Call For Papers >> AI and the New Era of Organizational Dynamics
Publication
Asia Pacific Journal of ManagementTitle
AI and the New Era of Organizational DynamicsType
Journal Special IssueEditors
Xianghua Lu (Fudan University), Noman Shaheer (University of Sydney), Weiguo Zhong (Peking University), Lin Tian (Fudan University), Jianyu Zhao (Harbin Engineering University)Deadline
December 31, 2024Description
Special Issue Editors
Xianghua Lu, Fudan University (lxhua@fudan.edu.cn)
Noman Shaheer, The University of Sydney (noman.shaheer@sydney.edu.au)
Weiguo Zhong, Peking University (zwg@gsm.pku.edu.cn)
Lin Tian, Fudan University (tianlin@fudan.edu.cn)
Jianyu Zhao, Harbin Engineering University (zjy@hrbeu.edu.cn)
Supervising Editor: Chinmay Pattnaik, University of Sydney (chinmay.pattnaik@sydney.edu.au)
In today’s era marked by rapid technological advancements, the confluence of Artificial Intelligence (AI) and organizational change emerges as a pivotal area for academic inquiry. AI, characterized as the pinnacle of computational progress reflecting human cognitive abilities, has evolved from an ambitious concept to a tangible reality. This evolution is reshaping business operations, strategic management, organization management and innovation management significantly (Berente, Gu, Recker, & Santhanam, 2021, p. 1435; Chen, Li, & Zhang, 2022; Fountaine, McCarthy, & Saleh, 2019). The adoption of AI technologies, including Large Language Model (LLM), machine learning, robotics, image recognition algorithms, and natural language processing, is not only augmenting decision-making processes but also fostering the creation of innovative products and services (Agrawal, Gans, & Goldfarb, 2018; Brynjolfsson & McAfee, 2014). This paper’s call to action is to examine the diverse effects of AI on organizational management and processes, emphasizing the necessity for extensive and cross-disciplinary studies in this rapidly changing and complex area (Bughin et al., 2018; Hirschberg & Manning, 2015).
AI’s widespread application in various organizational sectors has brought about a paradigm shift in strategic management and decision-making (Kaplan & Haenlein, 2019; Krakowski, Luger, & Raisch, 2023). AI’s capacity to analyze large datasets and predict trends is revolutionizing strategic planning, steering companies toward more data-driven and well-informed decisions (Davenport, Guha, Grewal, & Bressgott, 2020). This revolution is transforming not just conventional planning techniques but also establishing a new framework that incorporates AI insights for effective, long-term strategy and risk management (Fountaine, McCarthy, & Saleh, 2019). Concurrently, AI is driving a transformation in leadership and managerial approaches. Decision-making processes are becoming increasingly data-dependent, necessitating leaders to enhance their comprehension of AI’s potential and its broader implications (Brynjolfsson & McAfee, 2017). This change is spurring a shift in organizational culture, with a growing focus on innovation and adaptability (Iansiti & Lakhani, 2020; Sarwar, Gao, & Khan, 2023).
AI’s influence in organizational frameworks extends well beyond mere technological innovation and integration, impacting every aspect of business operations. AI presents vast opportunities for streamlining efficiency, innovating products and services, and securing a competitive advantage (Agrawal, Gans, & Goldfarb, 2018). Its role in automating routine tasks liberates valuable resources for operational initiatives (Brynjolfsson, Mitchell, & Rock, 2018) but also enhances decision-making accuracy through predictive analytics (Waller & Fawcett, 2013). In customer service, AI's presence is notable with the implementation of AI-driven chatbots and virtual assistants (Huang & Rust, 2018), and within human resources, it plays a crucial role in streamlining talent acquisition and management processes (Raisch & Krakowski, 2020). Nonetheless, while AI offers these benefits, it also introduces complex challenges, such as ethical issue, privacy concerns the implications of automation on operations management (Bughin et al., 2018). Consequently, organizations are tasked with the crucial responsibility of striking a balance between leveraging AI’s advantages and its responsible, ethical deployment, ensuring adept navigation in this new era of technology (Dignum, 2019).
