Pathways from Data to Value: Identifying Strategic Archetypes of Analytics-Based Services

Bibtex

Cite as text

						@Select Types{,
							 
							 
							 
							 
							 
							Journal   = "Band-1",
							 Title= "Pathways from Data to Value: Identifying Strategic Archetypes of Analytics-Based Services", 
							Author= "Fabian Hunke, Stefan Seebacher, Ronny Schüritz, and Gerhard Satzger", 
							Doi= "https://doi.org/10.30844/wi_2020_j7-hunke", 
							 Abstract= "The digital transformation offers organizations new opportunities to expand their existing service portfolio in order to achieve competitive advantages. A popular way to create new customer value is the offer of analytics-based services (ABS) – services that apply analytical methods to data to empower customers to make better decisions and to solve complex problems. However, research still lacks to provide a profound conceptualization of this novel service type. Similarly, actionable insights on how to purposefully establish ABS in the market to enrich the service portfolio remain scarce. Our cluster analysis of 105 ABS offered by start-ups identifies four generic ABS archetypes and unveils their specific service objectives and pronounced characteristics. The findings contribute to a more profound theorizing process on ABS by providing a detailed characterization of different ABS types and a systematization regarding strategic opportunities to enrich service portfolios in practice.", 
							 Keywords= "analytics-based services, archetypes, service portfolio, cluster analysis.", 
							}
					
Fabian Hunke, Stefan Seebacher, Ronny Schüritz, and Gerhard Satzger: Pathways from Data to Value: Identifying Strategic Archetypes of Analytics-Based Services. Online: https://doi.org/10.30844/wi_2020_j7-hunke (Abgerufen 22.11.24)

Abstract

Abstract

The digital transformation offers organizations new opportunities to expand their existing service portfolio in order to achieve competitive advantages. A popular way to create new customer value is the offer of analytics-based services (ABS) – services that apply analytical methods to data to empower customers to make better decisions and to solve complex problems. However, research still lacks to provide a profound conceptualization of this novel service type. Similarly, actionable insights on how to purposefully establish ABS in the market to enrich the service portfolio remain scarce. Our cluster analysis of 105 ABS offered by start-ups identifies four generic ABS archetypes and unveils their specific service objectives and pronounced characteristics. The findings contribute to a more profound theorizing process on ABS by providing a detailed characterization of different ABS types and a systematization regarding strategic opportunities to enrich service portfolios in practice.

Keywords

Schlüsselwörter

analytics-based services, archetypes, service portfolio, cluster analysis.

