Sociotechnical Systems Theory Research Articles

Supply chain learning and environmental performance: leveraging relational capital and information technology

PurposeWhile the importance of supply chain learning (SCL) is widely recognized by both academia and industry, the mechanisms through which SCL is developed and its effects on environmental performance remain insufficiently understood. This study conceptualizes and empirically investigates the role of relational capital and information technology (IT) in enhancing SCL and improving environmental performance.Design/methodology/approachDrawing on socio-technical system theory (STS) and a knowledge-based view, this research employs structural equation modeling to test the proposed model, utilizing survey data collected from Chinese manufacturing companies.FindingsThe results demonstrate that relational capital with supplier and customer significantly enhances both supplier and customer learning. While IT does not directly impact supplier and customer learning, it exerts an indirect influence through its positive effect on relational capital. In other words, relational capital mediates the relationship between IT and supplier and customer learning. Additionally, this study finds that both supplier and customer learning enhance environmental performance.Practical implicationsThis study provides actionable insights for managers, emphasizing the importance of relational capital in fostering SCL and improving environmental performance. By understanding these relationships, managers can develop more effective strategies for leveraging SCL as a tool for sustainability.Originality/valueThis study adds to the existing body of knowledge in supply chain management by offering a more nuanced and holistic model to explain how relational capital, IT and SCL interact to influence environmental performance, particularly within the context of Chinese manufacturing companies.

The Holistic Intelligent Healthcare Theory (HIHT): Integrating AI for Ethical, Transparent, and Human-Centered Healthcare Innovation

The rise of artificial intelligence (AI) in healthcare has the potential to significantly increase the efficiency, personalization, and overall efficacy of medical care delivery. However, its integration needs to be improved regarding ethical norms, data quality, openness, and human-centered care. This study proposes the Holistic Intelligent Healthcare Theory (HIHT), a comprehensive theory for guiding AI's ethical and practical use in healthcare. It draws on insights from theories such as the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), Socio-Technical Systems Theory (STS), Ethics of Care, Data Governance and Information Quality Theory, and Augmented Intelligence Theory. The HIHT highlights the importance of centralized governance, strong data quality, transparent AI methods, human-AI collaboration, and a reimagined role for healthcare practitioners—the "Intelligent Doctor." The study presents the theory's structures and investigates their implications for AI integration in healthcare, offering a road map for responsible and practical AI applications.

E-assessment and e-proctoring implementation during coronavirus pandemic: A participatory study using socio-technical systems theory

E-assessment and e-proctoring implementation during coronavirus pandemic: A participatory study using socio-technical systems theory

Artificial Intelligence (AI) in Higher Education: A Threat or Helping Hand in Improving Student-Instructor Communication

Purpose: The study aims to summarize current discussions among educational stakeholders regarding the potential benefits and threats posed by Artificial Intelligence. Methodology: In conducting comprehensive and systematic review, the study utilized the PRISMA flowchart and included only articles published between January 2020 and July 2024. The focus was strictly on peer-reviewed articles, with conference papers, dissertations, and other types of papers excluded. Data was sourced exclusively from EBSCOhost and Google Scholar. Additionally, only articles centered on the higher education industry were considered. Findings: The findings of this study reveal ongoing debates among educational stakeholders regarding the threats and benefits of AI models. It explains how AI is transforming academic environments by offering personalized learning experiences, enhancing learning outcomes, and increasing student engagement. However, concerns about data privacy have also emerged. To eliminate these concerns, the study recommends the introduction of consent forms that give users the option to allow or deny the use of their data for AI training. Unique Contribution to Theory, Practice and Policy: The study contributes to the ongoing debates by grounding its analysis in Sociotechnical Systems Theory (STS), emphasizing the need for a human-centered approach in AI adoption. This offers a holistic perspective on how educational institutions can implement AI effectively while addressing stakeholder concerns about privacy and control over data, making it a valuable resource for both academic and policy discussions surrounding AI in education.

