Search Results (366)

This study develops and tests an association-based model explaining how consumers interpret AI-enabled personalization in fashion e-commerce and how these interpretations relate to behavioral intentions. Integrating perspectives from Social Exchange Theory, the Antecedents of Trust Model, Self-Determination Theory, Psychological Contract Breach Theory, and Surveillance Capitalism, we examine the joint associations of perceived personalization, transparency, data control, and privacy concerns with brand trust, perceived surveillance, privacy violation perceptions, and purchase intention. Using PLS-SEM with data from 664 online shoppers, we find that personalization, transparency, and data control are each positively associated with brand trust, while personalization and privacy concerns are positively associated with surveillance perceptions. Brand trust is negatively associated with both surveillance and privacy violation perceptions, and privacy violation is negatively associated with purchase intention. Data control is directly associated with lower surveillance perceptions, whereas transparency operates indirectly through brand trust. Mediation analysis reveals that surveillance is associated with lower purchase intention only indirectly through privacy violation (full mediation), identifying perceived privacy violation as the central psychological pathway in the personalization-privacy paradox. Multi-group analysis identifies segment-level variations by gender and education: personalization is a stronger trust cue for men, while transparency is a stronger trust cue for women; trust buffers violation more strongly for higher-educated consumers. The results highlight a trust-first personalization strategy in which relevance must be paired with meaningful transparency and data-control features to mitigate surveillance and violation appraisals, supporting positive consumer outcomes in fashion e-commerce. Full article

In this study, a hybrid multi-criteria decision-making (MCDM) model was developed for analyzing consumer adoption decisions regarding generative artificial intelligence (Gen AI). By extending the technology acceptance model (TAM) into a structured decision system, the proposed framework integrates ethical and risk-related criteria, including perceived cost, perceived risk, transparency, accountability, intellectual property concerns, and data privacy, into a formal causal and evaluative structure. First, a Delphi-based consensus process is employed to identify and refine key adoption criteria. Subsequently, the decision-making trial and evaluation laboratory (DEMATEL) method is applied to quantify causal relationships among these criteria and to construct an influence network revealing prominence and directional effects. In total, 251 questionnaires were distributed in Taiwan, and 231 valid responses were collected. The results indicated the decision-making factors that underlie the adoption of Gen AI by consumers. The results highlighted transparency as a dominant causal factor that significantly influences multiple ethical and functional dimensions of Gen AI adoption. To address uncertainty and vagueness in human judgment, fuzzy importance–performance analysis was also incorporated. Best non-fuzzy performance values were obtained through defuzzification, enabling the classification and prioritization of critical adoption factors within a four-quadrant decision matrix. The proposed framework provides a mathematically grounded decision-support model for elucidating the structural interdependencies among adoption criteria and to facilitate strategic decision making for Gen AI system design and governance. This study contributes to the MCDM and operations research literature by transforming a behavioral acceptance model into a formal decision-analytic framework, thereby enhancing the analytical rigor and applicability of TAM-based adoption studies in complex socio-technical systems. 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

“Can ChatGPT become a general purpose technology?” “How does the “privacy paradox” play a role in adopting ubiquitous AI technologies in a humane AI ecosystem?” To answer these research questions, this study examined the roles of AI equality, trust in the large language model (LLM) ChatGPT, the need to belong, perceived benefits of ubiquitous AI, and privacy concerns about potentially ubiquitous generative artificial intelligence (GenAI) in a human-centered AI ecosystem. Drawing from the emerging literature on the AI divide (vs. AI equality) and AI-powered digital transformation, cross-sectional survey data were collected from current ChatGPT users. The results of testing PROCESS macro models with 5000 bootstrap samples showed the relationship between AI equality and purchase intention is mediated by trust in ChatGPT and is moderated by the need to belong. Privacy concerns about ChatGPT moderate the relationship between AI equality and perceived benefits of ubiquitous GenAI, which, in turn, mediates the relationship between AI equality and purchase intention. Ethical dilemmas in developing an equitable AI ecosystem, practical implications of the “privacy paradox” for designing trustworthy and ubiquitous AI interfaces in the dynamically evolving AI-powered digital transformation landscape and electronic marketplaces, and theoretical implications of the ChatGPT epidemic in a humane AI ecosystem for the literature on general purpose technology (GPT) are discussed. Full article

