MOR SI CFP|‘Opportunities and Challenges of Artificial Intelligence in Strategic Management and Organizational Innovation: New Theories, Perspectives, and Evidence’


Management and Organization Review

Special Issue on ‘Opportunities and Challenges of Artificial Intelligence in Strategic Management and Organizational Innovation: New Theories, Perspectives, and Evidence’

Guest Editors

Runtian Jing1, Weiling Ke2, Jianwen Liao3, En Xie4, and Jingtao Yi5

1Shanghai Jiao Tong University2Southern University of Science and Technology3Cheung Kong Graduate School of Business4Tongji University, and 5Renmin University of China

Proposal Submission Deadline: September 15, 2024

Paper Submission Deadline: April 30, 2025

Special Issue Theme Background

Artificial Intelligence (AI), as a rapidly evolving technology, has become integral to the operations of organizations worldwide (Benbya et al., 2020; Pietronudo et al., 2022). Here, AI can be defined as ‘the ability of a machine to perform cognitive functions that we associate with human minds, such as perceiving, reasoning, learning, interacting with the environment, problem solving, decision-making, and even demonstrating creativity’ (Collins et al., 2021: 10). With vast databases, deep learning capabilities, and intelligent algorithms, AI equips managers with data-driven decision-making abilities, transforming organizational capabilities and procedures (Borges et al., 2021; Krakowski, Luger, & Raisch, 2023; Raisch & Krakowski, 2021). Recently, generative AI, such as large-language models, has garnered significant attention from managers, challenging executives and boards to align them with their digital strategies (Li et al., 2021; Paschen et al., 2020). As Hatami and Segel (2023) have found in a recent McKinsey investigation report, the biggest business event in 2023 (or the past decade) was the arrival of generative AI, which has quickly become the top priority in the CEO agenda of thousands of companies. Additionally, the application of AI provides new opportunities and challenges for organizational strategic management research (Haefner et al., 2021; Von Krogh, 2018). 

This special issue, together with the special issue on Artificial Intelligence in Organizational Behavior and Human Resource Management Research: Opportunities and Challenges’, seeks to delve into the myriad ways that AI is reshaping the landscape of strategy formulation, organizational innovation, competitive dynamics, and job design, team dynamics, and human resource practice. The overarching research question includes: How is AI reshaping research in the fields of strategic management and organizational theory? What opportunities and challenges does AI present to strategic and organizational theories and practices? We seek to tackle these essential research questions at multiple levels of theorizing.

Previous studies have mostly taken a ‘tool’ perspective toward the application of information technologies in the organizational management context, and humans are assumed to be the sole source of agency in technology adoption and development. However, the arrival of generative AI has forced us to re-conceptualize the creativity and agency of this new generation of technology. Advanced algorithms and huge computing capabilities enable intelligent machines to approach or even surpass the level of human creativity and work-quality in many fields such as writing, painting, programming, or video-making. Currently, scholars begin to take the assumption of ‘material agency’ of technology, i.e., ‘the capacity for non-human entities to act on their own, apart from human intervention’ (Leonardi, 2011: 148). In other words, as non-human entities, intelligent machines can exercise their agency through ‘performativity’ without waiting for the human control, which will greatly change our understandings of many fundamental concepts in organizational context (e.g., Benbya et al., 2020; Bahoo et al., 2023; Johnson et al., 2022). Specifically, we highlight the challenges and opportunities for organizational innovation and strategic management created by the commercial application of AI from the following aspects. 

First, the relevance of organizational innovation and strategic management tools traditionally centered on ‘social beings’ has diminished (Budhwar et al., 2023; Mariani et al., 2021), and managers now face the dual challenges of effectively managing ‘AI employees’ and facilitating the interactions between human employees and intelligent robots (Curchod et al., 2020; Haefner et al., 2021; Nishant et al., 2020). In the organizational context, AI may create new persistent sources of competitive advantage by integrating humans’ cognitive capabilities and machines’ computational capabilities (Krakowski, Luger, & Raisch, 2023). Based on the understanding of this neo-diversity caused by ‘human-machine interaction’, organizations need to re-design their coordination and control system for tasks such as decision making or strategic implementation. 