AI technologies, such as machine learning, deep learning, and natural language processing, are reshaping the landscape of entrepreneurship (Chalmers, Shaw, & Carter, 2020). They provide novel tools for entrepreneurs to innovate, automate, and optimize various aspects of their ventures, from idea generation to scaling operations. For instance, AI can automate routine tasks, enhancing efficiency and freeing resources for innovations initiatives. The entrepreneurial landscape is changing due to AI's ability to predict trends and automate decision-making processes. This shift requires entrepreneurs to adapt their business models and strategies to harness AI's full potential. Future research in entrepreneurship should focus on understanding these changes, particularly how AI influences venture formation, decision-making processes within firms, and the overall outcomes of entrepreneurship, including financial returns and societal impacts.
AI is also rapidly transforming the landscape of international business, reshaping trade theories and policy implications. The burgeoning role of AI in global trade emphasizes the significance of economies of scale and knowledge creation (Olan et al., 2022). Firms with extensive datasets can generate more accurate predictions, leading to a competitive edge in the international market (Goldfarb & Trefler, 2018). This advantage is further augmented by the economies of scope, where AI's application across various domains within a company enhances its market position. However, the process of knowledge diffusion in AI exhibits a dual nature: while academic research and publications facilitate global knowledge sharing, the concentration of AI expertise in specific global tech hubs like Silicon Valley and Berlin indicates that much of AI's tacit knowledge remains regionally clustered. The strategic trade policy landscape is also being redefined by AI, with economies of scale, knowledge externalities, and the prominence of superstar scientists taking center stage. Privacy regulations have emerged as a critical factor in the AI-driven international business realm. Stricter privacy laws can constrain firms' innovative capabilities by limiting data accessibility, potentially leading to a competitive disadvantage in the global AI market. This scenario raises concerns about a potential "race to the bottom" in privacy standards, where countries might compete for AI industry investment through more lenient data usage policies.
Moreover, the advancement of AI signifies a shift from the industrial era to the information age, markedly improving data processing, business intelligence, and operational efficiencies across various industries (Bharadwaj, El Sawy, Pavlou, & Venkatraman, 2013). The integration of AI is particularly revolutionary, redefining organizational approaches to problem-solving and decision-making (Leonardi & Treem, 2020). As AI technologies continue to mature, they present new theoretical challenges, particularly in complex fields like neural networks and managing data uncertainty (Jordan & Mitchell, 2015). This dynamic environment underscores a range of emerging topics, including the application of machine learning in the expanding role of data science, and the deployment of deep learning in diverse sectors such as healthcare and environmental sciences (Agrawal, Gans, & Goldfarb, 2019).
Despite these technological strides, the assimilation of AI into existing systems necessitates significant investments, the upskilling of employees, and addressing critical issues around data privacy and security (Martin, 2019). Implementing AI effectively requires comprehensive change management strategies. This involves preparing the workforce for AI-driven alterations, promoting an innovative and learning-centric culture, and updating employee skills to match new technological requisites (Kane, Palmer, Phillips, Kiron, & Buckley, 2017). Therefore, the integration of AI into the business is significantly altering the employment landscape, presenting a mix of challenges and opportunities in workforce management (Chowdhury, Budhwar, Dey, Joel-Edgar, & Abadie, 2022). AI is transforming workplace functions, evident in its use in AI-enabled recruitment processes utilizing facial recognition and in automating sales operations (Tambe, Cappelli, & Yakubovich, 2019). While these innovations are remarkable, they also raise concerns about employee privacy and the potential adverse effects of labor optimization on the workforce (De Stefano, 2020). AI's application in tasks requiring complex reasoning, such as those involving advanced language models, prompts critical reflections on its influence on human involvement in judgment, decision-making, and creative endeavors (Daugherty & Wilson, 2018). The key challenge for organizations is to adapt to these transformations, fostering effective human-AIcollaboration, and addressing the ramifications of AI-driven decisions on organizational learning and individual career trajectories (Boudreau & Lakhani, 2017).