Postpartum depression (PPD) affects a substantial proportion of women globally and is often underdiagnosed due to barriers in screening, stigma, and limited access to care. This study presents the design and pilot evaluation of an mHealth application (“HeartHabit”) intended to support user awareness, self-monitoring, and potential identification of symptoms of PPD among Greek-speaking mothers. An alpha version of the application was evaluated through an online survey with 30 women within the first postpartum year, using a walkthrough video. The evaluation focused on perceived usability and acceptability rather than clinical outcomes or real-world use. Usability and app quality were assessed via the System Usability Scale (SUS) and a qualitative version of the user Mobile Application Rating Scale (uMARS), respectively, adopting a mixed-methods approach. Demographics, and mood and stress screening data were also captured. Quantitative data were analysed via descriptive statistics and qualitative responses via Framework Analysis. The results indicated high perceived usability (mean SUS = 83.7/100). Qualitative findings highlighted the importance of practical usability, self-regulation tools, personalisation, and connectivity with healthcare professionals. Privacy, data transparency, and user control over personal data were perceived as critical for trust. The application was perceived as a potentially useful adjunct to formal care or as at-home support when access to services is limited. Larger, controlled trials, clinical implementation protocols and clinician training are needed to promote the app’s safe integration into formal care. This mixed-methods evaluation, incorporating usability assessment and patient involvement, may offer a useful paradigm for early-stage digital mental health intervention development. Full article

(1) Background: The non-consensual dissemination of intimate images constitutes a severe form of online gender-based violence (OGBV) that inflicts profound harm on victims’ sexual privacy, psychological well-being, and social functioning. Taiwan enacted comprehensive legislative reforms in 2023—commonly referred to as the “Four Acts on Sexual Violence Prevention”—to strengthen criminal responses and expand victim protection mechanisms. However, the extent to which these reforms have translated into effective frontline practice remains insufficiently examined. (2) Methods: This qualitative study employed reflexive thematic analysis to investigate frontline professionals’ experiences with enforcing intimate image offence legislation and delivering victim support services. Semi-structured, in-depth interviews were conducted with 20 practitioners, including social workers, police officers, prosecutors, and lawyers. (3) Results: Three superordinate themes emerged across macro, meso, and micro structural levels. At the macro level, limited public awareness and persistent victim-blaming attitudes undermine prevention, help-seeking, and reporting. At the meso level, legislative fragmentation, challenges in preserving and analysing digital evidence, and inter-agency coordination gaps constrain enforcement capacity. At the micro level, procedural delays, risks of secondary victimization, and perceived inadequacies in compensation and support mechanisms weaken victims’ trust in institutional responses. (4) Conclusions: While Taiwan’s legislative reforms represent a significant institutional advancement, legal reform alone is insufficient to address digital sexual violence effectively. Comprehensive responses require integrated public education initiatives, enhanced inter-agency coordination, strengthened digital investigation capacity, and trauma-informed victim protection practices across all structural levels. In particular, the findings underscore an urgent public health need to establish rapid digital evidence preservation and takedown mechanisms to limit the proliferation of non-consensual sexual images and mitigate the associated mental health harms among victims. Full article

Learning Analytics (LA) can support student success through dashboards and early-support interventions, but adoption depends on students’ willingness to allow educational data use under privacy and data-protection requirements. This study examines predictors of students’ willingness to allow educational data use for LA in higher education, focusing on perceived benefits, perceived risks, control and transparency expectations, and institutional trust. A cross-sectional survey was administered to engineering students (N = 109); after an instructed-response attention check, N = 102 valid responses were retained. Composite Likert constructs (BENEFIT, RISK, CONTROL, TRANSPARENCY, TRUST) and two willingness outcomes were analyzed: academic-support LA (WILL_ACAD) and broader aggregated institutional reporting under safeguards (WILL_BROAD). Willingness was high in both scenarios, and the paired difference did not reach statistical significance. Regression models showed that institutional trust was the strongest predictor of willingness across both use cases; perceived benefits additionally predicted willingness for academic-support LA, while perceived risk was a positive predictor in the broader-use model. Descriptive results indicated that students prioritize human review before any action affecting a student and strong security measures as key safeguards. These provide initial evidence to inform privacy-aware learning analytics governance in similar technical-university contexts; broader generalization across higher education requires replication across disciplines and institutions. Full article

