Table 1. Research questions about AI in... | Emerald Publishing

Table 1.

Research questions about AI in management and organizational studies

Area	Research questions	References (e.g.)
Strategic management	How can organizations optimize human-machine collaboration to enhance decision accuracy and minimize errors, particularly in high-stakes contexts? How can organizations integrate generative AI with human expertise to reduce biases, inconsistencies and verification challenges in strategic evaluations? How can predictive models and symbolic data structures improve decision-making under complexity and ambiguity in organizational environments? What roles do different AI configurations and methods, such as generative models and LLM aggregation, play in enhancing strategic foresight and organizational adaptability? How do AI and digitalization reshape resource-based view applications in strategic management, including resource redeployment and competitive advantage? How can advanced analytical methods, like machine learning, deepen our understanding of resource evolution and the generalizability of existing management theories? What governance mechanisms can organizations implement to mitigate biases and opacity in AI-driven decision-making processes?	Doshi et al. (2024)
Innovation management	How can AI reduce the costs and risks of product development while tailoring offerings to evolving customer preferences? How can AI support the creation of new business models, market entry and strategic innovation during early phases of the innovation process? How can AI enhance the efficiency and accuracy of idea screening and creative problem-solving across industries? How can firms optimize the balance between human and computational capital in AI-driven R&D to maximize innovation outcomes and long-term R&D productivity? How can AI mitigate innovation decline in public firms and drive sustainable innovation across industries?	Ameen et al. (2024); Babina et al. (2024)
Organizational behavior/ human resource management	How can AI promote workplace equality and diversity, including addressing neurodiversity and reducing biases like the gender gap? How do minority groups perceive workplace AI use, and what benefits, challenges and ethical concerns emerge? How can AI enhance employee well-being, including mental health, job security and overall flourishing at work? How do AI-generated resources influence social and work processes that drive well-being and engagement? How can hybrid AI–human models improve recruitment, talent management and decision-making processes while fostering trust and learning? How do different AI designs impact adaptability, engagement and performance in human-machine collaboration? What novel job designs combine human and AI capabilities, and how can AI be tailored to diverse cognitive needs for productivity and inclusion? What job functions are best suited for delegation to AI, and how can organizations ensure effective integration into workflows? What factors influence leaders’ use of AI, and how does trust in AI shape employment relations and organizational dynamics? How do organizational factors, such as “stupidity” or resistance, affect AI adoption and effectiveness? How can generative AI like ChatGPT support recruitment, onboarding and talent development while addressing ethical and legal implications?	Bankins and Formosa (2023); Budhwar et al. (2023)
Supply chain management	How can AI models and hybrid AI–human decision-making systems mitigate the impact of fake news and disinformation on supply chain operations and resilience? How can AI and blockchain integration enhance transparency, traceability and sustainability in global supply chains? What are the technological, behavioral, organizational and regulatory barriers to effectively combining AI and blockchain in supply chain management?	Toorajipour et al. (2021); Charles et al. (2023)
Marketing	What configurations of AI characteristics lead to favorable or unfavorable consumer responses, including adoption, resistance and anthropomorphism? Which characteristics are most influential, and how can firms and society improve them to enhance consumer engagement? How does task consequentiality influence consumer perceptions of AI, and how do mechanical and feeling AI differ in their effects? What is the optimal configuration between AI intelligence types and customer journey stages)? How do different types of AI intelligence shape the consumer journey and the service ecosystem, including customer needs, engagement and satisfaction? What are the ethical and societal implications of AI integration in customer journeys, including issues of inequity and well-being?	Marvi et al. (2024); Mohammadi et al. (2024)
Entrepreneurship	How can entrepreneurs combine the novelty of human-generated ideas with the environmental and financial value of AI-generated ideas to optimize the ideation process? What theoretical mechanisms explain this balance? How does AI enable individual creativity and entrepreneurship while potentially reducing idea diversity at the macro level (creative convergence)? What principles explain this dual effect? How does AI influence disparities between resource-rich and resource-poor entrepreneurs, and does it empower less experienced or younger entrepreneurs to compete with seasoned ones? What mechanisms drive these effects? How does AI enhance entrepreneurs’ cognitive resources, influence decision-making under uncertainty and affect perceptions of uncertainty, intentions, and actions? What explains these changes? In what areas of the entrepreneurial process and for which tasks does AI improve outcomes like quality, productivity and efficiency? What explains these improvements? How does AI impact resource acquisition and allocation, including financial planning, venture capital decisions and challenging traditional methods like effectuation and the lean startup? Can AI reduce societal and personal costs associated with entrepreneurship while fostering more sustainable and responsible practices?	Lévesque et al. (2022); Obschonka et al. (2024)
Business ethics	What ethical challenges emerge from utilizing real-time web-sourced data in generative AI applications for business? How can organizations navigate the trade-off between pursuing innovation and addressing data privacy and security concerns? What strategies can organizations adopt to maintain ethical standards while implementing surveillance-based AI solutions?	Huang et al. (2022)

