Search Results (94)

This study examines user responses to an AI travel assistant in a cross-border tourism service system. Moving beyond adoption-centered technology acceptance research, it conceptualizes these responses as a staged appraisal process in which social and experiential cues shape performance expectancy and effort expectancy, which then influence attitude and two behavioral outcomes: users’ willingness to accept AI and objections to AI. Cosmopolitanism is introduced as an individual-level boundary condition. Survey data were collected from 499 Chinese tourists holding valid South Korean tourist visas after they evaluated Visit Seoul AI, an official AI-based travel-planning tool. The hypotheses were tested using partial least squares structural equation modeling. The results show that social influence, hedonic motivation, and perceived anthropomorphism significantly affect performance expectancy and effort expectancy, which in turn shape attitude. Attitude increases usersf’ willingness to accept AI and reduces objections to AI, with a stronger effect on users’ willingness to accept AI. Cosmopolitanism strengthens the negative effect of hedonic motivation on effort expectancy. This study extends AIDUA to cross-border AI service systems and shows that users may both accept and object to AI travel assistants. Full article

This study investigates the factors influencing customer engagement in AI-powered fitness applications and the subsequent impact on behavioral outcomes such as word of mouth and sustainable consumption. A quantitative research design was employed, with data collected through a structured survey from users of AI fitness applications, and analyzed using Structural Equation Modeling (SEM). Drawing on the Information Systems Success Model and engagement theory, the research examines the roles of service quality, system quality, information quality, health consciousness, anthropomorphism, and personal innovativeness. Findings reveal that higher perceived quality across service, system, and information dimensions, coupled with health consciousness and human-like features, significantly enhances user engagement. Engagement, in turn, drives both advocacy and sustainable behaviors, while personal innovativeness selectively amplifies the effect of system quality. The study advances theoretical understanding by adapting and extending established models to the context of AI-driven health technologies, while also providing practical insights for the development of intelligent, user-centric fitness applications that promote sustained engagement and responsible use. Full article

Anthropomorphism is a widely used marketing strategy, yet less is known about how baby-schema anthropomorphic cues embedded in eco-friendly logos function as compact visual identity cues to promote consumers’ green purchase intention through positive emotional attribution. Drawing on baby-schema theory and mental-state attribution, we examine the impact of anthropomorphic eco-friendly logos on green purchase intention, the mediating roles of perceived love and perceived hope, their sequential pathway, and the moderating effect of environmental attitude. A within-subjects study was conducted with 299 valid participants in China, using established and adapted scale items for data collection. Our results demonstrated that anthropomorphic eco-friendly logos significantly enhanced green purchase intention. Perceived love and perceived hope each mediated this relationship, and the sequential pathway from perceived love to perceived hope was also significant. Moreover, environmental attitude positively moderated the link between anthropomorphic logos and perceived love, with a stronger effect among consumers with higher pro-environmental attitudes. These findings highlight a positive emotional attribution mechanism through which anthropomorphic eco-friendly logo cues promote green consumption and clarify the boundary role of environmental attitude. Full article

Objectives: This study proposes an implementation-oriented design framework for multimodal conversational agents handling patient-generated health data and reports an exploratory experiment evaluating its instantiation in hypertension self-monitoring, focusing on user experience of conversational data-entry workflows. Methods: The framework operationalizes four complementary dimensions (social intelligence, communication style, anthropomorphic characteristics, and technological mapping) and was instantiated in two agents integrated into an eHealth platform. Each agent supports users by providing prompts, interpreting responses, checking data plausibility, and confirming submission. A three-arm, single-session feasibility experiment ($n=18$, $n=6$ per group) compared a conventional app interface with text-based and voice-based conversational agents. Evaluation triangulated three sources of evidence: open-ended qualitative responses analyzed through descriptive content analysis, session-level researcher observation notes, and the User Experience Questionnaire (UEQ) reported descriptively with one-way ANOVA and ${\eta }^2$ effect sizes. Results: All three modalities were acceptable to participants and produced UEQ scores in the positive range. Hesitation was observed in 2 of 6 Control participants, 1 of 6 Text participants, and 3 of 6 Voice participants, with self-reports indicating that voice-related difficulties were modality-specific (diction, command phrasing) and resolved within the session. Qualitative themes of acceptability and innovation, perceived effort, and modality-specific facilitators emerged across the corpus. Between-group ANOVAs did not reach statistical significance ($p>0.05$), as expected for an underpowered design, yet ${\eta }^2$ values were medium for Attractiveness, Efficiency, Dependability, and Pragmatic Quality and large for Stimulation and Hedonic Quality, converging with the qualitative innovation and engagement signal in the conversational conditions. Conclusions: The framework and feasibility experiment provide preliminary, hypothesis-generating evidence on the potential of multimodal conversational interfaces in healthcare. However, no clinical, behavioral, or longitudinal outcomes were assessed. The four design dimensions can be tentatively associated with themes recognizable in user discourse, and the observed effect-size pattern motivates adequately powered longitudinal studies that incorporate behavioral and clinical endpoints alongside user experience measures. Full article

