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Big Data and Blockchain for Service Operations Management pp 19–48Cite as

Strategy Formulation and Service Operations in the Big Data Age: The Essentialness of Technology, People, and Ethics

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Part of the book series: Studies in Big Data ((SBD,volume 98))

Abstract

Studies have shown that the sensible operation of big data may yield powerful insights that can improve the organisations’ strategic decision-making process and contribute to achieving an enhanced competitive advantage. In this manuscript, we explore the promise of big data in redefining strategy in service operations management (SOM) by means of investigating a rich range of bibliographic material. The SOM field has a plethora of research opportunities to capitalise on, which are enhanced by the presence of big data. SOM research in the big data age implies a shift in attention from being increasingly integrative across themes to being integrative across multiple disciplines, requiring the expertise of and tuning between different actors and expertise domains. Our aim is to stimulate debate in the field and set out a renewed research agenda by means of calling for additional considerations of strategic aspects, namely technology, people, and ethics, that can help guide and move the field forward.

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References

  1. Akkermans, H. A. (2018). Service operations dynamics: Managing in an age of digitization, disruption and discontent. Palgrave Pivot.

    Google Scholar 

  2. Amoako-Gyampah, K., & Meredith, J. R. (1989). The operations Management research agenda: An update. Journal of Operations Management, 8(3), 250–262.

    Article  Google Scholar 

  3. Baesens, B. (2014). Analytics in a big data world. Wiley.

    Google Scholar 

  4. Barbosa, C., & Azevedo, A. (2015). Operations strategy frameworks in manufacturing, services and product-service systems. Paper presented at the 6th IESM Conference. Seville, Spain.

    Google Scholar 

  5. Bayraktar, E., Jothishankar, M., Tatoglu, E., & Wu, T. (2007). Evolution of operations management: Past, present and future. Management Research News, 30(11), 843–871.

    Article  Google Scholar 

  6. Beyer, M. A., & Laney, D. (2012). The Importance of ‘Big Data’: A Definition. META Group (now Gartner). http://www.gartner.com/DisplayDocument?id=2057415&ref=clientFriendlyUrl. Accessed 1 Aug 2013.

  7. Bretthauer, K. M. (2004). Service management. Decision Sciences, 35(3), 325–332.

    Article  Google Scholar 

  8. Charles, V. (2016). 4^3 architecture for service innovation: Innovation doesn’t come out of nowhere. Strategia, 40, 105–114.

    Google Scholar 

  9. Charles, V., Aparicio, J., & Zhu, J. (Eds.). (2020). Data science and productivity analytics. Springer.

    Google Scholar 

  10. Charles, V., & Emrouznejad, A. (2018). Big data for the greater good: An introduction. In A. Emrouznejad & V. Charles (eds), Big data for the greater good (pp. 1–18). Springer.

    Google Scholar 

  11. Charles, V., & Gherman, T. (2013). Achieving competitive advantage through big data. Strategic implications. Middle-East Journal of Scientific Research, 16(8), 1069–1074.

    Google Scholar 

  12. Charles, V., & Gherman, T. (2018). Big data and ethnography: Together for the greater good. In A. Emrouznejad & V. Charles (eds.), Big data for the greater good (pp. 19–34). Springer.

    Google Scholar 

  13. Charles, V., Gherman, T., & Paliza, J. C. (2019). Stakeholder involvement for public sector productivity enhancement: Strategic considerations. ICPE Public Enterprise Half-Yearly Journal, 24(1), 78–87.

    Google Scholar 

  14. Charles, V., Tavana, M., & Gherman, T. (2015). The right to be forgotten—Is privacy sold out in the big data age? International Journal of Society Systems Science, 7(4), 283–298.

    Article  Google Scholar 

  15. Chase, R. B. (1996). The mall is my factory: Reflections of a service junkie. Production and Operations Management, 5(4), 298–308.

    Article  Google Scholar 

  16. Chase, R. B., & Apte, U. M. (2007). A history of research in service operations: What’s the big idea? Journal of Operations Management, 25(2), 375–386.

    Article  Google Scholar 

  17. Choi, T. M., Wallace, S. W., & Wang, Y. (2017). Big data analytics in operations management. Production and Operations Management, 27(10), 1868–1883.

    Article  Google Scholar 

  18. Chopra, S., Lovejoy, W., & Yano, C. (2004). Five decades of operations management and the prospect ahead. Management Science, 50(1), 8–14.

    Article  MATH  Google Scholar 

  19. Cohen, M. C. (2018). Big data and service operations. Production and Operations Management, 27(9), 1709–1723.

    Article  Google Scholar 

  20. Constantiou, I. D., & Kallinikos, J. (2015). New games, new rules: Big data and the changing context of strategy. Journal of Information Technology, 30(1), 44–57.

