The Effect of Voice AI on Digital Commerce

Sun, Chenshuo; Shi, Zijun; Liu, Xiao; Ghose, Anindya; Xueying, Li; Xiong, Feiyu

doi:10.2139/ssrn.3480877

Cited by 22 publications

(22 citation statements)

References 47 publications

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“…This represents an unprecedented opportunity for firms to create value (Bughin & Manyika, 2019). Thus, this article takes an initial step in extending prior research on AI applications in production and marketing (Aghion, Jones, & Jones, 2017; Aron, Dutta, Janakiraman, & Pathak, 2011; Brynjolfsson, Hui, & Liu, 2019; Schanke, Butch, & Ray, 2020; Sun et al, 2019) to investigate the role of AI in managing employees, particularly at the interface between AI and 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

168

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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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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

168

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“…When an NLP provides information to people, there may be more appropriate strategies for the situation, such as a clearer voice for giving information such as the news. Further, as AI develops, it will also require voices that can appeal or engage [45,46]. Therefore, the input and output appropriate for each situation should be utilized in natural language processing.…”

Section: Conclusion 41 Results and Implicationsmentioning

confidence: 99%

An Interdisciplinary Study of A Leaders’ Voice Characteristics: Acoustical Analysis and Members’ Cognition

2020

KSII TIIS

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The traditional roles of leaders are to influence members and motivate them to achieve shared goals in organizations. However, leaders such as top managers and chief executive officers, in practice, do not always directly meet or influence other company members. In fact, they tend to have the greatest impact on their members through formal speeches, company procedures, and the like. As such, official speech is directly related to the motivation of company employees. In an official speech, not only the contents of the speech, but also the voice characteristics of the speaker have an important influence on listeners, as the different vocal characteristics of a person can have different effects on the listener. Therefore, according to the voice characteristics of a leader, the cognition of the members may change, and, the degree to which the members are influenced and motivated will be different. This study identifies how members may perceive a speech differently according to the different voice characteristics of leaders in formal speeches. Further, different perceptions about voices will influence members' cognition of the leader, for example, in how trustworthy they appear. The study analyzed recorded speeches of leaders, and extracted features of their speaking style through digital speech signal analysis. Then, parameters were extracted and analyzed by the time domain, frequency domain, and spectrogram domain methods. We also analyzed the parameters for use in Natural Language Processing. We investigated which leader's voice characteristics had more influence on members or were more effective on them. A person's voice characteristics can be changed. Therefore, leaders who seek to influence members in formal speeches should have effective voice characteristics to motivate followers.

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“…Previous studies mainly focused on the benefit of the service AI. For example, some scholars found that AI application can improve purchase behavior [58], enhance clinical diagnosis and decision-making performance [59], and increase international trade [60]. However, the impact of service AI and customer negative reactions should be researched more.…”

Section: Theoretical and Practical Implicationsmentioning

confidence: 99%

Does Customers’ Emotion toward Voice-based Service AI Cause Negative Reactions? Empirical Evidence from a Call Center

Li¹,

Liu²,

Mao³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Many companies are introducing voice-based service artificial intelligence (AI) into their call centers. Little is known about the relationship between customers' emotions to voice-based service AI and customers' negative reactions. This study investigates the link between customers' emotions toward voice-based service AI and customers' negative reactions. Our results reveal that customers' emotion toward voice-based service AI could significantly affect their complaint behavior, and customers' complaint differ among different emotions types. Customers' negative and positive emotions toward voice-based AI services have asignificantly positive and negative effect, respectively, on customer complaint behavior than neutral emotion. We also find that the exchange round of human-AI interaction moderates the effect of the customer emotion on customer complaint. This study investigates the impact of customers' emotions toward voice-based service AI on customers' complaint behavior to make up the existing research insufficiency in a certain degree.

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The Effect of Voice AI on Digital Commerce

Cited by 22 publications

References 47 publications

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

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

An Interdisciplinary Study of A Leaders’ Voice Characteristics: Acoustical Analysis and Members’ Cognition

Does Customers’ Emotion toward Voice-based Service AI Cause Negative Reactions? Empirical Evidence from a Call Center

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