Healthcare organizations around the world face growing pressure to become more sustainable in environmental, social, and economic terms. Digital technologies such as artificial intelligence and cloud computing are seen as powerful tools that can help hospitals reach these sustainability goals. However, technology alone is not enough and many hospitals still fail to turn digital adoption into real sustainability results. This study examines how digital technologies improve Healthcare Sustainability Practices through Human Resource Management and Organizational Ambidexterity and whether Environmental Dynamism strengthens these effects. We surveyed 301 healthcare professionals in Bangladesh and analyzed the data using Partial Least Squares Structural Equation Modeling (PLS-SEM) with a bootstrapping procedure of 5000 resamples. The results show that digital technologies directly improved sustainability practices (β = 0.147 p = 0.005) and also strengthened Human Resource Management (β = 0.310) and Organizational Ambidexterity (β = 0.289). Human Resource Management was the strongest driver of sustainability (β = 0.662 p < 0.001) and it positively transferred the effect of digital technologies to sustainability (β = 0.205). Organizational Ambidexterity showed a negative effect on sustainability (β = −0.327) and also carried a negative indirect effect (β = −0.095). Environmental Dynamism improved sustainability practices (β = 0.170) and strengthened the link between digital technologies and sustainability (β = 0.122). These findings show that technology alone is not enough for sustainable healthcare. Hospitals need skilled people balanced structures and the ability to adapt to changing conditions to turn digital transformation into lasting sustainability.
Commercial organisations are increasingly reconfiguring sales–procurement work around data, models, and human judgement. In the post-generative AI (GenAI) landscape, flexibility must deliver speed without eroding accountability. This study contributes to flexible management theory by explaining how flexibility is organised when decision authority becomes elastic and partly machine-mediated within everyday organisational work. Drawing on a qualitative, abductive study of 24 semi-structured interviews with business-to-business (B2B) practitioners across fast-moving consumer goods, automotive, manufacturing, health technology, and consulting, this study integrates the dynamic capabilities perspective with the “situation-actor-process” and “learning-action-performance” framework. This study advances hybrid-intelligence flexibility as an organising logic for GenAI-mediated work, defined as the elastic allocation of decision rights and execution across humans and GenAI agents and contingent on task ambiguity and decision stakes. Hybrid-intelligence flexibility is operationalised through three micro-foundations: data asset liquidity, decision-scope elasticity, and interpretive governance. Evidently, liquidity strengthens sensing by accelerating reliable access to commercial signals, elasticity calibrates autonomy at the task level to support timely yet defensible actions, and interpretive governance reduces reversals as workflows reconfigure. The study develops analytically generalisable, middle-range propositions on when autonomy should expand or contract, and identifies boundary conditions shaping heterogeneous flexibility outcomes. It further advances responsible flexibility not as an ex-post constraint but as an antecedent of durable flexibility. Together, these contributions move the literature beyond GenAI use-case inventories towards a mechanism-based explanation of flexible management. For managers, a clear sequence emerges: increase data asset liquidity, codify the decision scope, and institutionalise interpretive governance.
This study examines the impact of strategic and flexible human resource management on the sustained performance of public sector organizations operating under the volatile, uncertain, complex, and ambiguous conditions necessitated by the COVID-19 pandemic. In exploring this topic, four goal-aligned flexible human resource management practices, namely recruitment, appraisal, training, and reward, were investigated alongside COVID-19 mitigation strategies. We collected survey data from 1272 employees of the Tanzania Ports Authority, which is a government parastatal in the Tanzanian maritime sector. Stereotype logistic regression was employed in our analysis and the results indicate that only the recruitment and appraisal of the four goal-aligned human resource functions have positive impacts on organizational performance, whereas training and rewards do not show significant effects. Furthermore, these HR functions maintain a significant impact on performance alongside COVID-19 mitigation strategies, suggesting that innovative and flexible HR approaches help organizations absorb pandemic-induced shocks. This study extends strategic human resource management theory by delineating how goal-aligned practices function at the critical intersection of rigid governance in a developing country and acute crisis constraints.
The rapid proliferation of Generative Artificial Intelligence (GenAI) creates a strategic paradox of managing the trade-off between leveraging emerging technologies and simultaneously adhering to rigorous ethical governance. This paper examines the concept of responsible flexibility as a strategic response for organizations navigating the dual pressures of technological agility and social responsibility in the digital age. This study investigates how firms balance adaptability with accountability across diverse sectors and proposes a Responsible AI Flexibility Framework (RAIFF). Using a multiple case study design of Indian companies, the analysis identifies how organizations position themselves along the responsibility–agility continuum. Findings highlight four patterns: responsible frontrunners, agile but fragile actors, cautious adapters, and balanced innovators. The results demonstrate that responsible flexibility is not merely a compliance imperative but a core competence and source of resilience, shaping competitive advantage in AI-driven contexts. The paper contributes to the literature on responsible innovation and digital transformation by offering empirical insights, a conceptual framework, and practical guidance for managers and policymakers.
