The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Artificial Intelligence Coaches for Sales Agents: Caveats and Solutions
Abstract
Firms are exploiting artificial intelligence (AI) coaches to provide training to sales agents and improve their job skills. The authors present several caveats associated with such practices based on a series of randomized field experiments. Experiment 1 shows that the incremental benefit of the AI coach over human managers is heterogeneous across agents in an inverted-U shape: whereas middle-ranked agents improve their performance by the largest amount, both bottom- and top-ranked agents show limited incremental gains. This pattern is driven by a learning-based mechanism in which bottom-ranked agents encounter the most severe information overload problem with the AI versus human coach, while top-ranked agents hold the strongest aversion to the AI relative to a human coach. To alleviate the challenge faced by bottom-ranked agents, Experiment 2 redesigns the AI coach by restricting the training feedback level and shows a significant improvement in agent performance. Experiment 3 reveals that the AI–human coach assemblage outperforms either the AI or human coach alone. This assemblage can harness the hard data skills of the AI coach and soft interpersonal skills of human managers, solving both problems faced by bottom- and top-ranked agents. These findings offer novel insights into AI coaches for researchers and managers alike.
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Cites
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The future of employment: How susceptible are jobs to computerisation?
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Algorithm aversion: People erroneously avoid algorithms after seeing them err.
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Algorithm appreciation: People prefer algorithmic to human judgment
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Resistance to Medical Artificial Intelligence
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Consumers and artificial intelligence: An experiential perspective
Puntoni, S., Reczek, R. W., Giesler, M., & Botti, S. (2021) · Journal of Marketing · doi:10.1177/0022242920953847 · Cited by 1,547 · cited 4×
“People tend to trust humans and resist AI, even if the former do not perform as well as the latter, because machines are perceived to be cold and less empathetic and lack interpersonal communication skills (Eastwood, Snook, and Luther 2012; O¨nkal et al 2009; Puntoni et al. 2020).”
BackgroundInfluentialLuo, X., Tong, S., Fang, Z., & Qu, Z. (2019) · Marketing Science · doi:10.1287/mksc.2019.1192 · Cited by 1,262 · cited 6×
“Our work extends prior literature on the negative impact of AI (Acemoglu and Restrepo 2017; Bessen et al. 2019; Frey and Osborne 2017) and customers’ aversion to AI automation and algorithm (Dietvorst, Simmons, and Massey 2015; Luo et al. 2019; Mende et al. 2019).”
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What can machine learning do? Workforce implications
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“Specifically, interpersonal communication skills (e.g., interpersonal empathy, encouragement, adaptivity, acknowledgment) are at the heart of human advantage over machines and are where AI falls short (Brynjolfsson and Mitchell 2017; Davenport and Ronanki 2018; Deloitte 2017; Deming 2017).”
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Cited by
Bankins, S., Ocampo, A. C., Marrone, M., Restubog, S. L. D., & Woo, S. E. (2024) · Journal of Organizational Behavior · doi:10.1002/job.2735 · Cited by 799
Tong, S., Jia, N., Luo, X., & Fang, Z. (2021) · Strategic Management Journal · doi:10.1002/smj.3322 · Cited by 531
Self-citationHui, X., Reshef, O., & Zhou, L. (2024) · Organization Science · doi:10.1287/orsc.2023.18441 · Cited by 200
Algorithmic Management: Bright and Dark Sides, Practical Implications, and Research Opportunities
Benlian, A., Wiener, M., Cram, W. A., Krasnova, H., Maedche, A., Möhlmann, M., Recker, J., & Remus, U. (2022) · Business & Information Systems Engineering · doi:10.1007/s12599-022-00764-w · Cited by 180
AI Companions Reduce Loneliness
De Freitas, J., Oğuz-Uğuralp, Z., Uğuralp, A. K., & Puntoni, S. (2026) · Journal of Consumer Research · doi:10.1093/jcr/ucaf040 · Cited by 165
Metadata
- Authors
- Luo, X., Qin, M. S., Fang, Z., & Qu, Z.
- Venue
- Journal of Marketing
- Publisher
- SAGE Publications
- Published
- 2020-10-14
- Volume
- 85
- Issue
- 2
- Pages
- 14-32
- ISSN
- 0022-2429 (ISSN-L)
- 1547-7185
- OpenAlex
- W3092728304
- Cited by
- 328
- References
- 13
- FWCI
- 25.19
- Type
- journal article
- Language
- en
- Open access
- closed
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