Balancing opportunity and risk: Perceived benefits, concerns and blockchain adoption intention among MSEs

ABSTRACT Despite rising environmental awareness, consumer adoption of sustainable innovations like 3D-printed clothing remains low, highlighting a persistent attitude – behavior gap. This study introduces the Dual Norms Model (DNM), which integrates the Theory of Planned Behavior (TPB) and Diffusion of Innovation (DOI) theory while distinguishing between social norms (perceived expectations) and behavioral norms (observed peer actions). Survey data from 221 participants analyzed using PLS-SEM reveal that behavioral norms – not social norms – significantly mediate the influence of key innovation attributes (e.g. environmental benefit, compatibility, trialability) on adoption intention. These findings address limitations in TPB and DOI and suggest that marketing sustainable innovations should emphasize peer modeling over moral or informational appeals. The DNM offers a stronger framework for understanding sustainable adoption under uncertainty.

Determinants of educational metaverse adoption intentions among business and management undergraduates: Motivations, individual differences, and barriers

Multiple pathways to generative AI entrepreneurial behavior: A mixed-methods study of Chinese undergraduates

Blockchain has gained attention for its potential to verify health insurance records, allow secure data sharing, and protect patient privacy. Despite these benefits, blockchain adoption in Egypt remains limited, and little research has examined its drivers and barriers. This study contributes to the limited research on blockchain adoption in Egyptian public hospitals by extending the TOE model with context-specific factors and examining adoption decisions at the organizational level. The study was conducted in public hospitals operating within the Universal Health Insurance System (UHIS) across six governorates. A quantitative approach was used, with data collected from a stratified random sample of 228 senior management and IT professionals across 53 public hospitals, analyzed using PLS-SEM. The results show that relative advantage, financial capability, and perceived trust positively affect adoption intention, whereas perceived risk and complexity negatively affect it. Security and privacy, as well as government support and regulations, significantly enhance perceived trust but do not directly affect adoption intention. Top management support and hospital readiness also strengthen perceived trust. The model explains 67.1% of the variance in blockchain adoption intention (R2 = 0.671) and shows predictive relevance (Q2 = 0.662). Perceived trust positively influences adoption intention and mediates selected relationships in the model, particularly for security and privacy, as well as government support and regulations, which indirectly influence adoption through trust. The proposed model provides practical insights for Egyptian public hospitals, blockchain providers, the Ministry of Health, and policymakers in designing strategies that may facilitate blockchain adoption in public hospitals in Egypt.

ABSTRACT This study investigates how mobile app features (AI transparency, rewards, and tracking) influence smart bin adoption. Grounded in nudge theory and the Stimulus-Organism-Response framework, an online experiment tested their effects on trust, perceived value, and adoption intention. Trust mediated the effect of all three features on adoption intention. Perceived value also mediated the effect of AI type and rewards, but only white-box AI showed a direct effect on intention. An interaction effect between rewards and tracking was found in the system with black-box AI. The findings offer theoretical contributions and practical guidelines for promoting sustainable technology use.

This study’s purpose was to examine factors influencing students’ intention to adopt 3D virtual prototyping technology and the moderating role of job relevance in textile and apparel education. An extension of the technology acceptance model (TAM) was adopted to frame the study. An online survey was conducted with 161 students enrolled in 12 textile- and apparel-related courses. The results indicated that perceived usefulness and enjoyment make a significant positive impact on intention to adopt the technology, while perceived ease of use did not exert a significant effect. Job relevance exerted a moderating effect on the relationship between technology attributes and adoption intention. These findings validate the extended TAM model’s theoretical implications and provide valuable insights for educators, highlighting the key motivational factors that drive students’ use of 3D virtual prototyping technology and supporting development of digital competencies in future decision-making amid the ongoing digital transformation of the apparel industry.

With the rapid development of Artificial Intelligence Generated Content (AIGC) platforms, users increasingly show cross-platform usage intentions. Existing research focuses on adoption and usage intentions in single-platform AIGC contexts. A theoretical gap remains in studies of cross-platform usage. This paper develops and empirically tests a three-stage multiple mediation model based on the cognitive-affective personality system (CAPS). The model integrates the optimum stimulation level (OSL) theory, complementarity theory, and perceived value theory, and it sets social influence and use experience as control variables to examine users’ multi-homing intention. The results show that: (a) OSL significantly enhances users’ perceived complementarity; (b) perceived complementarity positively affects perceived epistemic value; (c) perceived epistemic value significantly and positively predicts multi-homing intention; (d) OSL influences multi-homing intention through a chain mediation path of perceived complementarity and perceived epistemic value; and (e) social influence has a significant positive effect on multi-homing intention, while the effect of use experience is not significant.

