Search Results (187)

The existing literature highlights that artificial intelligence (AI) creates both hope and threat perceptions among managers and workers, particularly due to concerns about potential job losses and the negative effect on continued professional development. Employee trust in AI-based systems varies depending on their features and performance. Furthermore, regardless of the performance of such systems, some individuals are inherently opposed to AI, a phenomenon known as AI aversion. In this study, an Integrative AI Adoption Framework is developed, drawing upon principles from established theories, including the technology acceptance model, behavioral decision theory, and emotion-based frameworks, to assess how perceived usefulness and perceived ease of use, along with perceived threat, trust, and AI aversion, influence human resources (HR) professionals’ attitudes and behavioral intentions to use AI-based recruitment systems. In doing so, the study conceptualizes AI-based recruitment as a socio-technical system in which a technical subsystem (the system’s instrumental and AI-specific properties) and a social subsystem (the affective and trust-related responses of HR professionals) must be jointly considered to explain adoption. The model was tested using the partial least squares structural equation modeling (PLS-SEM) approach through survey-based data collected from 242 HR professionals. The study’s findings indicate that attitude plays an important role in shaping behavioral intention, and perceived usefulness is a key driver of attitude. AI aversion negatively influences attitudes, while trust has a twofold effect of reducing AI aversion and positively influencing attitude. Additionally, perceived threat significantly increases AI aversion, which is driven by concerns over job replacement and personal development. Full article

Small- and medium-sized enterprises (SMEs) operate under conditions of rapid change, competitive pressure and growing informational complexity, while their owner-managers often have limited time and cognitive bandwidth to interpret emerging strategic possibilities. Artificial intelligence (AI) is beginning to change this by extending how firms detect signals, interpret shifting environments and evaluate possible strategic responses. However, existing work in dynamic capabilities, sensemaking and microfoundations does not fully explain how AI-augmented cognitive systems shape organisational interpretive capacity, strategic adaptability and sustainable competitive positioning. This article addresses that gap by developing Strategic Edge Architecture (SEA), a sociotechnical microfoundational theory of how AI-augmented cognitive infrastructure enhances environmental sensing, prospective sensemaking, adaptive strategic response and sustainability integration in SMEs. Drawing on a multiparadigm theoretical synthesis, this article integrates insights from strategic management, organisational cognition, microfoundations, AI governance and sustainability strategy. SEA conceptualises strategic capability as an emergent property of cognitive infrastructure within which human and AI systems interact to support environmental interpretation, strategic adaptation and sustainable growth. The framework proposes a causal pathway through which AI augmentation strengthens sensing and sensemaking, with human-in-the-loop governance acting as a key moderating condition. The article concludes with formal propositions to guide future empirical research on AI-augmented organisational cognition, whilst recognising that the framework’s claims remain inferential and require empirical examination before SEA’s explanatory power can be assessed. Full article

The transition to automated vehicles (AVs) introduces complex human factors and system-level challenges across Society of Automotive Engineers (SAE) Levels 0–5, with profound implications for the long-term viability of future transport infrastructure. Drawing on a synthesis of socio-technical, cognitive, and behavioural adaptation theories, this study develops an integrated framework to analyse the evolving relationships among driving automation, human behaviour, system risks, and urban sustainability. The findings demonstrate that human-factor risks are inherently nonlinear, meaning they do not decrease proportionally as technology advances; instead, risk profiles peak significantly at intermediate automation levels (SAE 2–3) due to supervisory fatigue and delayed takeovers, introducing severe traffic flow volatility and localised micro-congestion that directly compromise the environmental efficiency of sustainable transport systems. As these risks reconfigure into institutional and digital infrastructure dependencies at higher levels (SAE 4–5), the primary constraint shifts toward network readiness. Through an analysis of real-world AV deployment case studies and a structured narrative literature review, this paper identifies critical operational discontinuities and mixed-traffic complexities that threaten urban grid resilience. This study proposes a conceptual framework that translates these cross-level socio-technical insights into actionable deployment pathways, providing policymakers with adaptive governance models, transportation planners with mixed-traffic management strategies aimed at preserving network efficiency, infrastructure agencies with physical and digital readiness criteria for long-term asset sustainability, and AV developers with human–machine interface optimisation frameworks to secure human-centric safety within sustainable smart city networks. Full article

