Search Results (1,244)

Artificial intelligence (AI) is increasingly transforming recruitment and selection. This conceptual study aims to develop an integrative socio-technical framework explaining HR professionals’ behavioral intention to adopt AI-based recruitment systems. Using a theory-driven conceptual review, the study integrates the Technology Acceptance Model (TAM), selected technological and organizational dimensions of the Technology–Organization–Environment (TOE) framework, and a socio-technical systems perspective. The framework combines perceived usefulness and relative advantage; trust, transparency, data privacy concerns, job replacement anxiety, and resistance to change; and HR readiness and top management support. Trust, transparency, perceived usefulness, and HR readiness are theorized to support behavioral intention; relative advantage is proposed to strengthen perceived usefulness; data privacy concerns are proposed to weaken trust; and job replacement anxiety and resistance to change are theorized to negatively influence behavioral intention. Top management support is conceptualized as moderating the resistance–intention relationship. The framework extends additive adoption models by emphasizing reciprocal socio-technical adaptation among technological characteristics, human interpretations, organizational practices, and governance conditions. As a conceptual model, it requires empirical validation and provides a basis for future research and responsible AI implementation in recruitment. Full article

Digital technologies are increasingly used in addiction care, yet telemedicine, ecological momentary assessment, mobile applications, wearables, electronic health records, digital therapeutics, and artificial intelligence commonly remain fragmented across separate platforms and clinical workflows. This Concept Paper addresses this information–integration gap by proposing the Digital Treatment Ecosystem (DTE), a person-centered, closed-loop information architecture for precision addiction psychiatry. The framework was developed through an integrative narrative synthesis of addiction, digital-health, health-informatics, artificial-intelligence, implementation, and regulatory literature. Its originality lies not in any individual technology, but in specifying a governed information-to-action cycle in which heterogeneous longitudinal data are integrated with provenance and uncertainty, interpreted against population-level and within-person baselines, translated into explicitly owned clinician-supervised actions, and returned as outcome feedback for treatment adaptation and organizational learning. Five functional layers are proposed: multimodal data acquisition; integration and interoperability; adaptive intelligence; clinical decision support; and intervention delivery with outcome feedback. Addiction-specific requirements include dynamic craving and recurrence risk, treatment disengagement, polysubstance use, medication continuity, overdose and withdrawal risk, stigma, confidentiality, and fragmented service pathways. Six operationalized propositions define how the DTE can be prospectively tested. The DTE is therefore proposed as a falsifiable socio-technical architecture rather than an established or clinically validated treatment system. Full article

Renewable energy transition management increasingly depends on the integration of digital technologies, organizational capabilities, and innovative forms of value creation. This study examines how digital transformation, organizational learning, and Sustainable Business Model Innovation jointly support renewable energy transition management. A convergent mixed-methods design was adopted, integrating comparative quantitative analysis of secondary data with qualitative evidence derived from strategic documents and complementary evidence obtained through structured expert assessments conducted in Poland, Germany, Denmark, and Spain. The comparative evidence suggests that higher levels of digital maturity are generally associated with stronger renewable energy performance, greater innovation intensity, and more advanced forms of Sustainable Business Model Innovation. Digitally advanced energy systems appear to exhibit stronger capacities for decentralized coordination, participatory governance, and adaptive learning processes, while less mature systems rely more heavily on localized experimentation and fragmented institutional arrangements. The comparative analysis also highlights differences in renewable energy transition pathways across the analyzed European countries, emphasizing the importance of organizational and institutional conditions in shaping transition processes. The study contributes to the literature by proposing an integrated analytical framework integrating digital transformation, organizational learning, and Sustainable Business Model Innovation within renewable energy transition research. The findings underline the importance of digitally enabled coordination and learning-based adaptation for supporting resilient renewable energy transition pathways and provide practical implications for policymakers, energy companies, and other stakeholders involved in sustainability-oriented energy transitions. Full article

