IDEAS home Printed from https://ideas.repec.org/a/inm/orserv/v12y2020i4p121-129.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/serv.2020.0262
    Download Restriction: no
    File URL: https://libkey.io/10.1287/serv.2020.0262?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Hamsa Bastani & Mohsen Bayati, 2020. "Online Decision Making with High-Dimensional Covariates," Operations Research, INFORMS, vol. 68(1), pages 276-294, January.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387.
    9. 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.
    10. Pavithra Harsha & Shivaram Subramanian & Joline Uichanco, 2019. "Dynamic Pricing of Omnichannel Inventories," Service Science, INFORMS, vol. 21(1), pages 47-65, January.
    11. Coase, R. H., 1995. "Essays on Economics and Economists," University of Chicago Press Economics Books, University of Chicago Press, edition 1, number 9780226111032, September.
    12. Vivek F. Farias & Andrew A. L, 2019. "Learning Preferences with Side Information," Management Science, INFORMS, vol. 65(7), pages 3131-3149, July.
    13. 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.
    14. 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.
    15. Jose Blanchet & Guillermo Gallego & Vineet Goyal, 2016. "A Markov Chain Approximation to Choice Modeling," Operations Research, INFORMS, vol. 64(4), pages 886-905, August.
    16. 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.
    17. Strausz, Roland, 2017. "A Theory of Crowdfunding," Rationality and Competition Discussion Paper Series 2, CRC TRR 190 Rationality and Competition.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fabian Schéele & Darek M. Haftor & Natallia Pashkevich, 2022. "Predicting delays in service operations," Service Business, Springer;Pan-Pacific Business Association, vol. 16(2), pages 211-226, June.
    2. Qi Feng & J. George Shanthikumar, 2022. "Developing operations management data analytics," Production and Operations Management, Production and Operations Management Society, vol. 31(12), pages 4544-4557, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Velibor V. Miv{s}i'c & Georgia Perakis, 2019. "Data Analytics in Operations Management: A Review," Papers 1905.00556, arXiv.org.
    3. Meng Qi & Ho‐Yin Mak & Zuo‐Jun Max Shen, 2020. "Data‐driven research in retail operations—A review," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(8), pages 595-616, December.
    4. Ilan Lobel, 2021. "Revenue Management and the Rise of the Algorithmic Economy," Management Science, INFORMS, vol. 67(9), pages 5389-5398, September.
    5. Qi Feng & J. George Shanthikumar, 2022. "Developing operations management data analytics," Production and Operations Management, Production and Operations Management Society, vol. 31(12), pages 4544-4557, December.
    6. Kameng Nip & Zhenbo Wang & Zizhuo Wang, 2021. "Assortment Optimization under a Single Transition Choice Model," Production and Operations Management, Production and Operations Management Society, vol. 30(7), pages 2122-2142, July.
    7. Ningyuan Chen & Guillermo Gallego & Zhuodong Tang, 2019. "The Use of Binary Choice Forests to Model and Estimate Discrete Choices," Papers 1908.01109, arXiv.org, revised Apr 2021.
    8. Antoine Désir & Vineet Goyal & Danny Segev & Chun Ye, 2020. "Constrained Assortment Optimization Under the Markov Chain–based Choice Model," Management Science, INFORMS, vol. 66(2), pages 698-721, February.
    9. Aydın Alptekinoğlu & John H. Semple, 2021. "Heteroscedastic Exponomial Choice," Operations Research, INFORMS, vol. 69(3), pages 841-858, May.
    10. Shipra Agrawal & Vashist Avadhanula & Vineet Goyal & Assaf Zeevi, 2019. "MNL-Bandit: A Dynamic Learning Approach to Assortment Selection," Operations Research, INFORMS, vol. 67(5), pages 1453-1485, September.
    11. Guiyun Feng & Xiaobo Li & Zizhuo Wang, 2017. "Technical Note—On the Relation Between Several Discrete Choice Models," Operations Research, INFORMS, vol. 65(6), pages 1516-1525, December.
    12. Shivaram Subramanian & Pavithra Harsha, 2021. "Demand Modeling in the Presence of Unobserved Lost Sales," Management Science, INFORMS, vol. 67(6), pages 3803-3833, June.
    13. Yinchu Zhu & Ilya O. Ryzhov, 2022. "Optimal data-driven hiring with equity for underrepresented groups," Papers 2206.09300, arXiv.org.
    14. Ali Aouad & Danny Segev, 2021. "Display Optimization for Vertically Differentiated Locations Under Multinomial Logit Preferences," Management Science, INFORMS, vol. 67(6), pages 3519-3550, June.
    15. Qi Feng & J. George Shanthikumar & Mengying Xue, 2022. "Consumer Choice Models and Estimation: A Review and Extension," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 847-867, February.
    16. Dam, Tien Thanh & Ta, Thuy Anh & Mai, Tien, 2023. "Robust maximum capture facility location under random utility maximization models," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1128-1150.
    17. Strauss, Arne K. & Klein, Robert & Steinhardt, Claudius, 2018. "A review of choice-based revenue management: Theory and methods," European Journal of Operational Research, Elsevier, vol. 271(2), pages 375-387.
    18. Srikanth Jagabathula & Paat Rusmevichientong, 2017. "Nonparametric Joint Assortment and Price Choice Model," Management Science, INFORMS, vol. 63(9), pages 3128-3145, September.
    19. Xi Chen & Chao Shi & Yining Wang & Yuan Zhou, 2021. "Dynamic Assortment Planning Under Nested Logit Models," Production and Operations Management, Production and Operations Management Society, vol. 30(1), pages 85-102, January.
    20. Yi-Chun Chen & Velibor V. Mišić, 2022. "Decision Forest: A Nonparametric Approach to Modeling Irrational Choice," Management Science, INFORMS, vol. 68(10), pages 7090-7111, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:orserv:v:12:y:2020:i:4:p:121-129. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.