Guest article by Ming-Hui Huang, recipient of the best service paper award 2018
I am multidisciplinary, with one foot in marketing and one foot in information systems (IS), which sometimes creates an identity crisis, as marketing people think I am an IS scholar while IS guys think I am a marketing person (not to mention the well-known phenomenon of half of the review team likes your paper and the other half hates it because it is multidisciplinary).
I am happy that technology has become more and more important in service, which makes people begin to recognize that technology and service are two sides of the same coin. It is only a degree issue. You can decide to lean toward the technology side more or the service side more, but you can’t really separate them, as we traditionally did with the distinction between high-tech or high-touch service.
My recent research stream on AI and service started from me attending the 2014 International Conference on Information Systems (ICIS) in Auckland, New Zealand. In the conference, the keynote speaker presented the Baby X project, an emotion recognition and simulation technology that can recognize human emotions and react to the emotions. It has great potential for service, illustrated by demonstrating how the technology (with a female flight attendant face) can serve in the frontline. Later I attended the first Organizational Frontline Research (OFR) symposium in Oklahoma in 2015 and people said that frontline service employees (FLEs) were irreplaceable because of the emotions involved in the interaction. I recalled the Baby X talk and was thinking that with the new emotional technology, FLEs can be replaced too. I thought people should be alerted to this new technology trend. I developed this idea into a three-generational technology evolution (from automated to thinking to feeling technology) (JSR 2017). That was still the time that the term “AI” was not popular yet.
In the JSR 2018 award-winning article, together with my co-author, Roland Rust, the breakthrough was to refine the three-generational technology idea into a multiple intelligences view, from mechanical to analytical to intuitive to empathetic. With AI progressing, more service tasks can be done by AI, and fewer human service employees are required. It emphasizes how AI replaces human workers progressively. This article is analytical and conceptual, predicting a somewhat pessimistic future about human service jobs and workers.
People have the natural tendency to ignore predictions that they don’t like. To provide empirical support for our theory, in the 2019 CMR (“The Feeling Economy”) article, we used government data to validate our prediction empirically. We demonstrated that although we are still in the Thinking Economy, with AI increasingly able to do the analytical and thinking tasks more efficiently and effectively than humans, human workers will need to re-skill to be more feeling and empathetic. For this disruptive innovative idea, we fought hard with editors and reviewers in numerous rounds of correspondences and revisions. To make it easier for people to accept the theory, we somewhat modified our emphasis from replacement to collaboration. I still believe that they are the two sides of the coin: AI replaces for whatever it can do, and before full replacement, AI augments and collaborates.
We also developed a managerial framework for managers to apply the multiple AI intelligences view to their business. This paper is conditionally accepted by the “Rise of the Machines” special issue of JSR. This framework proposes that mechanical AI can standardize service for cost leadership, thinking AI can personalize service for quality leadership, and feeling AI can relationalize for relationship leadership.
Putting all these together, my research program (with my co-author, Roland Rust) develops from scratch a multiple AI intelligences view, addresses their service implications both in human labor replacement and collaboration, at the micro-task level and the macro-economy level, and summarizes it in a managerial framework that service managers can draw upon for actionable service strategy.
The research program is not pre-planned, but plays out naturally (one idea leads to another) and nicely. We have more to come, including a comprehensive book on the Feeling Economy to be published in 2020 that summarizes and extends our thinking (especially those radical ideas that journal editors find difficult to accept) and several ongoing projects that will mature and test our theory in a broader scope.
In reflection, the research program on AI and service shows that being multidisciplinary has paid off. Important research questions and solutions do not have a disciplinary boundary. In this research stream, I recognized the importance of feeling AI due to my IS background, and realized that such AI could be good for communication and interaction-rich service encounters, based on my service background. I encourage more people to accept and utilize their multidisciplinary backgrounds when conducting service research.
Ming-Hui Huang, PhD
Distinguished Professor
National Taiwan University
References
• Rafaeli, Anat, Daniel Altman, Dwayne D. Gremler, Ming-Hui Huang, Dhruv Grewal, Bala Iyer, A. Parasuraman, and Ko de Ruyter (2017), “The Future of Frontline Research: Invited Commentaries,” Journal of Service Research, 20(1), 91-99.
• Huang, Ming-Hui and Roland T. Rust (2018), “Artificial Intelligence in Service,” Journal of Service Research, 21(2), 155-172.
• Huang, Ming-Hui, Roland T. Rust, and Vojislav Maksimovic (2019), “The Feeling Economy: Managing in the Next Generation of AI,” California Management Review, 64(4), 43-65.
• Huang, Ming-Hui and Roland T. Rust, “Engaged to a Robot? The Role of AI in Service,” Journal of Service Research, conditionally accepted.
• Rust, T. Roland and Ming-Hui Huang (2020), The Feeling Economy: How Artificial Intelligence Is Creating the Era of Emotion, Empathy, and Women, Palgrave-Macmillan, in writing.
