Utilizing Data and Analytics to Advance Service

Towards Enabling Organizations to Successfully Ride the Next Wave of Servitization
  • Fabian HunkeEmail author
  • Christian Engel
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 331)


For decades, servitization served as a strategy to gain a competitive advantage over competitors. However, due to its ubiquitous adoption, it is no longer a viable source for differentiation. In this context, data and analytics bear the potential to create new value and, thus, is believed to drive the next frontier of servitization. Yet, the majority of organizations fail to create new innovative services utilizing data and analytics, while research on this topic is also still very limited. Based on a structured literature review, we derive the following contributions to this research field: First, we provide a general overview over the topic, linking single discussions to a larger discourse. Second, we contribute to the fundamental understanding of the research field by pointing out the gaps in the existing literature. Third, we lay the foundation for future research by opening a research agenda to address the highlighted gaps.


Servitization Service advancement Big data Data analytics Literature review Research agenda Data- and analytics-based service 


  1. 1.
    Baines, T.S., Lightfoot, H.W., Kay, J.M.: Servitized manufacture: practical challenges of delivering integrated products and services. J. Eng. Manuf. 223(9), 1207–1215 (2009)CrossRefGoogle Scholar
  2. 2.
    Vandermerwe, S., Rada, J.: Servitization of business: adding value by adding services. Eur. Manag. J. 6(4), 314–324 (1988)CrossRefGoogle Scholar
  3. 3.
    Opresnik, D., Taisch, M.: The value of big data in servitization. Int. J. Prod. Econ. 165, 174–184 (2015)CrossRefGoogle Scholar
  4. 4.
    Neely, A.: The servitization of manufacturing: an analysis of global trends. In: Proceedings of the 14th European Operations Management Association Conference, pp. 1–10 (2007)Google Scholar
  5. 5.
    Neely, A.: Exploring the financial consequences of the servitization of manufacturing. Oper. Manag. Res. 1(2), 103–118 (2009)CrossRefGoogle Scholar
  6. 6.
    McAfee, A., Brynjolfsson, E.: Big data: the management revolution. Harv. Bus. Rev. 90(10), 61–67 (2012)Google Scholar
  7. 7.
    Atzori, L., Iera, A., Morabito, G.: The internet of things: a survey. Comput. Netw. 54(15), 2787–2805 (2010)CrossRefGoogle Scholar
  8. 8.
    Lavalle, S., Lesser, E., Shockley, R., Hopkins, M.S., Kruschwitz, N.: Big data, analytics and the path from insights to value. MIT Sloan Manag. Rev. 52(2), 21–32 (2011)Google Scholar
  9. 9.
    Schüritz, R., Satzger, G.: Patterns of data-infused business model innovation. In: Proceedings of the 18th IEEE Conference on Business Informatics (CBI), pp. 133–142 (2016)Google Scholar
  10. 10.
    Marshall, A., Mueck, S., Shockley, R.: How leading organizations use big data and analytics to innovate. Strateg. Leadersh. 43(5), 32–39 (2015)CrossRefGoogle Scholar
  11. 11.
    Chen, H., Chiang, R.H.L., Storey, V.C.: Business intelligence and analytics: from big data to big impact. MIS Q. 36(4), 1165–1188 (2012)Google Scholar
  12. 12.
    Watson, H.J.: Tutorial: business intelligence – past, present, and future. Commun. Assoc. Inf. Syst. 25(39), 487–510 (2009)Google Scholar
  13. 13.
  14. 14.
    Maglio, P., Lim, C.-H.: Innovation and big data in smart service systems. J. Innov. Manag. 4(1), 11–21 (2016)Google Scholar
  15. 15.
    Ostrom, A.L., Parasuraman, A., Bowen, D.E., Patrício, L., Voss, C.A.: Service research priorities in a rapidly changing context. J. Serv. Res. 18(2), 127–159 (2015)CrossRefGoogle Scholar
  16. 16.
    Webster, J., Watson, R.T.: Analyzing the past to prepare for the future: writing a literature review. MIS Q. 26(2), 13–23 (2002)Google Scholar
