Summary of the papers in this special issue
| Study (year) | Research question (data and method) | Context-industry | Conceptual lens | Key findings | |
|---|---|---|---|---|---|
| 1 | Manresa et al. (2025) | How does Generative AI (GenAI) influence employee performance in the workplace? (Quantitative method: Survey) | Sectors, including technology, education, hospitality finance and health in Spain | Stimulus-organism-response (SOR) framework | Positive attitudes towards GenAI correlate with enhanced engagement and performance. AI use improves attitudes and trust towards the adoption of GenAI |
| 2 | Asante et al. (2025) | What are the behavioural and social antecedents that produce a highly positive response to AI bias? (Quantitative method: Fuzzy-set qualitative comparative analysis (fsQCA) | Banking, insurance, media, oil, gas, telecommunication and manufacturing in Ghana | Social identity theory, norm activation theory and justice theory | Managers who regard their response to AI bias as a personal moral duty are likely to feel a strong sense of guilt towards the unintended consequences of AI logic and reasoning. Those who perceive the processes that guide AI algorithms’ reasoning as discriminating would be keen to address this prejudicial outcome |
| 3 | Mendy et al. (2025) | How do managers deploy AI systems to address underperformance? (Conceptual: literature review) | AI–HRM–Managerial decision-making literature | Thematic literature review | Capturing five critical areas— human, artificial intelligence, employees’ well-being, jobs, and organizational performance, the study develops a heuristic AI-HR framework that specifies how managers deploy AI systems to address underperformance |
| 4 | Siaw and Ali (2025) | How do human intelligence (HI) and artificial intelligence (AI) substitute and complement each other for organizational knowledge management (Conceptual: Systematic literature review) | AI-strategy-knowledge management literature | Dynamic capabilities | Substituting human intelligence (HI) with AI is suitable for external environmental scanning to identify opportunities, and AI substitution for HI is ideal for internal scanning through data analytics |
| 5 | Shao et al. (2025) | What are the relationships between the antecedents of GenAI adoption and the impact of GenAI adoption on employee efficiency, business value creation, and firm performance? (Quantitative methods: Survey data) | Indian retail sector | Technology adoption theory | Top management support, openness to innovation, and competitive pressures positively impact the adoption of GenAI |
| 6 | Zhao et al. (2025) | How does technophobia influence a manager’s intention to adopt generative artificial intelligence (AI) in management practices? (Quantitative methods: Survey data) | Chinese business managers | Cognitive-affective-normative (CAN) model | Technophobia negatively impacts manager’s intention to adopt generative AI, but self-regulated learning reduces the negative impact of technophobia on AI adoption |
| 7 | Amankwah-Amoah and Appiah (2025) | How does AI shape the process of human capital obsolescence (HCO) in different types of organizations, leading to different business failure pathways? (Conceptual: literature review) | Literature on AI advancement. And impact on employee knowledge, skills, and abilities | Human capital obsolescence (HCO) | Developed a four-domain framework which demonstrates how AI-induced HCO could trigger the obsolescence of individuals' knowledge, skills, and abilities in different types of organizations |
| 8 | Aziz et al. (2025) | What is the emerging relationship between AI and leadership (Systematic: literature review) | Broader literature on AI and leadership | AI-powered leadership | Unpacks a range of key challenges in AI-powered leadership, including ethical dilemmas, complications in human-AI interactions, hurdles in AI implementation within leadership contexts, and some long-term risks associated with AI integration |
| Study (year) | Research question (data and method) | Context-industry | Conceptual lens | Key findings | |
|---|---|---|---|---|---|
| 1 | How does Generative AI (GenAI) influence employee performance in the workplace? (Quantitative method: Survey) | Sectors, including technology, education, hospitality finance and health in Spain | Stimulus-organism-response (SOR) framework | Positive attitudes towards GenAI correlate with enhanced engagement and performance. AI use improves attitudes and trust towards the adoption of GenAI | |
| 2 | What are the behavioural and social antecedents that produce a highly positive response to AI bias? (Quantitative method: Fuzzy-set qualitative comparative analysis (fsQCA) | Banking, insurance, media, oil, gas, telecommunication and manufacturing in Ghana | Social identity theory, norm activation theory and justice theory | Managers who regard their response to AI bias as a personal moral duty are likely to feel a strong sense of guilt towards the unintended consequences of AI logic and reasoning. Those who perceive the processes that guide AI algorithms’ reasoning as discriminating would be keen to address this prejudicial outcome | |
| 3 | How do managers deploy AI systems to address underperformance? (Conceptual: literature review) | AI–HRM–Managerial decision-making literature | Thematic literature review | Capturing five critical areas— human, artificial intelligence, employees’ well-being, jobs, and organizational performance, the study develops a heuristic AI-HR framework that specifies how managers deploy AI systems to address underperformance | |
| 4 | How do human intelligence (HI) and artificial intelligence (AI) substitute and complement each other for organizational knowledge management (Conceptual: Systematic literature review) | AI-strategy-knowledge management literature | Dynamic capabilities | Substituting human intelligence (HI) with AI is suitable for external environmental scanning to identify opportunities, and AI substitution for HI is ideal for internal scanning through data analytics | |
| 5 | What are the relationships between the antecedents of GenAI adoption and the impact of GenAI adoption on employee efficiency, business value creation, and firm performance? (Quantitative methods: Survey data) | Indian retail sector | Technology adoption theory | Top management support, openness to innovation, and competitive pressures positively impact the adoption of GenAI | |
| 6 | How does technophobia influence a manager’s intention to adopt generative artificial intelligence (AI) in management practices? (Quantitative methods: Survey data) | Chinese business managers | Cognitive-affective-normative (CAN) model | Technophobia negatively impacts manager’s intention to adopt generative AI, but self-regulated learning reduces the negative impact of technophobia on AI adoption | |
| 7 | How does AI shape the process of human capital obsolescence (HCO) in different types of organizations, leading to different business failure pathways? (Conceptual: literature review) | Literature on AI advancement. And impact on employee knowledge, skills, and abilities | Human capital obsolescence (HCO) | Developed a four-domain framework which demonstrates how AI-induced HCO could trigger the obsolescence of individuals' knowledge, skills, and abilities in different types of organizations | |
| 8 | What is the emerging relationship between AI and leadership (Systematic: literature review) | Broader literature on AI and leadership | AI-powered leadership | Unpacks a range of key challenges in AI-powered leadership, including ethical dilemmas, complications in human-AI interactions, hurdles in AI implementation within leadership contexts, and some long-term risks associated with AI integration |
Source(s): Authors’ creation
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