Artificial intelligence (AI) is becoming increasingly embedded in the digital infrastructure of local government, creating new opportunities to improve public sector productivity while also influencing systemic risk and organisational resilience across interconnected public systems. As municipalities adopt AI to automate, support, and transform administrative processes, organisational performance becomes more dependent on the reliability of algorithms, the quality of data, effective governance, and coordination among public institutions. These growing interconnections create new vulnerabilities that can spread across public service networks, yet evidence on the productivity, risk, and resilience implications of AI adoption remains fragmented and dispersed across different fields of research. This study develops an integrative conceptual framework that examines the relationship between AI adoption, public sector productivity, systemic risk, and organisational resilience within interconnected sociotechnical systems. Drawing on insights from productivity economics, systems theory, and public governance, the framework positions total factor productivity (TFP) within a broader public value and risk governance perspective. Using the PRISMA 2020 methodology, the study systematically reviews 68 peer reviewed empirical studies published between 2015 and 2025, assessing productivity outcomes, methodological quality, effect sizes, and contextual factors relevant to local government and networked public administration. The findings show that productivity gains associated with AI are strongly influenced by organisational readiness, including digital maturity, workforce capabilities, governance quality, and institutional coordination. While AI has the potential to improve operational efficiency and strengthen adaptive capacity, inadequate readiness can increase systemic risks arising from algorithmic opacity, cybersecurity challenges, data dependence, coordination failures, and disruptions that may spread across interconnected administrative systems. The review also highlights that resilience depends on the ability of public organisations to anticipate, absorb, adapt to, and recover from AI-related disruptions while maintaining the continuity and quality of public services. The study contributes to theory by integrating perspectives from productivity economics, public administration, and systemic risk within a sociotechnical systems framework. It contributes empirically through a comprehensive synthesis of evidence on AI and public sector productivity and methodologically through the application of transparent PRISMA 2020 review procedures. From a practical perspective, the study offers a conceptual measurement framework and policy guidance for municipal decision makers seeking to improve productivity while strengthening resilience and reducing systemic risks in increasingly interconnected public governance systems. Full article

The shift to teleworking, defined as technology-enabled work arrangements in which employees perform organizational tasks remotely outside traditional office settings, has exposed the limitations of traditional performance management systems, including the lack of direct oversight, micromanagement risks, communication barriers, and employee isolation and well-being. These systems often rely on physical presence or intrusive surveillance rather than outcome-based evaluation. This paper asks how AI-driven performance management can be designed to address the documented challenges of teleworking while safeguarding employee autonomy, fairness, and well-being. The study integrates a comprehensive literature review on AI capabilities with empirical evidence from a sequential mixed-methods study of Canadian public servants, comprising machine learning analysis of over 205,000 tweets, document analysis of federal and provincial teleworking policies, a survey of 176 public servants analyzed using logistic regression, and semi-structured interviews with Government of Canada employees. Grounded in socio-technical theory and the Theory of Planned Behavior, the findings reveal that organizational support, workplace socialization, and attitudes are stronger predictors of teleworking success than digital skills or monitoring, while isolation functions as a measurable risk factor. These empirical patterns are mapped to specific AI capabilities to produce a socio-technical framework organized around three interdependent layers: technological, organizational, and human-centered. The paper contributes an empirically grounded alternative to purely speculative treatments of AI in performance management, offering design requirements derived from what teleworkers actually experience rather than from technological possibilities alone. While the framework is analytically grounded in empirical evidence, behavioral theory, and existing AI capabilities, it has not yet undergone full technical or longitudinal organizational validation. Accordingly, it should be understood as a theoretically and empirically informed design artifact intended to guide future implementation and evaluation efforts. It is worth acknowledging that the study’s key limitations include a Canada-specific public sector sample, modest survey and interview sizes, and the exploratory nature of several proposed AI capabilities; future cross-sectoral, comparative, and longitudinal research is needed to validate and extend the framework. Full article

