Digitalisation as a catalyst for supplier diversity, equity and inclusion

PurposeThe use of artificial intelligence (AI) in the workplace is on the rise. To help advance research in this area, the authors synthesise the academic research and develop research propositions on the antecedents and consequences of AI adoption and application in the workplace to guide future research. The authors also present AI research in the socio-technical system context to provide a springboard for new research to fill the knowledge gap of the adoption and application of AI in the workplace.Design/methodology/approachThis paper summarises the existing literature and builds a theoretically grounded conceptual framework on the socio-technical system theory that captures the essence of the impact of AI in the workplace.FindingsThe antecedents of AI adoption and application include personnel subsystem, technical subsystem, organisational structure subsystem and environmental factors. The consequences of AI adoption and application include individual, organisational and employment-related outcomes.Practical implicationsA research agenda is provided to identify and discuss future research that comprises not only insightful theoretical contributions but also practical implications. A greater understanding of AI adoption from socio-technical system perspective will enable managers and practitioners to develop effective AI adoption strategies, enhance employees' work experience and achieve competitive advantage for organisations.Originality/valueDrawing on the socio-technical system theory, the proposed conceptual framework provides a comprehensive understanding of the antecedents and consequences of AI adoption and application in the work environment. The authors discuss the main contributions to theory and practice, along with potential future research directions of AI in the workplace related to three key themes at the individual, organisational and employment level.

This paper looks at how the FitSM framework, together with Socio-Technical Systems (STS) theory, can help improve IT Service Management (ITSM) in a small software development firm. Limited resources often cause small businesses to manage services inefficiently, which leads to inconsistent delivery and dissatisfaction among clients. The study focuses on a single software company in Jakarta, where ITSM processes like incident management, change management, and configuration management were assessed. The research involved interviews with key staff, an evaluation of current practices, and a gap analysis to identify areas needing improvement. The results show that most processes are at an early stage, with some progress in Configuration Management. By using FitSM’s structured approach and addressing teamwork and communication issues through STS theory, the company can improve service reliability and efficiency. The study concludes that combining FitSM with socio-technical principles provides a practical solution for small companies to enhance their ITSM practices and overall service quality.

ABSTRACTBackground/Purpose: This paper presents the findings of an action research (AR) project to improve a health information system (HIS) at the Operating Theater Department (OTD) of a National Health Service (NHS) hospital in South East England, the UK. Methods: Informed by socio-technical systems (STS) theory, AR was used to design an intervention to enhance an existing patient administration system (PAS) to enable data entries in real time while contributing to the literature. The study analyzed qualitative data collected through interviews, participant observations, and document reviews. Results: The study found that the design of the PAS was unsuitable to the work of the three units of the OTD. Based on the diagnoses and STS theory, the project developed and implemented a successful intervention to enhance the legacy system for data entries in real time. Conclusions: The study demonstrates the value of AR from a socio-technical perspective for improving existing systems in healthcare settings. The steps adopted in this study could be applied to improve similar systems. A follow-up study will be essential to assess the sustainability of the improved system.

The growing use of artificial intelligence (AI) is transforming human resources management in all industries, including higher education institutions, where the ability to manage talent effectively is crucial for academic quality and institutional stability. Notwithstanding increasing recognition of the potential for AI in HR, research to date has been fragmented and focused on functional or technical aspects often leaving ethical governance and socio-technical fit in academic settings underexplored. It is this research gap that the current study aims to fill through an integrated and comprehensive conceptual model of AI-learning for talent management in higher education. Taking a qualitative narrative review method, the research integrates interdisciplinary literature from talent management to SHRM as well as higher education and AI governance. Grounded in the resource-based view, human capital theory, and socio-technical systems theory, this article investigates how AI is conceptually situated throughout the ITR of attraction, development, and retention. The synthesis underscores the strategic promise of AI-based talent management in enabling data-driven decision-making and individualized human capital development, while also underscoring the ethical and organizational challenges inherent in algorithmic bias, data privacy, transparency and employee trust. Grounded in these understandings, the study develops an integrative conceptual model suggesting that ethical governance and socio-technical readiness are prerequisites to responsible AI implementation in HE talent management. The paper adds to the literature by building on TM and SHRM literatures, integrating ethics and socio-technical systems perspectives explicitly in these frameworks and providing diagnostic understands and intervention strategies to institutional bosses who are managing digital transformation. The paper closes by suggesting lines for future empirical work to validate and improve the proposed framework in different higher education types.