Despite growing regulation and mature security tooling, cyberattacks continue to rise. This study examines how information security professionals perceive the challenges of securing modern systems. More specifically, we consider increasing technological complexity, the human factor, organizational change, and resilience through Evolutionary Governance Theory (EGT) and explore whether information security governance (ISG) co-evolves with the same velocity as the systems it manages. A closed-ended, five-point Likert questionnaire was developed, which was completed by 65 information security professionals recruited through the Global Information Assurance Certification (GIAC) Advisory Board and LinkedIn between October 2024 and March 2025. Responses were analyzed using descriptive statistics and Spearman correlations. Respondents were near-unanimous that technological evolution has increased complexity and interdependence, and that resilience is essential. 41.5% of respondents consider that governance lacks the processes to detect and respond to environmental changes, identifying a gap in governance capacity. Within this sample, no item was significantly associated with tenure, experience, or organization size, though the analysis is underpowered for small effects. This study applies EGT in the domain of information security, and reports practitioner evidence consistent with an interpretation of the ISG gap as an adaptive deficit of co-evolving systems rather than as an implementation failure. Full article

Hospital routines can elicit patients’ views without providing a reliable route for acting on them. The aim of this article is to develop a positive organizational model in which patient voice provides the basis for tailoring care, while professional responsibility remains with the clinical team and healthcare organization. We conducted a critical narrative review with conceptual synthesis, drawing on a previously assembled corpus of 105 full-text publications and a targeted update completed on 10 August 2026. The analysis compared patient input, organizational responses and limits of transferability. Search documentation was incomplete, and effects were not pooled. The framework distinguishes four types of input, five request dispositions, seven organizational actions and three response domains. These are working constructs informed by literature and conceptual reasoning. The proposed process retains the patient’s wording, assigns responsibility, explains constraints and checks the result. It places recovery, function and relief of suffering at the center of organizational decisions. Implementation could use existing nursing and care-management roles or a dedicated coordinator, with protected time and clear clinical boundaries. We propose patient and professional co-design followed by a prospective multisite feasibility pilot. The pilot would examine response completeness, accessibility, workload and measurement feasibility. A subsequent comparative study would evaluate goal concordance, quality of life, adherence, satisfaction, safety and costs. The accompanying pilot specification provides candidate questions, coding procedures and progression criteria. No pilot data or evidence of the model’s effectiveness are reported. Full article

Empirical research on organizational behavior contributes significantly to the examination of workplace impacts and to a deeper understanding of the underlying drivers of employee behavior. The long-term sustainability of organizations operating in VUCA environments depends largely on flexible adaptation and effective change management. The aim of this research is to examine the associations between workplace factors related to organizational change and employees’ intention to quit and perceived job insecurity during a crisis. Special emphasis is placed on stress caused by organizational change as an appraisal-based construct that may statistically mediate the association between employees’ psychological and physical strain and intention to quit. The research was conducted on a Hungarian sample, using a questionnaire survey among employees working in an SME environment. Based on the TSM (Transactional Stress Model), JD-R (Job Demands–Resources), and COR (Conservation of Resources) frameworks, hypotheses were formulated regarding the associations among workplace strain, stress caused by organizational change, intention to quit, and job insecurity. The hypotheses were tested using mediation models estimated within a structural equation modeling (SEM) framework. The results indicate a significant positive association between psychological strain and intention to quit, together with a significant indirect effect through stress caused by organizational change, a pattern consistent with partial mediation. Physical strain was also significantly and positively associated with intention to quit, but its indirect effect through stress caused by organizational change was not statistically significant. Indifferent leadership was significantly and positively associated with job insecurity, while the hypothesized indirect effect through stress caused by organizational change was not statistically significant. Full article

