Search Results (136)

Influencer marketing research has shown that source-related evaluations matter, yet less is known about how specific influencer cues are translated into consumer responses through differentiated internal psychological states. Drawing on the Stimulus–Organism–Response (S-O-R) framework, this study examines how influencer attractiveness and expertise shape consumer responses through parasocial interaction and trust. Attractiveness is conceptualized as a social-affective cue, whereas expertise is conceptualized as a competence-based cue. Parasocial interaction is modeled as a relational organismic state, and trust is modeled as a reliance-oriented organismic state. Survey data were collected from 532 Taiwanese social media users with prior experience following influencers and analyzed using partial least squares structural equation modeling (PLS-SEM). The results show that attractiveness positively predicts parasocial interaction, expertise positively predicts trust, and parasocial interaction further contributes to trust. Trust, in turn, positively influences loyalty, purchase intention, and recommendation intention, with the strongest effect observed for recommendation intention. These findings suggest that influencer effectiveness is better understood as a differentiated cue–mechanism–response process rather than as a generalized source-evaluation effect. By distinguishing attractiveness from expertise and by modeling parasocial interaction and trust as conceptually distinct but sequentially connected organismic states, this study provides a more precise S-O-R account of how influencer evaluations are translated into relational, transactional, and advocacy-oriented consumer responses. Full article

Tourism’s digital transformation has reshaped how travelers search for and evaluate destinations. However, relatively little empirical work has examined how user engagement translates into booking intent, especially under the emergent discovery channels mediated by artificial intelligence (AI). This study tests an engagement-driven referral framework using longitudinal behavioral data from a Mediterranean destination portal (April 2022–January 2026; 1.6 million sessions). Engagement depth, measured as average session time, significantly predicts booking intent click rate. Mobile drives 83% of sessions, but desktop users convert at nearly twice the rate (5.69% vs. 3.37%). High traffic, as it turns out, does not equal high commercial intent. Lower-volume international markets routinely outperform the dominant domestic market. The most striking result concerns AI referrals. Traffic arriving from AI assistants converts at 8.26%, more than double the organic search rate of 3.88%, despite shorter sessions, a pattern consistent with compressed decision-making under generative AI. These findings, grounded in real travel portal data, extend engagement theory beyond transactional settings and shed early light on how referrals from AI assistants like ChatGPT or Gemini differ behaviorally from organic search, with practical implications for portal managers, destination marketing organizations (DMOs), and sustainable demand management. Full article

This study examines stock market reactions to share repurchase announcements by firms listed on the Korean Stock Exchange from 2015 to 2024. Unlike the U.S. market, where share repurchases are generally viewed as a shareholder-friendly signal of strong firm performance, Korea’s institutional environment permits relatively discretionary treasury stock transactions, potentially leading to heterogeneous investor responses. Using an event-study methodology, we analyze short-term abnormal returns around repurchase announcements, differences across stated repurchase motives, and the moderating role of firm valuation. We document significantly positive short-term abnormal returns following repurchase announcements, consistent with signaling-based explanations. However, these positive market reactions are driven exclusively by repurchases explicitly intended to enhance shareholder value. Furthermore, the market response to shareholder-value-oriented repurchases is significantly stronger among firms with lower valuation levels, suggesting that undervaluation enhances the credibility of repurchase signals. Overall, our findings indicate that repurchase announcements are not interpreted uniformly in the Korean market. Instead, investors condition their reactions on both managerial intent and firm-specific valuation contexts. By jointly considering repurchase motives and valuation effects, this study contributes to the literature by showing that the informational content of repurchase announcements is contingent rather than universal, and that signaling effects materialize primarily when managerial actions align with credible undervaluation signals. Full article

