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The effect of implementing chatbot customer service on stock returns: an event study analysis

  • Original Empirical Research
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Abstract

Advancements in conversational Artificial Intelligence (AI) have led to rapid growth in firms’ use of AI chatbots in customer service roles. While the shareholder wealth effects of AI chatbots have yet to be investigated, recent findings suggest that AI investment may contribute negatively to firm value. This cautionary evidence, and the growing prevalence of AI chatbots, underscore that a clear understanding of their impact on firm value is urgently needed. An event study of 153 AI chatbot announcements demonstrates that implementation of AI customer service chatbots generates a .22% abnormal stock return, indicating investors respond favorably to this practice. Importantly, B2B (vs. B2C) firms have substantially more to gain from implementing AI chatbot customer service. However, we find chatbot anthropomorphism interacts with customer type, as investors respond less (more) favorably to anthropomorphized chatbots used in B2B (B2C) customer service roles. Two additional studies provide support for this pattern of findings.

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Notes

  1. In reference to AI, it can also be helpful to distinguish between a specific application of AI and more broad-level use of the term. Most AI in use today is narrow (or “weak”) AI, involving machines performing a specific set of tasks in a narrow area. Our focus is on such a specific application of AI (chatbots), as opposed to general (or “strong”) AI, which would be able to operate at a human-level intelligence across various contexts (Tegmark, 2017).

  2. Firms can leverage this advantage into greater market performance through higher prices, greater market share, advertising efficiencies, faster customer responsiveness, greater loyalty, etc.

  3. The other key type of market-based assets is intellectual market-based assets, i.e., the firm’s stock of market knowledge (Srivastava et al., 1998). By enabling firms to automate the acquisition and analyses of customer data (Iles, 2020), AI-driven chatbots may enable firms to increase their stock of intellectual market-based assets. This benefit may be especially important in B2B, given the difficulty of conducting traditional market research with those customers. We identify this as an area for future research.

  4. As such, there may be some situations where a chatbot would be at a distinct disadvantage compared to other service options such as engaging a human agent. In particular, it is likely that more complex situations would be less well suited for chatbot customer service. We capture this in our analysis and find, consistent with this intuition, that investors have less favorable views of chatbots claimed to handle complex requests. We thank a reviewer for this insight. The negative effect for chatbot task complexity did not vary between B2B and B2C contexts, however.

  5. As Facebook messenger was a frequently used platform for chatbot implementation (Jain et al., 2018), we included “Facebook messenger” as a search keyword.

  6. Assigning gender to an inanimate object (Waytz et al., 2014), giving it a human name (Mende et al., 2019; Waytz et al., 2014), or voice (Schroeder & Epley, 2016; Waytz et al., 2014) have shown to increase consumers’ tendency to anthropomorphize it. Another way to activate a human schema, and consequently increase object anthropomorphism, is by triggering cognitive associations congruent with human traits, relationships and behavior, for example by the depiction of an object performing actions and taking on roles that are typically associated with or performed by humans (Puzakova et al., 2013) or having a humanlike personality (Wan & Aggarwal, 2015).

  7. Following the suggestion of a reviewer, we consider voice as a separate chatbot characteristic in our empirical model. Results of the anthropomorphic index and its interaction with B2B/B2C were not affected by whether voice was included as part of the anthropomorphic index or not.

  8. Further support for the soundness of the exclusion restriction is provided by the low correlations between the inverse Mills ratio and the independent variables: with B2B (.02, p > .7) and anthropomorphism index (−.23, p = .01). Further, the first stage Heckman model displays a pseudo-R-square of .43, evincing strong explanatory power. These indicate our model does not suffer from a weak exclusion restriction (Bhagwat et al., 2020).

  9. The magnitude of the abnormal return associated with AI chatbots is comparable to the return associated with other similar investments, increasing confidence in our results. For instance, Lamey et al. (2021) report a. 18% abnormal return for retail service innovations and Im et al. (2001) report .27% abnormal return for IT investments.

  10. Additionally, our results are robust to 1) alternative benchmark models for calculating abnormal returns, 2) outliers, and 3) using cluster robust errors. (See Web Appendix D for the results of these robustness checks.)

  11. The order of the conditions and question blocks was counterbalanced in both studies, and the data was collapsed because the order did not interact with the measures. (See footnote 12 for one exception.)

  12. Item “easy to connect with and bond” interacted with the order. We excluded responses in the condition presented second and used between-subjects ANOVA for analysis. While consistent with the expected pattern (MB2B = 3.01, SDB2B = 1.83; MB2C = 3.16, SDB2C = 1.74), the difference was not significant, potentially due to the insufficient power.

  13. Participants also evaluated if B2C vs. B2B customers would appreciate specific benefits of chatbots more, using a bipolar scale (B2C = 1, B2B = 7). The results (see Panel A of Web Appendix G) suggest that investors expect B2B (vs. B2C) customers to value more chatbot’s capability to increase customer efficiency (4.97), to retrieve and integrate information across accounts (5.06), platforms (5.01), and applications (5.13). All the means were above the midpoint (p < .01). The chatbot’s ability to answer urgent questions on-demand (4.09) and enable customer self-sufficiency (4.19) was viewed as equally valuable to B2B and B2C customers (p = .56, p = .18 respectively).

  14. Participants also indicated which customers (B2B vs. B2C) would find anthropomorphized chatbots amusing, credible, easy to connect with and bond, enjoyable, engaging, and competent. Participants expected B2B (vs. B2C) customers to be less likely to associate these characteristics with anthropomorphized chatbots (p < .05). Chatbot credibility had similar ratings across the two customer types (p > .1). See Panel B of Web Appendix G for details.

  15. We thank a reviewer for these insightful suggestions.

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Acknowledgements

This research was supported in part by the Center for the Study of Economic Liberty at Arizona State University.

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  1. W. P. Carey School of Business, Arizona State University, Mail code 4106, Tempe, AZ, 85287-4106, USA

    Darima Fotheringham & Michael A. Wiles

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  1. Darima Fotheringham
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Correspondence to Darima Fotheringham.

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Table 6 Examples of AI chatbot launch events
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Fotheringham, D., Wiles, M.A. The effect of implementing chatbot customer service on stock returns: an event study analysis. J. of the Acad. Mark. Sci. 51, 802–822 (2023). https://doi.org/10.1007/s11747-022-00841-2

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