, which subsequently powers AI-enabled exploration to enhance innovation performance. Contrary to expectations, government support was not a significant predictor of sensing capability, suggesting that in resource-rich environments, external incentives are necessary but insufficient for capability building. Furthermore, competitive pressure was found to positively moderate the relationship between organizational readiness and exploration, acting as a critical catalyst that converts latent resources into active experimentation. These findings offer a theoretical roadmap for firms attempting to transition from AI-driven efficiency to AI-driven ambidexterity.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21060195

Authors: Yao Lu Guangming Li

Live streaming commerce is increasingly relying on high-intensity persuasive tactics, yet such tactics may activate consumers’ persuasion knowledge and trigger defensive processing. This research examines whether AI streamers mitigate this defense more effectively than human streamers. Drawing on the Persuasion Knowledge Model, two experiments reveal that, under conditions of high persuasive intensity, consumers perceive lower persuasive intent from AI streamers than from human streamers, which, in turn, reduces consumer suspicion and increases purchase intention. Moreover, this serial mediating effect is stronger for independent accounts than for brand official accounts. These findings provide evidence for a PKM-based mechanism of AI-mediated persuasion and suggest that platforms should consider using AI streamers in high-pressure promotional contexts.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21060194

Authors: Emre Yıldırım Faruk Dursun

The growing use of artificial intelligence (AI) in social media marketing has accelerated the emergence of AI-generated virtual influencers. While these influencers offer brands advantages such as scalability and message control, they also raise concerns regarding manipulation and ethical persuasion. Grounded in the Persuasion Knowledge Model (PKM), this study examines how different AI disclosure conditions influence perceived manipulation, perceived ethics, and purchase intention in AI influencer marketing. A three-condition between-subjects experimental design was employed to compare a human influencer, a disclosed AI influencer, and an undisclosed AI influencer using identical Instagram stimuli. Data were collected from 762 Generation Z female consumers in Türkiye. Structural equation modeling (SEM) was used to test the proposed relationships. The findings revealed that both disclosed and undisclosed AI influencer conditions significantly increased perceived manipulation. Perceived manipulation negatively affected perceived ethics, whereas perceived ethics positively influenced purchase intention. In addition, AI literacy positively affected perceived manipulation and perceived ethics while negatively affecting purchase intention. The findings further demonstrated that disclosure conditions indirectly influenced purchase intention through sequential cognitive and ethical evaluation processes. The study contributes to the AI influencer and digital persuasion literature by demonstrating that disclosure cues shape consumer responses through interconnected psychological mechanisms.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21060193

Authors: Wushuang Li Chin Fei Goh Yuping Wu Owee Kowang Tan

Although generative artificial intelligence (AI) has increasingly been used to create destination marketing images, tourists’ responses to such images remain unclear. Prior research has often attributed negative reactions to the visual characteristics of AI-generated images. However, limited attention has been paid to how tourists interpret these images within broader cultural contexts. Drawing on authenticity theory and cultural learning theory, this research examines the effect of image type (AI vs. human) on tourists’ perceived authenticity and visit intention, as well as the moderating roles of cultural learning cues in this process. Using three experiments, the results show that AI-generated images reduce perceived authenticity and visit intention compared with images taken by humans. Notably, while salient cultural learning cues enhance tourists’ perceived authenticity and visit intentions, different types of cues produce distinct outcomes: commodified cultural cues mitigate tourists’ negative responses to AI-generated images, whereas heritage cultural cues amplify authenticity concerns. These findings provide strategic insights for destination marketers on how to deploy AI-generated images effectively in tourism destination marketing.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21060192

Authors: Kexin Ding Tianjian Yang

Product mismatch, arising from consumers’ inability to physically experience products before purchase, is a major cause of returns in e-commerce, eroding e-tailer profits and intensifying consumers’ concerns about returns. To alleviate these concerns, e-tailers have increasingly adopted return insurance (RI), which reduces consumers’ return freight costs. However, RI may encourage consumers to defer product selection from the pre-purchase search stage to the post-purchase evaluation stage, thereby exacerbating mismatch and increasing return rates. As a countermeasure in live-streaming commerce, augmented reality (AR) provides an immersive product experience that can reduce mismatch and returns. This study develops a game-theoretic model to analyze the strategic interplay between an e-tailer’s RI decision and a live streamer’s AR decision while incorporating consumer search effort. The results show that consumer search effort changes the relationship between the two strategies. When search effort is low, RI and AR function as strategic substitutes; when search effort is high, they function as strategic complements. These findings indicate that the value of a return-management strategy depends on consumer behavior and on the presence of the partner’s AR strategy. The study contributes to the literature on interdependent return-management strategies and provides actionable insights for e-commerce practitioners.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21060191