Indoor environmental quality (IEQ) significantly impacts people’s comfort, health, and productivity in buildings, and modern green rating systems are primarily focused on energy efficiency rather than the direct user experience. This paper analyses the relationship between IEQ and the perceived quality of life (QoL) of certified and conventional office buildings in Malaysia using a mixed-methods design. The questionnaires were completed by 162 employees working in four open-plan offices: two were certified under the Green Building Index (GBI) established in Malaysia, and two were traditional. This was supplemented by 14 semi-structured interviews and 2 focus groups. The factors of IEQ were divided into ambient, designed, and behavioral environments. It was statistically determined that behavioral factors, such as visual privacy, personalization, ergonomics, and control, exhibited the strongest correlations with overall QoL, compared to ambient factors such as air quality or thermal comfort. Green buildings performed better in terms of daylighting and esthetics than conventional buildings, though they did not always deliver higher occupant satisfaction. The results indicate that current green certification frameworks pay insufficient attention to occupant-centered aspects. The proposed research adds a validated IEQ-QoL framework that predicts the incorporation of subjective user experience into building performance indicators, which can be important for certification reform, post-occupancy evaluation (POE), and human-centered sustainable design approaches. Full article

Although ego threat is known to influence workers’ aggressive behavior, little is understood about how support and narcissism shape this relationship. Accordingly, the present study conceptualized narcissistic traits as distinct self-regulatory strategies for maintaining self-worth and examined whether the meaning of support under ego threat varies depending on these traits. An online survey was conducted with 1621 Japanese workers, and the participants were classified into three types—Self-Assertion, Need for Attention and Praise, and Sense of Superiority and Competence—based on the highest scores on the three factors of the Narcissistic Personality Inventory Short version. Hierarchical multiple regression analyses were then conducted separately for each type. The results showed that the behavioral consequences of ego threat varied substantially across narcissistic types and that support did not uniformly suppress power harassment. For the Self-Assertion type, perceived organizational support was positively associated with Invasion of Privacy. For the Need for Attention and Praise type, men and managers tended to choose Excessive Demands. For the Sense of Superiority and Competence type, supervisor support reduced harassment; however, under strong ego-threatening conditions, such support paradoxically amplified harassment. These findings suggest that support functions as a socially meaningful cue whose interpretation depends on narcissistic self-regulatory strategies. Full article

Student attrition remains a persistent challenge in higher education and is shaped by interacting socioeconomic, academic, institutional, and wellbeing-related mechanisms. Although learning analytics and educational data mining increasingly support early-warning and intervention workflows, dataset reuse is often limited by incomplete documentation and inconsistent variable definitions. This Data Descriptor presents a structured cross-sectional survey dataset on factors influencing student persistence at a Colombian public university campus (La Paz). Data were collected between August and December 2025 through an online questionnaire and subsequently cleaned to remove duplicate entries and personally identifiable information. The released dataset contains 333 student records and 33 variables covering demographics (e.g., age, gender, first-generation status), socioeconomic conditions (e.g., residential stratum, housing, financial aid), academic experience and satisfaction (multiple 1–5 Likert items), perceived dropout intention across personal/socioeconomic/academic domains, thematically coded open-ended items describing challenges and motives, and a self-allocation of 0–100 weights across three dropout-factor domains. We provide a machine-readable codebook, a transparent preprocessing description, and technical validation checks (value ranges, category consistency, and composite-score integrity). The dataset is intended to support reproducible retention research, equity-oriented analyses, and benchmarking of predictive models, while encouraging responsible reuse through privacy-preserving release practices and FAIR-aligned metadata, repository deposition, and versioning. Full article