This study explores the influence of IT governance (ITG) mechanisms on strategic flexibility via dynamic capabilities (DC) and how these, in turn, enhance firm performance (FP) amid environmental uncertainty (EUN) in both developing and developed economies. The study employed partial least squares structural equation modeling (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA) to examine data from 278 Brazilian and English enterprises in the products and services industries. The findings show that ITG mechanisms consistently enable strategic flexibility via DC across both economies, challenging assumptions about development-level differences. Moreover, the empirical findings also highlighted the influence of DC as a mediator in the relationship between ITG and FP. To further explore the impact of contingent factors, a fsQCA was conducted across clusters of countries. The analysis identified multiple equifinal combinations of ITG that influence dynamic capabilities, depending on EUN’s absence, presence, or core presence and firm characteristics, ultimately leading to improved FP. This empirical study offers valuable insights on service and manufacturing enterprises on implementing an ITG framework to build DC and enhance FP. It highlights how different organizational factors and combinations of EUN can be leveraged within emerging and developed economic contexts. The finding fills knowledge gaps in the literature on ITG and strategic flexibility.
This study examines how integrating artificial intelligence (AI) with human intelligence (HI) reshapes alliance governance and, consequently, affects the firm’s performance. We propose a hierarchical architecture in which AI–HI augmentation acts as a foundational driver enabling the relational and formal governance flexibility, resulting in effective alliance performance. Considering the explorative nature of work and the need for diverse insights, we have used total interpretive structural modeling (TISM) to derive the architecture based on practitioners’ inputs. Furthermore, to synthesize the findings with the real-life scenarios, the SAP–LAP approach of case study analysis is used. This is used to demonstrate and ground the mechanisms in practice. We find that AI–HI augmentation improves alliance governance flexibility, resulting in effective goal and incentive alignment between the partners and eventually improving alliance performance.
In recent years, cutting-edge technologies such as artificial intelligence (AI) have streamlined operations and fostered continuous innovation across various manufacturing systems. Generative AI (Gen-AI), a subset of AI, helps generate new ideas and perspectives using large language or diffusion models. Gen-AI is transforming the ways of designing, developing, and operating industrial systems. While Gen-AI is widely used to create text, images and video content, its applications in intelligent and adaptive manufacturing remain limited and require precision, reliability, seamless integration, and security. Since the adoption of Gen-AI is still in its infancy, stakeholders need to understand the various factors influencing its implementation. In the present study, the SAP-LAP framework is used to analyze the situation-actors-process (SAP) and learning-actions-performance (LAP) dimensions for the effective implementation of Gen AI to empower flexible manufacturing systems (FMS). In conjunction with the efficient interpretive ranking process (e-IRP), the actors and actions were further ranked. The research findings identify AI technology providers and start-ups, OEMs, and system integrators as the most prominent actors in the implementation of Gen-AI in flexible manufacturing systems, with government and regulatory bodies as key enablers through policy support and governance frameworks. Further results show that ‘Develop modular Gen-AI toolkits tailored to different FMS architectures’ ‘Promote standards and protocols for secure and ethical use of Gen-AI’ are the topmost actions. Insights from this study will be helpful to practitioners and researchers interested in adopting Gen-AI applications to enhance agility and flexibility in production systems.
This research intends to analyze the impact of big data analytics capability (BDAC) toward supply chain innovation (SCI) and competitive advantage (CA) where green supply chain management (GSCM) performs the moderating role. A theoretical model was created, and hypotheses were formulated to investigate the links between these variables. Data collection was carried out through a survey of supply chain managers in manufacturing plants in Bangladesh. Total 341 valid responses were collected and analyzed with the method of partial least squares structural equation modeling (PLS-SEM) in WarpPLS. The findings of the study offer insights into how business can use BDAC to achieve SCI and CA. The results indicate that BDAC has a potential impact regarding a firm’s ability to enhance CA and SCI. Moreover, the moderating function of GSCM was examined, which was found to be a significant influence on one of these relationships. However, the industry age as a control variable was found to have no apparent impact on SCI or CA. The study also establishes the critical role of SCI in turning analytical skills into competitive results and partially mediates the relationship between BDAC and CA. In sum, this research illustrates how businesses can employ data-driven skills to increase productivity in complex and dynamic supply chain environments against the backdrop of a case study.
