Investigating the nexus between Artificial Intelligence and machine learning technologies in the case of Indian services industry
Abstract
The future in the services industry belongs to Artificial Intelligence (AI) driven machines, which is a major source of worry for the job market in India. Over 50% of India’s GDP constitutes services, and it is a major source of employment for the skilled manpower of India. The research measures the impact of AI on service jobs in India based on qualitative parameters such as logical, natural, physical, and compassion; and finds which aspect serves the jobs better between machines and humans. The jobs taken over by AI are primarily at the task level more than the job level and for the basic tasks predominantly. The replacement starts with the basic tasks involved in providing service and then it grows to perform all the tasks involved in services. The research finds out that the logical aspects of the service will slowly reduce in the coming 5–10 years as AI will perform all the logic-related tasks leaving more emotional tasks such as compassion for humans. Finally, even these emotional-related tasks will be taken over by the AI which provides us with a very interesting combination of man and machine in the Indian scenario still threatening human employment.
Keyword : Artificial Intelligence (AI), machine learning, human intelligence, big data, robots, automation, service sector, consumer
How to Cite
Ullal, M. S., Nayak, P. M., Dais, R. T., Spulbar, C., & Birau, R. (2022). Investigating the nexus between Artificial Intelligence and machine learning technologies in the case of Indian services industry. Business: Theory and Practice, 23(2), 323–333. https://doi.org/10.3846/btp.2022.15366
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Chung, T. S., Wedel, M., & Rust, R. T. (2016). Adaptive personalization using social networks. Journal of the Academy of Marketing Science, 44(1), 66–87. https://doi.org/10.1007/s11747-015-0441-x
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Davenport, T. H., & Kirby, J. (2015). Beyond automation. Harvard Business Review, 93, 58–65.
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Leachman, S. A., & Merlino, G. (2017). Medicine: The final frontier in cancer diagnosis. Nature, 542, 36–38. https://doi.org/10.1038/nature21492
Lerman, R. I., & Schmidt, S. R. (1999). An overview of economic, social, and demographic trends affecting the US labor market (Final Report). The Urban Institute for US Department of Labor, Washington, DC.
Marinova, D., de Ruyter, K., Huang, M.-H., Meuter, M., & Challagalla, G. (2017). Getting smart: Learning from technology empowered frontline interactions. Journal of Service Research, 20(1), 29–42. https://doi.org/10.1177/1094670516679273
Maza, C. (2017, October 26). Saudi Arabia gives citizenship to a non-Muslim, English-speaking robot. Newsweek. http://www.newsweek.com/saudi-arabia-robot-sophia-muslim-694152
Meuter, M. L., Ostrom, A. L., Roundtree, R. I., & Bitner, M. J. (2000). Self-service technologies: Understanding customer satisfaction with technology-based service encounters. Journal of Marketing, 64(3), 50–64. https://doi.org/10.1509/jmkg.64.3.50.18024
Miller, C. C. (2017, March 28). Evidence that robots are winning the race for American jobs. The New York Times. https://www.nytimes.com/2017/03/28/upshot/evidence-that-robots-are-winning-the-race-for-american-jobs.html
Miller, R., & Chandra, S. (2015, July 28). A world where man beats machine. Bloomberg.
Minsky, M. (2006). The emotion machine. Simon & Schuster.
Mitchell, T. (1997). Machine learning. McGraw Hill.
Nethravathi, P. S. R., Bai, G. V., Spulbar, C., Suhan, M., Birau, R., Calugaru, T., Hawaldar, I. T., & Ejaz, A. (2020). Business intelligence appraisal based on customer behaviour profile by using hobby based opinion mining in India: A case study. Economic Research – Ekonomska Istraživanja, 33(1), 1889–1908. https://doi.org/10.1080/1331677X.2020.1763822
Ng, A., & Jacobstein, N. (2017, March 6). How artificial intelligence will change everything. The Wall Street Journal. https://www.wsj.com/articles/how-artificial-intelligence-will-change-everything-1488856320
Poggi, J. (2017, January 3). CMO’s Guide to Chat bots. Advertising Age. http://adage.com/article/media/cmosguidechatbots/307199/
PricewaterhouseCoopers. (2017). What’s next for the 2017 data science and analytics job market? PwC. http://www.pwc.com/us/en/publications/data-science-and-analytics.html
Roberts, K., Roberts, J. H., Danaher, P. J., & Raghavan, R. (2015). Incorporating emotions in to evaluation and choice models: Application to Kmart Australia. Marketing Science, 34(6), 815–824. https://doi.org/10.1287/mksc.2015.0954
Russell, S. J., & Norvig, P. (2010). Artificial intelligence: A modern approach (3rd ed.). Pearson.