Artificial intelligence (AI) is increasingly embedded in organizational decision-making, requiring employees not only to use AI-generated recommendations but also to evaluate their quality and determine when reliance is appropriate. Although established research examines behavioral reliance on algorithmic and AI advice, fewer studies have approached performance under AI advice as an individual-differences assessment problem integrating psychometric structure, cognitive correlates, process indicators, and criterion-related evidence. This study developed and initially validated a CHC-informed, performance-based assessment of cognitive performance under AI advice using 24 organizational decision scenarios. The assessment was designed around three closely related content/performance dimensions—AI error detection, evidence integration, and cognitive control and adaptive reliance—while also capturing confidence, response time, and reliance behavior. The validation sample comprised 780 employed adults in Türkiye. Psychometric analyses included confirmatory factor analysis, multidimensional item response theory, response-time analyses, scenario-level logistic regression, measurement invariance, differential item functioning, and internal cross-validation. Results indicated a dominant general cognitive-performance component together with additional structure corresponding to the three theoretically specified dimensions. Assessment performance was positively associated with established cognitive measures, including ICAR-16 reasoning performance, working memory, processing speed, and attentional control, whereas associations with AI-related self-reports were generally weaker. Dimension-aligned analyses supported the expected associations of ICAR-16 with AI error detection and working memory with evidence integration. Performance was also moderately associated with concurrently assessed organizational decision quality (r = 0.408) and explained additional variance in this criterion beyond demographic and work characteristics, AI experience, conventional cognitive-performance measures, and AI-related self-reports (ΔR² = 0.103, p < .001). In contrast, several hypothesized scenario-specific associations involving AI confidence, time pressure, interruptions, and resistance to confidently inaccurate advice were not supported. Overall, the findings provide initial evidence for a performance-based approach to assessing how employees evaluate and respond to AI advice, while indicating that the proposed scenario-specific mechanisms and group-comparability findings require further replication before consequential applications are considered. Full article

Enhancing industrial chain resilience is important for maintaining stable production and supporting sustainable industrial development amid increasing economic uncertainty. Using Chinese A-share listed Specialized, Refined, Differential, and Innovative (SRDI) enterprises from 2012 to 2024 as the research sample, this study examines the impact of digital transformation on industrial chain resilience and its underlying mechanisms. The results reveal a significant N-shaped nonlinear relationship between digital transformation and industrial chain resilience. This finding remains robust after alternative variable measurements, sample adjustments, and tests addressing potential endogeneity. Mechanism analysis shows that the perception of economic policy uncertainty and total factor productivity constitute important channels through which digital transformation affects industrial chain resilience. Heterogeneity analysis further indicates that the nonlinear effect is more pronounced among SRDI “Little Giant” enterprises, firms in high-tech industries, and firms located in eastern China. The results suggest that the resilience effects of digital transformation vary with the level of digitalization and are shaped by the accumulation of firm capabilities and organizational adaptation. By examining SRDI enterprises, this study extends research on the relationship between digital transformation and industrial chain resilience and provides empirical evidence for differentiated digital transformation strategies and for strengthening industrial chain resilience as an important foundation for sustainable industrial development. Full article

Remote symptom monitoring using electronic patient-reported outcomes reduces symptom burden and acute care use in oncology, but most programs have been established in academic centers or funded trials using purpose-built software. We examined how the REmote Symptom and Patient mONitoring System (RESPONSe) was implemented and subsequently funded as a recurrent clinical service at a Canadian community cancer clinic using a configured commercial platform, patients’ own devices, and no formal institutional mandate. We conducted a qualitative post-implementation process evaluation at Richmond Cancer Clinic, British Columbia, reported following the Consolidated Criteria for Reporting Qualitative Research. Eight staff, purposively sampled by role and phase of involvement, were invited and participated, which consisted of one oncologist, three nurses, two virtual health staff, and two administrators. Interviews were analyzed using deductive–inductive thematic analysis. Three themes were identified: tailoring technology for clinical fit; designing care around patient and clinician users, centered on a dedicated symptom management nurse; and managing up towards operationalization. Operationalization was achievable using a customizable commercial platform, but depended on the interaction of iterative technology adaptation, dedicated clinical ownership, evolving care workflows, and sustained organizational advocacy. We propose five transferable implementation strategies mapped to established implementation frameworks. Full article