    Article  Google Scholar 

  21. Cox, M., & Ellsworth, D. (1997). Application-controlled demand paging for out-of-core visualization. In Proceedings of the 8th IEEE Conference on Visualization. Los Alamitos, CA: IEEE Computer Society Press.

    Google Scholar 

  22. Day, G. S., & Wensley, R. (1988). Assessing advantage: A framework for diagnosing competitive superiority. Journal of Marketing, 52(2), 1.

    Article  Google Scholar 

  23. Edvardsson, B., Gustafsson, A., & Roos, I. (2005). Service portraits in service research: A critical review. International Journal of Service Industry Management, 16(1), 107–121.

    Article  Google Scholar 

  24. Emrouznejad, A. (Ed.). (2016). Big data optimization: recent developments and challenges. Cham.

    MATH  Google Scholar 

  25. Emrouznejad, A., & Charles, V. (Eds.). (2018). Big data for the greater good. Springer.

    Google Scholar 

  26. Emrouznejad, A., & Marra, M. (2016). Big data: Who, what and where? Social, cognitive and journals map of big data publications with focus on optimization. In A. Emrouznejad (ed.), Big data optimization: Recent developments and challenges (pp. 1–16). Springer.

    Google Scholar 

  27. Epstein, E., & Hagen, C. (n/a). Big data and the creative destruction of today's business models. Retrieved October 1, 2020, from https://at.kearney.com/web/guest/analytics/article/?/a/big-data-and-the-creative-destruction-of-today-s-business-models

  28. Ericksson, T., Bigi, A., & Bonera, M. (2020). Think with me, or think for me? On the future role of artificial intelligence in marketing strategy formulation. The TQM Journal, 32(4), 795–814.

    Google Scholar 

  29. Field, J. M., et al. (2018). Service operations: What’s next? Journal of Service Management, 29(1), 55–97.

    Article  MathSciNet  Google Scholar 

  30. Fitzsimmons, J. A., & Fitzsimmons, M. J. (2004). Service management (4th ed.). McGraw Hill Irwin.

    Google Scholar 

  31. Fung, K. (2014). Google flu trends’ failure shows good data > Big Data. Harvard Business Review. https://hbr.org/2014/03/google-flu-trends-failure-shows-good-data-big-data

  32. Gherman, T. (2018). Spoken and embodied interaction in facilitated computer-supported workplace meetings (Doctoral dissertation). Retrieved from Loughborough University Institutional Repository.

    Google Scholar 

  33. Grant, R. M. (1999). Transforming uncertainty into success: The strategic leadership forum 1999. Strategy and Leadership, 27(4/5), 31–36.

    Article  Google Scholar 

  34. Greenfield, H. I. (2002). A note on the goods/services dichotomy. The Service Industries Journal, 22(4), 19.

    Article  Google Scholar 

  35. Grönroos, C. (1994). From scientific management to management service. A management perspective for the age of service competition. International Journal of Service Industry Management, 5(1), 5–20.

    Google Scholar 

  36. Grönroos, C. (2000). Service management and marketing: A customer relationship approach (2nd ed.). Wiley.

    Google Scholar 

  37. Hambrick, D. C., & Mason, P. A. (1984). Upper echelons: The organization as a reflection of its top managers. Academy of Manageent Review, 9(2), 193–206.

    Article  Google Scholar 

  38. Hammond, K. J. (2013). The value of big data isn’t the data. Harvard Business Review. http://blogs.hbr.org/cs/2013/05/the_value_of_big_data_isnt_the.html. Accessed 13 July 2013.

  39. Hansun, S., Charles, V., Gherman, T., Subanar, & Rini-Indrati, C. (2020). A tuned Holt-Winters white-box model for COVID-19 prediction. International Journal of Management and Decision Making, 1–22. In press.

    Google Scholar 

  40. Heineke, J., & Davis, M. M. (2007). The emergence of service operations management as an academic discipline. Journal of Operations Management, 25(2), 364–374.

    Article  Google Scholar 

  41. Herschel, R., & Miori, V. M. (2017). Ethics and Big Data. Technology in Society, 49, 31–36.

    Article  Google Scholar 

  42. Hill, A. V., Collier, D. A., Froehle, C. M., Goodale, J. C., Metters, R. D., & Verma, R. (2002). Research opportunities in service process design. Journal of Operations Management, 20(2), 189–202.