Role of innovative behaviour as a missing linchpin in artificial intelligence adoption to enhancing job security and job performance

AbstractBuilding upon the sociotechnical system theory, the present study contributes by examining the relationship between artificial intelligence (AI) adoption, employees' innovative behaviour on employee performance and job security (JS). The primary data is collected from 340 employees from firms located in the industrial hub of a developing economy using a simple random technique, and data is analysed using Smart‐PLS 3 from the manufacturing sector. The study evidences that employees' adoption and utilization of AI technologies positively influence their innovative behaviour, job performance (JP), and security. Moreover, the study finds a mediating role of innovative behaviour to connect the dots. Organizations can prioritize using AI‐driven training programmes so employees can use AI tools efficiently. Study findings also encourage employees to engage in innovative work behaviours like investigating novel concepts and experimenting with AI technologies to improve JP. This study invalidates that AI will replace employees at the workplace, as we can safely conclude that AI adoption enhances JP and JS.

Digital Transformation and Business Model Innovation: Moderating Role of Digital Leadership

Purpose: The manufacturing sector in Ghana has become increasingly important to the country's economic growth, driven by digital transformation and innovation. This research aims to explore the complexities of digital transformation within Ghanaian manufacturing companies, with a specific focus on the moderating role of digital leadership. Design/Methodology/Approach: This conceptual paper presents a framework that integrates socio-technical systems theory and the upper echelon theory. This framework offers a detailed understanding of how digital transformation, digital leadership, organizational adaptation, and business model innovation interact within the Ghanaian manufacturing sector. Findings: The Ghanaian manufacturing sector, a significant contributor to the country's GDP, has seen substantial growth, largely due to small and medium-sized enterprises (SMEs). However, many industries within the sector face challenges in embracing the digital age, including inadequate technological infrastructure and limited access to funding. The Ghanaian government has responded with policies to enhance digital capabilities and innovation, positioning the country for digital readiness. Originality: This paper contributes to the field by providing practical strategies for leadership development and decision-making within manufacturing organizations. It also advances theoretical perspectives and offers empirical insights specific to the Ghanaian context. Overall, this paper serves as a valuable resource for manufacturing leaders, policymakers, and researchers navigating digital transformation, innovation, and leadership in Ghana's manufacturing sector, with broader implications for emerging economies globally.

Unveiling the impact of the congruence between artificial intelligence and explorative learning on supply chain resilience

PurposeDrawing upon socio-technical system theory, this study intends to investigate the effects of the congruence and incongruence between artificial intelligence (AI) and explorative learning on supply chain resilience as well as the moderating role of organizational inertia.Design/methodology/approachUsing survey data collected from 170 Chinese manufacturing firms, we performed polynomial regression and response surface analyses to test our hypotheses.FindingsWe find that the congruence between AI and explorative learning enhances firms’ supply chain resilience, while the incongruence between these two factors impairs their supply chain resilience. In addition, compared with low–low congruence, high–high congruence between AI and explorative learning improves supply chain resilience to a greater extent. Moreover, organizational inertia attenuates the positive influence of the congruence between AI and explorative learning on supply chain resilience, while it aggravates the negative influence of the incongruence between these two factors on supply chain resilience.Originality/valueOur study expands the literature on supply chain resilience by demonstrating that the congruence between a firm’s AI (i.e. technical aspect) and explorative learning (i.e. social aspect) boosts its supply chain resilience. More importantly, our study sheds new light on the role of organizational inertia in moderating the congruent effect of AI and explorative learning, thereby extending the boundary condition for socio-technical system theory in the supply chain resilience literature.

Sociotechnical analysis of factors influencing IoT adoption in healthcare: A systematic review

This paper responds to previous calls for a deeper sociotechnical understanding of Internet of Things (IoT) adoption within healthcare. Through a systematic literature review, it synthesizes existing empirical research on the factors influencing IoT adoption, identifying 94 distinct factors grouped into 24 themes. By leveraging sociotechnical systems theory, the paper thoroughly analyzes these factors and proposes a specialized sociotechnical systems framework tailored specifically to IoT adoption in healthcare settings. This study underscores the current dearth of empirical research in this area and emphasizes the need for more studies, particularly those focused on the actual adoption processes of IoT solutions in healthcare contexts. By addressing these gaps, the review aims to significantly contribute to the literature, offering a novel sociotechnical perspective on IoT adoption that complements and enriches prior reviews that have predominantly focused on technical aspects.