As a path to promote undergraduate student success in Science, Technology, Engineering, and Mathematics (STEM), this paper introduces the Conceptual Organizational Framework for Expanding Excellence (COFFEE), which is informed by the longitudinal adaptation of the Meyerhoff Scholars Program at a large, research-intensive historically white institution. Drawing on over a decade of program evaluation and academic research, we articulate how foundational cultural ethos—shared values, STEM community, and affirming multidimensional identity environments—are transversally integrated across the structural, agentic, and academic architecture of institutional practice. The framework advances the literature by positioning core organizational conditions for STEM persistence and excellence. This work offers a theoretically grounded organizational model informed by organizational theory and prior empirical work documenting the ongoing success of a STEM program to guide research, practice, and systemic reform in STEM education. Full article

Micro and small enterprises (MSEs) often operate with limited financial slack, making the quality of financial judgment potentially important for their ability to withstand disruption. This study examines whether a composite of financial decision-making biases is associated with organizational resilience among formal MSEs in Ancash, Peru. Survey data from 384 owners, managers, and administrators were analyzed using PLS-SEM. The formative financial-bias composite was strongly and negatively associated with organizational resilience (β = −0.615, p < 0.001), accounting for 37.8% of its variance. Consistent negative associations were also observed for anticipation, coping, and adaptation. Complementary predictive and specification-sensitivity analyses were consistent with the main finding. The study contributes by connecting behavioral finance with organizational resilience and by identifying biased financial judgment as a behavioral factor associated with MSE resilience. For managers and MSE-support programs, the findings suggest that access to financial resources may need to be complemented by attention to decision quality. Because the study is cross-sectional and based on a non-probability sample of formal MSEs in Ancash, the findings do not establish causality or population-level representativeness and should not be generalized to informal enterprises, medium-sized or larger firms, other Peruvian regions, Latin America, or emerging economies without further replication. Sustainability performance was not directly measured. Full article

AI-enabled digital transformation can create governance gaps when model-level controls do not capture deployment-specific organizational relationships and enterprise-wide policies remain too general for materially different use cases. This article addresses that problem through a design-theoretic synthesis of Enterprise Systems Engineering, socio-technical governance, modularity theory, accountability scholarship, and organizational law, treating the AI use case as an intermediate enterprise-system boundary. It develops modular legal personhood as one possible legal-organizational architecture for structuring human and enterprise responsibility around that boundary; the concept does not attribute legal personality or autonomous responsibility to AI. The framework specifies connected governance functions, staged implementation, and assessment mechanisms linking boundary, configuration, authority, resources, evidence, corrective action, lifecycle adaptation, and enterprise-level coordination. The analysis indicates that legal differentiation is neither necessary nor sufficient for effective governance: its potential value arises when existing mechanisms leave these functions fragmented, while alternative legal, contractual, or internal arrangements may be preferable when they provide equivalent capabilities with lower burden. The framework remains a design-theoretic and empirically testable proposition; the hypothetical application is illustrative rather than validating, and practical feasibility, reliability, and comparative effectiveness require future organizational pilots and comparative research. Full article

