Theory‐Driven Perspectives on Generative Artificial Intelligence in Business and Management

摘要

Shuang Ren, Riikka M. Sarala, Paul Hibbert The advent of generative artificial intelligence (GAI) has sparked both enthusiasm and anxiety as different stakeholders grapple with the potential to reshape the business and management landscape. This dynamic discourse extends beyond GAI itself to encompass closely related innovations that have existed for some time, for example, machine learning, thereby creating a collective anticipation of opportunities and dilemmas surrounding the transformative or disruptive capacities of these emerging technologies. Recently, ChatGPT's ability to access information from the web in real time marks a significant advancement with profound implications for businesses. This feature is argued to enhance the model's capacity to provide up-to-date, contextually relevant information, enabling more dynamic customer interactions. For businesses, this could mean improvements in areas like market analysis, trend tracking, customer service and real-time data-driven problem-solving. However, this also raises concerns about the accuracy and reliability of the information sourced, given the dynamic and sometimes unverified nature of web content. Additionally, real-time web access might complicate data privacy and security, as the boundaries of GAI interactions extend into the vast and diverse Internet landscape. These factors necessitate a careful and responsible approach to evaluating and using advanced GAI capabilities in business and management contexts. GAI is attracting much interest both in the academic and business practitioner literature. A quick search in Google Scholar, using the search terms ‘generative artificial intelligence’ and ‘business’ or ‘management’, yields approximately 1740 results. Within this extensive repository, scholars delve into diverse facets, exploring GAI's potential applications across various business and management functions, contemplating its implications for management educators and scrutinizing specific technological applications. Learned societies such as the British Academy of Management have also joined forces in leading the discussion on AI and digitalization in business and management academe. Meanwhile, practitioners and consultants alike (e.g. McKinsey & Company, PWC, World Economic Forum) have produced dedicated discussions, reports and forums to offer insights into the multifaceted impacts and considerations surrounding the integration of GAI in contemporary business and management practices. Table 1 illustrates some current applications of GAI as documented in the practitioner literature. Zalando [online platform for fashion and lifestyle] Instacart [e-commerce application] Salesforce [cloud-based customer relationship software provider] DHL [logistics provider] Coca-Cola [beverage company] Nestlé and Mondelez [confectionary] Heinz [food processing company] Air India [airline] Duolingo [language learning application] Mastercard [financial services] In an attempt to capture the new opportunities and challenges brought about by this technology and to hopefully find a way forward to guide research and practice, management journals have been swift to embrace the trend, introducing special issues on GAI. These issues aim to promote intellectual debate, for instance in relation to specific business disciplines (e.g. Benbya, Pachidi and Jarvenpaa, 2021) or organizational possibilities and pitfalls (Chalmers et al., 2023). However, amidst these commendable efforts that reflect a broad spectrum of perspectives, a critical examination of the burgeoning hype around GAI reveals a significant gap. Despite the proliferation of discussions from scholars, practitioners and the general public, the prevailing discourse is often speculative, lacking a robust theoretical foundation. This deficiency points to the challenges to existing theories in terms of their efficacy in explaining the unique demands created by GAI and indicates an urgent need for refining prior theories or even r