References

Referenzen

1. Huang, M.H., Rust, R.T.: Technology-driven service strategy. J. Acad. Mark. Sci. 45, 906– 924 (2017).
2. Legner, C., Eymann, T., Hess, T., Matt, C., Böhmann, T., Drews, P., Mädche, A., Urbach, N., Ahlemann, F.: Digitalization: Opportunity and Challenge for the Business and Information Systems Engineering Community. Bus. Inf. Syst. Eng. 59, 301–308 (2017).
3. Davenport, T.H., Harris, J.G.: Competing on Analytics: The New Science of Winning. Harvard Business Review Press, Boston (2017).
4. Vargo, S.L., Lusch, R.F.: Why “service”? J. Acad. Mark. Sci. 36, 25–38 (2008).
5. Troilo, G., De Luca, L.M., Guenzi, P.: Linking Data-Rich Environments with Service Innovation in Incumbent Firms: A Conceptual Framework and Research Propositions. J. Prod. Innov. Manag. 34, 617–639 (2017).
6. Lehrer, C., Wieneke, A., vom Brocke, J., Jung, R., Seidel, S.: How Big Data Analytics Enables Service Innovation: Materiality, Affordance, and the Individualization of Service. J. Manag. Inf. Syst. 35, 424–460 (2018).
7. Hunke, F., Engel, C.: Utilizing Data and Analytics to Advance Service Towards Enabling Organizations to Successfully Ride the Next Wave of Servitization. In: Satzger, G., Patricio, L., Zaki, M., Kühl, N., and Hottum, P. (eds.) Exploring Service Science, 9th International Conference, IESS 2018. pp. 219–231. Springer, Cham (2018).
8. Hunke, F., Engel, C., Schüritz, R., Ebel, P.: Understanding the Anatomy of Analytics- Based Services – a Taxonomy To Conceptualize the Use of Data and Analytics in Services. ECIS 2019 Proc. 1–15 (2019).
9. BASF Digital Farming: FIELD MANAGER: Simply Smarter Crop Protection, https://www.xarvio.com/en/Field-Manager.
10. Demirkan, H., Bess, C., Spohrer, J., Rayes, A., Allen, D., Moghaddam, Y.: Innovations with smart service systems: Analytics, big data, cognitive assistance, and the internet of everything. Commun. Assoc. Inf. Syst. 37, 733–752 (2015).
11. Saarijärvi, H., Grönroos, C., Kuusela, H.: Reverse use of customer data: Implications for service-based business models. J. Serv. Mark. 28, 529–537 (2014).
12. Davenport, T.H., Lucker, J.: Running on data. Deloitte Rev. 5–15 (2015).
13. 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, 127–159 (2015).
14. Lim, C.-H., 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, 99–128 (2018).
15. Remane, G., Hanelt, A., Nickerson, R.C., Tesch, J.F., Kolbe, L.M.: A taxonomy of carsharing business models. ICIS 2016 Proc. 1–19 (2016).
16. Gimpel, H., Rau, D., Röglinger, M.: Understanding FinTech start-ups – a taxonomy of consumer-oriented service offerings. Electron. Mark. 28, 245–264 (2018).
17. Schilling, R.D., Haki, M.K., Aier, S.: Introducing Archetype Theory to Information Systems Research: A Literature Review and Call for Future Research. Proc. 13th Int. Conf. Wirtschaftsinformatik. 574–588 (2017).
18. Taran, Y., Nielsen, C., Thomsen, P., Montemari, M., Paolone, F.: Business Model Archetypes: a Mapping Tool for Fostering Innovation. R&D Manag. Conf. 885–902 (2015).
19. Criscuoloa, P., Nicolaoub, N., Salter, A.: The elixir (or burden) of youth? Exploring differences in innovation between start-ups and established firms. Res. Policy. 41, 319– 333 (2012).
20. Punj, G., Stewart, D.W.: Cluster Analysis in Marketing Research: Review and Suggestions for Application. J. Mark. Res. 20, 134–148 (1983).
21. Müller, O., Fay, M., vom Brocke, J.: The Effect of Big Data and Analytics on Firm Performance: An Econometric Analysis Considering Industry Characteristics. J. Manag. Inf. Syst. 35, 488–509 (2018).
22. Schüritz, R., Seebacher, S., Satzger, G., Schwarz, L.: Datatization as the Next Frontier of Servitization: Understanding the Challenges for Transforming Organizations. ICIS 2017 Proc. 1098–1118 (2017).
23. Chen, Y., Kreulen, J., Campbell, M., Abrams, C.: Analytics Ecosystem Transformation: A Force for Business Model Innovation. In: 2011 Annual SRII Global Conference. pp. 11–20 (2011).
24. 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, 1382–1406 (2016).
25. Lim, C.-H., Kim, K.H., Kim, M.J., Heo, J.Y., Kim, K.J., Maglio, P.P.: From data to value: A nine-factor framework for data-based value creation in information-intensive services. Int. J. Inf. Manage. 39, 121–135 (2018).
26. Martin, D., Kühl, N.: Holistic System-Analytics as an Alternative to Isolated Sensor Technology: A Condition Monitoring Use Case. Proc. 52nd Hawaii Int. Conf. Syst. Sci. 1005–1012 (2019).
27. Beverungen, D., Müller, O., Matzner, M., Mendling, J., Vom Brocke, J.: Conceptualizing smart service systems. Electron. Mark. 29, 7–18 (2019).
28. Woerner, S.L., Wixom, B.H.: Big data: Extending the business strategy toolbox. J. Inf. Technol. 30, 60–62 (2015).
29. Wuenderlich, N. V., Heinonen, K., Ostrom, A.L., Patricio, L., Sousa, R., Voss, C., Lemmink, J.G.A.M.: “Futurizing” smart service: implications for service researchers and managers. J. Serv. Mark. 29, 442–447 (2015).
30. Ackoff, R.L.: From data to wisdom. J. Appl. Syst. Anal. 16, 3–9 (1989).
31. Nickerson, R.C., Varshney, U., Muntermann, J.: A method for taxonomy development and its application in information systems. Eur. J. Inf. Syst. 22, 336–359 (2013).
32. Hambrick, D.C.: Taxonomic approaches to studying strategy: some conceptual and methodological issues. J. Manage. 10, 27–41 (1984).
33. Lovelock, C.H.: Classifying Services to Gain Strategic Marketing Insights. J. Mark. 47, 9– 20 (1983).
34. Allmendinger, G., Lombreglia, R.: Four Strategies for the Age of Smart Services. Harv. Bus. Rev. 83, 131–145 (2005).
35. Weking, J., Stöcker, M., Kowalkiewicz, M., Böhm, M., Krcmar, H.: Archetypes for Industry 4 . 0 Business Model Innovations. 24th Am. Conf. Inf. Syst. (AMCIS), New Orleans. 1–10 (2018).
36. Zolnowski, A., Christiansen, T., Gudat, J.: Business Model Transformation Patterns of Data-Driven Innovations. ECIS 2016 Proc. 1–16 (2016).
37. Rizk, A., Bergvall-Kåreborn, B., Elragal, A.: Towards a Taxonomy for Data-Driven Digital Services. Proc. 51st Hawaii Int. Conf. Syst. Sci. 9, 1076–1085 (2018).
38. Bryman, A.: Integrating quantitative and qualitative research: How is it done? Qual. Res. 6, 97–113 (2006).
39. Bryman, A.: Social Research Methods. Oxford University Press, Oxford (2012).
40. Saldana, J.: The Coding Manual for Qualitative Researchers. Sage, London (2009).
41. Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20, 37–46 (1960).
42. Landis, J.R., Koch, G.G.: The Measurement of Observer Agreement for Categorical Data. Biometrics. 33, 159–174 (1977).
43. Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2006).
44. Ward, J.H.: Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58, 236–244 (1963).
45. Sokal, R. R., & Michener, C.D.: A statistical method for evaluating systematic relationships. Univ. Kansas Bull. 38, 1409–1438 (1958).
46. Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987).
47. Kaufman, L., Rousseeuw, P.J.: Finding groups in data: an introduction to cluster analysis. John Wiley & Sons (1990).
48. Rust, R.T., Huang, M.-H.: The Service Revolution and the Transformation of Marketing Science. Mark. Sci. 33, 206–221 (2014).
49. Schüritz, R., Wixom, B., Farrell, K., Satzger, G.: Value Co-Creation in Data-Driven Services : Towards a Deeper Understanding of the Joint Sphere. ICIS 2019 Proc. 1–9 (2019).
50. Hinz, O., van der Aalst, W.M.P., Weinhardt, C.: Blind Spots in Business and Information Systems Engineering. Bus. Inf. Syst. Eng. 61, 133–135 (2019).

Most viewed articles

Meist angesehene Beiträge

GITO events | library.gito