This study examines how leadership agility, digital transformation, technology infrastructure, and organizational agility are interconnected in organizations operating in a developing economy with resource constraints and uneven digital maturity. Drawing on the Resource-Based View and Dynamic Capabilities Theory, the study explains how managerial coordination and technological renewal support organizational agility. Data from 303 respondents across diverse industries were analyzed using partial least squares structural equation modeling. The findings indicate that leadership agility is positively associated with both digital transformation and organizational agility. Digital transformation is also positively associated with organizational agility and partially mediates the relationship between leadership agility and organizational agility, indicating that leadership effects are realized through the deployment of capabilities. Furthermore, technology infrastructure moderates the relationship between digital transformation and organizational agility, suggesting that infrastructural flexibility and integration enhance the effectiveness of transformation. By examining these relationships in the context of a developing economy, the study extends the existing literature by demonstrating how contextual constraints shape dynamic capabilities. It contributes by positioning digital transformation as a core organizational capability, highlighting the conditional role of infrastructure, and explaining how leadership and technological capabilities jointly shape organizational agility.
Cryptocurrencies represent a significant financial innovation, yet their energy-intensive operations and perceived regulatory legitimacy under institutional ambiguity create substantial challenges for their broader acceptance, particularly in emerging markets. This study examines how digital trust, green trust, environmental awareness, and perceived regulatory legitimacy interact to shape cryptocurrency adoption intentions in environments characterized by institutional ambiguity. Building on established technology adoption research and trust-based perspectives in information systems, we develop and test an extended model that incorporates environmentally related trust perceptions and institutional legitimacy as key determinants of adoption. Using survey data from 462 Vietnamese respondents and Partial Least Squares Structural Equation Modeling, the findings show that environmental awareness enhances green trust, which subsequently drives adoption intentions. Digital trust mediates the influence of perceived risk, while perceived regulatory legitimacy strengthens both digital trust and green trust. Rather than examining sustainability outcomes at a macro level, the study clarifies how sustainability-related perceptions and trust mechanisms influence individual adoption decisions in developing markets. Additional robustness analyses were conducted across occupational subgroups (students, professionals, and individual investors) to ensure structural stability of the model, with results confirming consistency of core relationships. Practical implications are provided for policymakers, fintech managers, and IT strategists seeking to promote more trustworthy and environmentally responsible digital finance under regulatory uncertainty.
The study examines the causal relationships between the dimensions of enhancing human resource flexibility (HRF) and in turn, firm performance of construction companies in the automation-intensive industry. It aims to model the dynamics of impact under uncertainty and ambiguity, delivering a robust framework for developing HR strategies in a technology-transformed environment. The Grey Decision-Making Trial and Evaluation Laboratory (Grey-DEMATEL) technique is adopted to mitigate ambiguity and riskiness in decision-making. Initial data were collected through 76 expert surveys from senior human resources (HR) and project managers of national and multinational construction companies across India, using both online and offline participation. The complex causal relationships between the dimensions and sub-dimensions of human resource flexibility (functional, numerical, temporal, and cognitive) and key company performance indicators (efficiency, innovation, adaptability, and scheduled project performance) are derived, indicating that functional and cognitive flexibility are the main causal drivers, significantly impacting innovation, adaptability, and scheduled project performance in automation-enabled construction environments. The study is geographically constrained to firms operating in India and relies on subjective expert input, although this is mitigated through grey systems modelling. Future research should consider hybrid approaches such as Grey-DEMATEL-Artificial Neural Network (ANN) or real-time performance analytics to enhance generalizability and decision support. The study contributes to a nuanced, uncertainty-aware causal model that supports human resource-driven flexibility in the face of automation and digital transformation, thereby filling a significant gap in the performance management literature.
The rapid integration of information and communication technology (ICT), including cloud computing and virtual desktop infrastructures, has driven the expansion of remote and mobile work. This shift offers organizations opportunities to reduce costs, improve productivity, and enhance employee satisfaction. However, remote work remains constrained by challenges such as low consensus regarding its necessity, lack of diverse service solutions, and barriers to organizational culture. This study investigates how perceived innovation characteristics influence employees’ innovation resistance (IR) to remote work and examines the moderating role of behavioral and outcome control mechanisms. Covariance-based structural equation modeling was used on survey data from 519 employees to validate the proposed research model. The results show that compatibility and complexity significantly influenced IR, whereas relative advantage and self-efficacy had different effects. Outcome control reduces IR when the relative advantage is high but increases IR when complexity is high. These findings highlight the need for organizations to align control mechanisms with employees’ perceptions of innovation characteristics to reduce their resistance to remote work adoption.
