Doctoral dissertation: Data-driven business models often fail at scaling – growth strategies must match the maturity of the business model
What did you research?
Industrial companies collect vast amounts of data from their equipment and processes, yet turning that data into profitable business has proven difficult. Through three qualitative case studies, I examined how emerging technologies such as generative AI reshape value creation, the tensions the transition to data-driven business models creates between providers and their customers, and how firms scale outcome-based business models into sustainable offerings.
What is the main result of your research?
My research has three main results. First, the first sub-study identified four value logics enabled by generative AI – synthesizing, interactional, predictive, and diagnostic – in which AI most often augments people rather than replaces them. In the second sub-study, we focused on the tensions arising in the transition phases of different data-driven business models. We found that not all tensions created by the transition should be eliminated: some require ongoing balancing, while others act as catalysts for change.
Third, many firms succeed in data-driven pilots, yet turning these pilots into profitable, repeatable business often fails. In the third sub-study of my dissertation, we developed a maturity framework showing that the logic of scaling shifts as a business model matures: early on, value comes from learning, while later stages call for economies of scale, scope, and structure. Growth strategies must therefore be matched to the maturity of the business model.
How can the results be applied?
The findings offer practical guidance for executives, data professionals, industry associations, and customer organizations. The essential question is not whether to adopt AI, but which value logic to pursue, how to recognize and harness tensions, and how to match the scaling strategy to the maturity of the business model.
The public examination of Eveliina Lakka's doctoral dissertation in Marketing "Data-driven business models: Turning data-based insights into new business models and offerings" will be held on Friday, 28 August 2026 at 12 noon in the assembly hall (C1), Main Building, University of Jyväskylä. The Opponent is Professor Christian Kowalkowski (Linköping University) and the Custos is Associate Professor Joel Mero (University of Jyväskylä).
The dissertation is available online (the link will be updated later).
Further information:
Eveliina Lakka
eveliina.a.lakka@student.jyu.fi