Background/Objective: Clinical leadership is essential for improving care quality and patient safety in complex orthopedic emergency settings. This study examined perceptions of clinical leadership among Orthopedic and Trauma Clinical Nurse Specialists (OTCNSs) at a major regional trauma center in Romania. The objective was to apply and contextually adapt a previously developed methodological approach to identify candidate components for a clinical leadership development intervention tailored to the specific clinical demands of orthopedic trauma care and the Romanian legislative environment. Methods: A qualitative approach was utilized, systematically applying and contextually adapting the two-phase methodological architecture developed by Palermo et al. Semi-structured interviews were conducted with seven OTCNSs and analyzed using hybrid deductive–inductive thematic analysis. Following this, a consensus meeting was convened with nine OTCNSs, one Head Nurse, and one Clinical Researcher to co-construct a locally tailored intervention prototype. Results: Five overarching themes emerged from the interviews: individual attributes and professional capabilities, interprofessional collaboration and relational dynamics, team frameworks and organizational culture, role-based leadership and functional autonomy, and patient-centered trajectories. Based on these findings and stakeholder consensus, a four-component intervention prototype was co-constructed: (1) clinical orientation pathway and strategic onboarding, (2) core competency curriculum for advanced orthopedic nursing, (3) point-of-care mentorship and reflective clinical support, and (4) collaborative peer circles and case-based reflection. Conclusions: This study identified perceived leadership-development needs among OTCNSs and generated a preliminary, contextually tailored intervention prototype. The findings suggest that clinical leadership is shaped by a dynamic interplay of personal, relational, institutional, and patient-centered variables. The candidate intervention components require feasibility testing, refinement, and evaluation before they can be recommended for wider implementation. Full article

Maritime Education and Training (MET) is undergoing rapid digital transformation, yet adoption of adaptive learning technologies (ALT) remains uneven, constrained by the Standards of Training, Certification and Watchkeeping (STCW) regime and by human and organizational barriers. A persistent question in the technology-acceptance literature is how individual acceptance becomes organizational readiness in safety-critical, regulated settings. This study models the translation from educator attitudes to perceived institutional readiness through the mediating role of perceived usefulness. Partial least squares structural equation modeling was applied to survey data from 104 maritime educators. The model explained 42.8% of the variance in perceived institutional readiness and 34.8% in perceived usefulness. Educator attitudes significantly predicted perceived institutional readiness (β = 0.298), and the relationship was partially mediated by perceived usefulness (indirect β = 0.202, 95% CI 0.12 to 0.35): educators must first be convinced of an ALT’s practical value before judging their institution ready to adopt it. A hypothesized moderation by professional experience was not supported and is reported as exploratory. Because predictor and outcome were self-reported by the same respondents, the outcome is interpreted as perceived readiness rather than a verified organizational state. The study contributes a validated model of the individual-to-organizational translation mechanism in MET and guidance for MET leaders integrating ALT under STCW. Full article

Industrial production and logistics dashboards increasingly aggregate real-time data but remain monitoring tools with limited support for action prioritization, explainable recommendations, adaptive response, and organizational learning. This study proposes the Intelligent Acuity-Oriented Operations Dashboard (IAOOD), a conceptual, design-oriented framework that repositions operational dashboards toward active decision support. An exploratory user-preference study (10 interviews followed by a questionnaire with 41 professionals) provides preliminary evidence of the perceived relevance of the advanced capabilities to be added to the framework. Another evidence of relevance is a feature-availability comparison against selected industrial and academic dashboard approaches. This comparison is intended to assess conceptual coverage of key dashboard capabilities, not to demonstrate real-world performance improvement or operational superiority. The leading scientific contribution is a coherent integration of several recent key capabilities into a single operational design logic. These capabilities are: (1) closed-loop prediction–response–learning, (2) adaptive interfaces, (3) unified hard and soft metrics, (4) explainable AI, and (5) acuity classification. The second contribution is the empirical assessment of dashboard user preferences and the acuity dashboard hierarchy and its four-level acuity classification (urgent, acute, warning, weak link). A classification that organizes events by severity, urgency, confidence, impact, and transparent justification, enabling prioritized and governance-aligned interventions. Methodologically, IAOOD is developed through a structured conceptual framework-development process and illustrated via dashboard mock-ups of the proposed hierarchical structure. The study contributes architectural elements, evaluation dimensions, and six testable propositions linking framework mechanisms to expected outcomes (response timeliness, decision trust, recurrence reduction, strategic alignment, cognitive effectiveness, situational awareness). IAOOD is positioned as an integrative benchmark whose scientific value depends on subsequent empirical testing through industrial pilots, controlled user studies, and longitudinal KPI analysis. Full article