Authors: Ahmed S. Ajina

Rapid digital transformation has accelerated the frequency with which firms introduce product updates, platform enhancements, and feature innovations in e-commerce environments. While continuous innovation is typically associated with improved functionality and customer value, limited research has examined its potential psychological costs for consumers. This study introduces the concept of consumer innovation fatigue (CIF), defined as a psychological state of exhaustion arising from repeated exposure to ongoing innovation-related changes in digital products and online platforms. Following established scale development procedures, this research develops and validates a measurement scale for CIF. Item generation combined insights from prior literature with qualitative evidence from consumer interviews, followed by expert evaluation to ensure content validity. Using survey data from two independent samples of digital consumers in Saudi Arabia, exploratory factor analysis (n = 225) identified a unidimensional six-item scale. The scale was subsequently validated using partial least squares structural equation modeling (n = 361), confirming its reliability, convergent validity, and discriminant validity. The findings further demonstrate nomological validity by showing that exposure to continuous digital innovation significantly increases CIF, which in turn leads to innovation avoidance in online consumption contexts. By conceptualizing and empirically validating CIF, this study contributes to electronic commerce research by highlighting a previously underexplored psychological consequence of rapid innovation cycles in digital environments. The validated scale provides a useful instrument for future research examining consumer responses to ongoing technological change in e-commerce ecosystems.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21060190

Authors: Emre Özsalman Alptekin Ulutaş Halime Ünver

The significant growth in e-commerce has brought intense competition to the business sector. Businesses seeking to stand out in this competition are evaluated by customers not only by the quality of the products they sell, but also by their performance on digital platforms. To measure this evaluation, this study analyzed the website performance of 25 e-commerce companies in Türkiye using Multi-Criteria Decision Making (MCDM) methods with eleven technical and user-oriented performance criteria. MAXC (Maximum Criterion) and Skewness Impact Through Distributional Evaluation (SITDE) methods were used to determine the criterion weights of the websites, while CORASO (COmpromise Ranking from Alternative SOlutions) was used to rank the alternatives. According to the results, “total number of visitors” had the highest weight, while “bounce rate” had the lowest weight. According to the CORASO method, the top three performing e-commerce sites were EC1, EC2, and EC3, while the bottom three performing sites were EC11, EC14, and EC20.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21060189

Authors: Zheng-Jun Jin Kwang-Su Lee Chang-Hyun Jin Jungyong Lee

Despite rapid growth in the AI endorser market, the psychological mechanisms governing their effectiveness remain theoretically fragmented. This study proposes the AHICC (AI–Human Interface in Consumer Cognition) model—integrating the Stereotype Content Model, Uncanny Valley hypothesis, anthropomorphism theory, Source Credibility Model, and Parasocial Interaction theory—to explain consumer responses to AI endorsers. A fully crossed 3 (endorser type: AI vs. hybrid vs. human) × 3 (anthropomorphism level: low vs. moderate vs. high) × 2 (technological transparency: low vs. high) between-subjects factorial experiment (n = 252) was conducted. Twenty-one sub-hypotheses were tested using MANOVA, polynomial regression, SEM, and bootstrap mediation analysis. All 21 sub-hypotheses were supported. AI endorsers outperformed human counterparts on brand attitude and purchase intention. Polynomial regression confirmed an inverted U-shaped Uncanny Valley effect with an optimal anthropomorphism level of 4.7 (7-point scale). High technological transparency attenuated the Uncanny Valley effect by approximately 60%. Dual-pathway mediation through cognitive and affective routes was confirmed, and TRI and product complexity emerged as significant boundary conditions. The AHICC model offers the first comprehensive framework for the AI endorser context, providing theoretically grounded guidance on anthropomorphism calibration, transparency strategy, and product-category-specific endorser selection.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21060188

Authors: Hajin Shin Sulim Kim Hee-Dong Yang Yong-Young Kim

In the crowded landscape of social media, hashtags serve not only as navigational tools but also as strategic signals of meaning and intent. This study examines how hashtag strategies—specifically semantic similarity with post content and usage frequency—affect sales outcomes in influencer-driven electronic commerce. Drawing on self-presentation theory, we analyze panel data from Instagram posts to explore how these hashtag dimensions interact with influencers’ self-presentation styles. The results show that hashtag–posting similarity significantly enhances sales, particularly among influencers exhibiting a Judging (J)-type self-presentation marked by structured and purposeful communication. In contrast, hashtag frequency alone does not increase sales performance and may even diminish it, indicating that excessive use can be counterproductive. However, for J-type influencers, an increase in hashtag frequency yields positive effects when it is deployed within a coherent and structured self-presentation framework, strengthening trust and ultimately enhancing conversion rates. These findings highlight that both what is said (semantic content) and how much is said (frequency) matter, but their effects depend on influencers’ expressive styles. The study provides actionable insights for marketers and brands, suggesting that effective hashtag strategies should align with influencers’ self-presentation tendencies rather than follow standardized or quantitative rules.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21060186

