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Frontiers in Service Science: The Management of Data Analytics Services: New Challenges and Future Directions

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  • Francis de Véricourt

    (European School of Management and Technology, Berlin 10178, Germany)

  • Georgia Perakis

    (Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

Abstract

In this short paper, we discuss the impact of data analytics in services and delineate future research directions for the field. After illustrating how data analytics are transforming different service sectors, we consider the provision of data analysis as a service in its own right. We discuss how the very nature of data and certain features of the machine learning method give rise to new issues and pitfalls for the management of these services, which delineates as many future research directions. We also discuss the coproduction of services by humans and machines and call for more research on responsible data analytics services to tackle some of the most pressing ethical issues in our societies.

Suggested Citation

  • Francis de Véricourt & Georgia Perakis, 2020. "Frontiers in Service Science: The Management of Data Analytics Services: New Challenges and Future Directions," Service Science, INFORMS, vol. 12(4), pages 121-129, December.
    • Handle: RePEc:inm:orserv:v:12:y:2020:i:4:p:121-129
    DOI: 10.1287/serv.2020.0262
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    References listed on IDEAS

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    1. Hamsa Bastani & Mohsen Bayati, 2020. "Online Decision Making with High-Dimensional Covariates," Operations Research, INFORMS, vol. 68(1), pages 276-294, January.
    2. Roland Strausz, 2017. "A Theory of Crowdfunding: A Mechanism Design Approach with Demand Uncertainty and Moral Hazard," American Economic Review, American Economic Association, vol. 107(6), pages 1430-1476, June.
    3. Pavithra Harsha & Shivaram Subramanian & Joline Uichanco, 2019. "Dynamic Pricing of Omnichannel Inventories," Service Science, INFORMS, vol. 21(1), pages 47-65, January.
    4. Coase, R. H., 1995. "Essays on Economics and Economists," University of Chicago Press Economics Books, University of Chicago Press, edition 1, number 9780226111032, December.
    5. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, September.
    6. Vivek F. Farias & Andrew A. L, 2019. "Learning Preferences with Side Information," Management Science, INFORMS, vol. 65(7), pages 3131-3149, July.
    7. Assaf Avrahami & Yale T. Herer & Retsef Levi, 2014. "Matching Supply and Demand: Delayed Two-Phase Distribution at Yedioth Group—Models, Algorithms, and Information Technology," Interfaces, INFORMS, vol. 44(5), pages 445-460, October.
    8. Jose Blanchet & Guillermo Gallego & Vineet Goyal, 2016. "A Markov Chain Approximation to Choice Modeling," Operations Research, INFORMS, vol. 64(4), pages 886-905, August.
    9. Francis de Véricourt, & Huseyin Gurkan,, 2020. "Contracting, pricing, and data collection under the AI flywheel effect," ESMT Research Working Papers ESMT-20-01, ESMT European School of Management and Technology.
    10. Gah-Yi Ban & Cynthia Rudin, 2019. "The Big Data Newsvendor: Practical Insights from Machine Learning," Operations Research, INFORMS, vol. 67(1), pages 90-108, January.
    11. Logg, Jennifer M. & Minson, Julia A. & Moore, Don A., 2019. "Algorithm appreciation: People prefer algorithmic to human judgment," Organizational Behavior and Human Decision Processes, Elsevier, vol. 151(C), pages 90-103.
    12. Vivek F. Farias & Srikanth Jagabathula & Devavrat Shah, 2013. "A Nonparametric Approach to Modeling Choice with Limited Data," Management Science, INFORMS, vol. 59(2), pages 305-322, December.
    13. David H. Autor, 2015. "Why Are There Still So Many Jobs? The History and Future of Workplace Automation," Journal of Economic Perspectives, American Economic Association, vol. 29(3), pages 3-30, Summer.
    14. Dimitris Bertsimas & Vivek F. Farias & Nikolaos Trichakis, 2013. "Fairness, Efficiency, and Flexibility in Organ Allocation for Kidney Transplantation," Operations Research, INFORMS, vol. 61(1), pages 73-87, February.
    15. Velibor V. Mišić & Georgia Perakis, 2020. "Data Analytics in Operations Management: A Review," Manufacturing & Service Operations Management, INFORMS, vol. 22(1), pages 158-169, January.
    16. Sandeep Rath & Kumar Rajaram & Aman Mahajan, 2017. "Integrated Anesthesiologist and Room Scheduling for Surgeries: Methodology and Application," Operations Research, INFORMS, vol. 65(6), pages 1460-1478, December.
    17. Strausz, Roland, 2017. "A Theory of Crowdfunding," Rationality and Competition Discussion Paper Series 2, CRC TRR 190 Rationality and Competition.
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