    Article  Google Scholar 

  43. Iansiti, M. (2013). Operations strategy. Harvard Business Publishing.

    Google Scholar 

  44. IBM. (2013). What is big data? Retrieved July 20, 2012, from http://www-01.ibm.com/software/data/bigdata

  45. Johnston, R. (1999). Service operations management: Return to roots. International Journal of Operations and Production Management, 19(2), 104–124.

    Article  Google Scholar 

  46. Johnston, R. (2005). Service operations management: From the roots up. International Journal of Operations & Production Management, 25(12), 1298–1308.

    Article  Google Scholar 

  47. Judd, R. C. (1964). The case for redefining services. Journal of Marketing, 28(1), 58.

    Article  Google Scholar 

  48. Kant, I. ([1785] 1990). Foundations of a metaphysics of morals. McMillan.

    Google Scholar 

  49. Kundera, M. (1996). Slowness. Faber and Faber.

    Google Scholar 

  50. Kuo, Y., & Kusiak, A. (2018). From data to big data in production research: The past and future trends. International Journal of Production Research, 57(15–16), 4828–4853.

    Google Scholar 

  51. Laney, D. (2001). 3D Data Management: Controlling Data Volume, Velocity and Variety. Applications Delivery Strategies. META Group (now Gartner). http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velosity-and-Variety.pdf. Accessed 1 Aug 2013.

  52. Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of google flu: Traps in big data analysis. Science 343 (6176), 1203–1205.

    Google Scholar 

  53. Levitt, T. (1972). Production line approach to service. Harvard Business Review, 50(5), 41–52.

    Google Scholar 

  54. Lovelock, C., & Gummesson, E. (2004). Whither services marketing? In search of a new paradigm and fresh perspectives. Journal of Service Research, 7(1), 20–41.

    Article  Google Scholar 

  55. Machuca, J. A. D., Gonzalez-Zamora, M. D. M., & Aguilar-Escobar, V. G. (2007). Service operations management research. Journal of Operations Management, 25, 585–603.

    Article  Google Scholar 

  56. Manikas, A., & Boyd., L., Guan, J., & Hoskins, K. . (2020). A review of operations management literature: A data-driven approach. International Journal of Production Research, 58(5), 1442–1461.

    Article  Google Scholar 

  57. Maoz, M. (2013). How IT should deepen big data analysis to support customer-centricity. Gartner G00248980.

    Google Scholar 

  58. Markus, L. (2015). New games, new rules, new scoreboards: The potential consequences of big data. Journal of Information Technology, 30(1), 58–59.

    Article  Google Scholar 

  59. Maxwell, C. R. (2013). Accelerated pattern recognition, ethnography, and the era of big data. In B. Jordan (ed.), Advancing ethnography in corporate environments. Challenges and emerging opportunities (pp. 175–192). Left Coast Press.

    Google Scholar 

  60. Meredith, J. R., Raturi, A., Amoako-Gyampah, K., & Kaplan, B. (1989). Alternative research paradigms in operations. Journal of Operations Management, 8(4), 297–326.

    Article  Google Scholar 

  61. Miller, J. G., Graham, M. B. W., Freeland, J. R., Hottenstein, M., Maister, D. M., Meredith, J., & Schmenner, R. W. (1981). Production/operations management: Agenda for the 80s. Decision Science, 12(4), 547–571.

    Article  Google Scholar 

  62. Mintzberg, F. (1978). Patterns in strategy formation. Management Science, 24(9), 934–948.

    Article  Google Scholar 

  63. Morey, R. (1976). Operations management in selected nonmanufacturing organizations. Academy of Management Journal, 19(1), 120.

    Article  Google Scholar 

  64. Nie, W., & Kellogg, D. L. (1999). How professors of operations management view service operations? Production and Operations Management, 8(3), 339–355.

    Article  Google Scholar 

  65. Ohlhorst, F. (2013). Big data analytics: Turning big data into big money. Wiley.

    Google Scholar 

  66. Oliveira, P., Roth, A. V., Conley, M. V., & Voss, C. (2003). Knowledge assets in global services strategy. In D. V. Gibson, C. Stolp, P. Conceição, & M. V. Heitor (Eds.), Systems and policies for the global learning economy (pp. 511–536). Praeger Publishers.

    Google Scholar 

  67. Pannirselvam, G. P., Ferguson, L. A., Ash, R. C., & Siferd, S. P. (1999). Operations management research: An update for the 1990s. Journal of Operations Management, 18(1), 95–112.

    Article  Google Scholar 

  68. Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1985). A Conceptual model of service quality and its implications for future research. Journal of Marketing, 49(4), 41–50.

    Article  Google Scholar 

  69. Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12.

    Google Scholar 

  70. Pearce, E. (1957). History of the standard industrial classification. Executive office of the President, Bureau of the Budget, Office of Statistical Standards.

