Artificial Intelligence Coaches for Sales Agents: Caveats and Solutions

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).

Section: Uncovering Research Problems In the Real World Of Marketingmentioning

confidence: 99%

Reality Check: Infusing Ecological Value into Academic Marketing Research

Heerde¹,

Moorman²,

Moreau³

et al. 2021

“…They also inform the discussion about technology and marketing in that artificial intelligence can be used to monitor the seller's facial activity, provide real-time coaching, and thus assist in training salespeople to improve business outcomes (Grewal et al 2020;Luo et al 2021).…”

Section: Theoretical Contributionsmentioning

confidence: 99%

A New Livestream Retail Analytics Framework to Assess the Sales Impact of Emotional Displays

Bharadwaj¹,

Ballings²,

Naik³

et al. 2021

At the intersection of technology and marketing, the authors develop a framework to unobtrusively detect salespersons’ faces and simultaneously extract six emotions: happiness, sadness, surprise, anger, fear, and disgust. They analyze 99,451 sales pitches on a livestream retailing platform and match them with actual sales transactions. Results reveal that each emotional display, including happiness, uniformly exhibits a negative U-shaped effect on sales over time. The maximum sales resistance appears in the middle rather than at the beginning or the end of sales pitches. Taken together, in one-to-many screen-mediated communications, salespersons should sell with a straight face. In addition, the authors derive closed-form formulae for the optimal allocation of the presence of a face and emotional displays over the presentation span. In contrast to the U-shaped effects, the optimal face presence wanes at the start, gradually builds to a crescendo, and eventually ebbs. Finally, they show how to objectively rank salespeople and circumvent biases in performance appraisals, thereby making novel contributions to people analytics. This research integrates new types of data and methods, key theoretical insights, and important managerial implications to inform the expanding opportunity that livestreaming presents to marketers to create, communicate, deliver, and capture value.

“…Future research should investigate the boundary at which a human or a machine is categorized as such and explore other dehumanization types. Examples include marketing messages with mechanical voices and artificial intelligence software that blurs the line between humans and machines (Luo et al 2020;Puntoni et al 2020). Furthermore, researchers should examine the impact of human-as-machine representations on other types of food decisions as well as decisions in other domains.…”

Section: Theoretical Contributionsmentioning

confidence: 99%

Section: Theoretical Contributionsmentioning

confidence: 99%

“…Furthermore, consumers experience human-as-machine representations not only in targeted advertisements but also in everyday life. With rapid improvements in technology, virtual telepresence systems show people as human faces with mechanistic bodies, human enhancement technologies (e.g., augmented reality goggles, transcranial simulation headbands) represent humans as more machine-like, and artificial intelligence software further blurs the line between humans and machines (Castelo, Schmitt, and Sarvary 2019;Longoni, Bonezzi, and Morewedge 2019;Luo et al 2020). These technological advances are entering the retail and restaurant sectors (O'Reilly 2017) and are driving consumption decisions.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Portraying Humans as Machines to Promote Health: Unintended Risks, Mechanisms, and Solutions

Weihrauch¹,

Huang²

2021

To fight obesity and educate consumers on how the human body functions, health education and marketing materials often highlight the importance of adopting a cognitive approach to food. One strategy employed to promote this approach is to portray humans as machines. Five studies (and three replication and follow-up studies) using different human-as-machine stimuli (internal body composition, face, appearance, and physical movement) revealed divergent effects of human-as-machine representations. While these stimuli promoted healthier choices among consumers who were high in eating self-efficacy, they backfired among consumers who were low in eating self-efficacy (measured in Studies 1 and 3–5; manipulated in Study 2). This reversal happened because portraying humans as machines activated consumers’ expectation of adopting a cognitive, machine-like approach to food (Studies 3 and 4)—an expectation that was too difficult to meet for those with low (vs. high) eating self-efficacy. We tested a solution to accompany human-as-machine stimuli in the field (Study 5): Externally enhancing how easy and doable it was for consumers low in eating self-efficacy to meet the expectation of adopting a cognitive approach to food, which effectively attenuated the backfire effect on their lunch choices at a cafeteria.