Second, the dichotomous view of management attributes, which depicts organizational innovation and strategic management as both science and art, often overlooks the technical requirements of the implementation process (Essén & Värlander, 2019). Managers in the age of AI need to master a philosophy of technology that enables them to skillfully apply management theory to management practice (Li et al., 2021; Pietronudo et al., 2022). 

Third, the satisficing principle of decision-making is theorized based on the assumption of bounded rationality and the presence of incomplete and asymmetrical information (Chen et al., 2021; Pietronudo et al., 2022). AI, with its huge information processing capacity and super-powerful rationality, breaks through the limitations imposed by human factors (Mikalef & Gupta, 2021; Townsend, et al., 2022), and is expected to promote the transition of the decision-making criterion of organizational innovation and strategic management from satisficing decision-making to optimal decision-making (Gama & Maistretti, 2023; Wilson & Daugherty, 2018). 

Fourth, the excessive pursuit of technological rationality for organizational innovation may intensify the tension between management efficiency and management ethics (Huang et al., 2018; Raisch & Krakowski, 2021), and it is necessary for the government, social organizations, and industry associations to build a multi-level constraint mechanism from a variety of aspects, including policies, laws, and industry standards (Borges et al., 2021). As the fields of organizational management, and AI research generally focus on how to activate intelligent behavior through procedural optimization and design (Csaszar & Steinberger, 2022), concepts such as search, representation, and aggregation originating from the field of AI also provide inspiration for re-examining and re-judging the relationship between subject and object, job design principles, decision-making guidelines, and ethical requirements of organizational innovation and strategic management (Csaszar & Steinberger, 2022).

Over the past decade, China has enacted many national-level policies to accelerate the development of AI technologies in various industrial sectors. For example, in April 2023, the National Cyberspace Administration of China launched the ‘Management Measures for Generative AI Services’, which has specified the admission qualifications, responsibilities, and obligations of industrial application of generative AI, and served as a policy framework for firms to adopt and commercialize this technology in the market. Recognizing AI’s potential to reshape and refine the management discipline, the Business Administration Division of the Department of Management Sciences in the National Natural Science Foundation of China (NSFC) has consistently advocated for the integration of AI into management research. Recently, a series of key projects of NSFC that focus on fundamental research and frontier topics in AI-driven management have been announced, aiming to propel the evolution of the management discipline. In alignment with these initiatives, this special issue aims to ignite a robust dialogue on how AI is transforming strategic management research and practice, and what this means for the future of organizational competitive strategies.

Different from the SI on AI in other journals (such as AMR, SMJ, JMS), this MOR SI highlights the research perspective and evidence from firms in China or global comparative contexts. For example, compared to the Western respondents in the case of ‘trolley dilemmas’, Chinese people are less prone to support utility-maximizing alternatives (Ahlenius & Tännsjö, 2012), which may imply different ethical principles in the context of human-machine coordination. Compared with Western societies, China has taken a more lenient policy toward the data protection regulation in the past decade, which has provided the institutional architecture in supporting the business model innovation and high-speed growth of many Internet companies (Roberts et al., 2021). Under the impacts of these institutional or cultural forces, Chinese firms need to figure out that how the emerging generative AI technologies will reshape their business and industry.

We invite submissions of theoretical, empirical, and case study papers that not only contribute to academic discourse but also offer practical insights for business leaders of Chinese firms grappling with the integration of AI into their strategic and organizational innovation. Through this collection of articles, we aspire to chart future research directions and provide a forum for discussing the multifaceted implications of AI in strategic and organizational research fields.