The transition towards circular economy models requires, alongside technological innovation and regulatory instruments, design configurations capable of making circular practices operationally legible, executable, correct, and stable over time. Within this framework, the paper investigates the relationship between smart circular economies and gamification as complementary components of socio-technical configurations oriented towards behaviour activation, action quality, and continuity of practice. The aim is to develop a design-led framework that connects design configuration, behavioural outcomes, and accountability. The study adopts a theory-driven approach based on a critical review of the literature, the construction of a case-agnostic analytical grid, and the qualitative comparison of three paper-based case studies: a gamified recycling station in Sweden, a marketplace for urban logistics, and a smart and gamified mobile application for waste recycling. The results show that the synergy between smartness and gamefulness is more robust when the target action is unambiguously defined, the quality of the action is at least partly observable, feedback is located at the point of action, and incentives reward what the system can credibly verify. The comparison identifies three recurring profiles, two cross-cutting trade-offs, and the risk of “performative circularity”. The contribution consists of an exploratory, preliminary, and controlled analytical–comparative device for examining existing cases, clarifying the added explanatory value of integrating smartness and gamefulness, and formulating hypotheses for subsequent empirical validation. Its applicability is bounded by the robustness of the available evidence, the observability of action quality, and the explicitness of accountability conditions. Full article

Ensuring that government-led large-scale online public services evolve from formal participation to substantive quality represents a key governance challenge. This study extends the Unified Theory of Acceptance and Use of Technology (UTAUT) by integrating value identity as a core value-rational driver and by taking self-reported teaching practice quality as the ultimate outcome variable. Based on a cross-sectional survey of 2226 teachers analyzed via structural equation modeling, our findings reveal a stark ‘Governance Paradox’: at the aggregate level, both Behavioral Intention (β = −0.213) and Social Influence (β = −0.098) are unexpectedly associated with lower self-reported teaching practice quality, despite Value Identity being a powerful predictor of intention (β = 0.900). We conceptualize this statistical paradox not as an anomaly, but as a diagnostic for misaligned subsystems within a complex socio-technical system. Crucially, this paradox is systematically resolved through multi-group analysis; disaggregating the sample by institutional context reveals the expected positive relationships within homogeneous subgroups. This suggests that hidden heterogeneity and contextual factors are responsible for distorting the aggregate picture. Theoretically, this research offers two contributions: it reframes statistical aggregation artifacts as a systems-diagnostic framework for governance and introduces “Motivation Fusion” as a micro-foundational mechanism to explain the institutional and psychological conditions that produce such artifacts. Practically, the study provides a micro-foundational diagnostic framework for designing more targeted and effective public service policies. Full article

Artificial intelligence (AI)-related harms are increasingly attributed to governance failures rather than to isolated technical malfunctions. This article reframes AI governance as a core managerial competence grounded in leadership authority, accountability design, and organizational communication. The study addresses a persistent gap in higher education and managerial training, namely the insufficient preparation of future leaders to govern AI-mediated decision systems responsibly. Using a structured conceptual synthesis grounded in socio-technical systems theory and the organizational governance literature, the paper identifies recurring governance failure modes, including authority drift from human decision-makers to automated systems, diffusion of accountability, governance debt accumulation, and reliance on average-case performance metrics that obscure worst-case risks. To illustrate early governance readiness, an exploratory survey of senior university students—representing early-stage managerial cohorts—was conducted, resulting in the AI Governance Readiness Composite Score (AGRCS). The findings illustrate preliminary patterns in self-assessed governance readiness among early-stage managerial cohorts, without implying statistical generalization or population-level conclusions. The study does not seek statistical generalization but uses empirical signals to support conceptual arguments. The main contribution lies in positioning leadership authority, intervention capacity, and governance-related communication as central pillars of sustainable AI governance. The article translates these governance principles into an educational agenda, proposing sustainable pedagogy practices such as authority mapping, escalation rehearsals, worst-case simulations, and governance-focused learning environments. By framing AI governance as a leadership and communication challenge rather than a narrow technical problem, the study contributes to sustainable organizational development, responsible decision-making, and long-term societal trust aligned with the United Nations Sustainable Development Goals. Full article

