Decision authority and the returns to algorithms

• 成果类型: Article
• 署名作者: Kim, Hyunjin; Glaeser, Edward L.; Hillis, Andrew; Kominers, Scott Duke; Luca, Michael
• 署名单位: INSEAD Business School; Harvard University; Harvard University; Harvard University
• 刊物名称: STRATEGIC MANAGEMENT JOURNAL
• ISSN/ISSBN: 0143-2095
• DOI: 10.1002/smj.3569
• 发表日期: 2024
• 页码: 619-648
• 关键词: algorithms; decision-making; data-driven decision-making; future of work; Organizational economics

摘要

Research SummaryWe evaluate a pilot in an Inspections Department to explore the returns to a pair of algorithms that varied in their sophistication. We find that both algorithms provided substantial prediction gains, suggesting that even simple data may be helpful. However, these gains did not result in improved decisions. Inspectors often used their decision authority to override algorithmic recommendations, partly to consider other organizational objectives without improving outcomes. Interviews with 55 departments find that while some ran pilots seeking to prioritize inspections using data, all provided considerable decision authority to inspectors. These findings suggest that for algorithms to improve managerial decisions, organizations must consider both the returns to algorithms in the context and how decision authority is managed.Managerial SummaryWe evaluate a pilot in an Inspections Department to explore the returns to algorithms on decisions. We find that the greatest gains in this context come from integrating data into the decision process in the form of simple heuristics, rather than from increasing algorithmic sophistication or additional data. We also find that these improvements in prediction do not fully translate into improved decisions. Decision-makers were less likely to follow data-driven recommendations, partly in consideration of other organizational objectives, but without substantially improving on them overall. These findings suggest that organizations should consider the returns to technical sophistication in each context, and that the design and management of decision authority can be a key choice that impacts the value organizations can capture from using predictive analytics.