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. Full article

Large language models (LLMs) have been widely adopted in educational settings, particularly among university students. However, the behavioral mechanisms through which these systems influence academic outcomes remain insufficiently understood. This study develops and empirically tests a framework explaining how the technological attributes of LLMs—perceived usefulness, ease of use, system reliability, accessibility, and interface design—affect student motivation and personalization, which foster anthropomorphic perception and enhance self-efficacy and academic performance. Data were collected from university students in Saudi Arabia using a structured survey and analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that technological attributes positively influence motivation and personalization, which strengthen anthropomorphism and subsequently improve self-efficacy and academic performance. The results provide practical insights into the effective application of LLMs in higher education and highlight the role of generative AI in supporting sustainable educational practices. Full article

As an emerging application, AI shopping has received increasing attention from both enterprises and users. Based on the push–pull–mooring (PPM) model, this research examined user switching intention from traditional online shopping to AI shopping. We conducted an online survey to collect 422 valid responses and adopted a mixed method of structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA). The results show that choice overload and perceived inefficiency lead to online shopping fatigue, while perceived convenience, perceived anthropomorphism, and perceived coolness affect AI shopping attractiveness. Online shopping fatigue, AI shopping attractiveness, and inertia determine user switching intention. These results provide a comprehensive understanding of the mechanism underlying user switching from traditional online shopping to emerging AI shopping. They also imply that e-commerce platforms need to mitigate online shopping fatigue and increase AI shopping attractiveness in order to expand their user base and maintain a competitive advantage. Full article

Industrial robots are increasingly deployed in human-centered settings, where appearance and motion critically shape worker trust and acceptance. This study employed a 2 × 2 factorial design manipulating robot appearance (Sleek vs. Industrial) and motion (Adaptive vs. Rigid) to examine effects on perceived lifelikeness, intelligence, engagement, trust, and predictability. Participants rated each measure using Likert scales, and data were analyzed using descriptive statistics, two-way ANOVA, and Pearson correlations. Results revealed significant main effects of appearance and movement across multiple perceptual dimensions, with a significant interaction effect observed for trust. Findings suggest that anthropomorphic cues, both visual and behavioral, may enhance perceptions of intelligence, relatability, and trust. This work contributes to the limited literature on anthropomorphism in industrial contexts and provides empirical evidence to guide the design of human-centered collaborative robotic systems. Full article

Recycled products have evolved from environmental substitutes to an important development direction in the future consumer market. However, consumers’ active avoidance of recycled product advertisements is still prevalent, which restricts their market acceptance and promotion. This study aims to systematically explore the relationship between recycled product anthropomorphism and consumer advertising avoidance, and reveal the mediating role of perceived risk, as well as the moderating effects of technology readiness and time orientation. A mixed exploratory method combining Smart PLS and fsQCA was adopted to conduct an in-depth analysis of 728 questionnaires. The results show that recycled product anthropomorphism has a significant negative impact on consumer advertising avoidance, and this effect is partially realized through the mediating mechanism of perceived risk, which is regulated by technology readiness and time orientation. The research results not only enrich the application of anthropomorphism theory in the field of sustainable consumption but also provide empirical evidence and practical guidance for companies to formulate effective recycled product advertising strategies and reduce consumer advertising avoidance. Full article

