File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.25300/MISQ/2022/16813
- Scopus: eid_2-s2.0-85180134044
- WOS: WOS:001110245400012
- Find via
Supplementary
- Citations:
- Appears in Collections:
Article: Let Artificial Intelligence Be Your Shelf Watchdog: The Impact of Intelligent Image Processing-Powered Shelf Monitoring on Product Sales
Title | Let Artificial Intelligence Be Your Shelf Watchdog: The Impact of Intelligent Image Processing-Powered Shelf Monitoring on Product Sales |
---|---|
Authors | |
Keywords | Economic value of artificial intelligence field experiment monitoring quasi-experiment shelf management |
Issue Date | 22-Aug-2023 |
Publisher | Management Information Systems Research Center |
Citation | MIS Quarterly, 2023, v. 47, n. 3, p. 1045-1072 How to Cite? |
Abstract | 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. |
Persistent Identifier | http://hdl.handle.net/10722/340358 |
ISSN | 2023 Impact Factor: 7.0 2023 SCImago Journal Rankings: 4.105 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Deng, Yipu | - |
dc.contributor.author | Zheng, Jinyang | - |
dc.contributor.author | Huang, Liqiang | - |
dc.contributor.author | Kannan, Karthik | - |
dc.date.accessioned | 2024-03-11T10:43:34Z | - |
dc.date.available | 2024-03-11T10:43:34Z | - |
dc.date.issued | 2023-08-22 | - |
dc.identifier.citation | MIS Quarterly, 2023, v. 47, n. 3, p. 1045-1072 | - |
dc.identifier.issn | 0276-7783 | - |
dc.identifier.uri | http://hdl.handle.net/10722/340358 | - |
dc.description.abstract | <p>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.</p> | - |
dc.language | eng | - |
dc.publisher | Management Information Systems Research Center | - |
dc.relation.ispartof | MIS Quarterly | - |
dc.subject | Economic value of artificial intelligence | - |
dc.subject | field experiment | - |
dc.subject | monitoring | - |
dc.subject | quasi-experiment | - |
dc.subject | shelf management | - |
dc.title | Let Artificial Intelligence Be Your Shelf Watchdog: The Impact of Intelligent Image Processing-Powered Shelf Monitoring on Product Sales | - |
dc.type | Article | - |
dc.identifier.doi | 10.25300/MISQ/2022/16813 | - |
dc.identifier.scopus | eid_2-s2.0-85180134044 | - |
dc.identifier.volume | 47 | - |
dc.identifier.issue | 3 | - |
dc.identifier.spage | 1045 | - |
dc.identifier.epage | 1072 | - |
dc.identifier.isi | WOS:001110245400012 | - |
dc.identifier.issnl | 0276-7783 | - |