Artificial Intelligence Coaches for Sales Agents: Caveats and Solutions

Luo, Xueming; Qin, Marco Shaojun; Fang, Zhihui; Qu, Zhe

doi:10.2139/ssrn.3704283

Cited by 4 publications

(2 citation statements)

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“…Try to flip this assumption into a research question that you think is interesting. For example, Luo et al (2021b) challenge the view that AI is unequivocally beneficial when used for marketing activities such as sales force training. Conversations with managers led them to understand that there may be AI overload for weak salespeople and AI aversion for strong salespeople, uncovering the need to develop AI tools to accommodate those differences.…”

Section: Bringing Ecological Value To the Research Problemmentioning

confidence: 99%

Reality Check: Infusing Ecological Value into Academic Marketing Research

Heerde¹,

Moorman²,

Moreau³

et al. 2021

Journal of Marketing

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1 This value need not be appreciated immediately by the stakeholder or its value evident in the short run. Science and its dissemination may take time to reach and clarify the benefits to such stakeholders. 2 Proximity to the real world is not a new aspiration. Indeed, there is a long history in the social sciences. In the Brunswik lens model, a cue's "ecological validity" is correlated with some important perceptual criterion in the environment (Brunswik 1956). While important, we are less interested in this strict view of validity, which has been relegated mostly to measurement and design. 3 There is an important distinction between research that generalizes across the population of interest and research that generalizes to the population of interest. Lynch (1982) argues that no research in the social sciences can claim to be universally generalizable. Instead, research should content itself with generalizing across subpopulations, where subpopulations are based on background variables that may interact with key independent variables to affect the behavior of interest, also known as heterogeneous treatment effects (Chen et al. 2020).

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Section: Bringing Ecological Value To the Research Problemmentioning

confidence: 99%

Reality Check: Infusing Ecological Value into Academic Marketing Research

Heerde¹,

Moorman²,

Moreau³

et al. 2021

Journal of Marketing

View full text Add to dashboard Cite

show abstract

“…Artificial intelligence (AI) is playing an increasingly important role in firm management (Agrawal, Gans, & Goldfarb, 2018; Iansiti & Lakhani, 2020; Lee, 2018; Luo, Qin, Fang, & Qu, 2021). A burgeoning area of AI application is to conduct job evaluation and provide performance feedback to employees.…”

Section: Introductionmentioning

confidence: 99%

The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance

Tong

Jia

Luo

et al. 2021

Strategic Management Journal

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179

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Companies are increasingly using artificial intelligence (AI) to provide performance feedback to employees, by tracking employee behavior at work, automating performance evaluations, and recommending job improvements. However, this application of AI has provoked much debate. On the one hand, powerful AI data analytics increase the quality of feedback, which may enhance employee productivity (“deployment effect”). On the other hand, employees may develop a negative perception of AI feedback once it is disclosed to them, thus harming their productivity (“disclosure effect”). We examine these two effects theoretically and test them empirically using data from a field experiment. We find strong evidence that both effects coexist, and that the adverse disclosure effect is mitigated by employees' tenure in the firm. These findings offer pivotal implications for management theory, practice, and public policies. Managerial abstract Artificial intelligence (AI) technologies are bound to transform how companies manage employees. We examine the use of AI to generate performance feedback for employees. We demonstrate that AI significantly increases the accuracy and consistency of the analyses of information collected, and the relevance of feedback to each employee. These advantages of AI help employees achieve greater job performance at scale, and thus create value for companies. However, our study also alerts companies to the negative effect of disclosing using AI to employee that results from employees' negative perceptions about the deployment of AI, which offsets the business value created by AI. To alleviate value‐destroying disclosure effect, we suggest that companies be more proactive in communicating with their employees about the objectives, benefits, and scope of AI applications in order to assuage their concerns. Moreover, the result of the allayed negative AI disclosure effect among employees with a longer tenure in the company suggests that companies may consider deploying AI in a tiered instead of a uniform fashion, that is, using AI to provide performance feedback to veteran employees but using human managers to provide performance feedback to novices.

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