Scope of the Special Issue

This special issue invites research that enriches and/or challenges existing theories, evidence, and practices on organizational design and change, strategic decision-making, technological innovation, competitive landscape, managerial ethics, etc., concerning the impact of AI on strategy and organization research in the Chinese context. We welcome a diverse range of submissions, including quantitative and qualitative studies, review articles, and conceptual papers. Potential topics include, but are not limited to:

Potential Topics and Research Questions

Decision Making and Strategy Formulation:

  • How can artificial intelligence (AI) be leveraged to enhance strategic decision-making processes in organizations? What are the implications of AI-generated content on organizational communication and decision-making? How does the integration of AI in these processes enhance strategic outcomes such as decision quality and speed, and interpretability of judgment?
  • How can AI be used to predict market trends and inform strategic planning? How can these predictions be effectively incorporated into strategic planning?
  • How does the use of AI affect the managerial cognitive processes of strategic decision-making? How would it challenge or transform the boundaries and assumptions about rational behavioral choice in organizations?
  • Are there any AI-related heuristics managers use in making strategic decisions? What are the cognitive biases and ethical considerations associated with incorporating AI into strategic decision-making? How can organizations address these ethical considerations?

Strategic Innovation and Competitive Dynamics: 

  • How to identify the challenges firms may face when implementing AI-driven strategies? What are the promotors and inhibitors of firms’ adoption of AI tools like machine learning and LLM in strategic management practices?
  • Whether and how does the adoption of AI change firms’ preference for some specific strategic moves like specification or diversification, efficiency or flexibility, low cost or differentiation, etc.? 
  • How can firms’ scientific research efforts (even collaboration with universities) in AI impact their follow-up innovation volume and quality (e.g., patents, and trade secrets like algorithms), as well as their competitive advantage?
  • How does the adoption of AI influence firms’ competitive moves and business model innovation in the global marketplace? How would different kinds of AI technologies work as new forms of resources to enable operational efficiency of firms?
  • How can AI contribute to fostering innovation in product development and service delivery? How can AI indirectly foster innovation by improving processes or creating new opportunities?
  • How can firms use AI to enhance their innovation processes and develop disruptive technologies? How can firms achieve competitive advantage based on the development of specific LLMs (rather than using general LLMs)? What are the impacts of AI on open innovation strategies and collaborations between organizations?

Organizational Design and Change:

  • How and why may the information processing view of organizational design be affected by the emerging attributes of artificial intelligence? How to design appropriate coordination and control system for organizations to maximize the AI properties and human expertise? How to put people in the center of AI-based organizational design? 
  • How would AI technologies shape and impact organizational design and corporate governance? Can AI-driven analytics optimize organizational hierarchies and decision-making processes for increased efficiency? What is the role of AI in monitoring and shaping the emergence of organizational platform ecosystems?
  • What are the leadership challenges for organizations that heavily rely on AI-driven strategies? How does AI influence the role and effectiveness of strategic leaders in shaping organizational direction?
  • How does the use of AI in strategic management impact employee roles and responsibilities? What changes might be required in the organizational culture to facilitate effective AI integration? How could organizations effectively manage the transformation from traditional operational practices to AI-enabled routines?
  • What role does AI play in shaping and evolving organizational culture over time? How can organizations leverage AI to align their culture with strategic goals and adapt to changing external factors?
  • How does the integration of AI impact organizational learning and its ability to adapt to changing environments? Can AI contribute to more effective organizational learning and knowledge management strategies?

Industrial Impacts and Societal Implications:

  • How can AI provide a competitive edge in different industry contexts? What are some examples of successful AI integration leading to competitive advantage?
  • How can institutional arrangements and government policy making ensure the transparency and fairness of application of AI technologies in organizational context? How would AI technologies help organizations cooperate with external suppliers to create new business models in industrial ecosystem context? 
  • What are the dark sides of AI adoption in the strategic management context? For example, homogeneity and then competitive parity. 
  • How do different kinds of AI technologies contribute to creating competitive advantages or disrupting existing competitive dynamics? In what ways can AI be leveraged to gain and sustain competitive advantages in different industries?
  • How does AI contribute to the development and implementation of sustainable innovation practices? How can AI applications help firms reduce negative environmental impact and promote sustainability?

Proposal Submission Requirements

1. Research proposals should not exceed 5,000 words and should include the research questions, a brief and relevant literature review, disciplinary perspectives for the study, hypotheses or research questions, the sample, measures used, data, and analytical plan.