This article presents a contextual entropic model for understanding, explaining, and resolving cognitive biases associated with thinking under uncertainty. This article applies a unique performance evaluation of teamwork method, enabling a qualitative and quantitative assessment of symptom-based context and ensuring a clear and rational interpretation of decision making in ambiguous and risky situations. This human reliability assessment method also contributes to a deeper understanding of the iterative and complementary nature of cognition and decision making. The main idea is the quantum-like understanding of the dual image of a symptom as a wave and a bit of information in thought processes. Thus, each context alternative is identified by a unique combination of three-valued states of the socio-technical system at discrete points in time—recognized, unrecognized, and unrecognizable. Judgment is a wave-like process driven by the interfering sum of the amplitudes of symptom recognition shifts. By modeling stepwise cognitive processes by adding and subtracting symptoms or stimuli, we can estimate and compare the likelihood ratios between biased judgments. This article presents three canonical filters used to improve cognitive theories and decision-making methods—Ellsberg’s two-color, three-color, and four-color paradoxes—to demonstrate the power of the symptom-based context model. Full article

The accelerating climate crisis and resource depletion demand new architectural paradigms that move beyond linear models of production and consumption. While the concept of Intelligent Ecologies is often associated with digital and artificial intelligence systems, this study reinterprets it through the lens of Traditional Ecological Knowledge (TEK), vernacular architecture, and constraint-based innovation. Grounded in a critical reading of key references in ecological knowledge, vernacular studies, circular economy theory, and responsible innovation, the paper develops a conceptual framework tracing a trajectory from TEK to adaptive and circular design. Two architectural case studies, the ARCò kindergarten in Sant’Alessio (biological cycle) and the Parabase Elementa housing project in Basel (technical cycle), are analysed to demonstrate how natural and collective intelligence can be operationalised in contemporary practice. The findings show that circularity emerges not as an added sustainability layer but as the logical outcome of design under ecological and material constraints. The study concludes that Intelligent Ecologies should be understood as socio-ecological systems in which architecture participates in living processes through adaptive, regenerative, and temporally open strategies, thereby repositioning innovation as continuity with historically embedded forms of ecological intelligence rather than technological rupture. Full article

The rapidly aging global population is placing immense pressure on healthcare systems, which are struggling to meet the needs of older adults. Information and communication technologies (ICTs) are considered a key driver in supporting the development of age-friendly healthcare models. This scoping review aims to map and structure the multifaceted applications of ICTs in age-friendly healthcare, focusing on their design, benefits, challenges, and implementation in different contexts. We followed the PRISMA-ScR guidelines and conducted a systematic search of five major databases (Web of Science, Scopus, PubMed, ScienceDirect, and IEEE Xplore), supplemented with backward citation chaining to improve the robustness of literature identification. The results show that ICTs can help older adults by improving their access to healthcare information, enhancing their care coordination, supporting their independent living, and personalizing their health management. Key challenges include user experience issues for older adults, data privacy and security concerns, and implementation barriers related to resources and professional support. Effective implementation of ICTs requires greater emphasis on age-centered design, robust data governance, and scalable integration with existing healthcare systems. We further propose a Technology Design–Scenario Application–Effect Evaluation (TD-SA-EE) analytical framework for ICT application in age-friendly healthcare; the framework is grounded in sociotechnical systems theory to provide explanatory insights beyond descriptive classification. This research provides insights into optimizing age-friendly healthcare through ICTs and contributes to fully leveraging ICTs in building sustainable and equitable age-friendly healthcare systems. Full article

The integration of digital technologies is increasingly recognized as a critical enabler of lean practices in prefabricated construction projects. However, a systematic understanding of the underlying factors that drive this lean–digital transformation remains limited. To address the gap, this study identified 18 factors through an in-depth review of 30 papers and a follow-up questionnaire survey. The factors are divided into five dimensions, i.e., organizational, social, technological, economic and environmental, according to an extended framework of the Socio-Technical Systems (STS) and Technology–Organization–Environment (TOE). These 18 factors were then analyzed via a back propagation (BP) neural network model. The empirical data were collected from 148 practitioners across 11 regions in China where PC industrialization, digital technology adoption, and lean-related practices are relatively mature. These regions were selected because digitalization-driven lean practices are more observable in such contexts, allowing the BP model to capture the comprehensive contribution of key factors more effectively. The findings reveal that the effective implementation of the smart lean practices via digitalization is primarily driven by a systematic process, where greater attention should be directed toward simulation-based process optimization, robust information management, integrated design and construction, lean management systems, and the workers’ digital skills. Although the empirical evidence is derived from relatively mature PC and digital construction markets in China, the identified factors provide reference insights for broader PC projects including less mature regions to make effective measures to improve lean implementation. This study contributes to the existing knowledge body of lean in PC by extending the theories of STS and TOE to advance the understanding of digital drivers. Additionally, the results serve as a reference for stakeholders by informing strategic priorities such as resource allocation for workforce development, advancing the realization of smart lean prefabricated construction. Full article