The Effectiveness of Enterprise Resource Planning (ERP) Systems in Knowledge Management

Purpose: The general objective of the study was to evaluate the effectiveness of Enterprise Resource Planning (ERP) systems in knowledge management. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the effectiveness of Enterprise Resource Planning (ERP) systems in knowledge management. Preliminary empirical review revealed that ERP systems significantly enhanced knowledge management by integrating business processes, eliminating data silos, and improving decision-making and operational efficiency. Success depended on factors like organizational culture, top management support, and user training. ERP systems facilitated continuous improvement and innovation by providing real-time data access, enabling swift responses to market changes. Despite challenges like high costs and resistance to change, these could be mitigated through careful planning, strategic alignment, and effective change management, making the investment in ERP systems worthwhile for improved KM and organizational performance. Unique Contribution to Theory, Practice and Policy: The Resource-Based View (RBV) Theory, Socio-Technical Systems (STS) Theory and Knowledge-Based View (KBV) Theory may be used to anchor future studies on Enterprise Resource Planning (ERP) systems. The study recommended integrating ERP systems into KM frameworks to enhance theoretical models, emphasizing strategic alignment and continuous user engagement for practical implementation. It advised organizations to develop policies prioritizing ERP and KM alignment, foster knowledge-sharing cultures, and implement robust change management strategies. Continuous improvement and innovation were highlighted, with recommendations for regular system evaluation and incorporation of technological advancements. Long-term strategic planning was also emphasized, including comprehensive plans for ERP implementation, maintenance, and alignment with organizational goals to maximize the effectiveness of ERP systems in knowledge management.

Influence of Machine Learning on Predictive Maintenance in Manufacturing in Kenya

Purpose: The aim of the study was to evaluate the influence of machine learning on predictive maintenance in manufacturing in Kenya. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Machine learning has significantly enhanced predictive maintenance in Kenya's manufacturing sector. By analyzing large datasets from equipment sensors, ML algorithms can predict equipment failures before they occur, minimizing downtime and maintenance costs. This proactive approach improves operational efficiency and extends equipment lifespan, benefiting manufacturers by reducing unplanned disruptions and enhancing overall productivity. Unique Contribution to Theory, Practice and Policy: Diffusion of innovations theory, resource-based view (RBV) & sociotechnical systems theory may be used to anchor future studies on the influence of machine learning on predictive maintenance in manufacturing in Kenya. Manufacturing firms should invest in comprehensive training programs to equip their workforce with the necessary skills to manage and interpret ML-driven maintenance systems. Governments and industry regulators should create incentives for the adoption of ML technologies in manufacturing.

Quality 4.0 practices toward sustainable excellence in the manufacturing sector

In the Industry 4.0 context, it is very important for companies to apply a comprehensive and sustainable business model to grow steadily and quickly adapt to the fast-changing environment. Although the existing literature has explored Quality 4.0 by integrating Industry 4.0 tools into the TQM system, the question of how Quality 4.0 practices drive sustainable excellence (SE) remains unexplored. Therefore, to fill the gap, this research aims to investigate the relationship between Quality 4.0 practices and SE as well as the role of digital transformation (DT) and digital leadership in this connection, anchoring on the stakeholder theory, the natural resource-based view (NRBR) theory, and the socio-technical system (STS) theory. The research employs the quantitative Structural Equation Model method to analyse empirical data in the manufacturing industry in Vietnam. The findings demonstrate that Quality 4.0 practices positively influence both digital transformation and SE. This study confirmed the mediating role of digital transformation and the moderating role of digital leadership in the relationship between Quality 4.0 practices and SE. The findings of this study contributed to applications of Quality 4.0 practices and digital transformation to improve SE in the manufacturing sector.

Digitally transforming the organization through knowledge management: a socio-technical system (STS) perspective

PurposeIn today’s rapidly evolving business landscape, innovation is the cornerstone for every organization. Knowledge management (KM) is crucial for developing sustainable competitive advantage by fostering innovation. This study aims to identify the key drivers of KM in the context of digital transformation through qualitative research.Design/methodology/approachThis study employs a qualitative approach based on in-depth interviews with senior KM officers, including chief knowledge officers and directors who spearhead KM in their respective organizations. This research identifies four key dimensions, shedding new light on the drivers of KM in the context of digital transformation.FindingsThis study’s findings reveal that the integration of important drivers from the lens of social-technical system (STS) theory is categorized into the four dimensions of KM, namely, motivation, technology, people interaction and organizational drivers. These factors jointly impact and design the effectiveness of KM in the digital age.Originality/valueThis study makes a unique contribution to the field of digital transformation. It presents a conceptual framework from the lens of the STS theory that encompasses four critical dimensions of KM: motivation, technology, people interaction and organizational dimensions, each with sub-codes. This framework can be utilized by practitioners and scholars alike.