ABSTRACT The application of XR technology is beneficial to enhancing the digital cultural heritage tourism (DCHT) experience. This study employed mixed approaches (qualitative interviews and quantitative analysis) to uncover the attributes of XR for DCHT, including simulated presence, conceptual understanding, content innovativeness, participatory entertainment, embodied sensation, immersive interactivity, and aesthetic fusion. The technology acceptance model was expanded with two new variables (perceived advantages and engagement) and a moderator of past experience. Multiple attributes of XR for DCHT significantly influenced consumers’ perceived ease of use, usefulness, and advantages, thereby driving their positive engagement, attitude, and adoption intention. Past experience served as a key moderator in the framework. The fuzzy-set qualitative comparative analysis (fsQCA) revealed the complex asymmetry in the formation of behavioral intentions by identifying causal recipes of antecedent variables. Research findings provide crucial insights into how to enhance technology innovation and operations, and tourist experience in cultural heritage destinations.

BackgroundLung cancer remains the leading cause of cancer deaths in the United States; however, uptake of lung cancer screening (LCS) with low-dose computed tomography (LDCT) among eligible individuals remains low. Evidence suggests that limited knowledge, stigma, and false health beliefs contribute to the underuse of LDCT screening.ObjectiveThis pilot study aimed to examine an online educational intervention designed to improve knowledge, attitudes, health beliefs, behavioral intentions, perceived importance, and confidence related to LCS among high-risk individuals.MethodsA single-group preintervention and postintervention design was used. High-risk individuals who smoke, defined according to the US Preventive Services Task Force criteria, completed baseline questionnaires followed by 5 self-directed online educational modules delivered through Research Electronic Data Capture (REDCap). Postintervention questionnaires assessed changes in lung cancer and screening knowledge, lung cancer stigma, health beliefs based on the health belief model and precaution adoption process model, and intentions, perceived importance, and confidence regarding LDCT screening. LCS uptake was assessed via follow-up email 3 months after the intervention. Data were analyzed using descriptive statistics and paired-samples two-tailed t tests.ResultsA total of 25 participants completed the intervention. Significant improvements were observed across all major study outcomes. Knowledge scores increased markedly (score=3.76-8.60; P<.001), while lung cancer stigma decreased (score=25.52-19.16; P<.001). Health belief model constructs showed significant improvements, including perceived susceptibility, perceived benefits, cues to action, and self-efficacy, alongside reductions in perceived barriers and perceived severity (all P<.001). Self-reported intentions, perceived importance, and confidence related to obtaining LDCT screening increased significantly. Of the 22 (88%) participants who completed the 3-month follow-up, 13 (59.1%) reported obtaining LDCT screening. Participant satisfaction with the intervention was high, with a mean score of 18.32 (SD 2.33) out of 20.ConclusionsFindings from this pilot study support the feasibility, acceptability, and preliminary efficacy of an online educational intervention created to promote LCS among high-risk individuals. The intervention improved knowledge; reduced stigma; positively influenced health beliefs; and increased screening intentions, perceived importance, confidence, and uptake. Results provide a foundation for a larger-scale study and suggest that online educational platforms may be an effective strategy to reach geographically diverse high-risk populations and promote LDCT screening.

Cutaneous interstitial fluid (ISF) is a minimally invasive biofluid that reflects clinically relevant biochemical and inflammatory biomarkers comparable to blood, highlighting its potential role in autoimmune and inflammatory dermatoses. However, effective clinical translation of ISF-based diagnostics depends not only on technological validation but also on the awareness and readiness of future healthcare professionals. To assess healthcare students’ knowledge, attitudes, perceptions, and willingness to adopt cutaneous ISF-based biomarker technologies for autoimmune dermatoses, and to identify factors associated with adoption intent. A cross-sectional survey was conducted among 282 healthcare students using a structured, self-administered questionnaire. Data were summarized using frequencies and percentages for categorical variables and means ± standard deviations, medians, and interquartile ranges for continuous variables. Pearson correlation and multivariable linear regression analyses were performed to examine associations and identify predictors of willingness to adopt ISF biomarkers. Among participants, 36.2% had heard of cutaneous ISF diagnostics and 27.3% could identify a specific ISF biomarker, with mean knowledge scores indicating low to moderate awareness (range: 1.57 ± 0.72 to 2.06 ± 0.83). Despite limited awareness, attitudes were favorable, with 68.8% perceiving ISF as a promising approach (mean 3.40 ± 1.05) and 73.0% expressing trust in its scientific validity (mean 3.63 ± 1.05). Willingness to adopt ISF diagnostics was high, particularly in the absence of cost constraints (73.4%, mean 3.94 ± 1.03) and with access to training (72.7%, mean 3.94 ± 1.03). Support for curriculum integration was also strong (mean scores: 3.95 ± 1.03 and 3.99 ± 1.00; Cohen d = 0.95–0.99). Knowledge and attitudes were positively correlated with adoption intent (r = 0.42–0.51). Multivariable analysis identified attitudes (β = 0.62, P < .001) and knowledge (β = 0.18, P < .001) as significant predictors, explaining 47% of the variance (R2 = 0.47). A clear gap exists between limited knowledge and strong readiness to adopt ISF biomarker diagnostics among healthcare students. Despite low awareness, participants demonstrated positive attitudes, confidence in scientific validity, and willingness to pursue training and future use. These findings highlight the importance of curricular integration and targeted education to support informed adoption of ISF diagnostics in clinical practice.

