Background and Objective Employee cybersecurity compliance is shaped by the cognitive, motivational, and social conditions under which security requirements are performed. This study examines cross‐sectional associations of risk perception, social influence, psychological reactance, self‐efficacy, and cognitive load with compliance. Methods A sequential mixed‐methods design was used. Phase 1A comprised a complete‐case survey of 847 working professionals in Yemen and Saudi Arabia using 29 seven‐point Likert items; CFA and latent‐variable SEM were re‐estimated from the anonymized dataset. Phase 1B comprised 42 semistructured interviews analyzed using thematic analysis. Results The CFA showed close fit, χ 2 (362) = 363.11, CFI = 0.9999, TLI = 0.9999, and RMSEA = 0.0019. Reliability ranged from α = 0.877 to 0.923, and the maximum HTMT ratio was 0.430. In the latent SEM, social influence was positively associated with compliance ( β = 0.609, p < 0.001), risk perception was negatively associated ( β = −0.671, p < 0.001), and psychological reactance had a small positive adjusted association ( β = 0.102, p = 0.016). Self‐efficacy and cognitive load were not significant, and the observed‐composite SE × CL interaction was small with a bootstrap 95% CI that included zero. Conclusions The findings support a cautious, human‐centered interpretation of cybersecurity compliance. Social context and perceived risk showed the clearest adjusted associations, while the proposed self‐efficacy and cognitive‐load pathways were not confirmed. Longitudinal designs, objective behavioral measures, and direct indicators of security‐interface burden are needed before causal direction can be claimed. Security‐interface burden and security‐workflow design are likewise implicated in the observed patterns and warrant direct measurement.
Research Background Evaluating user satisfaction is essential for understanding interaction with wearable technology in healthcare, sports science, and research settings. Aims and Objective This study aimed to adapt and evaluate the psychometric properties of an Arabic version of the User Satisfaction Evaluation Questionnaire (USEQ) for use with wearables providing a reliable instrument for assessing user satisfaction with wearable technologies. Research Methods We employed a cross‐cultural adaptation approach that included two phases: translation and validation. The translation phase involved forward and backward translation, expert committee reconciliation, and user comprehension testing. Psychometric evaluation was conducted with 127 participants to examine content and face validity, internal consistency (Cronbach’s α and McDonald’s ω ), and factorial validity via exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Results The translated USEQ demonstrated acceptable validity and reliability. The content validity indices ranged 0.8–1.0 with (S‐CVI = 0.88), and face validity indices ranged 0.93–1.0 with (S‐FVI = 0.96). The reliability was acceptable with Cronbach’s α = 0.79 and McDonald’s ω = 0.855. EFA (KMO = 0.834, significant Bartlett’s test) revealed high factor loadings (0.771–0.891) explaining 63.18% variance. CFA further supported the model fit, with CFI (0.986) and TLI (0.977) exceeding conventional benchmarks for good fit, and RMSEA (0.063) and SRMR (0.058) within acceptable ranges. Conclusion The Arabic USEQ demonstrates preliminary validity and reliability for measuring user satisfaction in this context. The instrument provides a usable tool for researchers evaluating wearable user experiences in Arabic‐speaking populations.
As Artificial Intelligence (AI) becomes more common in everyday living, there is an increasing demand for intelligent systems that are both performant and understandable. Explainable AI (XAI) systems aim to provide comprehensible explanations of decisions and predictions. At present, however, evaluation processes are rather technical and not sufficiently focused on the needs of human users. Consequently, evaluation studies involving humans can serve as a valuable guide for conducting user studies. This paper presents a comprehensive review of 65 user studies evaluating XAI systems across different domains and application contexts. As a guideline for XAI developers, we provide a holistic overview of the properties of XAI systems and evaluation metrics focused on users (human centered). We propose goals for the human-centered design (design goals) of XAI systems for different user groups (AI novices and data experts). The first part of our results includes the analysis of XAI system characteristics. Importantly, we distinguish between the core system and the XAI explanation, which together form the whole system. Further results include the distinction of evaluation metrics into affection, cognition, usability, interpretability, and explanation metrics. Furthermore, the users, along with their specific characteristics and behavior, can be assessed. For AI novices, the relevant extended design goals include responsibility, acceptance, and user experience. For data experts, the focus is performance oriented, including human-AI collaboration and system task performance. Future literature trends include the design of context-aware and inclusive systems, adapting to users and the situational context. Several limitations in the reviewed literature were identified: a lack of validation, standardization, and documentation; evaluation is focused on particular metrics, rather than holistic; evaluation of explanatory and transparency components of the system is often neglected in user studies. These research gaps could be addressed by integrating existing XAI evaluation frameworks and developing validated questionnaires.
The rapid proliferation of wearable health technologies for older adults has not been matched by sustained adoption. Whilst existing literature predominantly focuses on theoretical acceptance models, it provides limited empirical guidance for practitioners seeking to improve usability. This systematic review analyses the usability and acceptance of wearable health devices amongst older adults aged 65 and older to identify usability challenges. Adhering to PRISMA protocols, we systematically searched Web of Science, CINAHL Complete, PubMed Central, Scopus, JMIR, IEEE Xplore, MDPI, SpringerLink, Taylor & Francis, and the ACM Digital Library for studies published between 2014 and 2025 evaluating the usability and/or acceptance of consumer wearable health technologies. Data extraction focused on usability challenges, acceptance frameworks, device ergonomics, and ecosystem integration. Of the 446 records identified, 11 studies involving 727 participants met the inclusion criteria. Key findings revealed predominant reliance on psychological models over engineering-focused usability research. Significant gaps emerged in physical design studies despite their influence on long-term use. Unstandardised training approaches limited scalability, and fragmented methodologies impeded evidence synthesis. Future research requires standardised evaluation frameworks, materials-focused usability studies, and longitudinal analyses. We propose an interdisciplinary research agenda bridging behavioural science and engineering design to enhance evidence-based development for geriatrics.
The emergence of Digital Human CEOs marks a significant development in the relationship between artificial intelligence, organizational authority, and corporate governance. This article develops a conceptual framework to explain how Digital Human CEOs may acquire, or fail to acquire, legitimacy as organizational leaders. Drawing on anthropomorphism theory, social presence theory, and transportation theory, the paper proposes an integrated mechanism through which human likeness, perceived presence, and narrative engagement shape stakeholder trust and acceptance. The analysis argues that legitimacy in AI-driven leadership does not depend on technical sophistication alone, but on the interaction between design realism, communicative effectiveness, transparency, and governance arrangements. Particular attention is given to the conditions under which trust may weaken, including excessive realism, limited explainability, cultural variation in authority expectations, and unresolved questions of accountability. The article also examines the governance implications of assigning executive authority to digitally embodied agents, emphasizing the need for clear oversight structures, auditable decision processes, and identifiable human responsibility. By clarifying the psychological and organizational pathways through which Digital Human CEOs may be evaluated, this paper contributes a structured basis for future empirical research while offering a governance-oriented perspective on the responsible development of AI-driven leadership.