Area	Research questions	References (e.g.)
Strategic management	How can organizations optimize human-machine collaboration to enhance decision accuracy and minimize errors, particularly in high-stakes contexts? How can organizations integrate generative AI with human expertise to reduce biases, inconsistencies and verification challenges in strategic evaluations? How can predictive models and symbolic data structures improve decision-making under complexity and ambiguity in organizational environments? What roles do different AI configurations and methods, such as generative models and LLM aggregation, play in enhancing strategic foresight and organizational adaptability? How do AI and digitalization reshape resource-based view applications in strategic management, including resource redeployment and competitive advantage? How can advanced analytical methods, like machine learning, deepen our understanding of resource evolution and the generalizability of existing management theories? What governance mechanisms can organizations implement to mitigate biases and opacity in AI-driven decision-making processes?	Doshi et al. (2024)
Innovation management	How can AI reduce the costs and risks of product development while tailoring offerings to evolving customer preferences? How can AI support the creation of new business models, market entry and strategic innovation during early phases of the innovation process? How can AI enhance the efficiency and accuracy of idea screening and creative problem-solving across industries? How can firms optimize the balance between human and computational capital in AI-driven R&D to maximize innovation outcomes and long-term R&D productivity? How can AI mitigate innovation decline in public firms and drive sustainable innovation across industries?	Ameen et al. (2024); Babina et al. (2024)
Organizational behavior/ human resource management	How can AI promote workplace equality and diversity, including addressing neurodiversity and reducing biases like the gender gap? How do minority groups perceive workplace AI use, and what benefits, challenges and ethical concerns emerge? How can AI enhance employee well-being, including mental health, job security and overall flourishing at work? How do AI-generated resources influence social and work processes that drive well-being and engagement? How can hybrid AI–human models improve recruitment, talent management and decision-making processes while fostering trust and learning? How do different AI designs impact adaptability, engagement and performance in human-machine collaboration? What novel job designs combine human and AI capabilities, and how can AI be tailored to diverse cognitive needs for productivity and inclusion? What job functions are best suited for delegation to AI, and how can organizations ensure effective integration into workflows? What factors influence leaders’ use of AI, and how does trust in AI shape employment relations and organizational dynamics? How do organizational factors, such as “stupidity” or resistance, affect AI adoption and effectiveness? How can generative AI like ChatGPT support recruitment, onboarding and talent development while addressing ethical and legal implications?	Bankins and Formosa (2023); Budhwar et al. (2023)
Supply chain management	How can AI models and hybrid AI–human decision-making systems mitigate the impact of fake news and disinformation on supply chain operations and resilience? How can AI and blockchain integration enhance transparency, traceability and sustainability in global supply chains? What are the technological, behavioral, organizational and regulatory barriers to effectively combining AI and blockchain in supply chain management?	Toorajipour et al. (2021); Charles et al. (2023)
Marketing	What configurations of AI characteristics lead to favorable or unfavorable consumer responses, including adoption, resistance and anthropomorphism? Which characteristics are most influential, and how can firms and society improve them to enhance consumer engagement? How does task consequentiality influence consumer perceptions of AI, and how do mechanical and feeling AI differ in their effects? What is the optimal configuration between AI intelligence types and customer journey stages)? How do different types of AI intelligence shape the consumer journey and the service ecosystem, including customer needs, engagement and satisfaction? What are the ethical and societal implications of AI integration in customer journeys, including issues of inequity and well-being?	Marvi et al. (2024); Mohammadi et al. (2024)
Entrepreneurship	How can entrepreneurs combine the novelty of human-generated ideas with the environmental and financial value of AI-generated ideas to optimize the ideation process? What theoretical mechanisms explain this balance? How does AI enable individual creativity and entrepreneurship while potentially reducing idea diversity at the macro level (creative convergence)? What principles explain this dual effect? How does AI influence disparities between resource-rich and resource-poor entrepreneurs, and does it empower less experienced or younger entrepreneurs to compete with seasoned ones? What mechanisms drive these effects? How does AI enhance entrepreneurs’ cognitive resources, influence decision-making under uncertainty and affect perceptions of uncertainty, intentions, and actions? What explains these changes? In what areas of the entrepreneurial process and for which tasks does AI improve outcomes like quality, productivity and efficiency? What explains these improvements? How does AI impact resource acquisition and allocation, including financial planning, venture capital decisions and challenging traditional methods like effectuation and the lean startup? Can AI reduce societal and personal costs associated with entrepreneurship while fostering more sustainable and responsible practices?	Lévesque et al. (2022); Obschonka et al. (2024)
Business ethics	What ethical challenges emerge from utilizing real-time web-sourced data in generative AI applications for business? How can organizations navigate the trade-off between pursuing innovation and addressing data privacy and security concerns? What strategies can organizations adopt to maintain ethical standards while implementing surveillance-based AI solutions?	Huang et al. (2022)

Source(s): Own elaboration