Digital transformation (DT) is becoming of strategic importance for Small, Medium and Micro Enterprises (SMMEs), especially in the retail sector, where a significant portion of customer engagement, operational efficiency, and market competitiveness is shaped by digital technologies. Even though there is a growing availability of smartphones, mobile payment systems, and social media platforms, many South African retail SMMEs struggle to achieve a sustained and meaningful DT. Existing studies offer limited insights into the dynamic interactions between technological, organisational, and human agency factors that enable digital uptake over time. This study investigates the sociotechnical dynamics of DT among retail SMMEs in the Eastern and Western Cape provinces of South Africa. The research integrates Adaptive Structuration Theory (AST) with the Limits to Success Archetype (LSA) to conceptualise DT as an evolving process shaped by the interplay of technology, organisational structures (formal arrangement of roles, responsibilities, authority, and communication patterns within an organisation), and human agency. Using an exploratory qualitative research design, purposively sampled semi-structured interviews were conducted with 23 retail owners, directors and managers. The interviews were transcribed, and the data were analysed thematically using the Braun and Clarke six-step thematic analysis framework on Atlas.ti 25. Findings indicate that DT in retail SMMEs is enabled by pragmatic, tool-level digital adoption, training, education, ongoing skill development, alignment with business capacity, regulatory clarity, operational realities, addressing scams, fraud, data security, a user-friendly interface, and the availability of native language digital tools, structural interventions that reduce inequality, and DT ecosystem support. The study contributes to DT scholarship by integrating sociotechnical and systems-thinking perspectives to explain the trajectories of DT in retail SMMEs. It also provides practical insights for policymakers, support institutions, and digital ecosystem actors seeking to democratise DT in emerging-market retail contexts. Full article

Building services systems in comprehensive hospitals must support safety-critical clinical workflows within dense spatial and technical interfaces. Coordination among owners, designers, contractors, operators, and clinical users is often fragmented across planning, design, construction, and operation. This study adopts an exploratory qualitative case-study design using grounded theory coding procedures. Semi-structured interviews and field observations were conducted with 44 stakeholders involved in 10 tertiary hospitals in Shenzhen, China. Through open, axial, and selective coding, the study identifies contextual conditions, recurrent coordination breakpoints, and four lifecycle coordination mechanisms: requirement stabilization, technical integration, verification and handover, and feedback optimization. The findings show that failures in hospital building services systems are not merely technical defects. They are cumulative socio-technical failures generated by unstable clinical requirements, discontinuous responsibilities, weak knowledge translation, and delayed decisions at stage interfaces. The proposed model reframes coordination as an iterative lifecycle process and provides an analytically grounded framework for diagnosing coordination risks and organizing stakeholder responsibilities in complex hospital projects. Its effects on project outcomes require further validation through future implementation and comparative studies. Full article

A shift towards Industry 5.0 with a focus on sustainability, human centricity, and resilience requires the implementation of sustainable manufacturing (SM). Implementing SM is challenging as it requires commitment to sustainable practices at all organizational levels. Leadership is a crucial success factor but is largely neglected in the literature on SM. To address this gap, we introduce a novel approach focusing on multiple leadership dimensions. The aim is to develop effective leadership perspectives by integrating sustainable leadership (SL) theory into SM. A systematic literature review and qualitative content analysis are used to identify and analyze 79 peer-reviewed articles from the Scopus and Web of Science databases. SL serves as a frame of reference to identify relevant aspects for sustainable leaders in the scientific discourse on SM. The findings are summarized in 24 categories for integrated SM. They form a conceptual framework comprising three levels: context; systems and processes; and leadership and social aspects. Examining these aspects with interrelationships enriches the SM discourse and sheds a new, human-centered light on it. The findings can support training and knowledge transfer, thereby enabling leaders to navigate operational complexity. Furthermore, the categorization provides a foundation for developing socio-technical models and assessments for Industry 5.0 from a systemic perspective. Full article