Amsterdam University of Applied Sciences
A design method for data driven business models
Haaker, Timber; Groot, Wouter; Hekman, Erik
Publication date 2019
Document Version Final published version Published in
4th International Conference on New Business Models
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Citation for published version (APA):
Haaker, T., Groot, W., & Hekman, E. (2019). A design method for data driven business models. In F. Lüdeke-Freund, & T. Froese (Eds.), 4th International Conference on New Business Models: New Business Models for Sustainable Entrepreneurship, Innovation, and Transformation (pp. 554-560). ESCP Europe Berlin.
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thInternational Conference on New Business Models
New Business Models for
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Edited by Florian Lüdeke-Freund and Tobias Froese, with support from Claire Eckmann and Harris Azhar
Chair for Corporate Sustainability, ESCP Europe Business School, Berlin
Edited in June 2019
We thank the numerous authors and reviewers without whom these proceedings would not have been possible!
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Reference
Lüdeke-Freund, F. & Froese, T. (2019): Proceedings of the 4th International Conference on New Business Models: New Business Models for Sustainable Entrepreneurship, Innovation, and Transformation, Berlin, Germany, 1-3 July 2019. Berlin: ESCP Europe.
ISBN 978-3-96705-001-1
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Table of Contents
Partners ... 2
Words of Welcome ... 4
Instead of a Prologue ... 5
Conference Programme ... 9
Introductory Note & Fishbowl Discussion ... 12
Keynotes ... 13
ABC Panel – Academic / Business / Consulting ... 16
Journal Panel ... 19
Conference Tracks ... 21
Track I: Business Models For A Circular Economy ... 23
Track II: New Business Models, Sustainable Development & Corporate Strategic Management ... 168
Track III: Social Entrepreneurship as Transformative Force towards Sustainability ... 336
Track IV: Circular Communication in a Circular Economy, How Social Media Communication Shapes Sustainable Business Models ... 446
Track V: New Business Models for Sustainable Entrepreneurship ... 476
Track VI: Open Innovation Enabled by Emerging Technologies: What Are the Implications for New Business Models? ... 551
Track VII: New Business Models for Sustainability Transition ... 625
Track VIII: Insights on New Business Models from Young Academics ... 761
Track IX: Sustainable Business Models for A Sharing Economy ... 903
Track X: Practitioner Sessions ... 924
Instead of an Epilogue ... 927
Conference Team ... 928
About ESCP Europe Berlin ... 929
A design method for data driven business models
Timber Haaker1,*, Wouter Groot2, Erik Hekman3
1Saxion University of Applied Sciences; 2Amsterdam University of Applied Sciences; 3University of Applied Sciences Utrecht
*t.i.haaker@saxion.nl
Extended abstract
Introduction
The growing availability of data offers plenty of opportunities for data-driven innovation of business models. This certainly applies to interactive media companies. Interactive media companies are engaged in the development, provisioning, and exploitation of interactive media services and applications.
Through the service interactions, they may collect large amounts of data which can be used to enhance applications or even define new propositions and business models. According to Lippell (2016), media companies can publish content in more sophisticated ways. They can build a deeper and more engaging customer relationship based on a deeper understanding of their users. Indeed, research from Weill & Woerner (2015) suggests that companies involved in the digital ecosystem that better understand their customers than their average competitor have significantly higher profit margins than their industry averages. Moreover, the same research suggests that businesses need to think more broadly about their position in the ecosystem. Open innovation and collaboration are essential for new growth, for example combining data within and across industries (Parmar et al., 2014). However, according to (Mathis and Köbler, 2016), these opportunities remain largely untapped as especially SMEs lack the knowledge and processes to translate data into attractive propositions and design viable data driven business models (DDBM). In this paper, we investigate how interactive media companies can structurally gain more insight and value from data and how
they can develop DDBM. We define a DDBM as a business model relying on data as a key resource (Hartmann et al., 2016).
Background
There exist several generic approaches for developing service propositions and designing BMs like the Business Model Canvas (Osterwalder et al., 2010) or STOF method (De Vos & Haaker, 2008), and there are conceptual models, development approaches and patterns concerning DDBM specifically (Hartmann et al., 2016;
Brownlow et al., 2015; Mathis and Köbler, 2016; Schaefer et al., 2017). Hartmann develops a conceptual model, i.e., the DDBM Framework, with typical business model components like an offering, customers and revenue model but adding Data Sources and Key (data) Activities as key components of a DDBM. For each concept, Hartmann also proposes typical solutions or patterns (Remane et al., 2017). Brownlow (2015) builds on Hartmann’s conceptual model by adding a development approach based on asking key business model questions. Schaefer (2017) did an analysis of data-driven business models in industry 4.0 and considered common characteristics in a hybrid Business Model Canvas. Mathis and Köbler (2016) developed the Data Canvas and Data-Need Fit to systematically document the available data and a process to match available data with user needs, respectively.
