Manager Appraisal of Artificial Intelligence Investments

• 成果类型: Article
• 署名作者: Queiroz, Magno; Anand, Abhijith; Baird, Aaron
• 署名单位: State University System of Florida; Florida Atlantic University; University of Arkansas System; University of Arkansas Fayetteville; University System of Georgia; Georgia State University
• 刊物名称: JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
• ISSN/ISSBN: 0742-1222
• DOI: 10.1080/07421222.2024.2376383
• 发表日期: 2024
• 页码: 682-707
• 关键词: information-technology investment; THEORETICAL FRAMEWORK; Agency theory; big data; ai; systems; automation; FUTURE; mechanisms; generation

摘要

Artificial intelligence (AI) is an important source of competitive advantage as it enables task augmentation and automation. However, while AI can create significant value, it is important to note that AI investments are fraught with risks and uncertainties. Thus, managers are likely to carefully evaluate potential AI investments before committing to investing. However, we know little about how managers' appraisal of AI influences their investment choices. Drawing upon theorization in the areas of business value of AI, agentic information systems (IS) appraisal, and time-situated agency, we extend existing theory in two ways: (1) development of an AI classification (foundational typology) that proposes two dimensions (action autonomy and learning autonomy) for classifying AI by type and level of autonomy; and (2) development of propositions that leverage time-situated agency and the AI classification to explicate how managers' delegation preferences influence their AI investment appraisal. This paper contributes a foundational theoretical platform for furthering AI investment appraisal research. In addition, the paper sets an agenda for future research in this area.