Theory-driven perspectives on generative artificial intelligence in business and management
Brown, Olivia and Davison, Robert M. and Decker, Stephanie and Ellis, David A. and Faulconbridge, James and Gore, Julie and Greenwood, Michelle and Islam, Gazi and Lubinski, Christina and MacKenzie, Niall G. and Meyer, Renate and Muzio, Daniel and Quattrone, Paolo and Ravishankar, M. N. and Zilber, Tammar and Ren, Shuang and Sarala, Riikka M. and Hibbert, Paul (2024) Theory-driven perspectives on generative artificial intelligence in business and management. British Journal of Management, 35 (1). pp. 3-23. ISSN 1045-3172 (https://doi.org/10.1111/1467-8551.12788)
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Abstract
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.
ORCID iDs
Brown, Olivia, Davison, Robert M., Decker, Stephanie, Ellis, David A., Faulconbridge, James, Gore, Julie, Greenwood, Michelle, Islam, Gazi, Lubinski, Christina, MacKenzie, Niall G. ORCID: https://orcid.org/0000-0003-3769-7086, Meyer, Renate, Muzio, Daniel, Quattrone, Paolo, Ravishankar, M. N., Zilber, Tammar, Ren, Shuang, Sarala, Riikka M. and Hibbert, Paul;-
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Item type: Article ID code: 90488 Dates: DateEvent19 January 2024Published19 January 2024Published Online14 December 2023AcceptedSubjects: Social Sciences > Commerce > Business Department: Strathclyde Business School > Hunter Centre for Entrepreneurship, Strategy and Innovation Depositing user: Pure Administrator Date deposited: 05 Sep 2024 13:35 Last modified: 03 Oct 2024 10:46 URI: https://strathprints.strath.ac.uk/id/eprint/90488