Smart home technology (SHT) holds considerable promise for aging in place, yet older adults’ adoption lags markedly behind younger generations. Drawing on Gibson’s Affordance Theory and the Andersen Behavioral Model, this study employs an intergenerational comparative design to identify the capabilities gap underlying this divide. Data were drawn from a nationally representative survey, comprising a younger group (aged 18–25, n = 2,022) and an older group (aged >65, n = 875). Regression analyses identified new media use and self-rated global health as shared positive predictors of SHT adoption intention, and traditional media use as a negative predictor. Oaxaca-Blinder decomposition revealed that endowment differences in new media use and self-rated global health largely explained the generational gap, whereas family health, despite higher endowments among older adults, exhibited larger coefficients among younger adults. Moderation analysis localized the primary barrier to intention formation rather than behavioral follow-through, and mediation analysis confirmed new media use as an actionable intervention pathway. These findings reframe the generational adoption gap as a structural phenomenon rooted in differential user capabilities, informing the design of age-targeted interventions.

ABSTRACT Employing an online experiment, this study explores how awareness of AI bias could affect users’ intention to adopt generative AI. Results showed a three-way interaction effect between bias-based engagement, bias-awareness information cue priming, and social norms on AI trust, which could further affect users’ adoption intention. That is, with high social norms of AI use, if users had not been primed that AI was biased, their trust in AI decreased after experiencing AI bias on their own. However, if they had been primed that AI was biased, their own experience with AI bias did not significantly affect trust in AI.

Artificial intelligence (AI) has demonstrated strong potential in breast cancer diagnostics by improving accuracy, efficiency, and clinical workflow. However, adoption among physicians remains variable. Existing research often overlooks the contextual and experiential differences between clinicians who use AI and those who do not. A comprehensive understanding of barriers and facilitators, especially across user groups, is essential to inform equitable and effective AI implementation in real-world settings. This study aimed to (1) identify key barriers and facilitators influencing the use of AI tools in breast cancer diagnostics, with a specific focus on comparing current users and nonusers, and (2) examine how social, technological, and individual-level factors are linked to physicians' attitudes toward AI, intention to use it, and perceived likelihood of future adoption. A cross-sectional, embedded mixed methods survey was conducted with 46 Austrian physicians. Quantitative items were based on the technology acceptance model and its extensions. Open-ended responses were analyzed using conventional content analysis and integrated with quantitative results via joint displays. Ordinary least squares regressions examined factors associated with attitudes, intention, and the likelihood of future AI use. Among the 46 participating physicians, 52% (n=24) reported current AI use. Common facilitators included improved quality of work, efficiency, and expanding knowledge. Nonusers highlighted barriers such as limited access (17/21, 81%), high costs, and lack of training. AI users highlighted barriers related to limited integration with existing systems and concerns about trust. Despite these differences, both groups expressed strong future adoption intentions. Perceiving multiple facilitators was significantly associated with more favorable attitudes (B=0.83; P=.02), stronger intention to use AI (B=1.32; P=.01), and higher perceived likelihood of future use (B=1.56; P=.001). AI-related skills positively predicted intention (B=1.00; P=.04) and likelihood of future use (B=1.16; P=.01), while colleagues' positive views about AI predicted both attitudes (B=0.34; P=.02) and intention (B=0.39; P=.01). In contrast, perceiving multiple barriers was associated with lower intention (B=-0.84; P=.047) and likelihood (B=-1.48; P<.001). Being aged 50 or older was significantly associated with more negative attitudes (B=-1.11; P=.002) and lower likelihood of future use (B=-0.82; P=.02). This study offers preliminary insights into the implementation of AI in breast cancer diagnostics within the Austrian health care context. AI adoption appears to be a staged process with evolving support needs. Early-stage users may benefit from improved access and training, while experienced users require support for workflow integration and trust-building. Promoting peer support, addressing demographic disparities, and embedding AI training into clinical routines may support more sustainable and equitable adoption. These findings inform tailored implementation strategies and offer recommendations that may be transferable to other health systems.