Artificial intelligence (AI) is increasingly being deployed in various sectors of e-commerce. Consequently, it becomes necessary to identify the impact of algorithmic design parameters on buyer behaviour. This study examines the impact of algorithmic anthropomorphism (ANT), algorithmic transparency (TRAN) and perceived algorithmic fairness (FAIR) on consumer purchase intentions (PI) in the Turkish e-commerce market. In addition, this study examines technology acceptance—operationalised through the Technology Acceptance Model (TAM)—as a boundary condition, with particular attention to the differential moderating roles of perceived ease of use (PEOU) and perceived usefulness (PU). A structured questionnaire was distributed among 384 online consumers in Türkiye via Qualtrics. A confirmatory factor analysis (CFA) established the psychometric adequacy of the measurement model (all AVE > 0.50, all CR > 0.87; HTMT < 0.85 across theoretically distinct constructs). The proposed model was tested using the PROCESS macro for sequential mediation and moderation analyses, with bootstrap confidence intervals based on 5000 resamples. The results reveal that: (1) algorithmic anthropomorphism positively affects both algorithmic transparency and perceived algorithmic fairness; (2) algorithmic transparency has a significant positive effect on both perceived fairness and purchase intention; (3) perceived algorithmic fairness mediates the relationships between algorithmic anthropomorphism and purchase intention, as well as between algorithmic transparency and purchase intention; and (4) although the composite technology acceptance level (TAL) measure does not significantly moderate the anthropomorphism–purchase intention path (p = 0.075), disaggregating TAL into its sub-dimensions reveals that PEOU significantly moderates this relationship (p < 0.001), whereas PU does not (p = 0.199). The composite-TAL result is therefore not statistically supported, but the dimension-specific PEOU finding is robust. These findings offer theoretical contributions to AI-driven consumer behaviour research and practical implications for the design of algorithmic e-commerce systems in emerging digital markets. Full article

Introduction: Simulation-based training is a key component of surgical education; however, existing models, such as dry-laboratory and virtual reality simulators, have limitations in terms of realism and accessibility. The use of human cadaveric material is also challenging because of its high cost and limited availability. Objectives: To evaluate the effectiveness of biological models based on large animal cadaveric material, specifically cattle and pigs, for practicing head and neck surgical skills. Materials and Methods: The study included 100 third- and fourth-year students, who were divided into a study group and a comparison group, with 50 participants in each group. The study group practiced surgical skills using animal cadaveric material: a porcine mandible for bone graft harvesting and a bovine head for resection craniotomy. The comparison group practiced using 3D-printed models. The results were assessed using an anonymous 8-item Likert-scale questionnaire, followed by statistical analysis using the Mann–Whitney U test. Results: In the study group, statistically significant increases were observed in satisfaction with participation, fulfillment of expectations, perceived subjective acquisition of manual skills, and overall satisfaction with the training process (p < 0.001; median scores: 38.0 and 34.0, respectively). The greatest differences were observed in satisfaction with participation, where 54% of participants rated it as “Excellent” compared with 6% in the comparison group, and in perceived subjective acquisition of manual skills, reported by 80% of participants in the study group compared with 24% in the comparison group. Conclusions. The use of cadaveric specimens from large animals is associated with higher satisfaction and represents an accessible alternative for practicing basic and commonly performed head and neck surgical procedures that do not require fine dissection of neurovascular bundles. This model provides a high degree of tactile realism and anatomical context and is subjectively preferred over non-anthropomorphic simulators. Full article

As Gen-AI shopping chatbots become increasingly prevalent in e-commerce, limited research has examined how consumers’ appraisals of interactive cues shape trust and continued use in privacy-sensitive retail settings. Drawing on Cognitive Appraisal Theory (CAT) and the AIDUA framework, this study investigates how novelty value, anthropomorphic attribute, and social presence influence performance anticipation, effort anticipation, and perceived privacy risk and how these appraisals subsequently shape perceived trust and continued use. Data from 549 experienced users in mainland China were analyzed using partial least squares structural equation modeling (PLS-SEM). The results show that while novelty value enhances performance and effort anticipation, it does not significantly elevate perceived privacy risk. Anthropomorphic attribute positively affects performance anticipation and negatively affects perceived privacy risk, while social presence enhances performance anticipation and effort anticipation and reduces perceived privacy risk. Performance anticipation and effort anticipation positively predict perceived trust, whereas perceived privacy risk negatively predicts perceived trust; perceived trust, in turn, strongly predicts continued use. Mediation analyses further show that cognitive appraisal variables mediate the effects of primary appraisal factors on perceived trust, while perceived trust mediates the effects of cognitive appraisal variables on continued use. Serial mediation results additionally indicate that primary appraisal factors influence continued use through cognitive appraisal and trust formation. These findings deepen understanding of the cognitive and trust-building mechanisms underlying consumer interactions with Gen-AI shopping chatbots and offer practical implications for e-commerce platforms. Full article

