Skip to main content

Setting Sail for Data-Driven Decision-Making an Action Design Research Case from the Maritime Industry

  • Chapter
  • First Online:
Design Science Research. Cases

Part of the book series: Progress in IS ((PROIS))

  • 3555 Accesses

Abstract

To react to new market dynamics, OEM, one of the largest marine equipment manufacturers in the world, was facing the task of transforming its aftersales business from key-account-manager-driven sales processes to more proactive and customer-centric processes. The company had recently implemented an organization-wide customer relationship management (CRM) system to facilitate this transformation. However, the system was not fully used because of a lack of proactive work practices that the system could support. Based on this diagnosis, we developed and applied a method for data-driven lead-generation that uses advanced analytics and automation to leverage internal and external data sources to identify and assess sales leads. To guide the design process, we ingrained the artifact with theory about data-driven decision-making (DDD) and value creation in the form of initial design principles. After several iterations of building the artifact, examining the organizational context, and evaluating the changes that those interventions introduced, we formalized a set of design principles and abstracted them to the broader class of DDD artifacts, highlighting decision quality but also the importance of model comprehensibility, domain knowledge, and actionability of results.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    Earlier stages and iterations of the artifact of this ADR study have been reported in an unpublished master’s thesis and, with a focus on developed design theory, in the proceedings of the European Conference on Information Systems (Thiess & Müller, 2018).

References

  • Abe, M. (2008). ‘Counting your customers’ one by one: A hierarchical bayes extension to the Pareto/NBD model. Marketing Science, 28(3), 541–553.

    Article  Google Scholar 

  • Beverungen, D., Müller, O., Matzner, M., Mendling, J., & Vom Brocke, J. (n.d.). Conceptualizing smart service systems.

    Google Scholar 

  • Brynjolfsson, E., Hitt, L. M., & Kim, H. H. (2011). Strength in numbers: How does data-driven decisionmaking affect firm performance? SSRN Electronic Journal.

    Google Scholar 

  • Chandra, L., Seidel, S., & Gregor, S. (2015). Prescriptive knowledge in IS research: Conceptualizing design principles in terms of materiality, action, and boundary conditions. In: 2015 48th Hawaii International Conference on System Sciences (pp. 4039–4048). IEEE.

    Google Scholar 

  • Davenport, T. H. (2013, December). ‘Analytics 3.0’.

    Google Scholar 

  • Dearden, A. (2001). IDA-S: A conceptual framework for partial automation. In People and computers XV—interaction without frontiers (pp. 213–228). London: Springer.

    Google Scholar 

  • Efron, B., & Morris, C. (1977). Stein’s paradox in statistics. Scientific American, 236(5), 119–127.

    Google Scholar 

  • Fader, P. S., & Hardie, B. G. S. (2009). Probability models for customer-base analysis. Journal of Interactive Marketing, 23(1), 61–69.

    Article  Google Scholar 

  • Fader, P. S., & Hardie, B. G. (2013). The Gamma-Gamma model of monetary value.

    Google Scholar 

  • Gregor, S., & Benbasat, I. (1999). Explanations from intelligent systems: Theoretical foundations and implications for practice. MIS Quarterly.

    Google Scholar 

  • Hevner, A. (2007). A three cycle view of design science research. Scandinavian Journal of Information Systems, 19(2).

    Google Scholar 

  • Hollander, E. P., Vroom, V. H., & Yetton, P. W. (1973). Leadership and decision-making. Administrative Science Quarterly.

    Google Scholar 

  • International Maritime Organization. (2015). Survey Guidelines under the Harmonized System Of Survey And Certification (HSSC).

    Google Scholar 

  • Kayande, U., De Bruyn, A., Lilien, G. L., Rangaswamy, A., & van Bruggen, G. H. (2009). How incorporating feedback mechanisms in a DSS affects DSS evaluations. Information Systems Research.

    Google Scholar 

  • Kotter, J. P. (1995). Leading change: Why transformation efforts fail.

    Google Scholar 

  • LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT Sloan Management Review, 52(2), 21.

    Google Scholar 

  • Leek, J. T., & Peng, R. D. (2015). What is the question? Science, 347(6228), 1314–1315.

    Google Scholar 

  • Müller, O., Fay, M., & vom Brocke, J. (2018). The effect of big data and analytics on firm performance: An econometric analysis considering industry characteristics. Journal of Management Information Systems, 35(2), 488–509.

