Purpose This paper investigates how employee voice, well-being, and resistance to change influence the journey of digital maturity in smart factories. Whereas existing models have stressed infrastructure and a system-based approach, this research explores a socio-technical perspective in bringing forward human dynamics necessary toward Industry 4.0 transformation. Design/methodology/approach The Employee-Centric Digital Maturity Framework (EC-DMF) has been proposed and tested to integrate the voice of employees, their wellbeing, and resistance to digital maturity structures. Measurement items were adapted from prior studies. Data from 388 employees in 47 smart factories in Bangladesh were collected through a structured survey. The proposed model was tested through Partial Least Squares Structural Equation Modelling (PLS-SEM) with SmartPLS 4.0. Findings The findings show that, in fact, employee voice, well-being, and resistance to change are significant drivers of a smart factory's digital maturity. The study confirms that employee voice enhances both well-being and digital maturity, while well-being positively drives digital maturity and mediates the effects of voice and resistance. In contrast, resistance to change negatively impacts both well-being and digital maturity. Such findings emphasise not only how these factors interplay to impact digital transformation but also leverage the socio-technical evolution of smart factories. Originality/value This research validates the EC-DMF as a socio-technical framework, extending digital maturity models through psychosocial dimensions. It advances theory by positioning employee voice as a structural enabler, highlighting well-being as both predictor and mediator, and reframing resistance as a barrier and diagnostic signal. Practically, it shows that inclusive communication, well-being support, and constructive management of resistance are essential for accelerating digital maturity in smart factories.

The rapid acceleration of modern industry driven by automation and AI requires a systematic approach to aligning workers, technology, and organizations, a role addressed by macro ergonomics. This study maps the intellectual structure and evolution of macro ergonomics in modern industrial contexts through a hybrid systematic literature review and bibliometric analysis of 233 Scopus-indexed documents. Using R-based bibliometric methods, key trends, collaboration networks, and thematic clusters were identified. The findings show that macro ergonomics has evolved into a critical sociotechnical framework for Industry 4.0/5.0, supporting organizational resilience, safe human–AI integration, and the reduction of systemic risks in automated environments. Three dominant research themes emerge: digital integration, organizational optimization, and human-centered design, alongside notable gaps in global collaboration and SME applications. Overall, this study consolidates the scientific landscape of macro ergonomics and provides guidance for future research and practical implementation of holistic, adaptive, and human-centric work systems.

Purpose This study examines how the integration of artificial intelligence (AI) into journalism shapes public perceptions of news credibility. It examines differences in credibility toward news authored by AI, human journalists and human–AI collaborations. It also investigates whether AI-related public discussion serves as a mediating mechanism and social trust as a moderating factor. Design/methodology/approach Drawing on communicative and social capital theories, the study tests a conceptual model linking AI-related news consumption, public discussion and social trust to perceived news credibility across three authorship types. Using data from a nationally representative US panel (N = 1,252), structural equation modeling was conducted to assess both direct and moderated relationships. Findings News co-produced by AI and human journalists is perceived as more credible than AI-generated news and comparably credible to human-written news. AI-related public discussion serves as a key mechanism that enhances the perceived credibility of AI-generated content. Crucially, social trust moderates these relationships: at higher levels of trust, the positive association between discussion and credibility is stronger across all authorship types, especially for AI-only and collaborative news. Originality/value Using data collected in April–May 2022, prior to the public release of ChatGPT, this study offers a pre-generative-AI baseline for understanding audience responses to AI-generated news. It advances a socio-technical framework that integrates public discussion (communication) and social trust (social capital) to explain credibility perceptions across AI-, human- and human–AI-authored news. The findings highlight the roles of human–AI collaboration and community-level trust in sustaining credible and socially accepted uses of AI in journalism.

Purpose Prior e-HRM research mainly studies knowledge workers in developed economies, overlooking frontline employees who constitute the majority workforce in labor-intensive sectors. This study addresses three gaps: (1) insufficient examination of social-technical subsystem interplay in resource-constrained retail, (2) absence of socio-technical frameworks for frontline workers in emerging markets and (3) limited understanding of e-HRM effectiveness for low-skilled, mobile-dependent employees with minimal digital literacy. Design/methodology/approach Comparative case studies of two Indian hypermarket chains employing grounded theory methodology. Unlike survey-based e-HRM research, this processual approach reveals micro-level socio-technical realignments during execution through expert interviews. Findings E-HRM effectiveness depends on four interconnected domains: workforce planning, employee lifecycle stages, process improvements and performance management. Critically, mobile-first design emerges as essential, social ecosystems fundamentally shape technology acceptance beyond TAM predictions and cost-efficiency trade-offs manifest differently in the context of emerging markets. Practical implications Practicing managers can learn the integral considerations to be kept in mind when developing the vision for e-HRM in their organizations. These include the social ecosystem, ease of resources, the trade-off between cost and efficiency, and the employee lifecycle touchpoints, among others. The socio-technical systems influence the final outcome of e-HRM implementation, which is the job redesign. Originality/value Three theoretical contributions include the development of a contextualized socio-technical framework for frontline employees, the demonstration that social affordances are more consequential than technical sophistication for frontline performance, and the extension of socio-technical theory by explicating how resource constraints necessitate “frugal innovation” approaches, harmonizing minimal technical features with maximal social support.

