LET ARTIFICIAL INTELLIGENCE BE YOUR SHELF WATCHDOG : THE IMPACT OF INTELLIGENT IMAGE PROCESSING-POWERED SHELF MONITORING ON PRODUCT SALES

• 成果类型: Article
• 署名作者: Deng, Yipu; Zheng, Jinyang; Huang, Liqiang; Kannan, Karthik
• 署名单位: University of Hong Kong; Purdue University System; Purdue University; Zhejiang University; University of Arizona
• 刊物名称: MIS QUARTERLY
• ISSN/ISSBN: 0276-7783
• DOI: 10.25300/MISQ/2022/16813
• 发表日期: 2023
• 页码: 1045-1072
• 关键词: information-technology; space allocation; management; determinants; incentives; algorithms; cancer; MODEL; work; WILL

摘要

We collaborated with a leading fast-moving consumer goods (FMCG) manufacturer to investigate how intelligent image processing (IIP)-based shelf monitoring aids manufacturers' shelf management by using data from a quasi-experiment and a field experiment. We discovered that such artificial intelligence (AI) assistance significantly and consistently improves product sales. Several underlying mechanisms were revealed by our quantitative and qualitative analysis. First, retailers are more likely to comply due to the greater monitoring effectiveness enabled by AI assistance. Second, the positive effect of IIP-based shelf monitoring partially persists after it is terminated, implying that human learning takes place. Third, the value of IIP-based shelf monitoring can be attributed to independent retailers rather than chain retailers. Since the degree of contract heterogeneity is the major difference between these retailers in terms of monitoring, this finding further suggests that AI is relatively more scalable when coping with more heterogeneous instances. Apart from these great benefits, we demonstrate the low marginal costs of implementing IIP-powered shelf monitoring, which indicates its long-term applicability and potential to generate incremental value. Our research contributes to several literature streams and provides managerial insights for practitioners who consider AI-assisted operational models.