Authors: Zichao Jia Junfeng Tian Chenyu Tian Yong Liu

In a duopoly market comprising an e-tailer and a physical retailer, we develop analytical models to explore the e-tailer’s strategy of introducing a virtual showroom to alleviate consumers’ fit uncertainty. While a virtual showroom can increase online consumer traffic, consumers may instead purchase from physical stores (i.e., webrooming) or reduce offline browsing before buying online (i.e., showrooming). Our findings indicate that consumers with a moderate online hassle cost tend to showroom when offline travel cost is not high, whereas those with a high online hassle cost rely on the virtual showroom for webrooming. Therefore, we identify the conditions under which a virtual showroom should be introduced. Specifically, when the cost of travel to the store is relatively high, the e-tailer can profit from introducing a virtual showroom if its return-handling cost is not too low. Under moderate travel cost, the e-tailer can leverage a virtual showroom to weaken competition if the return-handling cost is not too high, enabling both retailers to benefit. Notably, the impact of product fit probability on virtual showroom strategy decisions reverses between high and moderate travel costs. Under specific conditions, the virtual showroom can achieve a “win-win-win” situation for the e-tailer, the physical retailer, and consumers.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21060187

Authors: Ying Xie Dan Zhou Tingting Wang Hanyu Sun

With frequent exposures of influencer false advertising and persona fabrication, “authenticity” has become an increasingly critical consumer demand. However, existing research employs inconsistent perspectives on “authenticity” (encompassing both consumer-perceived authenticity and influencer strategic authenticity) and remains fragmented, focusing narrowly on specific experiential dimensions such as positive emotions and social identity without systematic integration across the full customer journey. Therefore, this study adopts meta-analysis, systematically searching Chinese and English databases including Web of Science, Google Scholar and CNKI (2015–2026), and incorporates 170 effect sizes from 56 studies with 63 samples (n = 22,563) to systematically examine the effects of strategic authenticity (SA) and perceived authenticity (PA) on the customer experience journey (affective experience, cognitive experience, physical experience, and social-identity experience). Publication bias tests (fail-safe N > 5k + 10) indicated no significant bias. The results reveal that affective experience is more driven by strategic authenticity (ρ = 0.535), whereas cognitive experience (ρ = 0.591), physical experience (ρ = 0.355), and social-identity experience (ρ = 0.515) are more significantly influenced by perceived authenticity. Regarding influencer type moderation, virtual influencer SA exerts a stronger effect on social-identity experience (ρ = 0.574) than human influencer SA (ρ = 0.170, QB = 3.980, p = 0.046); conversely, human influencer PA shows a stronger effect on social-identity experience (ρ = 0.571) than virtual influencer PA (ρ = 0.016, QB = 8.189, p = 0.004). Regarding cultural differences, individualistic cultures exhibit stronger PA effects on physical experience (ρ = 0.419) and social-identity experience (ρ = 0.742); long-term orientation cultures show stronger SA effects on social-identity experience (ρ = 0.574). These findings contribute to the authenticity literature and offer actionable insights for influencer marketing practices.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21060185

Authors: Min Qin Zhensong Jiang Zhao Pan

Live streaming e-commerce has emerged as a dominant force in electronic commerce. Although extensive research has examined service failures and recovery in conventional e-commerce environments, there is limited understanding of how buyers interpret and respond to service recovery information provided by streamers in live streaming contexts, where streamers fulfill dual roles as both idols and sellers. This study, grounded in signaling theory, investigates the relationships among buyers’ observation, interpretation, and feedback of service recovery information. Specifically, it aims to assess how buyers’ perceived justice regarding service recovery information affects their repeat purchase intentions following live streaming e-commerce failures. Meanwhile, this study elucidates the mediating role of trust in the streamer (idol role and seller role) and verifies whether responsibility attribution and product involvement have moderating effects on the effect of perceived justice. Employing PLS-SEM analysis, the study analyzes data from 401 buyers who have experienced service failures in live streaming e-commerce. The findings reveal that perceived justice positively influences repeat purchase intentions to a certain extent. Trust in the streamer mediates the link between perceived justice and repeat purchase intentions. The inhibiting moderation effect of responsibility attribution and product involvement is generally significant. This study contributes to the theoretical understanding of signaling theory by expanding the signaling model to encompass service recovery information in live streaming e-commerce. Furthermore, it provides practical guidance for live streaming e-commerce practitioners on managing service recovery information, including recommendations for the sequence of sending such information, enhancing its transmission, and improving its overall effectiveness.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21060184