Conventional research on digital business development offers a limited view, overwhelmingly concerned with the isolated effects of individual variables while overlooking their synergistic relationships. This study challenges this reductive perspective by applying fuzzy set Qualitative Comparative Analysis (fsQCA) to Chinese city-level data. We specifically investigate how elements from the socio-technical framework interact synergistically to shape the urban digital business ecosystem. The results demonstrate that no single factor is sufficient as a determinant. Instead, we observe equifinality, meaning multiple distinct configurations can lead to equally high performance. Furthermore, the causal configurations for failure are not mirror images of those for success but instead exhibit a distinctive pattern. The influence of government size exemplifies this asymmetry. For policymakers, the implication is that effective strategies for urban digital business must be holistic and context sensitive, moving beyond universal prescriptions.

Purpose This study aims to propose a conceptual framework for metaverse tourism from a sociotechnical perspective, highlighting the interactions between metaverse technological capabilities and travelers’ behaviors to enhance the understanding of tourism experiences in the metaverse, and suggesting research opportunities to further advance the discourse on metaverse tourism. Design/methodology/approach A framework for metaverse tourism is developed by applying a sociotechnical perspective to the contexts of hospitality and tourism. This framework is based on a review of the literature on the metaverse in the fields of hospitality and tourism, marketing and information systems. Findings Existing frameworks on metaverse tourism have emphasized experiential dimensions and motivations, often overlooking the technological factors that shape user decisions. The proposed framework identifies the key technological capabilities and digital infrastructure in metaverse tourism that influence travelers’ behaviors. It also highlights the interconnections between the virtual and physical worlds via smart tourism layers, shaping immersive and absorptive involvement. Research limitations/implications This study identifies new research opportunities and highlights potential applications of metaverse tourism in the field of hospitality and tourism. Originality/value This study introduces a novel sociotechnical framework for understanding metaverse tourism, offering insights into how tourism experiences emerge from the interplay between technological capabilities and traveler behaviors.

This study develops an optimized change-management framework to mitigate Manufacturing Execution System (MES) 3.0 implementation failure by integrating quantitative and qualitative findings from the ADKAR model through a socio-technical lens. Using explanatory sequential mixed-methods design, quantitative data from 248 respondents were analyzed via multiple and moderated regression, followed by in-depth interviews with 10 key stakeholders analyzed in NVivo. The phases were triangulated using a socio-technical framework to identify systemic patterns. Quantitatively, employee resistance significantly moderated the relationship between ADKAR components and implementation failure (β = 0.657, p = 0.040), with awareness (β = −0.778, p = 0.000) and knowledge (β = −0.168, p = 0.012) showing significant negative effects. Qualitative findings revealed five major themes: multidimensional resistance (active 15%, passive 35%, concealed 20%, neutral 30%), ADKAR implementation gaps, systemic contextual factors, and mitigation strategies. Triangulation exposed hierarchical cultural barriers, digital-literacy gaps, and insufficient reinforcement mechanisms. We propose an integrated hexagonal socio-technical model with six components and 24 sub-elements: Goals (4), People (4), Infrastructure (4), Technology (4), Culture (4), and Processes (4) for sustainable MES 3.0 implementation. This study contributes empirical evidence of resistance as a moderator and provides actionable guidance for digital transformation in manufacturing organizations.