Research goal
The main goal of this research is to design and evaluate a practical method for developing DDBM in the context of interactive media companies. The method builds on the DDBM framework of Hartmann and the process model of Brownlow.
Business model patterns and pre-defined solutions are integrated into the method. In the evaluation, the goal is to test the efficacy of the method. In particular, the efficacy of the use of patterns vis-à-vis a method without such patterns, and the contribution to supporting collaborative multi-actor innovation.
Research design
This research is part of a national project on DDBM and involves researchers and students from academia and practitioners from interactive media companies. The development of the DDBM design method follows a design science research (DSR) approach (Gregor & Hevner, 2013), as summarized in Table 1.
Table 1 DSR approach, objectives and research methods (adapted from Gregor & Hevner, 2013).
Steps Objectives this paper Research Methods
(1) Introduction Problem statement, research objective
Literature review, practitioners’ interviews (2) Background Theories behind the problem Literature review (3) Research design Description of the DSR approach Literature review (4) Results Development and description of
the DDBM design method (the artifact)
Action research; iterative
development with
practitioners (5) Evaluation First evaluation results from
pilot with master students
Experimental design, reflection
(6) Discussion Interpretation of results Qualitative analysis of data (7) Conclusions Communicating main findings so
far and collect feedback on future steps
Interpretation, peer review
In the first step (‘Introduction’) we held interviews with interactive media companies to better understand their needs for developing DDBM. For the second step (‘Background’) we reviewed literature on DDBM, business model design methods and data driven innovation in the context of media companies. The third step (‘Research design’) is about the DSR approach that was followed as described in this section. For the fourth step (‘Results’) we first developed the conceptual basis for our DDBM design method by extending the framework of Hartmann (2016) with concepts from the process model of Brownlow (2015), the concept of value networks, and concepts from business model implementation (De Reuver et al., 2017). Based on this adapted framework, a first version of the method was developed. The method was practically elaborated into a workshop format with accompanying templates and supporting materials. Subsequently, the method was iteratively tested and redesigned in a series of workshops with practitioners from the interactive media companies involved in the project and with students.
The formative evaluations have led to a first full version of the DDBM design method, a step-by-step approach for developing a DDBM. Templates and materials were designed together with an external design agency.
For the fifth step (‘Evaluation’), we will evaluate the efficacy of the DDBM design method. In particular the efficacy of the use of patterns vis-à-vis a method without such patterns, the contribution to collaborative multi-actor innovation, and the added value of a facilitated use of the method. In the sixth step (‘Discussion’) we
discuss preliminary results and in the seventh step (‘Conclusion’) the main contribution and practical implications.
Results
The main result is a six-step method for developing DDBM. It starts with determining the intended business goals and intended data innovation.
Consequently, in steps 2 and step 3, the required data sources and data activities are determined, respectively. In step 4 we develop the value network, i.e. the set of business roles and actors fulfilling these roles and value flows between them.
Next in step 5, the business model is complemented with the revenue model(s) and the financial flows between the actors. Finally, in step 6, the actions to be taken to implement the (new) business model are prioritized and positioned on a roadmap with desired actions. Table 2 summarizes the approach indicating goals, intended results, and available support materials.
Table 2 Overview of DDBM design method with steps, goals, results and support materials
Step and goal Intended results Support materials
(1) Determine Goals Company goal, data innovation goal, target group, proposition
Theory card, inspiration cards, goal cards, Goal-Data- Innovation game board (2) Select Data Sources Overview of required data
and data sources
Theory card, data source cards
(3) Define Data Activities Overview of required data activities
Theory card, data activity cards, data activity game board (4) Sketch Business Model Business model as a value
network with actors and value flows
Theory card, actor cards, business model game board
(5) Choose Revenue Model Business model as a value network with revenue model and financial flows
Theory card, revenue model cards
(6) Develop Roadmap Overview of actions in roadmap
Priority cards, roadmap template
Each step is supported by a card deck with pre-defined solutions or patterns for inspiration, see Figure 1. In step 1, the method provides inspiration cards highlighting companies with successful data driven business models. At step 2, the method provides cards with typical data sources (customer data, partner data, open data, etc.), and for step 3, typical data activities (data acquisition, data
aggregation, etc.). For each step, users design solutions by selecting appropriate pre-defined cards and/or formulate their solutions on blank cards. Game boards are available to collect and annotate cards. For example, in step 4, users create actor cards for each involved actor. Actor cards describe the key activity and key resource that the actor brings to the network. The game board allows users to arrange cards in a value network and to add the value exchanges and flows between the actors. The end result is a clear overview of a new DDBM. In the final step, users think of practical steps towards implementation of the DDBM and place them on the roadmap game board.