    Google Scholar 

  71. Peinado, J., Graeml, A. R., & Vianna, F. (2018). Operations management body of knowledge and its relevance to manufacturing and service organizations. Revista De Gestão, 25(4), 373–389.

    Article  Google Scholar 

  72. Penrose, E. T. (1959). The theory of the growth of the firm. Wiley.

    Google Scholar 

  73. Plumb, I., & Zamfir, A. (2011). Noi ipoteze privind managementul serviciilor în lumea globală [New hypotheses regarding service management in the global world]. Economie teoretică şi aplicată, XVIII(1), 122–130.

    Google Scholar 

  74. Rallis, S. F., & Rossman, G. B. (2003). Learning in the field: An introduction to qualitative research (2nd ed.). Sage.

    Google Scholar 

  75. Rendon, R., Apte, A., & Apte, U. (2012). Services contracting in the DoD: A comparison of management practices in the Army, Navy, and Air Force. Defense Acquisition Research Journal, 19(1), 3–32.

    Google Scholar 

  76. Roda, W. C., Varughese, M. B., Han, D., & Li, M. Y. (2020). Why is it difficult to accurately predict the COVID-19 epidemic? Infectious Disease Modelling, 5, 271–281.

    Article  Google Scholar 

  77. Roth, A. V., & Menor, L. J. (2003). Insights into service operations management: A research agenda. Production and Operations Management, 12(2), 145–164.

    Article  Google Scholar 

  78. Sampson, S. E. (2012). Visualizing service operations. Journal of Service Research, 15(2), 182–198.

    Article  Google Scholar 

  79. Shi, D., Guan, D., Zurada, J., & Manikas, A. (2017). A data-mining approach to identification of risk factors in safety management systems. Journal of Management Information Systems, 34(4), 1054–1081.

    Article  Google Scholar 

  80. Smith, J. S., Karwan, K. R., & Markland, R. E. (2007). A note on the growth of research in service operations management. Production and Operations Management, 16(6), 780–790.

    Article  Google Scholar 

  81. Spohrer, J., & Maglio, P. P. (2008). The emergence of service science: Toward systematic service innovations to accelerate co-creation of value. Production and Operations Management, 17(3), 238–246.

    Article  Google Scholar 

  82. Vargo, S. L., & Lusch, R. F. (2004). The four service marketing myths: Remnants of a goods-based, manufacturing model. Journal of Service Research, 6(4), 324–335.

    Article  Google Scholar 

  83. Vargo, S. L., & Lusch, R. F. (2010). Advancing service science with service-dominant logic. In P. P. Maglio, C. Kieliszewski, & J. C. Spohrer (Eds.), Handbook of service science (pp. 133–156). Springer.

    Chapter  Google Scholar 

  84. Victorino, L., et al. (2018). Service operations: What have we learned? Journal of Service Management, 29(1), 39–54.

    Article  Google Scholar 

  85. Vosbergen, S., Janzen, J., Stappers, P. J., van Zwieten, M., Lacroix, J., Idema, K., van den Brook, I., Kemps, H. M., Kraaijenhagen, R. A., & Peek, N. (2013). A qualitative participatory study to identify experiences of coronary heart disease patients to support the development of online self-management services. International Journal of Medical Informatics, 82(12), 1183–1194.

    Article  Google Scholar 

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Acknowledgements

The authors are thankful to the reviewers for their valuable comments on the previous version of this work.

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Authors and Affiliations

  1. CENTRUM Católica Graduate Business School, Lima, Peru

    Vincent Charles

  2. Pontifical Catholic University of Peru, Lima, Peru

    Vincent Charles

  3. Surrey Business School, The University of Surrey, Guildford, UK

    Ali Emrouznejad

  4. Faculty of Business and Law, University of Northampton, Northampton, UK

    Tatiana Gherman

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  1. Vincent Charles
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  2. Ali Emrouznejad
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  3. Tatiana Gherman
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Correspondence to Vincent Charles .

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Editors and Affiliations

  1. Surrey Business School, The University of Surrey, Guildford, UK

    Ali Emrouznejad

  2. Center for Value Chain Innovation, CENTRUM Católica Graduate Business School, Pontifical Catholic University of Peru, Lima, Peru

    Vincent Charles

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Charles, V., Emrouznejad, A., Gherman, T. (2022). Strategy Formulation and Service Operations in the Big Data Age: The Essentialness of Technology, People, and Ethics. In: Emrouznejad, A., Charles, V. (eds) Big Data and Blockchain for Service Operations Management. Studies in Big Data, vol 98. Springer, Cham. https://doi.org/10.1007/978-3-030-87304-2_2

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