Metaverse technologies, specifically digital twins (DT) and blockchain, are increasingly influencing the evolution of supply chain management (SCM). This study explores how these technologies support adaptability, flexibility, and resilience in complex environments. Using a mixed-method approach incorporating BERTopic modeling, the research identifies four thematic areas: real-time operational optimization via DTs; immersive workforce training through AR/VR; transparency improvements via blockchain; and Industry 5.0 technological trends. The result indicated the DTs in developing flexible, adaptive supply chains by facilitating predictive analytics, dynamic scenario planning, and stakeholder coordination. Integrated with machine learning and blockchain, DTs help optimize responsiveness, collaboration, and reconfiguration strategies, core principles of flexible systems management. The study further applies the antecedents–decisions–outcomes (ADO) framework to propose an outline for further research avenues and practice, grounded in three key questions focusing on how metaverse technologies enhance flexibility, support collaboration, and advance the sustainability–resilience nexus. The analysis also identifies governance, scalability, and ethical implementation as persistent challenges. Methodologically, the work demonstrates how topic modeling can support theory development in digitally transformed organizational systems. Expected outcomes include greater operational agility, improved sustainability alignment, and enhanced trust across supply chain networks. These insights contribute to ongoing efforts to build digitally enabled, ethically governed, and dynamically flexible supply chains aligned with United Nations Sustainable Development Goals (SDG) such as SDG 8 (work), SDG 9 (innovation), SDG 12 (consumption), SDG 13 (climate), and SDG 16 (governance).
This study examines the behavioral and organizational factors that influence the adoption of digital twin (DT) technologies in healthcare, particularly in resource-constrained environments such as Bangladesh. It emphasizes the often-overlooked human and institutional aspects of DT adoption, alongside technical considerations. An integrated model was developed by combining the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) with the Technology-Organization-Environment (TOE) framework, drawing on six key constructs from each. Data collected from 439 healthcare professionals were analyzed using a hybrid approach of structural equation modeling (SEM) and artificial neural networks (ANNs). The results show that all predictors have a significant impact on behavioral intention, with complexity having a negative effect. ANN sensitivity analysis identified regulatory support, effort expectancy, facilitating conditions, and complexity as the most influential factors. These contribute to flexible management dimensions in hospitals. While the model explains 88.4
Since the emergence of generative artificial intelligence, its adoption has steadily increased, generating significant business value by transforming business models and management processes. In Ghana, despite awareness of this technology, businesses are lagging competitors for various reasons, including inadequate managerial capability, an unsustainable business ecosystem, and the premature stage of generative artificial intelligent (GAI). The study seeks to assess the digital maturity of industries in Ghana concerning GAI adoption, utilizing Technology-Organization-Environment (TOE) Theory. Data was gathered from 250 respondents across diverse firms, and a model tested through Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) using SmartPLS 4. The analysis provided moderate support for the model, showing that firms were more prone to adopt GAI when they perceived its compatibility as mature. However, the results did not support the significance of top management support (TMS). Additionally, relative advantage (RAD) and competitive pressure (COP) exhibited a negative influence on adoption, while government support (GOS) had a positive and significant impact. Study implications and limitations are further discussed and recommendations for future research provided.
This research investigates how MSMEs may become more sustainable by combining responsible AI (RAI) with green innovation. Although AI has revolutionary potential to boost eco-innovation and operational efficiency, the leadership’s role in coordinating new technologies with sustainable goals is not well acknowledged. The study adds to the expanding debate over the sustainable performance of MSMEs by investigating how leadership influences ethical, environmental, and economic responsibility within the organisational landscape. A mixed-methods approach was employed, beginning with quantitative analysis on 321 MSME respondents, followed by qualitative analysis. A disjoint two-stage approach using PLS-SEM is followed by meta-inferences to evaluate the hypothesised model. The findings suggest the RAI significant effect on MSMEs’ sustainable performance as well as green innovation. The influence of green innovation and RAI on sustainability is favourably moderated by sustainable transformational leadership (STL), underscoring the crucial role that leadership plays in promoting environmental and digital change. The research provides implementable recommendations for MSME leaders, industry professionals, and policymakers to incorporate RAI into business strategies, thus fostering green innovation and ensuring long-term sustainability. It affirms the key contribution of STL in overcoming barriers to AI adoption, aligning technology with sustainable goals. The study integrates RAI, green innovation and STL into a single paradigm, particularly in the unexplored setting of MSMEs. It reconceptualises RAI as a strategic factor behind sustainable performance and leadership, providing a novel viewpoint on how responsible behaviour may improve organisation performance and contribute to broader sustainability goals.