2. Proposals should include a short version of all authors’ academic vitae, which should be fewer than five pages long and highlight the author’s educational background, professional experience, and academic achievements.

3. Proposals should include a timeline for completing the project from the date of approval.

Special Issue Timeline

1. Proposal Submission (deadline September 15, 2024): The preliminary proposal should specify the targeted data source, format, develop intended research questions, and justify the motivation. Please submit proposals to Xingxing Zhao ( with the subject line: ‘Artificial Intelligence for Strategic Management and Organizational Innovation Proposal’.

2.  Paper Development Workshop for Accepted Proposals (October 2024, Business School, Renmin University of China): Accepted proposals will be invited to a developmental workshop to further refine the focused research questions. At the end of the workshop, we will extend invitations to some promising proposals to submit a revised version of the proposal.

3. Paper Submission Deadline (April 30, 2025): Papers for the special issue should be submitted electronically through MOR’s ScholarOne Manuscripts site at and identified as submission to the ‘Artificial Intelligence for Strategic Management and Organizational Innovation’ special issue.

4. Publication of the Special Issue (TBD)

Special Issue Guest Editors

Runtian Jing (Email: is a professor of organizational management at Shanghai Jiaotong University, and was granted the Yangtze-River Distinguished Professorship by the Ministry of Education of China. His research interests include organizational change and leadership behavior. He currently serves as the president of the International Association for Chinese Management Research. He received his PhD from Xi’an Jiaotong University in 1997.

Weiling Ke (Email: is a professor of Information Systems and Management Engineering at Southern University of Science and Technology. Her research interests include digital transformation, digital innovations, and platform ecosystems. She currently serves on the editorial board of multiple journals such as Decision Support Systems, Information & Management and IT & People. She received her PhD from the National University of Singapore in 2004.

Jianwen (Jon) Liao (Email: is an executive fellow at Harvard Business School, a professor of strategy and innovation at Cheung Kong Graduate School of Business and the senior advisor to the Chairman, JD Group. His research interests cut across strategy, innovation, and entrepreneurship. He was among the early contributor to the field of entrepreneurship and was actively involved in Panel Study of Entrepreneurial Dynamics (PSED). His work has been published in the Journal of International BusinessStrategic Entrepreneurship JournalEntrepreneurship Theory and PracticesHarvard Business Review, among others. He received his PhD from Southern Illinois University at Carbondale in 1996. 

En Xie (Email: is a professor of the School of Economics and Management, Tongji University, Shanghai, China. His research interests include business strategy in emerging economies, international business, and strategic alliances. He has published papers in reputable academic journals like Strategic Management JournalJournal of Operations ManagementJournal of World Business, and other leading management journals in China. Prof. Xie has presented his works at AOM, AIB, and refereed management conferences in China.

Jingtao Yi (Email: is a professor of Economics, School of Business, Renmin University of China. His research interests lie at internationalization strategy, emerging market multinationals, innovation and international business, and international business in the digital economy, with a focus on platform ecosystems, platform competition, and globalization. His work has been published in Journal of International Business StudiesJournal of ManagementJournal of Management Studies, among others. He received his PhD from the University of Nottingham in 2007. 


Ahlenius, H., & Tännsjö, T. (2012). Chinese and Westerners respond differently to the trolley dilemmas. Journal of Cognition and Culture, 12, 195–201.

Bahoo, S., Cucculelli, M., & Qamar, D. (2023). Artificial intelligence and corporate innovation: A review and research agenda. Technological Forecasting and Social Change, 188, 122264.

Benbya, H., Davenport, T. H., & Pachidi, S. (2020). Artificial intelligence in organizations: Current state and future opportunities. MIS Quarterly Executive, 19(4), 9–21.

Borges, A. F., Laurindo, F. J., Spínola, M. M., Gonçalves, R. F., & Mattos, C. A. (2021). The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. International Journal of Information Management, 57, 102225.

Budhwar, P., Malik, A., De Silva, M. T., & Thevisuthan, P. (2022). Artificial intelligence–challenges and opportunities for international HRM: A review and research agenda. The International Journal of Human Resource Management, 33(6), 1065–1097.