Figure 1 Examples of predefined cards for data source and data activity, respectively.
The six steps are embedded in a format for a facilitated workshop, which we consider the main use case for the method. However, the method with the materials can also be used by practitioners in a self-service setting.
Contribution and practical implication
First, the conceptual model underlying the DDBM design method extends current conceptual models. In particular, the value network view adds a multi-actor perspective to DDBM, and provides insights in the complex value flows within data-ecosystems. Next, as part of the design science approach, design knowledge
regarding data-driven business models will be generated. In particular knowledge about the added value of applying patterns and pre-defined solutions within business model design methods.
The research provides a practical and tested approach for developing DDBM for interactive media companies and the broader community of data-rich service providers. With this method, practitioners will able to develop and test new collaborative DDBM faster.
Keywords
Business Model, Data driven, Design method, Open innovation, Multi actor
References
De Vos H., & Haaker T. (2008) The STOF Method. In: Bouwman H., De Vos H., Haaker T. (eds). Mobile Service Innovation and Business Models. Springer, Berlin, Heidelberg, pp. 115-136.
Brownlow, J., Zaki, M. Neely, A., & Urmetzer, F. (2015) Data and Analytics - Data- Driven Business Models: A Blueprint for Innovation. Working paper, Cambridge Service Alliance, University of Cambridge. Available from https://cambridgeservicealliance.eng.cam.ac.uk/resources/Downloads/Mo nthly%20Papers/2015MarchPaperTheDDBMInnovationBlueprint.pdf.
[Accessed: 31st January 2019]
De Reuver, M., Bouwman, H., & Haaker, T. (2013), T. Business model roadmapping:
a practical approach to come from an existing to a desired business model.
International Journal of Innovation Management 17/01, 2013.
Gregor, S., & A. R. Hevner (2013) Positioning Design Science Research for Maximum Impact. Management Information Systems Quarterly, 37 (2), 337–
355.
Hartmann, P., Zaki, M. E., Feldmann, N., & Neely, A. D. (2016) Capturing value from big data – a taxonomy of data-driven business models used by start-up firms.
International Journal of Operations and Production Management, 36 (10), pp. 1382-1406.
Lippell, H. (2016) Big Data in the Media and Entertainment Sectors, In: Cavanillas, J.M., Curry, E. & Wahlster, W. (eds.) New Horizons for a Data-Driven Economy: A Roadmap for Usage and Exploitation of Big Data in Europe.
Springer International Publishing, pp. 245-259.
Mathis, K., & Köbler, F. (2016). Data-Need Fit – Towards data-driven business model innovation, In: Morelli, N., De Götzen, A. & Grani, F. (eds.). Service Design Geographies. Proceedings of the ServDes.2016 Conference, Copenhagen, Denmark. Linköping University Electronic Press. Available from http://www.ep.liu.se.tudelft.idm.oclc.org/ecp/contents.asp?issue=125 [Accessed: 31st January 2019].
Osterwalder, A., Pigneur, Y., Clark, T., & Smith, A. (2010) Business model generation: A handbook for visionaries, game changers, and challengers.
John Wiley & Sons, Inc., Hoboken, New Jersey.
Parmar, R. Mackenzie, I., Cohn, D. & Gann, D. (2014) The new patterns of innovation. Harvard Business Review, January-February 2014. Available from: https://hbr.org/2014/01/the-new-patterns-of-innovation [Accessed 31st January 2019]
Remane, G., Hanelt, A., Tesch, J. & Kolbe, L. (2017) The business model pattern database—a tool for systematic business model innovation. International Journal of Innovation Management. 21 (1), 1750004.
Schaefer, D., Walker, J. & Flynn, J. (2017) A Data-driven Business Model Framework for Value Capture in Industry 4.0. In: Gao, J., El Souri, M., &
Keates, S. J. (eds.) Advances in Manufacturing Technology XXXI, IOS Press, Amsterdam, The Netherlands.
Weill, P. & Woerner, S. L. (2015) Thriving in an increasingly digital ecosystem. MIT Sloan Management Review. 56(4), 27-34.
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ISBN: 978-3-96705-001-1