The protection of personal information has become a defining challenge for higher education institutions, particularly in developing contexts where regulatory frameworks are often strong on paper but weak in practice. This study investigates student perceptions of privacy within Zimbabwe’s higher education system, focusing on three constructs: awareness, expectations, and confidence across nine core privacy components derived from international principles (FIPPs, OECD, GDPR) and the Zimbabwe Data Protection Act (ZDPA). Using survey data from 287 students across diverse programmes and modes of study, descriptive and comparative analyses reveal a striking pattern: students demonstrate high awareness and very strong expectations, yet their confidence in institutional compliance remains significantly lower. The largest deficits were found in privacy education, consent, and notice/openness, suggesting that institutions are perceived as technically competent in data handling but weak in transparency, accountability, and student engagement. The research extends privacy perception models by considering the discrepancy between the students’ expectations and the institutional trust. It also encourages universities to go beyond mere compliance by implementing concrete measures such as privacy training, clear consent, and frequent data audits. The findings contribute to global debates on privacy by offering evidence from the Global South, showing that the key challenge is not student ignorance but institutional trustworthiness. Bridging this awareness-confidence gap is essential for building a privacy-conscious academic environment. Full article

Background/Objectives: Patient dignity is one of the central values in nursing, equivalent to justice, freedom, and individuality. The purpose of this study was to investigate the psychometric properties of the Greek version of the Inpatient Dignity Scale (IPDS). Methods: In this descriptive, cross-sectional study, 280 patients from three Hemodialysis Units (HD) completed the IPDS, a self-completed questionnaire assessing patients’ expectations regarding dignity and patients’ satisfaction with dignity. Items are categorized into four dimensions, both for expectations and satisfaction: Respect as a Human Being, Respect for Personal Feeling and Time, Respect for Privacy, and Respect for Autonomy. For the translation into Greek, a double forward-backward translation process was followed, and subsequently, cultural adaptation was carried out. Construct validity was tested using the Confirmatory Factor Analysis (CFA) conducted in AMOS 26.0. Convergent validity was assessed through correlations with the Caring Behaviors Inventory-16 (CBI-16) and correlations between the dimensions of the IPDS. Repeatability was assessed using the Intraclass Correlation Coefficient (ICC), and internal consistency using Cronbach’s alpha. The SPSS 26.0 statistical program was used for the descriptive and correlational analyses (p < 0.05). Results: The mean age of participants was 64.8 years old. CFA revealed an acceptable fit for the questionnaire (CFI 0.92–0.93, TLI 0.91–0.94, and RMSEA < 0.08 for both expectations and satisfaction). The IPDS was significantly and positively correlated with the CΒΙ-16, indicating good convergent validity. Cronbach’s alpha was >0.70 in all dimensions of the IPDS, indicating good internal consistency. Conclusions: The Greek version of IPDS is a valid and reliable tool to measure patients’ perceived dignity. Full article

This study examines the psychological mechanisms through which anthropomorphic artificial intelligence (AI) relates to consumer adoption intentions in fragile, low-trust economies. Integrating the Stimulus–Organism–Response framework with the Computers Are Social Actors paradigm, Institutional Trust Theory, and Privacy Calculus Theory, we investigate how human-like AI design shapes cognitive and affective responses within Sierra Leone’s banking sector. Using survey data from 277 banking customers and partial least squares structural equation modeling, we find that AI anthropomorphism exhibits no direct association with adoption intention (β = −0.013, p = 0.760). Instead, its influence is entirely indirect—transmitted in parallel through perceived social presence (β = 0.144, 95% CI [0.062, 0.226]) and trust in the AI system (β = 0.139, 95% CI [0.068, 0.210]). Critically, customer skepticism—shaped by institutional fragility—functions as a boundary condition that substantially attenuates both pathways: among highly skeptical users (+1 SD), anthropomorphism’s conditional effect on social presence becomes non-significant (β = 0.098, p = 0.124) compared to low-skepticism users (β = 0.412, p < 0.001), while its effect on trust is reduced by more than half (β = 0.118 vs. 0.284). These findings identify a critical boundary condition on human-like AI design: in low-trust environments, anthropomorphism operates not as a standalone adoption driver but as a relational amplifier whose efficacy depends on foundational trust and is substantially weakened when skepticism is high. The study challenges universalist assumptions in human–AI interaction research and underscores the need for institutionally sensitive design approaches in fragile economies. Full article