The hotel industry is widely induced by emotional transactions between frontline employees and their guests leading to unintentional transfer of emotions, a phenomenon known as emotional contagion (EC). EC can result in positive or negative outcomes in the workplace influencing employees’ well-being and performance. This research paper explored direct effects of emotional contagion (EC) on (H1) employees’ well-being (PW), (H2) job performance (JP), and (H3) turnover intention (IL). Based on the affective events theory (AET) and the social exchange theory (SET), employee’s psychological well-being was employed as a mediating factor (H6-H7) and leader–member exchange (LMX) as a moderator variable that might alleviate the adverse consequences of EC (H8). Cross-sectional survey data were collected online from 792 frontline employees. The proposed model was evaluated with partial least squares structural equation modeling (PLS-SEM). The findings revealed that EC can significantly weaken PW, which accordingly decreases JP and increases IL. Nonetheless, strong levels of LMX can alleviate these harmful influences, emphasizing the main significant role of LMX in regulating emotional dynamics in the service workplace. This study expands our understanding of how emotional mechanisms and LMX practices can adjust employee resilience, retention, and performance in the context of high-emotion service. Full article

In evolutionary computation, distinct clusters that address different subproblems evolve independently of each other, which makes it difficult to exchange genetic information between them. However, a vaguely defined task within one system may be expressed more clearly within another. Effective interaction methods enable subsystems to collaborate more effectively in solving global tasks. By analysing how ambiguous intentions regarding electricity consumption influence actual behaviour in real-world scenarios, we discovered that transaction and negotiation patterns within electricity markets can effectively support this process. By introducing time and third parties, the study presents a semiautomatic, interpretable reasoning community logic system that enables machines to express transaction negotiation patterns. Through formalised operations, it facilitates the conversion of intentions, uncovering hidden relationships within global structures through this liberated form of expression. This paper examines its impact on computational and search paradigms through case studies, enabling collaborative approaches and granularity control via dynamic anchor points, and explores automated peer-to-peer transactions and electricity monetisation within highly abstracted power trading processes. Full article

Virtual live streaming enables consumers to engage with virtual anchors, facilitating product information acquisition and online transactions. Despite its promising prospects, the field currently grapples with insufficient purchase intention. Anthropomorphizing virtual anchors in such contexts is common, yet the uncanny valley effect can undermine consumer acceptance. Drawing on mind perception and anthropomorphism theories, we explore factors influencing purchase intention in virtual live streaming. Analyzing data from 197 Taobao virtual live streaming consumers, we find that utility and responsiveness positively affect perceived agency, while friendliness and empathy enhance perceived experience. Moreover, perceived agency and experience positively affect purchase intention. Anthropomorphism strengthens the link between utility/responsiveness and perceived agency but weakens the association between friendliness and perceived experience. Our findings offer insights for both research and practice, though limitations are acknowledged and discussed. Full article

Agentic AI is increasingly framed as enabling consumers to delegate commerce decisions and actions to digital assistants, yet consumer-facing evidence still centers on assistive chatbots and recommender-like systems, with scarce evaluation of execution-level delegation. This study provides an evidence-mapping review of empirical work on agentic commerce and synthesizes determinants and outcomes of delegation across three questions: (RQ1) how systems are operationalized (autonomy, task scope, interaction mode, and transaction capability/evidence realism), (RQ2) what facilitates or inhibits delegation, and (RQ3) what downstream outcomes follow for marketing performance and consumer experience. We searched Scopus and Web of Science for English-language, peer-reviewed primary studies (2015–2026) and applied conservative coding rules that distinguish claimed capability from simulated or demonstrated execution. The mapped literature is concentrated in text-based, low-autonomy assistants focused on recommendation and post-purchase support; coverage drops sharply for workflow-level autonomy, cart building, checkout/payment execution, and negotiation. Across studies, findings cluster into two motifs: a utility/assurance pathway in which performance cues and interaction quality increase perceived usefulness, satisfaction, and trust, and a governance pathway in which autonomy cues and system-initiated control trigger reactance/powerlessness and reduce acceptance unless mitigated by safeguards; urgency can attenuate governance resistance. Because most outcomes are intention- or vignette-based, calibration, verification, and error-recovery behaviors remain under-measured. Overall, delegation appears to depend less on maximizing autonomy than on coupling capability with user governance (consent, oversight, recourse, accountability), and we outline measurement priorities for evaluating execution-capable agents. Full article

