Wei-Hung Hsiao

Purpose: This study examines how resource-constrained small and medium enterprises (SMEs) in Taiwan’s emerging AI-driven self-storage market mitigate the liability of newness. It conceptualizes trust transfer from tangible service cues to brand trust as an associational cognitive hierarchy, providing a perception-based signaling framework for nascent ventures to cultivate adoption intentions. Design/methodology/approach: Grounded in signaling and trust transfer theories, the research identifies six antecedents across entrepreneurial signals and tangible trust cues. Structural equation modeling analyzed survey data collected from 362 Taiwanese consumers utilizing a category-level evaluation protocol. Findings: Environmental safety, economic feasibility, and locational convenience drive service trust. While reputation and communication positively influence brand trust, enterprise size shows no statistically significant direct association. A full mediation model confirms that service trust indirectly drives usage intention through brand trust, which acts as the primary direct driver. Practical implications: Rather than attempting to artificially project enterprise scale, SME operators should strategically allocate capital toward AI-specific operational touchpoints (such as smart access and automated safety monitoring). Furthermore, managers must implement transparent communication, data privacy standards, and mandatory human-in-the-loop emergency escalation protocols. Originality/value: This research resolves the human-absent physical touchpoint boundary and reverse cognitive pathway gap by integrating signaling and trust transfer frameworks in an unstaffed physical setting. It demonstrates how technology-enabled SMEs can structure localized service trust into brand-level credibility to drive adoption.

Purpose: This study aims to explore how AI digital signage functions as an experiential interface in retail settings. Drawing from Schmitt's experiential marketing framework, AI digital signage is examined as a touchpoint that delivers sensory (SENSE), affective (FEEL), cognitive (THINK) and behavioral (ACT) experiences through personalized, real-time and interactive features. By identifying the key experiential factors and performance gaps that shape consumer interactions with AI-enabled technologies, it helps to make the application of AI in retail scenarios more abundant and improve better consumer experience. Design/methodology/approach: A mixed-method design was employed to investigate the factors influencing consumer experience with AI digital signage in retailing. The research framework was developed based on a comprehensive literature review and expert consultation. The Analytic Hierarchy Process (AHP) was used to determine the relative importance of experiential dimensions and criteria. Subsequently, a modified VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method was applied to identify performance gaps across the evaluated factors. Data were collected through an online survey in Taiwan, yielding 365 valid responses for AHP and 373 valid responses for the modified VIKOR analysis. Findings: The results indicate that among the four experiential dimensions, sensory experience (SENSE) is considered the most influential in shaping consumer interaction with AI digital signage, followed by behavioral experience (ACT), affective experience (FEEL) and cognitive experience (THINK), based on the AHP weighting results. In contrast, the performance gap analysis using the modified VIKOR method reveals that affective experience (FEEL) has the largest gap value, followed by cognitive experience (THINK), sensory experience (SENSE) and behavioral experience (ACT). This suggests that while sensory and behavioral experiences are highly valued, affective and cognitive aspects are currently underdelivered, highlighting critical facets for improvement in enhancing consumer engagement with AI-enabled touchpoints. Practical implications: This study employs a hybrid multiple-attribute decision-making approach to empirically investigate the evaluation dimension and criteria related to factors influencing consumers' experiences with AI digital signage in retailing. The results of this study provide valuable insights for researchers and practitioners alike in the field, offering a valuable reference for determining the factors those impact consumers' experiences with AI digital signage when it serves as the new marketing medium. Originality/value: This study bridges experiential marketing and intelligent retail technology by treating AI signage as an experiential touchpoint and evaluating its impact through a hybrid AHP–VIKOR model, offering both theoretical insight and practical guidance for smart retail engagement.

Purpose: This article investigates the impact of consumers’ promotion strategies on the effectiveness of promotional campaigns for subscribing to Over-the-Top (OTT) streaming services, from a prospective theoretical perspective. Additionally, it explores the moderating effects of platform type and subscriber type on promotion strategies in the context of OTT streaming services.Design/methodology/approach: This study employs an experimental design to separately examine the promotion strategies of subscribers. The first part investigates the effect of promotion strategies on promotional effectiveness, regardless of platform and subscriber types, while the second part considers the moderating effects of both platform type and subscriber type on promotional effectiveness.Findings: The analysis reveals that renewed subscribers exhibit a positive attitude toward incremental promotions and have stronger intentions to subscribe and recommend. Nonrenewed subscribers, in contrast, prefer discount promotions, demonstrating a positive attitude toward them and expressing higher intentions to subscribe and recommend.Originality/value: This study applies prospect theory to OTT promotion strategies, uncovers the psychological drivers behind promotion effectiveness and examines the moderating role of subscriber type and platform type, thereby providing actionable insights to improve consumers’ attitudes and behavioral intentions.

Purpose: The rise of artificial intelligence (AI) applications has driven enterprises to provide many intelligent services to consumers. For instance, customers can use chatbots to make relevant inquiries and seek solutions to their problems. Despite the development of customer service chatbots years ago, they require significant improvements for market recognition. Many customers have reported negative experiences with customer service chatbots, contributing to resistance toward their use. Therefore, this study adopts the innovation resistance theory (IRT) perspective to understand customers’ resistance to using chatbots. It aims to integrate customers’ negative emotions into a predictive behavior model and examine users’ functional and psychological barriers. Design/methodology/approach: In this study, we collected data from 419 valid individuals and used structural equation modeling to analyze the relationships between resistance factors and negative emotions. Findings: The results confirmed that barrier factors affect negative emotions and amplify chatbot resistance influence. We discovered that value and risk barriers directly influence consumer use. Moreover, both functional and psychological barriers positively impact negative emotions. Originality/value: This study adopts the innovation resistance theory perspective to understand customer resistance to using chatbots, integrates customer negative emotions to construct a predictive behavior model and explores users’ functional and psychological barriers. It can help in developing online customer service chatbots for e-commerce.

The emergence of the COVID‐19 pandemic dramatically lowered the foodservice industry's income overnight. Conversely, the practical measure of remaining at home to deal with the pandemic's impact has boosted the online food delivery business. In this study, a consumer perspective was adopted and an adapted version of the extended unified theory of acceptance and use of technology (UTAUT2) was used alongside multi‐attribute decision‐making methods (DEMATEL, DANP and modified VIKOR) to construct a model for evaluating and selecting a food delivery platform (FDP). The results of the INRM (influential network relation map) revealed that the first dimension to be improved upon and adjusted should be security, followed by effort expectancy, performance expectancy, social influence, facilitating conditions, hedonic motivation and habit. The DANP influential weights suggested that habits were the most important dimension, followed by hedonic motivation, while performance expectancy was the least important. According to the results of the gap analysis, the first dimension that required improvement was performance expectancy, followed by effort expectancy, facilitating conditions, security, social influence, habits and hedonic motivation. It is expected that the findings of this study can serve as a reference for consumers selecting FDPs to better satisfy their dining needs. The novel model is discussed in terms of theoretical, practical and managerial implications.