  17. 17.
    vom Brocke, J., Simons, A., Riemer, K., Niehaves, B., Plattfaut, R., Cleven, A.: Standing on the shoulders of giants: challenges and recommendations of literature search in information systems research. Commun. Assoc. Inf. Syst. 37(1), 205–224 (2015)Google Scholar
  18. 18.
    Boell, S.K., Cecez-Kecmanovic, D.: A hermeneutic approach for conducting literature reviews and literature searches. Commun. Assoc. Inf. Syst. 34, 257–286 (2015)Google Scholar
  19. 19.
    Davenport, T.H.: Analytics 3.0. Harv. Bus. Rev. 91(12), 64 (2013)Google Scholar
  20. 20.
    Legner, C., Eymann, T., Hess, T., Matt, C., Böhmann, T., Drews, P., et al.: Digitalization: opportunity and challenge for the business and information systems engineering community. Bus. Inf. Syst. Eng. 59(4), 301–308 (2017)CrossRefGoogle Scholar
  21. 21.
    Huberty, M.: Awaiting the second big data revolution: from digital noise to value creation. J. Ind. Compet. Trade 15, 35–47 (2015)CrossRefGoogle Scholar
  22. 22.
    Alvertis, I., et al.: Challenges laying ahead for future digital enterprises: a research perspective. In: Persson, A., Stirna, J. (eds.) CAiSE 2015. LNBIP, vol. 215, pp. 195–206. Springer, Cham (2015). Scholar
  23. 23.
    Herterich, M.M., Uebernickel, F., Brenner, W.: Stepwise evolution of capabilities for harnessing digital data streams in data-driven industrial services. MIS Q. Exec. 15(4), 299–320 (2016)Google Scholar
  24. 24.
    Heiskala, M., Jokinen, J.P., Tinnilä, M.: Crowdsensing-based transportation services - an analysis from business model and sustainability viewpoints. Res. Transp. Bus. Manag. 18, 38–48 (2016)CrossRefGoogle Scholar
  25. 25.
    Lopez, P.G., Tinedo, R.G., Montresor, A.: Towards Data-driven software-defined infrastructures. Procedia Comput. Sci. 97, 144–147 (2016)CrossRefGoogle Scholar
  26. 26.
    Wolfert, S., Ge, L., Verdouw, C., Bogaardt, M.J.: Big data in smart farming – a review. Agric. Syst. 153, 69–80 (2017)CrossRefGoogle Scholar
  27. 27.
    Schüritz, R., Seebacher, S., Satzger, G., Schwarz, L.: Datatization as the next frontier of servitization – understanding the challenges for transforming organizations. In: Proceedings of the 38th International Conference on Information Systems (ICIS), pp. 1–21 (2017)Google Scholar
  28. 28.
    Chen, Y., Kreulen, J., Campbell, M., Abrams, C.: Analytics ecosystem transformation: a force for business model innovation. In: Proceedings of the Annual SRII Global Conference, pp. 11–20 (2011)Google Scholar
  29. 29.
    Schüritz, R., Seebacher, S., Dorner, R.: Capturing value from data: revenue models for data-driven services. In: Proceedings of the 50th Hawaii International Conference on System Sciences (HICSS), pp. 5348–5357 (2017)Google Scholar
  30. 30.
    Teece, D.: Business models, business strategy and innovation. Long Range Plann. 43(2), 172–194 (2010)CrossRefGoogle Scholar
  31. 31.
    Herterich, M.M., Uebernickel, F., Brenner, W.: The impact of cyber-physical systems on industrial services in manufacturing. Procedia CIRP 30, 323–328 (2015)CrossRefGoogle Scholar
  32. 32.
    Zolnowski, A., Christiansen, T., Gudat, J.: Business model transformation patterns of data-driven innovations. In: Proceedings of the 24th European Conference on Information Systems (ECIS), pp. 1–16 (2016)Google Scholar
  33. 33.
    Krishnamoorthi, S., Mathew, S.K.: Business analytics and business value: a case study. In: Proceedings of the 36th International Conference on Information Systems (ICIS), pp. 1–17 (2015)Google Scholar
  34. 34.
    Delen, D., Demirkan, H.: Data, information and analytics as services. Decis. Support Syst. 55(1), 359–363 (2013)CrossRefGoogle Scholar
  35. 35.