Purpose Smart manufacturing promises operational efficiency and enhanced sustainability, yet SMEs frequently struggle to translate digital investments into tangible environmental outcomes. This study examines how organizational alignment, emergent socio-technical mechanisms and contextual factors influence the effectiveness of digital technologies in driving sustainability, with a focus on high-impact manufacturing sectors. Design/methodology/approach A qualitative multi-case research design was employed, drawing on in-depth semi-structured interviews with senior representatives from 16 manufacturing firms across diverse sectors and regions. Data were analyzed using thematic analysis in ATLAS.ti. Findings Digital technologies consistently improve operational efficiency, but their environmental impact is contingent on organizational alignment and socio-technical configuration. Governance maturity, digital accessibility, and sustainable agility emerged as key mediators, explaining why similar technologies produce divergent outcomes across firms. Sectoral and contextual differences further shape implementation trajectories, revealing that SMEs can achieve measurable sustainability gains through pilot-based, adaptive approaches even under resource constraints. The study highlights that sustainability outcomes are not an automatic by-product of technology adoption but require coordinated alignment of strategy, systems, culture and operational practices. Originality/value The research advances theory by extending the McKinsey 7S model into sustainability-driven digital contexts and integrating Lean and Agile practices into the concept of sustainable agility. It provides a novel socio-technical framework that explains how SMEs can convert digital adoption into measurable environmental outcomes, addressing gaps in literature dominated by analyses of large firms. By combining organizational, operational and contextual perspectives, the study offers both conceptual clarity and guidance for embedding sustainability into digital transformation initiatives in resource-constrained, high-impact manufacturing sectors.

This study presents a sociotechnical framework for mitigating ethnic violent conflicts by integrating information and communication technologies (ICTs) with community-based social capital. Drawing on longitudinal case studies from three conflict-prone regions in Kenya, Mt. Elgon, Muhoroni, and the Turkana–West Pokot borderlands, the research examines how ICT-enabled peace networks, particularly the Early Warning and Early Response System (EWERS), mobilize bonding, bridging, and linking social capital to reduce violence. The study employs a multi-phase qualitative design, combining retrospective analysis, key informant interviews, focus group discussions, action participation, and thematic coding of EWERS data collected between 2009 and 2021. This approach enabled the reconstruction of system evolution, stakeholder dynamics, and community responses across diverse socio-political contexts. Findings demonstrate that embedding ICTs within trusted social structures fosters inter-ethnic collaboration, inclusive decision-making, and trust-building. EWERS facilitated confidential reporting, timely alerts, and coordinated interventions, leading to reductions in livestock theft, improved leadership accountability, emergence of inter-ethnic business networks, and enhanced visibility and response to gender-based violence. The system’s effectiveness was amplified by faith-based legitimacy, local governance integration, and adaptive training strategies. The study argues that ICTs can become effective enablers of peace when sensitively contextualized within local norms, relationships, and community trust. Operationalizing social capital through digital infrastructure strengthens community resilience and supports inclusive, sustainale peacebuilding. These insights offer a scalable model for ICT-integrated violence mitigation in low- and middle-income countries. This is among the first studies to operationalize bonding, bridging, and linking social capital within ICT-enabled peace networks in rural African contexts. By embedding digital infrastructure into trusted community relationships, the framework offers an analytical approach that can inform inclusive violence mitigation strategies across low- and middle-income settings. While the framework demonstrates potential for scalability, its outcomes depend on contextual adaptation and cannot be assumed to replicate uniformly across all environments.

Community-based organisations increasingly rely on digital systems to support membership coordination, activity tracking, and capability-building programs. However, small volunteer-led associations often lack platforms that reflect their community values, participatory practices, and user-centered needs. This paper proposes a conceptual data-integration architecture for the Trefoil Guild Melaka, guided by contemporary perspectives in Community Informatics, Participatory Information Systems, and User-Centered Design. Adopting a conceptual modeling approach rather than empirical evaluation, the paper synthesises socio-technical frameworks to outline an integrated platform comprising membership, activity-tracking, and capability-building modules. The contribution is both theoretical and methodological, demonstrating how socio-technical principles can inform platform design for grassroots organisations and support digital inclusion, collective agency, and participatory engagement. The discussion also highlights implications for digital empowerment strategies, participatory system development, and future implementation pathways, emphasizing how carefully conceptualised architectures can provide community-driven organisations with sustainable trajectories for digital transformation.