The exponential progress of AI systems today compels scientists and philosophers to redefine their conceptual frameworks to better understand the nature of these new technologies and their moral status. Among the various theories that are used to respond to the challenges posed by intelligent systems are instrumentalism, Socio-technical Systems Theory (STST) and Mediation Theory (MT), all widely adopted in the field of Science and Technology Studies (STS). This paper intends to present the main features of these theories and provide a comparative analysis of them in order to assess their contribution to the process of understanding the moral status of artificial intelligence. Our investigation intends to show how (1) instrumentalism is inadequate to account for the moral status of AI, (2) STST, while helping to highlight the link between AI, society and morality, lends itself to the criticism of anthropocentrism, (3) MT in its Latourian version has the merit of highlighting the active character of technological artefacts and thus of artificial intelligence in the moral sphere. However, the principle of symmetry it proposes poses the problem of the de-accountability of the human agent. (4) MT in its postphenomenological form seems to partially resolve the problem of moral responsibility, but the opacity of the terminology it employs exposes it to various criticisms. In light of these results, we intend to show how an understanding of the moral status of intelligent systems cannot be based on the diametrically opposed positions that consider technologies either morally neutral or else moral agents similar to humans, whereas particularly useful elements can be found in STST and in postphenomenological MT.

This article critically examines algorithmic bias and discrimination in the Indian context, focusing on the intersection of rapid artificial intelligence (AI) adoption and existing societal inequities rooted in historical injustices. It explores how AI systems, developed without deliberation and representation, risk perpetuating biases related to caste, religion, gender and socio-economic status, posing significant dangers to marginalised communities. Using a multi-theoretical framework, including algorithmic bias theory, sociotechnical systems theory and feminist theories of structural injustice, the study highlights AI’s entanglement with societal norms and power dynamics. The qualitative research involved in-depth interviews with five experts from academia, law, science and policy to examine AI’s sociotechnical landscape in India. Key findings include underrepresentation of marginalised communities in AI datasets, lack of ethical AI guidelines, risks of surveillance misuse and limited awareness of AI ethics among developers and policymakers. A novel insight is the danger of addressing AI bias reactively, as this could entrench further harm. The study advocates for public policy solutions to promote data equity, foster responsibility among AI developers, establish independent oversight and encourage public discourse on AI ethics. Prioritising equity and ethical considerations is crucial to harnessing AI’s potential while protecting the rights and dignity of all citizens.

Artificial Intelligence (AI) is increasingly transforming Environmental Assessment (EA), enhancing how environmental planning and decision-making are conducted. This systematic literature review critically examines the current landscape of AI applications in EA by analyzing 63 peer-reviewed studies through the PRISMA methodology and NVivo-assisted thematic coding. Four primary AI application areas emerged: prediction and modelling, data collection and processing, impact assessment and decision support, and real-time monitoring and management. To advance the conceptual understanding of AI integration, this review incorporates four key dimensions: (1) Socio-technical systems theory, to contextualize the interplay between technological innovations and institutional practices in EA; (2) Responsible innovation, to highlight ethical, inclusive, and anticipatory approaches in deploying AI tools; (3) Environmental governance, to explore how AI reshapes regulatory structures, participation, and transparency; and (4) Digital transformation and data-driven decision-making, to assess the systemic changes brought about by AI adoption in environmental management. The findings reveal both convergence and divergence in AI use across sectors and regions, with challenges including data quality, algorithmic transparency, and regulatory gaps. The study concludes with actionable recommendations to harmonize AI integration with environmental goals through ethical, scalable, and inclusive innovations, offering critical insights for policymakers, researchers, and practitioners.

Purpose This paper aims to introduce the concept of “ethics in motion,” which serves as a foundational element for a dynamic and integrative accounting education system. Using a repetition-based pedagogy, it reframes ethics education from a static, fragmented module into a continuous, reflective practice, interwoven with sustainability awareness and the responsible application of artificial intelligence (AI). It offers a distinct teaching approach based on repetition theory and socio-technical systems (STS) theory to reconceptualize accounting education for a more ethically and technologically sophisticated future. Design/methodology/approach The paper uses a critical reflection approach and literature review to construct an ethical synthesis addressing the teaching of accounting and its integration with ethics, sustainability and AI. Findings The study integrates accounting ethics, sustainability accounting and AI using repetition-based pedagogy in the proposed conceptual model. Iterative engagement deepens the ethics of sustainability, informs the responsible use of AI and educates ethics beyond compliance-driven approaches. Research limitations/implications The model requires empirical testing in practical teaching contexts. Subsequent studies may use case studies and mixed methods, and are encouraged to conduct cross-nation comparisons to examine cultural and institutional dimensions of ethics, sustainability and AI. Practical implications This model provides a pedagogy to foster ethical engagement and interdisciplinary praxis. It equips accounting graduates with the necessary critical and adaptive competencies for their future careers. Originality/value This paper develops an ethics pedagogy model for sustained transformation through repetition and STS. It expands the discourse of accounting ethics and sustainability accounting by advocating for the ethics of character, environmental stewardship and responsible technological engagement.