Psychological predictors of financial technology adoption: The role of trust, attitude, and demographics in AI based financial ChatBots use.

As explainable AI gains prominence, the question of how its communication design influences user behavior in online health platforms remains underexplored. This study introduces the Human–AI Communication Pathway Optimization Model (HACPOM). This model integrates AI design nudges to explain how transparency of thinking (opaque vs. transparent), source referencing (no source vs. journal), and response latency (fast vs. slow) can influence adoption intention. In a 2 × 2 × 2 between-subjects experiment (N = 723), transparency of thinking and source referencing significantly increased users’ adoption intention. This outcome thus validated the direct route. Response latency showed no direct effect. However, it interacted with transparency, with the strongest advantage occurring under fast response conditions. Structural equation modeling supported HACPOM’s theorized indirect pathway. AI design nudges shaped the perceived risks and benefits, which influenced trust and thereby also influenced adoption intention. A supplementary qualitative interview with Shenzhen residents further corroborated the underlying mechanisms of the HACPOM framework. The implications for designing trustworthy AI health consultations are also discussed herein.

Conventional wisdom—and much of the trust literature—assumes that consumers prize benevolence in human experts but competence in AI. We put this stereotype to a stringent test through two 2 × 2 between‑subjects experiments (Study 1: N = 328; Study 2: N = 329) that manipulated benevolence (high vs. low) and competence (high vs. low) in health advertisements featuring either a human or an AI nutritionist. PROCESS Models 4 and 7 evaluated a dual‑mediation pathway in which perceived health benefits and trust linked experimental cues to adoption (purchase) intention. Results overturned the stereotype. Across all cue combinations, ads featuring human nutritionists outperformed their AI counterparts on perceived benefits, trust, and adoption intention. The human advantage widened under low‑benevolence or low‑competence conditions and narrowed—yet persisted—when both cues were high. Mediation analyses confirmed that source effects operated sequentially through perceived benefits and trust. These findings expose the limits of competence signaling for overcoming algorithmic aversion in healthcare contexts. To approach parity with human advisers, AI nutritionists must demonstrate not only technical skill but also convincing cues of benevolence, suggesting new design imperatives for AI‑enabled health communication.

Although multidomain interventions, which simultaneously address several risk factors are an evidence-based strategy for dementia prevention, their implementation status and factors driving their adoption by local governments remain unclear. This implementation gap is critical, as limited uptake on dementia prevention programs may restrict opportunities to reduce dementia risk at the population level. This study aimed to clarify the landscape of dementia prevention activities across Japanese municipalities and identify factors associated with the intention to implement comprehensive multidomain programs. A nationwide cross-sectional survey was conducted, targeting all 1,741 municipalities in Japan. A structured questionnaire was developed and used to assess the implementation of 12 World Health Organization (WHO)-recommended prevention items as well as the municipalities' intention to adopt a multidomain program. A multinomial logistic regression analysis was performed to identify factors associated with a high adoption intention (Active Group) compared to a clear non-adoption intention (Non-Adoption Group), adjusting for municipal characteristics. Of 941 included municipalities (54.0% response rate), current prevention efforts were predominantly focused on cognitive, social, and physical activities, with less emphasis on cardiovascular risk management. The strongest predictor of adoption intention was the implementation of measures to improve residents' health literacy (relative risk ratio [RRR] = 2.8, 95% confidence interval [CI]: 1.2-6.5). Other significant factors included prior experience with multidomain programs (RRR = 1.4, 95% CI: 1.2-1.7), awareness of the WHO guidelines (RRR = 1.4, 95% CI: 1.0-2.0), and the number of currently implemented items (RRR = 1.2, 95% CI: 1.1-1.3). Structural factors such as staffing levels were not significant. The adoption of evidence-based dementia prevention programs by the Japanese municipalities appears to depend more on programmatic readiness and a commitment to public health communication than on structural capacity alone. These findings highlight the importance of strengthening readiness and health literacy to support municipalities in translating evidence-based dementia prevention strategies into practice and advancing population-level risk reduction.

The micromobility mindset: Socio-technical drivers of bike share scheme adoption in the UK

Incentives or dedicated bike lanes: what drives non-users’ intentions for e-bike adoption?