Rust, R. T., & Huang, M.-H. (2012). Optimizing service productivity. Journal of Marketing, 76(2), 47–66. https://doi.org/10.1509/jm.10.0441
Rust, R. T., & Huang, M.-H. (2014). The service revolution and the transformation of marketing science. Marketing Science, 33(2), 206–221. https://doi.org/10.1287/mksc.2013.0836
Sawhney, M. (2016, September). Putting products into services. Harvard Business Review, 82–89.
Schlinger, H. D. (2003). The myth of intelligence. The Psychological Record, 53(1), 15–32.
Schwab, K. (2017). The fourth industrial revolution. World Economic Forum.
Simonite, T. (2017, January 18). AI software learns to make AI software. MIT Technology Review.
Soucy, P. (2016, July 7). Self-learning intelligent search, explained. KM World, S34.
Sternberg, R. J. (1984). Toward a triarchic theory of human intelligence. Behavior and Brain Sciences, 7(2), 269–287. https://doi.org/10.1017/S0140525X00044629
Sternberg, R. J. (1997). A triarchic view of giftedness: Theory and practice. In N. Coleangelo & G. A. Davis (Eds.), Handbook of gifted education (2nd ed., pp. 43–53). Allyn and Bacon.
Sternberg, R. J. (1999). The theory of successful intelligence. Review of General Psychology, 3(4), 292–316. https://doi.org/10.1037/1089-2680.3.4.292
Sternberg, R. J. (2005). The theory of successful intelligence. Interamerican Journal of Psychology, 39(2), 189–202.
Ullal, M. S., Hawaldar, I. T., Mendon, S., & Joseph, N. R. (2020). The effect of artificial intelligence on the sales graph in Indian market. Entrepreneurship and Sustainability Issues, 7(4), 2940–2954. https://doi.org/10.9770/jesi.2020.7.4(24)
Ullal, M. S., Spulbar, C., Hawaldar, I. T., Popescu, V., & Birau, R. (2021). The impact of online reviews on e-commerce sales in India: A case study. Economic Research – Ekonomska Istraživanja, 34(1), 2408–2422. https://doi.org/10.1080/1331677X.2020.1865179
Vargo, S. L., & Lusch, R. F. (2004). Evolving to a new dominant logic for marketing. Journal of Marketing, 68(1), 1–17. https://doi.org/10.1509/jmkg.68.1.1.24036
Vinge, V. (1993, March 30–31). Technological singularity. In VISION-21 Symposium. NASA Lewis Research Center and the Ohio Aerospace Institute. https://www.frc.ri.cmu.edu/~hpm/book98/com.ch1/vinge.singularity.html
Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97–121. https://doi.org/10.1509/jm.15.0413
Winkler, R. (2017, March 27). Elon Musk launches Neuralink to connect brains with computers. The Wall Street Journal.
Wünderlich, N. V., Wangenheim, F. V., & Bitner, M. J. (2013). High Tech and High Touch: A framework for understanding user attitudes and behaviors related to smart interactive services. Journal of Service Research, 16(1), 3–20. https://doi.org/10.1177/1094670512448413
Xiao, L., & Ding, M. (2014). Just the faces: Exploring the effects of facial features in print advertising. Marketing Science, 33(3), 338–352. https://doi.org/10.1287/mksc.2013.0837
Yoo, J., & Arnold, T. J. (2016). Frontline employee customer-oriented attitude in the presence of job demands and resources: The influence upon deep and surface acting. Journal of Service Research, 19(1), 102–117. https://doi.org/10.1177/1094670515589956