From humans to algorithms: A sociotechnical framework of workplace surveillance

This study proposes a conceptual-analytical framework for human-centric AI integration in Industry 5.0 by employing a Systematic Literature Review (SLR) and Applied Framework Analysis (AFA). A structured PRISMA-based screening process was applied to ensure transparency in identifying and selecting 48 scholarly sources that inform the framework. The review reveals three foundational dimensions enabling responsible AI adoption: ethical governance grounded in transparency and accountability, inclusive innovation addressing bias and digital inequality, and futureready human capital oriented toward capability enhancement. These dimensions are synthesized into a sociotechnical framework that bridges normative humancentric principles with organizational practice. To demonstrate its analytical utility, the framework is applied to Gojek Indonesia as an illustrative case using publicly available secondary information, without claiming empirical verification or access to proprietary algorithmic processes. The analysis indicates partial alignment between Gojek’s documented initiatives and human-centric principles, particularly in interface transparency, communication design, and worker-support mechanisms, while also exposing persistent tensions related to power asymmetry and limited algorithmic visibility. The study concludes that although secondary evidence suggests opportunities for human-centric implementation, comprehensive evaluation requires multi-method empirical research capable of capturing lived experiences and internal decision-making structures.

Background: Agile project management has gained increasing relevance in sustainable development due to its capacity for flexibility, iterative decision-making, and stakeholder-centered processes. Objectives: This study examines how Agile principles enhance the performance of sustainability-oriented projects by evaluating their influence on project success, stakeholder engagement, and resource efficiency. Methods: A mixed-methods design was applied, combining a 30-item survey administered to 150 participants with 20 semi-structured interviews to integrate quantitative outcomes with contextual insights. Results: The findings demonstrate that Agile-driven projects achieve a 90% success rate compared to 75% in traditionally managed projects, while stakeholder satisfaction improves by 2.4 points and resource utilization increases by 20%. Regression modelling (R² = 0.68) further shows that iteration frequency and stakeholder engagement are the strongest predictors of success. Conclusion: The study contributes new evidence on how Agile supports behavioral mechanisms—such as collaboration, trust formation, and adaptive team learning—within sustainability initiatives. These findings underline the potential of Agile as a socio-technical framework capable of strengthening project responsiveness and long-term sustainability outcomes.

Human resource management (HRM) plays a pivotal role not only in structuring organizational processes but also in shaping how employees cognitively appraise, affectively respond to, and behaviorally adapt to workplace demands. Drawing on structuration theory (Giddens, 1984) and integrating insights from stress and coping theory (Lazarus & Folkman, 1984) and cognitive evaluation theory (Deci & Ryan, 2012), this study investigates how HRM influences employee coping behaviors—specifically adaptation, resistance, and manipulation—and how these behaviors, in turn, affect job performance. The research addresses three core objectives: (1) to examine how HRM practices shape employees’ cognitive and affective responses, with attention to boundary conditions such as fairness, transparency, and employee participation; (2) to identify the types of coping behaviors employees employ in response to HRM and elucidate how cognitive and affective appraisals drive the selection of these strategies; and (3) to analyze the mediating role of coping behaviors in the relationship between HRM and dual dimensions of job performance— in-role task execution and extra-role organizational citizenship behaviors. By centering employee agency within a dynamic socio-technical framework, this study provides a micro-foundational understanding of HRM’s impact on performance and offers practical guidance for designing HR systems that are both effective and human-centered.

Abstract The decline in indigenous food consumption in India—accelerated by the emphasis of the Green Revolution on high-yield crops—has led to loss of biodiversity, reduced dietary diver-sity, and erosion of cultural food heritage. Although agricultural research recognises the nutri-tional and ecological value of traditional crops, technological interventions to raise consumer awareness remain limited. Meanwhile, AI-based food recommendation systems feature mainly global or calorie-centric cuisines, overlooking culturally significant Indian dishes and their sea-sonal logic. This paper proposes HeritageBites, a socio-technical framework developed through sec-ondary analysis that integrates cultural research with AI-based personalisation to promote in-digenous food and local ingredients through a digital platform. The framework demonstrates how technology can preserve culinary heritage, improve nutritional and seasonal awareness, and strengthen rural livelihoods by connecting traditional recipes with digital engagement. The findings of the study reveal that indigenous diets align with modern nutritional science and that digital promotion can promote cultural revival and sustainable market opportunities. Keywords: Indigenous food, Traditional Indian cuisine, Seasonal diets, AI-based recommen-dation system, Cultural preservation, Nutritional diversity, Sustainable food systems

Transforming neonatal care through informatics: A review of artificial intelligence, data, and implementation considerations.