This paper focuses on the role of managers’ self-construal as they shape their jobs in remote work environments, drawing on job crafting theory. Based on twenty seven in-depth interviews of middle and senior managers across ten industries, the study examines managers’ approaches to productivity in remote work settings. This study employed analytic pluralism by combining interpretative phenomenological analysis and thematic analysis to generate rich and nuanced insights. The results suggest that a manager’s self-construal plays a crucial role in job crafting. In particular, idiocentric managers (those with an independent self-construal) felt well suited to remote work as it allowed them to present their authentic selves and increased their sense of comfort and engagement. Nevertheless, they experienced difficulties receiving proper work support. Conversely, allocentric managers (those with an interdependent self-construal) found remote work more challenging, experiencing weak socialization and difficulty in integrating their work with that of their teams. This research can contribute to the body of remote work literature by introducing the concept of self-construal and showing how job redesign is influenced in this context. The results have significant policy implications for managers and employees in flexible environments.
Generative artificial intelligence (GenAI) presents transformative potential for optimising supply chain and logistics operations by enhancing efficiency, supporting sustainability initiatives, and improving stakeholder experiences. Despite these promising prospects, multifaceted and interdependent barriers constrain the integration of GenAI into sustainable supply chains, necessitating a systematic and strategic evaluation. Anchored in the technology-organisation-environment (TOE) framework and Diffusion of Innovation (DOI) theory, this study extends the analytical scope through the incorporation of the supply chain and external contexts. A dual-method approach is employed, leveraging the SAP-LAP (situation–actor–process–learning–action–performance) framework to capture the flexible nature of technological adoption, alongside the Bayesian best–worst method (BWM) to ensure robustness in the prioritisation of critical barriers. Empirical findings highlight that environmental external context (0.2702) emerges as the most significant barrier, followed closely by technological context (0.2689), organisational context (0.2572), and supply chain context (0.2035). These results underscore persistent regulatory ambiguities, limited technological readiness, limited organisational capacities, and inherent complexities within supply chain networks as principal impediments to GenAI adoption. This research offers actionable policy recommendations, including the formulation of clear regulatory frameworks that balance innovation with sustainability compliance and strategic investments in digital infrastructure and literacy.
This study examines the effect of digital leadership on organizational performance in digital transformation, a strategic priority for public-sector organizations seeking to enhance service quality, transparency, and public value. While digital leadership has been increasingly recognized as a key driver of transformation, less is known about the conditions under which it translates into improved organizational performance in rule-bound public-sector contexts. Drawing on dynamic capability theory, this study examines the role of strategic agility as an enabling organizational capability that shapes the effectiveness of digital leadership. Using survey data from public-sector organizations, this study empirically examines the relationships among digital leadership, strategic agility, and organizational performance. The findings indicate that digital leadership is positively associated with organizational performance; however, this relationship becomes substantially stronger when organizations exhibit higher levels of strategic agility. Specifically, strategic agility enhances organizations’ ability to align leadership with intent with coordinated action and timely resource reconfiguration. This study contributes to the literature by clarifying the boundary conditions under which digital leadership yields performance outcomes in public-sector environments characterized by institutional constraints. From a practical perspective, the findings suggest that public-sector digital transformation initiatives should move beyond technology adoption and leadership development alone and, instead, emphasize building strategic agility to ensure effective implementation and sustained performance improvements.
This study investigates the impact of artificial intelligence (AI)-responsive agile leadership (AL) on sustainable organizational outcomes (OO) in AI-integrated environments. Despite growing interest in AI, the mechanisms linking leadership to outcomes remain underexplored. This study proposes a multi-layered framework that incorporates perceived trust and organizational justice (TOJ) as contextual moderators, and AI-supportive organizational culture (OC), employee attitude toward AI (EA), AI technology adoption (TA), and strategic flexibility for AI integration (SF) as serial mediators, illustrating the impact of leadership on organizational performance. A cross-sectional design was adopted and data were collected from 400 telecom employees in Oman using structured questionnaires. Structural Equation Modelling (SEM) with SMARTPLS assessed the proposed moderated serial mediation model, and extended serial mediation and moderated parallel mediation models were also tested for robustness. These findings indicate that AL indirectly influences OO through EA, TA, and SF. AL significantly influenced OC, but OC did not directly influence OO. TOJ strengthens the effects of AL on OC and EA. Among the three tested models, the moderated serial mediation model demonstrated the strongest explanatory support, enabling the identification of multiple mediation pathways. This study provides insights into the role of agile leadership in promoting AI readiness through structural, cognitive, and contextual mechanisms, thereby contributing to the flexible system management paradigm.