Comprehending the dynamics of how AI catalyzes organizational change is imperative for leaders and policymakers. The Asia pacific region provides an excellent context to explore and extend the organizational change research considering the substantial and intensive application of AI in this region. In addition, the Asia pacific region differs from other geographical regions in terms of political and socio-cultural contexts, which provides an opportunity for alternative conceptualizations of AI and organizational change.
In this special issue of Asia Pacific Journal of Management (APJM), we provide an opportunity for scholars to address both under-researched areas and unresolved issues related to a shared understanding of AI and organizational change. We welcome manuscripts on a large variety of topics, especially seeks contributions that deepen the understanding of AI’s role in organizational innovation and transformation, particularly emphasizing its impact on management. This invitation is extended to authors to engage in and enrich the ongoing conversation on the intricate relationship between AI and organizational change. Studies that provide unique insights of AI and organizational change in the contexts of Asia Pacific region with innovative methodologies are especially encouraged. A representative, but by no means exhaustive, listing of topics includes:
1. Strategic Management in an AI-Driven Era: How is AI redefining competitive advantage in various industries, and what implications does this have for strategic planning and execution? Studies in this domain could provide valuable insights into strategically leveraging AI for business growth and competitive advantages.
2. AI-Driven Innovation, Strategic Innovation Management in New Technology Context: How does AI contribute to organizational learning and innovation? What role does AI play in enhancing the production and service innovation process within organizations? How does AI enable organizations to capitalize on open innovation and collaborative ecosystems? How can AI drive disruptive innovation in established industries?
3. Entrepreneurship in the Age of AI: How does AI empower entrepreneurs in identifying and capitalizing on new market opportunities? In what ways can startups leverage AI to gain a competitive edge in rapidly evolving markets? What are the challenges and opportunities for entrepreneurs in building AI-driven business models?
4. Navigating Global Markets with AI and Strategies for International Business Expansion and Adaptation:How can AI assist companies in navigating the complexities of international markets and global competition? In what ways does AI enable businesses to adapt their products and services for diverse international markets? How does AI influence the decision-making process in international mergers, acquisitions, and partnerships?
5. AI Integration in Organizational Systems: How does the strategic incorporation of AI redefine organizational goals and competitive dynamics? What strategic planning is required to synchronize AI initiatives with long-term business strategies and objectives? How can AI integration ensure agility and adaptability in a rapidly evolving business environment?
6. AI and the Evolution of Organization Management: What impact does AI have on organizational structures, cultures, and processes? This line of inquiry aims to explore how AI-induced changes are reshaping organizational dynamics, redefining employee roles, and transforming the overall workplace environment. The goal is to understand AI's transformative potential for organizational design and culture (Zuboff, 2019) , the new skill sets required in AI-enhanced work environments, and strategies to nurture effective collaboration between humans and AI systems (Kellogg et al., 2020).
7. AI-Driven Decision Making in Organizations: How does AI influence decision-making processes at various organizational levels? This area of study should investigate the impact of AI on different tiers of decision-making, exploring how AI tools can enhance human judgment and decision-making abilities.
8. AI as a Strategic Tool in Enhancing Business Operations: How can AI-driven analytics strategically enhance the operations management in modern organization? What are the strategies for leveraging AI in streamlining operational processes and improving productivity? In what ways can AI contribute to the development of long-term relationships with business partners and improve value creation among ecosystems?
9. Ethical Dimensions of AI in Organizations: What ethical considerations arise from the deployment of AI in organizational contexts, and how can these be effectively managed? Investigations in this field should examine the ethical implications of AI, focusing on issues such as data privacy, algorithmic bias, and the formulation of ethical AIframeworks (Martin, 2019).
Further Details are available at: https://link.springer.com/journal/10490/updates/26984326