Healthcare organizations are experiencing a swift increase in cyberattacks targeting valuable patient data and essential systems. Healthcare often spends less on cybersecurity infrastructure than other industries, which makes human weaknesses a major risk factor. Staff and insiders are often to blame for major breaches. Objective: This study seeks to examine the primary human-factor vulnerabilities in healthcare cybersecurity and to suggest strategies for reducing insider threats in medical facilities. Methods: We executed a systematic literature review of peer-reviewed studies, industry reports, and breach data (2015–2024) in accordance with PRISMA guidelines. Qualitative thematic coding was employed to identify persistent human-risk themes and assess current mitigation frameworks. The review shows that most healthcare breaches are caused by mistakes made by people or people who work for the company. Some of the most important weaknesses are being open to social engineering (especially phishing) and being careless because of not enough training, being tired, or a bad security culture. Malicious insiders (data theft, sabotage) and compromised credentials (phishing victims) make the risk even higher. Good ways to reduce risk include technological controls (like access management and monitoring), organizational policies (like role-based privileges and zero-trust), and human-centered measures (like regular training and stress management). Our proposed multi-layered framework integrates these methodologies. Conclusion: This paper provides a thorough understanding of the predominance of human factors in healthcare cyber risk and presents a sociotechnical framework for mitigation. The study enhances practice and policy by integrating behavioral insights with technical controls and policy alignment, such as compliance with HIPAA and GDPR. Subsequent research ought to investigate AI-facilitated insider detection and cross-cultural analyses of cybersecurity within the health sector.

This systematic literature review investigates advancements in intelligent computer-aided design and computer-aided manufacturing (CAD–CAM) integration and toolpath generation, analyzing their evolution across Industry 4.0 and emerging Industry 5.0 (I5.0) paradigms. Using the theory–context–characteristics–methodology framework, the study synthesizes 51 peer-reviewed studies (from 2000 to 2025) to map theoretical foundations, industrial applications, technical innovations, and methodological trends. Findings reveal that artificial intelligence (AI) and machine learning dominate research, driving breakthroughs in feature recognition, adaptive toolpath optimization, and predictive maintenance. However, human-centric frameworks central to I5.0, such as socio-technical collaboration, remain underexplored. High-precision sectors (aerospace, biomedical) lead adoption, while small and medium enterprises (SMEs) lag due to resource constraints. Technologically, AI-driven automation and STEP-NC standards show promise, yet interoperability gaps persist due to fragmented data models and legacy systems. Methodologically, AI-based modeling prevails (49 % of studies), but experimental validation and socio-technical frameworks are sparse. Key gaps include limited real-time adaptability, insufficient AI training datasets, and slow adoption of sustainable practices. The review highlights the urgent need for standardized data exchange protocols, scalable solutions for SMEs, and human-AI collaboration models to align CAD–CAM integration with I5.0’s sustainability and resilience goals. By bridging these gaps, this work provides a roadmap for advancing intelligent, human-centered manufacturing ecosystems.

This paper proposes the Epistemic Calibration Network (ECN), a novel sociotechnical framework designed to bridge the widening Epistemic Gap between human perception and AI output veracity. This gap is critically amplified by the proliferation of high-fidelity multimodal generative systems. The central problem is the divergence between the Human Illusion, wherein cognitive biases such as automation and normalization bias lead to critically miscalibrated human trust (over-reliance or under-reliance), and the AI’s Objective Signal, which is often poorly calibrated and susceptible to catastrophic failures like mismatched grounding. The ECN framework integrates three core, computationally modeled modules: (A) a Metacognitive Objective Signal Generator (M-OSG) utilizing cross-modal consistency for robust Uncertainty Quantification (UQ); (B) a Computational Human Bias Modeler (C-HBM) which predicts miscalibration risk based on derived cognitive profile ; and (C) a Dynamic Calibration Loop Interface (D-CLI) that employs adaptive, friction-based interventions . ECN provides an architectural blueprint for achieving genuine epistemic alignment, which is essential for fostering appropriate trust, ensuring robust decision-making, and facilitating ethical AI deployment in sensitive, high-stakes environments.

Disaster loss estimations are valuable risk reduction tools but rarely consider the loss trade-offs when a building stock is subjected to multi-hazard impacts. Here, we developed an approach to simulate direct economic losses of a housing stock and explore loss reduction across scenarios of housing typology distributions. We used multi-objective optimisation to model wind and seismic losses in Itbayat, Batanes, Philippines. Using Monte Carlo simulation, 11,628 housing stock scenarios were modelled under two cases of paired extreme hazard intensity thresholds, identifying Pareto optimal solutions that were further analysed against a socio-technical framework. We show that the current housing stock distribution can sustain lower multi-hazard losses by achieving more optimal combinations of lightweight and reinforced concrete typologies. However, transitioning to this desired stock distribution becomes a trade-off of not just wind-seismic loss reductions but also of socio-technical considerations such as households’ risk perceptions. Our study advances risk reduction strategies by streamlining loss estimations to inform collective and safer multi-hazard construction practices.