This study investigates how two key characteristics of AI-enabled chatbots in mobile banking applications—perceived intelligence and perceived anthropomorphism—influence users’ cognitive and hedonic evaluations, namely perceived usefulness, confirmation, and perceived enjoyment, and how these evaluations subsequently shape user satisfaction and continuance intention. Grounded in the Expectation–Confirmation Model (ECM), the study also examines the moderating role of users’ need for interaction with service employees in these relationships. Using a quantitative research design, data were collected through a structured survey from 402 users of AI-enabled mobile banking applications in Türkiye. The proposed model was tested using partial least squares structural equation modeling (PLS-SEM), and moderated mediation effects were analyzed using Hayes’ PROCESS Macro (Model 58). The results reveal that perceived intelligence positively affects perceived anthropomorphism, perceived usefulness, perceived enjoyment, and confirmation, while perceived anthropomorphism further reinforces these effects. Cognitive and emotional evaluations significantly enhance user satisfaction, which in turn strongly predicts continuance intention toward chatbot usage. Moreover, the need for interaction with service employees significantly moderates the indirect effects of perceived usefulness, perceived enjoyment, and confirmation on satisfaction and continuance intention. By extending the expectation–confirmation model with both cognitive and emotional dimensions, this study offers novel insights into user-centered chatbot design in mobile banking and highlights the importance of individual differences in shaping sustained technology use. Full article

As AI-powered learning tools become more common in educational settings, understanding their acceptance mechanisms is increasingly important. This study examines how the design attributes of AI museum guides—anthropomorphism, interactivity, and personalization—are associated with the acceptance intention and perceived learning outcomes among Chinese high school students with prior museum experience. Using structural equation modeling with 324 participants, we test whether these features relate to acceptance through two affective pathways: perceived warmth and anxiety reduction. The results reveal distinct patterns: anthropomorphism shows an indirect-only association with anxiety reduction through perceived warmth; interactivity is associated with anxiety reduction through responsive feedback; and personalization serves dual functions, enhancing both pathways. Anxiety reduction shows strong positive associations with both acceptance intention and perceived learning outcomes. The multi-group analysis shows that most pathways function equivalently across genders, with one exception where anxiety reduction more strongly predicts learning outcomes for females than males. These findings reveal distinct psychological functions within the Chinese educational context: anthropomorphism influences anxiety reduction exclusively through perceived warmth, while personalization and interactivity provide both affective and cognitive support. The implications for AI museum guide design in similar contexts are discussed. The generalizability to other cultural contexts and populations, such as Western students or adult learners, requires further investigation. Full article

In the context of digital tourism development, artificial intelligence has become one of the major techniques for tourists’ information acquisition and interaction in the field of intangible cultural heritage (ICH) tourism. However, whether AI with anthropomorphism elements attracts tourists to visit cultural heritage sites and how AI anthropomorphism design affects visitors’ visit intentions remains unclear. Therefore, based on the stimulus–organism–response (S–O–R) theory, this study proposes an “AI anthropomorphism–AI trust–visit intention” model and investigates the role of AI anthropomorphism in visit intention. In particular, this study tests the effects of perceived intelligence and perceived risk on AI anthropomorphism, as well as the role of AI trust and perceived cultural sustainability on the relationship between AI anthropomorphism and visit intention. With a sample of 478 Chinese respondents who are intangible cultural heritage (ICH) tourists, the hypothesized relationships are tested by employing structural equation modeling. The results show that perceived intelligence exerts a positive effect on AI anthropomorphism, while perceived risk exerts a negative effect on AI anthropomorphism. Moreover, AI anthropomorphism exerts an effect on AI trust, which in turn yields a great influence on visit intention. In addition, further analysis shows that AI type intensifies the effect of anthropomorphism on AI trust, and the relationship between AI trust and visit intention is regulated by perceived cultural sustainability. This study reveals how AI anthropomorphism functions in ICH tourism, and the findings provide practical guidance for advancing intelligent services and giving cultural sustainability top priority in order to support the sustainable growth of ICH tourism. Full article