The Impact of Edge Computing on Real-Time Data Processing

Purpose: The study sought to explore the impact of edge computing on real-time data processing. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the impact of edge computing on real-time data processing. Preliminary empirical review reveled that edge computing significantly reduced latency and enhanced efficiency in real-time data processing across various industries by bringing computational resources closer to data sources. It highlighted the technology's ability to handle large volumes of IoT-generated data, improve security by localizing data processing, and drive innovation and economic growth through new applications and services. Edge computing's decentralized approach proved essential for reliable and robust data handling, particularly in critical sectors like healthcare and finance, ultimately solidifying its importance in the digital transformation landscape. Unique Contribution to Theory, Practice and Policy: The Diffusion of Innovations Theory, Resource-Based View (RBV) and Sociotechnical Systems Theory may be used to anchor future studies on edge computing on real-time data processing. The study recommended expanding theoretical frameworks to include the unique aspects of edge computing, investing in robust edge infrastructure, and developing standardized protocols and best practices. It emphasized the need for government incentives and supportive regulatory frameworks to promote adoption, and suggested that academic institutions incorporate edge computing into curricula. Additionally, the study called for ongoing research to address emerging challenges and opportunities, ensuring continuous advancement and effective implementation of edge computing technologies.

Investigating circular procurement management and sustainability performance: The interactive moderating effects of digital traceability and collaboration

Whether and how manufacturing firms can enhance their overall sustainability performance through the implementation of circular procurement management is still unclear and under-researched. Drawing on the socio-technical systems theory, this study develops a theoretical model and explores the potential impacts of circular procurement management on manufacturing firms’ sustainability performance (i.e., economic, environmental, and social performance) while examining the moderating role of digital traceability and collaboration. Based on survey data from 250 Chinese circular economy manufacturing firms, this study uses hierarchical regression to analyze the impact mechanism and boundary of circular procurement management on sustainability performance. The empirical results show that (1) Circular procurement management in manufacturing firms can significantly improve economic, environmental, and social performance; (2) Digital traceability positively moderates the effect of circular procurement management on both environmental and social performance; (3) Collaboration positively moderates the effect of circular procurement management on both economic and environmental performance; and (4) The impact of circular procurement management on environmental performance is positively and interactively moderated by digital traceability and collaboration.

Examining Ethical and Social Implications of Digital Mental Health Technologies Through Expert Interviews and Sociotechnical Systems Theory

This paper aims to understand how science and technology experts working in the digital mental health field interpret the ethical and social implications of its technologies, combining an ‘expert interview’ methodology with insights from sociotechnical systems theory. Following recruitment of experts in science and technology fields who had experience of supporting the development of DMH interventions, 11 semi-structured interviews were conducted and analyzed in accordance with the Framework Method. A single theme of ‘complexity of implications’ is presented here and divided into the categories of ‘implications for users’, ‘implications for healthcare professionals and systems’, and ‘implications for society’. Participants identified a range of ethical and social implications of digital mental health technologies at the three different levels, which this discussion relates to three key aspects of complex sociotechnical systems identified in existing theoretical work. These are ‘heterogeneity’, ‘interdependence’ and ‘distribution’, each of which raises important questions for future research about how complex values, relationships and responsibilities should be negotiated in digital mental health. The paper concludes that this study’s approach provides a model for understanding the implications of digital health more broadly, with participants’ combined experience and knowledge shedding light on key interventions at the forefront of digitalization in healthcare.

What Motivates Consumers’ Purchase Intentions in E-Commerce Live Streaming: A Socio-Technical Perspective

The surge in popularity of e-commerce live streaming is evident among sellers and consumers. However, a holistic examination of the influencing factors of consumers’ purchase intention from the perspective of social and technical systems is still limited. Utilizing the socio-technical systems theory, we investigate the effects of social and technical system factors on consumers’ flow experience and how flow affects consumer purchase intention in the live streaming setting. Furthermore, we identify how optimal stimulation level moderates the relationship between socio-technical system factors and flow. Empirical results (N = 355) indicate that flow can be strengthened through social system factors (financial bonds, social bonds) and technical system factors (visibility, guidance shopping), consequently affecting consumer purchase intention. In terms of the moderating effects, the optimal stimulation level positively moderates the relationships between social bonds, visibility, guidance shopping, and flow.