As cross-border e-commerce expands in emerging economies, consumer participation increasingly depends on perceived transaction risk linked to digital payments, settlement, dispute resolution, and institutional enforceability. This study reconceptualizes online purchase intention (OPI) as a decision embedded in retail international finance. Extending the Theory of Planned Behavior (TPB), it integrates Internet Trustworthiness Behavior (ITB) and Country of Origin (COO) as risk-relevant signals shaping consumer judgment under cross-border uncertainty. Survey data from 390 digitally active consumers in the Dominican Republic were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that ITB strengthens perceived behavioral control, attitudes toward online purchasing, and subjective norms, while also exerting a direct positive effect on OPI. COO emerges as a strong direct predictor of OPI, functioning as a heuristic indicator of country credibility when formal safeguards appear weak. Contrary to standard TPB expectations, perceived behavioral control negatively predicts OPI, suggesting that greater digital competence may heighten awareness of expected losses and limited recourse in high-risk environments. The findings advance international business and finance research by showing how micro-level trust practices and macro-level country signals jointly shape consumer risk management in cross-border digital markets, with implications for inclusive participation and consumer protection. Full article

Turnover is a major concern for the hotel and hospitality industry on a global scale. This research focuses on several Middle Eastern five-star hotels in terms of transactional leadership and its relationship with employees’ turnover intentions. In addition, the mediating effect of organizational climate, along with the moderating influence of psychological contract fulfillment are examined. This research combines the premises of social exchange, organizational support, and psychological contract theories to support the development of hypotheses. A total of 350 employee data from five-star hotels in Saudi Arabia, Lebanon, Egypt, Jordan, and Bahrain was gathered using surveys using a convenience sampling method. The research used Partial Least Squares—Structural Equation Modeling (PLS-SEM) to analyze the hypotheses, which were supported by the results obtained. The importance of organizational climate and fulfilling psychological contracts is highlighted, which, under transactional leadership, can reduce turnover intentions. This is critical for the labor-intensive hotel sector with high turnover rates and random and unpredictable tasks. Transactional leadership is found to be a good fit for the Middle Eastern hotel sector through routinization and a structural approach. The current findings can be beneficial for scholars and practitioners. Full article

Research on users’ switching intentions in peer-to-peer (P2P) mobile payment systems, particularly in developing markets, remains limited. This study examines how two satisfaction dimensions, transaction-based satisfaction and experience-based satisfaction, influence switching intentions through two layers of trust: institution-based trust and disposition to trust. Grounded in Expectancy-Disconfirmation Theory, data from 529 users of Haiti’s leading P2P mobile payment platform were analyzed using structural equation modeling. Results show that while transaction-based satisfaction has minimal impact on switching intentions, experience-based satisfaction strengthens institution-based trust, which in turn significantly reduces switching intentions. These findings highlight the central role of institutional reliability in shaping post-adoption behavior in duopolistic and resource-constrained markets. The study extends satisfaction-trust theory to digital financial ecosystems and offers practical insights for improving user retention through sustained institutional credibility and long-term service reliability. Full article

This study investigates the influence of access to mobile cashless technology on enterprise continuity intention and cash flow management skills. It also explores the influence of cashless technology, knowledge, and experience on enterprise continuity intention and cash flow management skills, and examines the direct relationship between cash flow management skills and enterprise continuity intention among micro and small enterprises during crises and in an unstable context. The 259 responses collected from micro and small entrepreneurs were analyzed by Partial Least Squares Structural Equation Modeling. The hypotheses tested reported a positive and significant relationship between access to mobile cashless technology and enterprise continuity intention and cash flow management skills. Furthermore, it was found that cashless technology knowledge and experience have a positive and significant relationship with enterprise continuity intention, as well as cash flow management skills. Finally, cash flow management skills were found to positively influence enterprise continuity intention. The study offers theoretical and practical implications for policymakers and other stakeholders to improve cashless transactions in the context of the study. Full article