Authors: Bi Li Chaoshan Wang Yan Wu Boyu Chen Zhifeng Hao

With the rapid development of e-commerce, data-driven models have significantly enhanced service experience. We can obtain the optimal values for the price but have also intensified consumer privacy concerns. Among various privacy protection policies, which are more effective? Is there a governance framework that balances commercial efficiency with privacy safety? To address this, we develop a duopoly game-theory model that analyzes consumer behavior characterized by heterogeneous privacy costs and preferences, aiming to evaluate the impact of differentiated privacy protection policies within digital ecosystems. We analyze whether opt-in requirement or inference regulation is more advantageous for consumer and firm competition. We find that, in a competitive environment, imposing opt-in requirement on one party can yield competitive advantages and profit increases, whereas imposing inference regulation on the other may result in a competitive disadvantage. Such differentiated policies create an asymmetric competitive landscape, effectively avoiding a prisoner’s dilemma and, under certain conditions, increasing both consumer and total surplus. Furthermore, our study reveals significant differences in the impact of these policies on data-driven and usage-driven firms. Based on these findings, we recommend that regulators carefully tailor privacy protection policies according to industry-specific data characteristics, adopting differentiated regulatory strategies when appropriate and providing compensation mechanisms for disadvantaged firms to optimize total welfare.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21060183

Authors: Qing Gu Xintao Yu Ding Yuan Qiang Yang

Although existing studies have shown that AI recommendation systems have potential in enhancing consumers’ purchase intention, there remains a lack of systematic research. This study aims to explore how the interaction between information presentation formats and AI role types influences consumers’ purchase intention. Based on metacognitive experience theory, two experiments are designed to analyze processing fluency as a mediator and consumers’ AI knowledge as a moderator. The results reveal that the interaction between information presentation format and AI role type significantly affects consumers’ purchase intention, while their separate effects are insignificant. Different from existing studies on separate factors, this study demonstrates that AI interactive marketing performance hinges on the matching of design and role positioning. This study extends the application of the theory of metacognitive experiences in the field of human–AI interaction and provides targeted recommendations for the interface design of AI recommendation systems.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21060182

Authors: Xiao Fu Hao Tan

This study addresses supply uncertainty in the transportation and production of agricultural products within e-commerce-driven supply chains by developing a stochastic supply chain framework. By embedding an endogenous trust-based punishment mechanism, we characterize the strategic interplay between relational governance and operational decisions. In the single-period setting, higher retailer trust leads to larger order quantities while reducing the supplier’s optimal supply effort. This occurs because increased trust lowers the minimum delivery commitment ratio, allowing the retailer to voluntarily share more supply risk in exchange for lower-cost products. This creates a mutually beneficial risk-sharing arrangement between the retailer and supplier. When extending the framework to a dual-channel setting with a reliable backup option, the retailer consistently leverages trust and risk-sharing to lower sourcing costs. Concurrently, fluctuations in trust prompt the retailer to strategically shift order allocations between channels based on the primary supplier’s maximum effort capacity. Multi-period analysis shows a positive relationship between trust and the supplier’s expected profit, with optimal maximum supply effort stabilizing under repeated cooperation. Numerical experiments demonstrate that dual-channel decentralized decision-making achieves bullwhip effect mitigation quantitatively comparable to the centralized benchmark, while also alleviating moral hazard issues and benefiting retailers. These results indicate that the proposed mechanism effectively curbs opportunistic behavior and promotes sustainable, mutually advantageous supply chain collaboration.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21060181