Purpose: This study examines the role of Artificial Intelligence (AI) and Machine Learning (ML) in transforming Indonesia’s financial industry by comparing their implementation in a conventional fintech company (JULO) and a Sharia-compliant fintech company (ALAMI). The study focuses on differences in AI adoption, value orientation, and their implications for operational efficiency and ethical compliance. Method: This research employs a qualitative comparative case study using secondary data sources, including corporate reports and relevant literature. The analysis is guided by the Technology–Organization–Environment (TOE) framework, Socio-Technical Systems Theory, and Maqasid al-Shariah. Result: The findings show that both companies utilize AI and ML to enhance decision-making and operational efficiency. However, JULO prioritizes speed and scalability, while ALAMI integrates Sharia principles such as justice, transparency, and the avoidance of riba. Implication: The study highlights the importance of aligning AI innovation with ethical and Sharia values in Islamic fintech development. Originality: This study offers a comparative, value-based analysis of AI-driven fintech from conventional and Sharia perspectives in Indonesia.

Digital heritage (DH) research serves as a bridge between technological applications and broader cultural, social, and policy issues. A comprehensive understanding of DH requires the integration of multiple fields. To address this, this work applies a socio-technical systems (STS) perspective to DH as a strategy to bridge the technological and social aspects. It first examines how DH functions as STSs, analyses the dynamic interactions between technological and social subsystems, and explains the need to achieve joint optimisation to tackle the complexity of DH research. Second, a comparative analysis of six STS models is conducted, using the Venice Time Machine project as a representative case, to explore both the potential and limitations of STSs as a theoretical framework for DH. Third, STS theory is applied to emphasise that the approach needs to incorporate cultural expression, technological feasibility, diverse stakeholder interests, and long-term adaptability in order to address the complexity of current DH challenges. Finally, an STS-DH framework is proposed to guide the design, implementation and evaluation of DH projects using the elements identified through the present analysis. This work extends STS theory applications to cultural heritage digitisation; provides stakeholders with new practical tools; recognises the lack of empirical research in this field and highlights the need for further research.

The study of management of innovation is crucial to media management research, helping explain how technology diffusion affects the communication industry and providing a glimpse into its future. This meta-analysis looks at the recent history of mass communication scholarship (1983–2008), addressing newsroom changes to determine whether a mainstream managerial theory—sociotechnical literature—garners adequate use. The researchers discovered numerous references—mostly indirect—to sociotechnical systems (STS) theory, yet found sporadic use of a true managerial emphasis as expressed through the analysis of STS theory's principles- and frames-specific theoretical framework. Implications of the findings and future, alternative directions for research are proposed.

This paper has explored the role of Artificial Intelligence (AI) in advancing sustainable development practices and its impacts across key sectors includes energy, waste management, agriculture, and urban infrastructure. Using a model that joins together Socio-Technical Systems Theory with the Circular Economy framework, plus Sustainability Transitions Theory, this paper looks at AI's help in achieving green results. The DeepMind AI helped Google reduce the energy use for cooling data centers by 40%. AI-enabled sorting systems have improved recycling accuracy by up to 20%. AI-driven agricultural solutions increased crop yield by 40%. AI-driven projects helped improve traffic management, lowering emissions, and achieving cost savings. This study has an interdisciplinary approach. The paper is a combination of insights from technology, sustainability and ethics.

The rapid advancement of artificial intelligence (AI) is ushering in a new era of service innovation, with agentic AI systems, characterized by autonomy, contextual reasoning, and adaptive decision-making, poised to transform the hospitality and tourism industry. Grounded in socio-technical systems theory, this conceptual paper examines the technical foundations of agentic AI and proposes a typology of five functional roles: service agents, planning agents, monitoring agents, engagement agents, and meta-agents. These systems offer opportunities for enhanced personalization, efficiency, and guest engagement. However, their adoption introduces critical risks, including data privacy concerns, algorithmic bias, cultural misinterpretation, and workforce displacement. The paper outlines governance strategies emphasizing ethical design, human–AI collaboration, and regulatory adaptation. It concludes with a research agenda to guide empirical inquiry into the social, cultural, and psychological impacts of agentic AI across hospitality and tourism contexts.

This study aims to examine how the integration of Artificial Intelligence (AI) and Cloud-Based Accounting Information Systems (CAIS) enhances the reliability of financial reporting and strengthens cybersecurity. Using a qualitative Systematic Literature Review (SLR) and guided by Sociotechnical Systems Theory, the research synthesizes 28 academic sources. Results show that AI and CAIS improve accuracy, fraud detection, and real-time reporting while mitigating cyber threats through encryption and automated monitoring. However, challenges include algorithmic bias, regulatory complexity, and organizational misalignment. The study concludes that joint optimization of technical and social systems is essential for achieving secure, transparent, and resilient digital financial reporting environments.

This study investigates the organizational factors influencing technological readiness for Artificial Intelligence (AI) adoption, using a theoretical lens grounded in Sociotechnical Systems Theory (STS). A survey of 210 participants from educational and industrial institutions in China was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results confirm that AI adoption significantly enhances organizational efficiency and employee engagement, which in turn mediate its impact on technological readiness. By incorporating mediation analysis, the study provides a nuanced understanding of the indirect mechanisms through which AI adoption influences readiness, offering both theoretical and managerial insights for AI-enabled transformation. The integration of STS and rigorous empirical modeling presents a methodologically sound and practically relevant contribution to the field.