    Sun, Z., Zou, H., Strang, K.: Big data analytics as a service for business intelligence. In: Janssen, M., et al. (eds.) I3E 2015. LNCS, vol. 9373, pp. 200–211. Springer, Cham (2015). Scholar
  36. 36.
    Schüritz, R., Brand, E., Satzger, G., Bischhoffshausen, J.: How to cultivate analytics capabilities within an organization ? – design and types of analytics competency centers. In: Proceedings of the 25th European Conference on Information Systems (ECIS), pp. 389–404 (2017)Google Scholar
  37. 37.
    Lim, C.H., Kim, M.J., Heo, J.Y., Kim, K.J.: Design of informatics-based services in manufacturing industries: case studies using large vehicle-related databases. J. Intell. Manuf. 29(3), 497–508 (2015)CrossRefGoogle Scholar
  38. 38.
    Marjanovic, O.: From analytics-as-a-service to analytics-as-a-consumer-service: exploring a new direction in business intelligence and analytics research. In: Proceedings of the 48th Hawaii International Conference on System Sciences (HICSS), pp. 4742–4751 (2015)Google Scholar
  39. 39.
    Dremel, C., Wulf, J., Herterich, M.M., Waizmann, J.-C., Brenner, W.: How AUDI AG established big data analytics in its digital transformation. MIS Q. Exec. 16(2), 81–100 (2017)Google Scholar
  40. 40.
    Ross, J., Sebastian, I., Beath, C., Mocker, M., Moloney, K., Fonstad, N.: Designing and executing digital strategies. In: Proceedings of the 37th International Conference on Information Systems (ICIS), pp. 1–17 (2016)Google Scholar
  41. 41.
    Patrício, L., Gustafsson, A., Fisk, R.: Upframing service design and innovation for research impact. J. Serv. Res. 21(1), 3–16 (2018)CrossRefGoogle Scholar
  42. 42.
    Lim, C., Kim, M.-J., Kim, K.-H., Kim, K.-J., Maglio, P.P.: Using data to advance service: managerial issues and theoretical implications from action research. J. Serv. Theory Pract. 28(1), 99–128 (2018)CrossRefGoogle Scholar
  43. 43.
    Hunke, F., Seebacher, S., Schuritz, R., Illi, A.: Towards a process model for data-driven business model innovation. In: Proceedings of the 19th IEEE Conference on Business Informatics (CBI), pp. 150–157 (2017)Google Scholar
  44. 44.
    Patrício, L., Fisk, R., Constantine, L.: Multilevel service design: from customer value constellation to service experience blueprinting. J. Serv. Res. 14(2), 180–200 (2011)CrossRefGoogle Scholar
  45. 45.
    Nickerson, R.C., Varshney, U., Muntermann, J.: A method for taxonomy development and its application in information systems. Eur. J. Inf. Syst. 22(3), 336–359 (2013)CrossRefGoogle Scholar
  46. 46.
    Hartmann, P., Zaki, M., Feldmann, N., Neely, A.: Capturing value from big data – a taxonomy of data-driven business models used by start-up firms. Int. J. Oper. Prod. Manag. 36(10), 1382–1406 (2016)CrossRefGoogle Scholar
  47. 47.
    Hunke, F., Schüritz, R., Kuehl, N.: Towards a unified approach to identify business model patterns: a case of e-mobility services. In: Za, S., Drăgoicea, M., Cavallari, M. (eds.) IESS 2017. LNBIP, vol. 279, pp. 182–196. Springer, Cham (2017). Scholar
  48. 48.
    Edvardsson, B., Olsson, J.: Key concepts for new service development. Serv. Ind. J. 16(2), 140–164 (1996)CrossRefGoogle Scholar
  49. 49.
    Fitzsimmons, J.A., Fitzsimmons, M.J. (eds.): New Service Development: Creating Memorable Experiences. Sage Publication Inc., Thousand Oaks (2000)Google Scholar
  50. 50.
    Stummer, C., Kundisch, D., Decker, R.: Platform launch strategies. Bus. Inf. Syst. Eng. 60(2), 167–173 (2018)CrossRefGoogle Scholar
  51. 51.
    Porter, M.E., Heppelmann, J.: How smart, connected products are transforming companies. Har. Bus. Rev. 93, 10 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Karlsruhe Service Research Institute (KSRI), Karlsruhe Institute of Technology (KIT)KarlsruheGermany

Personalised recommendations