Artificial intelligence and human integration: a conceptual exploration of its influence on work processes and workplace learning

ABSTRACT This paper examines the influence of Artificial Intelligence (AI) on job and organizational processes, drawing on sociotechnical systems (STS) theory. It discusses the emergence of AI and its implications for HRD on individual, group, and organizational learning. AI has changed the trajectory of work processes and outcomes, paving the way for the future of work. An AI-human integration process model is proposed to reinforce its influence on job design in response to AI applications in the workplace. Learning is experienced between humans and between humans and AI. AI’s cognitive intelligence is capable of learning from each other through a process of deep learning. Humans, too, can learn from AI to modify their response towards working with AI-enabled applications. The model illustrates the relationships between individuals, work processes, and sociotechnical systems through multilevel interactions. Of relevance to HRD are two critical processes, namely learning and innovating, that facilitate the continuation of AI-human integration, enabling employees to embrace AI as an integral part of their work life. The paper offers several implications for HRD research and practice, recognizing opportunities for workplace learning and the potential constraints and risks arising from AI adoption that are applicable globally and internationally.

Unpacking Digital Transformation: Identifying key enablers, transition stages and digital archetypes

This study aims to identify recurring Digital Transformation (DT) archetypes within firms and explore the capabilities and values associated with each archetype. Underpinned by the Resource-Based View (RBV) and Socio-Technical Systems (STS) theory, the study provides a framework explicating the enabling factors, their characteristics, interdependencies, operational capabilities, and resultant value creation within the context of digitalizing firm operations. Employing a deductive-inductive approach, an exhaustive literature review combined with an in-depth, multiple case study analysis of sixteen firms provides rich insights into the digital transformation journey. Findings reveal technology, data, human capital, processes, and organization structure as key enablers alongside their critical, complementary roles for transformation. Cross-case analysis identified instances of the DT framework, uncovering three archetypes: process efficiency, responsiveness, and strategic agility. Each archetype exhibits distinct socio-technical enabler configurations and improvements to business processes, resulting in unique operational capabilities and value generation. This research provides actionable guidance for managers in leveraging socio-technical enablers to achieve successful digital transformation. It underscores the need for tailored strategies, recognizing that a one-size-fits-all approach is inadequate. Managers must strategically select desired values and develop the necessary socio-technical system components, processes, and operational capabilities to attain them. OriginalityThis study develops digital archetypes of firm operations linking sociotechnical components, operational capabilities, and goals of firm operations. To the best of our knowledge, no prior research has developed such digital archetypes.

Technological power, complex systems, and boundary objects in global energy transitions

This study makes a case for why the field of political ecology (PE) would benefit from deep engagement with technological power, and exemplifies this within the domain of energy studies and the ongoing transformation of the energy sector. Technology is not among the core interests or traditional topics of PE—and is therefore often disregarded or treated as a black box. The argument presented in this study asserts that this omission undermines the analytical power and the relevance of the field. Technology is the central mechanism whereby socionatures evolve and how relationships are negotiated and enacted. The attitude towards technical things is partly based on the idea that technologies are just artefacts of little interest. Cross-field dialogue is also hindered by perceived ontological conflicts. This study draws upon research from the philosophy of technology and sociotechnical systems theory to introduce a dynamic understanding of technological power. Seeing what technology does and has the potential to do requires looking beyond the common emphasis on dominance and control, to the myriad ways in which technologies shape our everyday lives, ontologies, and imagining of the future. Taking on a contentious concept, I advocate for the use of “systems” as a boundary object suitable for cross-field dialogue. As an analytical construct without inherent scale, it works as a framing device for moving power and knowledge claims to the forefront, while also allowing dialogue outside academia.

Reconfiguring the Customer Service Domain: Perspectives of Managers, Conversational Designers, and Human Agents on Human–Chatbot Collaboration

This study explored the criteria for evaluating human–chatbot collaboration in customer service using the socio-technical systems theory. Interviews with 28 customer service managers, conversational designers, and human agents revealed their evaluation criteria diverged. Managers prioritized the chatbot’s technical capabilities and preferred a standalone customer service chatbot, while conversational designers prioritized the social aspect, focusing on the chatbot as a support tool for human agents and customers. Human agents mainly evaluated the chatbot as a means to increase job satisfaction. These distinct criteria highlight the importance of an aligned approach in human–chatbot collaboration. To improve this collaboration, integrating the chatbot into customer data systems was suggested.