Background: Generative artificial intelligence (GenAI) is rapidly permeating healthcare; yet, U.S. clinicians still report mixed feelings about its reliability, impact on workflow, and ethical implications. Current data on provider sentiment are needed to guide safe, patient-centered AI implementation in healthcare. Objective: This study aimed to assess U.S. healthcare providers’ perceptions of generative AI adoption, perceived usefulness, training needs, barriers, and strategies for safe integration. Methods: A nationwide, IRB-approved, cross-sectional survey was administered to healthcare professionals using Qualtrics. A convenience sample of clinicians was recruited via professional listservs and e-mail invitations. The 20-page questionnaire captured demographics, GenAI exposure, organizational adoption status, perceived usefulness (5-point scale), barriers, and mitigation strategies. SPSS v27 and Microsoft Excel were used for statistical analysis. Results: Of 130 respondents, 109 completed the core survey (completion rate 83.8%). Participants were 38.5% physicians, 16.5% nurses, 12.8% allied professionals, and 32.2% other providers; 54.2% were women, and 64.8% were ≥50 years. Overall, 86.9% agreed that GenAI is useful in current patient care, rising to 92.9% when asked about future usefulness. Only 42.4% had received formal GenAI training, and just 23.2% reported that their organization had begun adopting AI. The top perceived benefits were improved documentation/clerking (57.0%) and error reduction (49.4%). Dominant barriers included limited AI knowledge (24.7%) and fear of job loss (16.9%). Despite concerns, 72% expressed willingness to support broader GenAI adoption, favoring human oversight (67.1%) and staff training (60.8%) as key safeguards. There were statistically significant findings in perceived AI usefulness by gender (χ² = 29.2; p < 0.001); organizational adoption of AI (χ² = 31.6.2; p = 0.047) and where AI is most useful (χ² = 101.1; p < 0.001) by qualifications; and support for AI adoption by age (χ² = 18.0; p = 0.02). Conclusions: U.S. clinicians in our survey viewed GenAI as useful but reported limited training and organizational infrastructure needed for confident use while also expressing concerns regarding data privacy and ethical risk. Education programs and transparent, provider-led implementation strategies may accelerate responsible GenAI assimilation while addressing ethical and workforce concerns. Also, health administrators should use the efficiency gains to improve provider–patient relationships and clinicians’ work–life balance while reducing clinician burnout rates. Full article

Given the global aging of the population and the rising retirement age, the development of cross-generational technologies is crucial for a sustainable workforce supply. While AI-powered smart glasses can provide continuous cognitive support, current industrial solutions often prioritize work efficiency at the expense of the physical, cognitive, and socio-emotional needs of older workers. This study employed a mixed-methods approach grounded in embodied cognition. First, semi-structured interviews with ten participants were analyzed using grounded theory to develop a four-dimensional model of embodied experience: Perceived Pressure, Action Feedback, Collaboration Embedding, and Belonging. Subsequently, four interaction strategies—Rhythm Control, Transparent Feedback, Non-intrusive Assistance, and Legible Privacy & Social Signaling—were formulated and implemented. A high-fidelity prototype was developed to embody these strategies. Finally, a team of eight multidisciplinary experts evaluated the device using the System Usability Scale (SUS) and a proprietary twelve-item questionnaire. The results showed that the device’s overall usability was borderline acceptable (SUS = 68.13 ± 8.94). While the devices received stronger ratings for Control & Safety, the ratings for dignity and social acceptance were comparatively low. These findings contribute to the development of wearable device operation strategies suitable for users of different generations, and underline the importance of social and emotional compatibility as a prerequisite for future practice tests. Full article