    Google Scholar 

  • Orlikowski, W. J., & Iacono, C. S. (2001). Research commentary: Desperately seeking the ‘IT’ in IT research—A call to theorizing the IT artifact. Information Systems Research, 12(2), 121–134.

    Google Scholar 

  • Platzer, M., & Reutterer, T. (2016). Ticking away the moments: Timing regularity helps to better predict customer activity. Marketing Science, 35(5), 779–799.

    Google Scholar 

  • Purao, S., Rossi, M., & Sein, M. K. (2010). On integrating action research and design research (pp. 179–194).

    Google Scholar 

  • Ransbotham, S., Kiron, D., & Prentice, P. K. (2015). The talent dividend. MIT Sloan Management Review, 56(4), 1.

    Google Scholar 

  • Rossi, P. E., & Allenby, G. M. (2003). Bayesian statistics and marketing. Marketing Science, (3), 304.

    Google Scholar 

  • SAS. (2012). The evolution of decision making: How leading organizations are developing a data-driven culture—Sponsor content from SAS. Retrieved from https://hbr.org/sponsored/2016/04/the-evolution-of-decision-making-how-leading-organizations-are-developing-a-data-driven-culture.

  • Seidel, S., Chandra Kruse, L., Székely, N., Gau, M., & Stieger, D. (2018). Design principles for sensemaking support systems in environmental sustainability transformations. European Journal of Information Systems.

    Google Scholar 

  • Sein, H., Purao, R., & Lindgren, R. (2011). Action design research. MIS Quarterly.

    Google Scholar 

  • Shaikhina, T., & Khovanova, N. A. (2017). Handling limited datasets with neural networks in medical applications: A small-data approach. Artificial Intelligence in Medicine, 75, 51–63.

    Google Scholar 

  • Sharma, R., Mithas, S., & Kankanhalli, A. (2014). Transforming decision-making processes: A research agenda for understanding the impact of business analytics on organisations. European Journal of Information Systems.

    Google Scholar 

  • Shearer, C., Watson, H. J, Grecich, D. G., Moss, L., Adelman, S., Hammer, K., & Herdlein, S. a. (2000). The CRISP-DM model: The new blueprint for data mining. Journal of Data Warehousing.

    Google Scholar 

  • Susman, G. I., & Evered, R. D. (1978). An assessment of the scientific merits of action research. Administrative Science Quarterly, 23(4), 582.

    Google Scholar 

  • Thiess, T., & Müller, O. (2018). Towards design principles for data-driven decision making-an action design research project in the maritime industry. In Proceedings of the 26th European Conference on Information Systems (ECIS). Portsmouth: European Conference on Information Systems (ECIS).

    Google Scholar 

  • Van De Schoot, R., Broere, J. J., Perryck, K. H., Zondervan-Zwijnenburg, M., & Van Loey, N. E. (2015). Analyzing small data sets using Bayesian estimation: The case of posttraumatic stress symptoms following mechanical ventilation in burn survivors. European Journal of Psychotraumatology.

    Google Scholar 

  • vom Brocke, J., Debortoli, S., Müller, O., & Reuter, N. (2014). How in-memory technology can create business value: Insights from the hilti case. Communications of the Association for Information Systems.

    Google Scholar 

  • Wagner, M. M., & Hogan, W. R. (1996). The accuracy of medication data in an outpatient electronic medical record. Journal of the American Medical Informatics Association, 3(3), 234–244.

    Google Scholar 

  • Watson, H. J. (2014). Tutorial: Big data analytics: Concepts, technologies, and applications. CAIS, 34, 65.

    Google Scholar 

  • Wu, L., & Hitt, L. M. (2016). How do data skills affect firm productivity: Evidence from process-driven versus innovation-driven practices.

    Google Scholar 

  • Wu, L., Hitt, L. M., & Lou, B. (2017, February 28). Data analytics skills, innovation and firm productivity.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Oliver Müller .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Thiess, T., Müller, O. (2020). Setting Sail for Data-Driven Decision-Making an Action Design Research Case from the Maritime Industry. In: vom Brocke, J., Hevner, A., Maedche, A. (eds) Design Science Research. Cases. Progress in IS. Springer, Cham. https://doi.org/10.1007/978-3-030-46781-4_12

Download citation

Publish with us

Policies and ethics