Csaszar, F. A., & Steinberger, T. (2022). Organizations as artificial intelligences: The use of artificial intelligence analogies in organization theory. Academy of Management Annals, 16(1), 1–37.

Chen, L., Yi, J., Li, S., & Tong, T. (2021). Platform governance design in platform ecosystems: Implications for complementors’ multihoming decision. Journal of Management, 48(3): 630–656.

Collins, C., Dennehy, D., Conboy, K., & Mikalef, P. (2021), ‘Artificial intelligence in information systems research: a systematic literature review and research agenda’. International Journal of Information Management, 60, 102383.

Curchod, C., Patriotta, G., Cohen, L., & Neysen, N. (2020). Working for an algorithm: Power asymmetries and agency in online work settings. Administrative Science Quarterly, 65(3), 644–676.

Essén, A., & Värlander, S. W. (2019). How technology-afforded practices at the micro-level can generate change at the field level: Theorizing the recursive mechanism actualized in Swedish rheumatology 2000–2014. MIS Quarterly, 43(4), 1155–1176.

Gama, F., & Magistretti, S. (2023). Artificial intelligence in innovation management: A review of innovation capabilities and a taxonomy of AI applications. Journal of Product Innovation Management, 9, 12698.

Haefner, N., Wincent, J., Parida, V., & Gassmann, O. (2021). Artificial intelligence and innovation management: A review, framework, and research agenda. Technological Forecasting and Social Change, 162, 120392.

Hatami, H. & Segel, L. H. (2023). What matters most? Eight CEO priorities for 2024. McKinsey Report, December 12, 2023.

Huang, L., Tan, C. H., Ke, W., & Wei, K. K. (2018) Helpfulness of online review content: The moderating effects of temporal and social cues. Journal of the Association for Information Systems, 19(6): 308–327.

Johnson, P. C., Laurell, C., Ots, M., & Sandström, C. (2022). Digital innovation and the effects of artificial intelligence on firms’ research and development–Automation or augmentation, exploration or exploitation? Technological Forecasting and Social Change, 179, 121636.

Krakowski, S., Luger, J., & Raisch, S. (2023). Artificial intelligence and the changing sources of competitive advantage. Strategic Management Journal, 44(6), 1425–1452.

Leonardi, P. M. 2011. When flexible routines meet flexible technologies: Affordance, constraint, and the imbrication of human and material agencies. MIS Quarterly, 35(1): 147–167.

Li, J., Li, M., Wang, X., & Jason, T. (2021). Strategic directions for AI: The role of CIOs and boards of directors. MIS Quarterly, 45(3), 1603–1643.

Mariani, M. M., Machado, I., & Nambisan, S. (2023). Types of innovation and artificial intelligence: A systematic quantitative literature review and research agenda. Journal of Business Research, 155, 113364.

Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58(3), 103434.

Nishant, R., Kennedy, M., & Corbett, J. (2020). Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. International Journal of Information Management, 53, 102104.

Paschen, U., Pitt, C., & Kietzmann, J. (2020). Artificial intelligence: Building blocks and an innovation typology. Business Horizons, 63(2), 147–155.

Pietronudo, M. C., Croidieu, G., & Schiavone, F. (2022). A solution looking for problems? A systematic literature review of the rationalizing influence of artificial intelligence on decision-making in innovation management. Technological Forecasting and Social Change, 182, 121828.

Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210.

Roberts, H., Cowls, J., Morley, J., Taddeo, M., Wang, V. C., & Floridi, L. (2021). The Chinese approach to artificial intelligence: an analysis of policy, ethics, and regulation. AI & Society, 36(1): 59–77.

Townsend, D. M., Hunt, R. A., Rady, J., Manocha, P., & Jin, J. Y. (2022). Are the futures computable? Knightian uncertainty and Artificial Intelligence. Academy of Management Review

Von Krogh, G. (2018). Artificial intelligence in organizations: New opportunities for phenomenon-based theorizing. Academy of Management Discoveries, 4(4), 404–409.