Artificial Intelligence and Machine Learning: Exploring Drivers, Barriers, and Future Developments in Marketing Management
| Item Type | Journal paper |
| Abstract | Companies neither fully exploit the potential of Artificial Intelligence (AI), nor that of Machine Learning (ML), its most prominent method. This is true in particular of marketing, where its possible use extends beyond mere segmentation, personalization, and decision-making. We explore the drivers of and barriers to AI and ML in marketing by adopting a dual strategic and behavioral focus, which provides both an inward (AI and ML for marketers) and an outward (AI and ML for customers) perspective. From our mixed-method approach (a Delphi study, a survey, and two focus groups), we derive several research propositions that address the challenges facing marketing managers and organizations in three distinct domains: (1) Culture, Strategy, and Implementation; (2) Decision-Making and Ethics; (3) Customer Management. Our findings contribute to better understanding the human factor behind AI and ML, and aim to stimulate interdisciplinary inquiry across marketing, organizational behavior, psychology, and ethics. |
| Authors | Volkmar, Gioia Valentina; Fischer, Peter Mathias & Reinecke, Sven |
| Journal or Publication Title | Journal of Business Research |
| Language | English |
| Subjects | business studies computer science information management behavioral science |
| HSG Classification | contribution to scientific community |
| HSG Profile Area | Global Center for Customer Insight |
| Refereed | Yes |
| Date | 3 April 2022 |
| Publisher | Elsevier |
| Depositing User | Dr. Peter Mathias Fischer |
| Date Deposited | 04 Apr 2022 07:21 |
| Last Modified | 09 Mar 2023 16:03 |
| URI: | https://www.alexandria.unisg.ch/publications/266103 |
DownloadFull text not available from this repository.CitationVolkmar, Gioia Valentina; Fischer, Peter Mathias & Reinecke, Sven (2022) Artificial Intelligence and Machine Learning: Exploring Drivers, Barriers, and Future Developments in Marketing Management. Journal of Business Research, Statisticshttps://www.alexandria.unisg.ch/id/eprint/266103
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