The transition toward decentralized energy systems has amplified interest in peer-to-peer electricity trading. However, research on prosumer behavior in such markets remains fragmented, hindered by a lack of benchmarkable experimental infrastructure. Addressing this gap, the LabChain system was developed—a modular, interactive prototype designed to study human behavior in synthetic P2P electricity markets under controlled laboratory conditions. This system integrates real-world technologies, such as blockchain-based transaction backends, flexibility market interfaces, and asset control tools, allowing fine-grained observation of strategic and perceptual dimensions of prosumer activity. The research followed an iterative design approach to develop the infrastructure for experimental energy economics research, and to assess its effectiveness in aligning participant experience with design intentions. Based on the meta-requirements generality, affordance-centric design, and technological grounding, 13 detailed peer-to-peer market, software, and system requirements that allow for system evaluation were developed. As a proof of concept, seven participants simulated prosumer behavior over a week through interaction with the system. Their interaction with the system was analyzed through simulation data and focus group interviews, using a modified thematic content analysis with a hybrid inductive–deductive coding approach. The main achievements are (i) the design and implementation of the LabChain system as a modular infrastructure for P2P electricity market experiments, (ii) the development of an associated experimental workflow and research design, and (iii) its demonstration through an illustrative, proof-of-concept evaluation based on thematic content analysis of a single focus group session focusing on interaction and perceptions. The behavioral results from an initial session are limited, exploratory, and demonstrative in nature and should be interpreted as illustrative only. They nevertheless revealed tension between system flexibility and cognitive usability: while the system supports diverse strategies and market roles, limitations in interface clarity and information feedback constrain strategic engagement. Full article

Short-video platforms such as TikTok, Douyin, and Instagram Reels have transformed digital consumption into an immersive, algorithmically mediated commerce ecosystem. This study examines how compulsive short video use (CSV), a maladaptive pattern linked to diminished self-regulation, shapes purchase intention (PI). Drawing on compulsive consumption theory, dual-process perspectives, and socio-technical systems theory (STST), we estimate a structural equation model using survey data from 542 active short-video users. The results show that CSV exerts a strong and consistent positive effect on PI, indicating that compulsive engagement functions as a commercially consequential psychological state. This relationship is contingent on socio-technical conditions: technical support and platform familiarity substantially amplify the CSV–PI pathway, social belonging provides weaker but positive reinforcement, and social interaction attenuates the effect by redirecting attention away from transactional cues. These findings position CSV as both a form of digital pathology and a commercially activating mechanism within socio-technical environments. The study also offers guidance for platform managers seeking to balance monetization with ethical responsibility in short-video commerce ecosystems. Full article

In data mining and exploration, outliers are specific and infrequent data that require special attention, as they may reveal potentially hazardous information. Detecting outliers can support, e.g., identification fraudulent credit card usage or unauthorized access to transactions, even hacking banking systems, etc. The paper proposes a definition of outlier in terms of fuzzy representations of expert knowledge and its application to detect outliers. The approach proposed has the potential to enhance the performance of outlier detection in various fields, including finance and banking data storage and analysis. By “enhance” we mean that the intention of the new method is to cooperate with known numerical methods, e.g., LOF, rather than supersede or deprecate them. The usefulness of the method is proven via providing new outlying observations for given datasets using input data expressed in an imprecise, linguistic manner. Full article

Although the use of mobile payments has become increasingly prevalent, understanding the factors that persuade users to continue to rely on the transaction method remains limited. This study applied the Uses and Gratifications Theory and a contingency perspective to examine the relationship between personalized artificial intelligence and users’ intention to continue using mobile payments. Drawing on the contingency perspective, we also assessed four moderating factors: age, educational level, social network, and technological diversity. Using survey data collected from 515 Chinese users and applying hierarchical regression analysis, our results reveal that personalized artificial intelligence significantly enhances users’ intention to continue using mobile payments. Educational level, social network, and technological diversity also have a positive influence but age has a deterring effect. Our empirical findings constitute an academic contribution to a deeper understanding of the dynamics that persuade mobile payment users not to change their routine. Full article