Authors: Calvin Steve Nyagudi Wenbing Wu

Technology-driven content is increasingly reshaping how tourists perceive and evaluate destinations, yet the underlying content evaluative processes remain insufficiently investigated. This study, therefore, integrates the Stimulus–Organism–Response (S–O–R) framework with Anthropomorphism Theory to examine how destination anthropomorphic content (DAC) relates to destination image (DI) and destination visitation intention (DVI) in digitally mediated environments. Using a cross-sectional survey design and multi-group Structural Equation Modeling, the study compares relationships across two information sources: AI- and social media influencer-generated content. The results show that DAC is positively associated with both DI and DVI across groups. Permutation-based multi-group analysis indicates that the differences in structural paths between AI and influencer groups are not statistically significant. This finding provides the basis for interpreting group comparisons, suggesting that the observed relationships do not differ meaningfully across content sources. While bootstrapping and effect size (f2) results indicate relatively stronger coefficients in the influencer group, these results are interpreted as descriptive tendencies rather than statistically confirmed differences. These findings suggest that tourists may respond positively to both human and technologically mediated agents’ content when human-like social cues are perceived. This study contributes to the growing discourse on AI and digital content in tourism by unveiling the critical concern of whether the content source matters in anthropomorphic perception. The study further extends the application of S–O–R in AI-mediated marketing contexts. The findings offer practical insights for destination marketers seeking to leverage both AI and influencer-based strategies in shaping tourist perceptions and intentions.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21060180

Authors: Zhenyu Zhang Xueji Wang

Drawing on the uncanny valley framework, trust theory, and similarity attraction theory, this study examines how customers’ multidimensional perceptions of humanoid service robots shape their approach and avoidance behaviors through two relational states: trust and rapport. Subsequently, structural equation modeling and mediation analysis were employed for testing. The results indicate that customers’ overall perceptions of service robots not only encourage approach behaviors but may simultaneously intensify avoidance tendencies, reflecting the ambivalent nature of human–robot interactions. We interpret this dual activation through the uncanny valley framework, in which humanlike robots simultaneously elicit attraction and aversion. Trust and rapport play critical mediating roles in this process, effectively reducing avoidance responses while strengthening customers’ approaches. Further analyses reveal that different perceptual dimensions operate through distinct mechanisms in the formation of trust and rapport. This study aims to deepen the comprehension of customer response mechanisms to humanoid service robots through a relational perspective, and offers practical insights for hotels seeking to balance operational efficiency with emotional experience in robot design and management.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21060179

Authors: Nermin Yildirim Bora Gündüzyeli

Artificial intelligence (AI) has transformed programmatic advertising (PA) by enabling automated, data-driven personalization within digital marketing ecosystems. However, this transformation has also intensified concerns regarding privacy, consumer trust, and the long-term sustainability of these ecosystems. In this study, sustainability is conceptualized as the capacity of digital marketing systems to maintain long-term functionality without eroding consumer trust or amplifying privacy-related tensions that may undermine system stability over time. This study addresses the research question: How does AI-driven PA affect the sustainability of digital marketing ecosystems through the personalization–privacy paradox? A systematic literature review, conducted in line with PRISMA guidelines, synthesizes prior research across four interrelated clusters: AI-driven personalization, privacy concerns, consumer trust, and consumer attitudes and behavior. The findings indicate that consumer responses to AI-driven personalization are not linear and are shaped by factors such as transparency, perceived control, and the perceived legitimacy of data practices. The review further shows that the paradox of personalization and privacy operates as a persistent condition within AI-driven advertising ecosystems, where the benefits of personalization coexist with ongoing privacy-related tensions. This study contributes to the literature by synthesizing fragmented research on AI-driven PA and by highlighting the central role of trust and transparency in understanding how personalization and privacy tensions shape consumer responses in digital marketing ecosystems.

Journal of Theoretical and Applied Electronic Commerce Research doi: 10.3390/jtaer21060178

Authors: Yunbo Chen Jing Huang Yin Zhang Yixiang Zhang

Personalized advertising is now routine in digital commerce and new media, but relevance does not always translate into acceptance. When consumers read tailored messages as signs of tracking or inference rather than as useful assistance, they may feel exposed and avoid the advertising. Drawing on advertising avoidance theory, privacy calculus theory, and control agency theory, this study examines a privacy-risk account of personalized advertising avoidance. Survey data from 502 consumers and structural equation modeling show that privacy concern, privacy fatigue, and prior negative experiences are directly associated with advertising avoidance. Privacy concern, prior negative experiences, and perceived personalization are also positively associated with perceived risk; perceived industry self-regulation is negatively but non-significantly associated with perceived risk; and perceived risk is positively associated with advertising avoidance. These results position perceived risk as a close downstream appraisal through which consumers make sense of data-driven targeting. The study contributes to digital commerce and interactive marketing research by showing that consumers evaluate personalized advertising not only as a cue of relevance, but also as a possible source of privacy vulnerability. It also offers implications for transparent targeting, visible user control, platform governance, and less intrusive personalization.