This study examines comparative effectiveness of human and virtual influencers in shaping purchase intention, word-of-mouth intention, and willingness to follow. Using Social exchange theory and source-based persuasion models, 2 × 2 experiment tests responses to hedonic and utilitarian offerings in product–service and service–service contexts. Findings indicate that human influencers generate higher purchase intention and greater perceived expertise, attractiveness, and relatability, whereas virtual influencers evoke stronger follow intention. Mediation effects of expertise, attractiveness, and homophily are confirmed. Interaction effects reveal virtual influencers as more persuasive for utilitarian, while human influencers for hedonic offerings, with stronger effects in product–service than service–service contexts.

This study investigates the determinants of customer switching intention in online tourism platforms using an integrated fuzzy multi-criteria decision-making approach. Nineteen critical criteria were identified through the Fuzzy Delphi Method (FDM) based on expert consensus. Subsequently, the Fuzzy DEMATEL technique was employed to analyze the causal relationships among these factors and categorize them into four strategic quadrants based on their prominence and relation. Beyond identifying key determinants, the primary contribution of this study lies in revealing how these factors interact within a structured causal system, thereby offering strategic insights for managing switching behaviors in digital tourism platforms. The study contributes to the literature by incorporating emerging digital elements such as virtual influencers and privacy concerns into an analysis of switching behavior. Practically, it offers a decision-making framework for platform managers to prioritize strategic actions that enhance customer retention and minimize switching. The findings also provide a foundation for future research on interdependent switching mechanisms in digital service ecosystems.

This study examines how cognitive dissonance influences consumer loyalty toward online education platforms in emerging regions. Drawing on Cognitive Dissonance Theory, the research investigates the effects of product and emotional dissonance on attitudinal and behavioral loyalty, while testing the mediating roles of technology adoption behavior and social media engagement, and the moderating effect of gender. Data collected from 620 online education platform users were analyzed using PLS-SEM. The findings indicate that product dissonance intensifies emotional dissonance, which negatively affects loyalty outcomes. Mediation effects highlight the role of adaptive technology use and social engagement in mitigating post-adoption discomfort.

Our results suggest that customer incivility sequentially intensifies FLEs’ emotional exhaustion, moral disengagement, and counterproductive behaviors (service sabotage, organizational deviance, and turnover intentions). Unexpectedly, managerial exchange does not appear to buffer the intensity of customer incivility-induced emotional exhaustion and/or prevent the subsequent cascade of counterproductive consequences. Taken together, mistreated FLEs appear to deactivate their moral compass and seek restoration/retribution through a ‘fight’ (service sabotage, organizational deviance) and/or ‘flight’ (turnover intentions) response. Despite a resurgence in the dysfunctional outcomes of customer incivility little to no research has examined it in tandem with moral disengagement, especially with emotional exhaustion as an antecedent.

This study aims to examine key factors influencing customer loyalty toward branded food delivery applications. The proposed conceptual framework examines the effects of post-adoption performance and effort expectancies on perceived utilitarian and hedonic values, which, in turn, influence user loyalty. The moderating roles of customers’ gender and age are also analyzed. This study contributes to the existing literature by investigating the main drivers of customer loyalty from a technology post-adoption and brand-focused perspective. It also offers practical recommendations for managers and policymakers to cultivate lasting customer relationships and segment the market by customer gender and age.

This study examines customer responses to automated service recovery in online travel booking platforms using Expectancy Disconfirmation Theory and the Stimulus–Organism–Response framework. Survey data from 420 Indonesian users were analyzed using Partial Least Squares Structural Equation Modeling. The results show that automated service recovery quality influences switching intention indirectly through recovery satisfaction and trust. Contrary to expectations, higher recovery quality is negatively associated with satisfaction, revealing a boundary condition of Expectancy Disconfirmation Theory in technology mediated recovery. The Need for human interaction functions as a quasi-moderator, shaping satisfaction, trust, and switching intentions.

As AI reshapes service delivery, this study examines how AI-enabled service experiences influence customer engagement in Saudi Arabia’s banking sector. Grounded in social exchange theory and social cognitive theory, it explores the mediating role of customer satisfaction and the moderating effect of proactive personality. Survey data from 325 customers across 13 banks were analyzed using hierarchical regression and Hayes’ PROCESS macro with 5,000 bootstrap samples to test moderated mediation. Results show that AI experiences enhance engagement directly and indirectly through satisfaction. Proactive personality strengthens the satisfaction–engagement relationship. The findings offer theoretical contributions and practical guidance for banks adopting AI technologies.

As artificial intelligence (AI) transforms digital travel services, understanding its role in customer value co-creation (CVC) becomes crucial. Drawing on Service-Dominant Logic and Cognitive Effort Theory, this study examines how AI-enabled CVC affects choice stress, perceived effort, satisfaction, and intention to use. Data from 283 Dutch consumers with AI-assisted travel experience were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results reveal that CVC reduces choice stress and enhances satisfaction while its effect on perceived effort is nonsignificant, reflecting a facilitative rather than effort-increasing role. Satisfaction partially mediates the effects of CVC and choice stress on intention, underscoring its pivotal role in user engagement. The moderating effect of AI usage is modest, positively strengthening the relationship between perceived effort and intention and amplifying the stress-reducing impact of CVC. The findings deepen understanding of AI’s cognitive–emotional dynamics in service encounters and offer actionable guidance for service marketers and travel managers to design more adaptive, user-centered AI experiences.

This article explores the impact of prompting on the performance and service quality of artificial intelligence (AI) customer service chatbots, and the relationship between the prompting process and service quality elements. The data were collected in September 2024 from responses generated by three chatbots, each using a range of prompting techniques. The chatbots responded to real customer inquiries collected by the Visit Helsinki customer service office in summer 2020. The results show that prompting has a significant impact on the quality of online customer service that AI chatbots can provide.