Data-driven culture and orchestrated data ecosystems: a conceptual model based on the resource-based view

Authors

DOI:

https://doi.org/10.1108/REGE-12-2024-0184

Keywords:

Data ecosystems, Data-driven culture, Resource-based view, Conceptual paper

Abstract

Purpose

This conceptual paper examines how orchestrated data ecosystems influence the development of a data-driven culture based on the resource-based view, proposing a conceptual model and theoretical propositions.

Design/methodology/approach

The study is grounded in the resource-based view and theory-building from literature, employing an integrative review to structure a conceptual model. The analysis connects concepts related to data-driven culture, orchestrated data ecosystems and sustainable competitive advantage.

Findings

The paper argues that, within orchestrated data ecosystems, the orchestrator promotes the adoption of data-driven practices, which can be considered a strategic resource from the resource-based view perspective. The adoption of these practices supports the development of sustainable competitive advantages for both organizations and the ecosystem. The study presents a conceptual model and two theoretical propositions that structure this relationship.

Originality/value

This paper extends the application of resource-based view to data ecosystems by considering data-driven practices as strategic resources that contribute to sustainable competitive advantage. The originality lies in the proposition that, in orchestrated data ecosystems, the orchestrator facilitates the dissemination of these practices, shaping sustainable competitive advantages at both the organizational and ecosystem levels.

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References

Acquah, I. S. K., Naude, M. J., & Soni, S. (2021). How the dimensions of culture influence supply chain collaboration: An explanatory sequential mixed-methods investigation. Revista de Gestão, 28(3), 241–262. https://doi.org/10.1108/rege-11-2020-0105

Agyei-Owusu, B., Acquah, I., Asante, M., & Andoh-Baidoo, F. (2021). The effect of data-driven culture on customer development and firm performance. In D. Dennehy, A. Griva, N. Pouloudi, Y. Dwivedi, M. Mäntymäki, & I. Pappas (Eds.), Responsible AI and analytics for an ethical and inclusive digitized society, 651-662. Springer. https://doi.org/10.1007/978-3-030-85447-8_40

Al Wahshi, J., Foster, J., & Abbott, P. (2022). An investigation into the role of data governance in improving data quality: A case study of the Omani banking sector. In Proceedings of the 25th European Conference on Information Systems. AISEL. https://aisel.aisnet.org/ecis2022_rp/121

Anderson, C. (2015). Creating a data-driven organization. O'Reilly Media.

Andrade-Rojas, M. G., Kathuria, A., & Konsynski, B. R. (2021). Competitive brokerage: How information management capability and collaboration networks act as substitutes. Journal of Management Information Systems, 38(3), 667-703. https://doi.org/10.1080/07421222.2021.1962596

Bagnoli, C., Meggiorin, K., & Lucchese, M. (2022). The integration of digital business models: The Amazon case study. In Digital business models for Industry 4.0, 45-67. https://doi.org/10.1007/978-3-030-97284-4_4

Barney, J. B. (1986). Organizational culture: Can it be a source of sustained competitive advantage? Academy of Management Review, 11(3), 656-665. https://www.jstor.org/stable/258317

Barney, J. B. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120. https://doi.org/10.1177/014920639101700108

Barney, J. B., & Clark, D. N. (2007). Resource-based theory: Creating and sustaining competitive advantage. Oxford University Press.

Barney, J. B., & Hesterly, W. S. (2006). Organizational economics: Understanding the relationship between organizations and economic analysis. In S. R. Clegg, C. Hardy, T. B. Lawrence, & W. R. Nord (Eds.), The SAGE handbook of organization studies, 111-148. SAGE Publications Ltd.

Barney, J. B., Ketchen, D. J., & Wright, M. (2011). The future of resource-based theory: Revitalization or decline? Journal of Management, 37(5), 1299-1315. https://doi.org/10.1177/0149206310391805

Berndtsson, M., Forsberg, D., & Olsson, B. (2018). Becoming a data-driven organisation. Proceedings of the 26th European Conference on Information Systems. https://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-16398

Cambridge Spark. (2024). Building a data-driven culture: 5 examples from world-leading organisations. https://www.cambridgespark.com/info/building-a-data-driven-culture-5-examples-world-leading-businesses

Chan, Y. E., Shaffer, M. A., & Snape, E. (2004). In search of sustained competitive advantage: The impact of organizational culture. The International Journal of Human Resource Management, 15(1), 17-35. https://doi.org/10.1080/0958519032000157320

Chatterjee, S., Chaudhuri, R., & Vrontis, D. (2021). Does data-driven culture impact innovation and performance of a firm? Annals of Operations Research. https://doi.org/10.1007/s10479-020-03887-z

Curry, E., & Ojo, A. (2020). Enabling knowledge flows in an intelligent systems data ecosystem. Real-time linked dataspaces, 1-20. https://doi.org/10.1007/978-3-030-29665-0_1

Fleetwood, S. (2005). Ontology in organization and management studies: A critical realist perspective. Organization, 12(2), 197-222. https://doi.org/10.1177/1350508405051188

Forbes. (2024). Beyond the tools: Building a data-driven culture. https://www.forbes.com/councils/forbestechcouncil/2024/05/20/beyond-the-tools-building-a-data-driven-culture/

Geisler, S., Otto, B., & Gelhaar, J. (2021). Knowledge-driven data ecosystems toward data transparency. ACM Journal of Data and Information Quality, 14(1), 1-27. https://doi.org/10.1145/3467022

Gelhaar, J., Groß, T., & Otto, B. (2021). A taxonomy for data ecosystems. Proceedings of the 54th Hawaii International Conference on System Sciences, 1-10. https://hdl.handle.net/10125/71359

Gelhaar, J., & Otto, B. (2020). Challenges in the emergence of data ecosystems. Twenty-Third Pacific Asia Conference on Information Systems, 1-10. https://www.researchgate.net/publication/341930759_Challenges_in_the_Emergence_of_Data_Ecosystems

Guggenberger, T. M., Otto, B., & Gelhaar, J. (2020). Ecosystem types in information systems. Proceedings of the 28th European Conference on Information Systems. https://www.researchgate.net/publication/341188637

Hannila, H., Silvola, R., Harkonen, J., & Haapasalo, H. (2022). Data-driven begins with DATA: Potential of data assets. Journal of Computer Information Systems, 62(1), 29-38. https://doi.org/10.1080/08874417.2019.1683782

Jaakkola, E. (2020). Designing conceptual articles: Four approaches. AMS Review, 10, 18-26. https://doi.org/10.1007/s13162-020-00161-0

Kira, B., Sinha, V., & Srinivasan, S. (2021). Regulating digital ecosystems: Bridging the gap between competition policy and data protection. Industrial and Corporate Change, 30(5), 1337-1360. https://doi.org/10.1093/icc/dtab053

KPMG. (2021). Becoming a data-driven company is more than investing in technology. https://home.kpmg/nl/nl/home/insights/2021/06/becoming-a-data-driven-company-is-more-than-investing-in-technology.html

Kremser, W., & Brunauer, R. (2019). Do we have a data culture? In Data-driven culture in the 21st century, 187-204. Springer. https://doi.org/10.1007/978-3-658-27495-5_11

Lindebaum, D. (2022). Demystifying Essays as an “A-Typical” Publication Format. Business & Society, 61(4), 845–850. https://doi.org/10.1177/00076503211065409

MacInnis, D. J. (2011). A framework for conceptual contributions in marketing. Journal of Marketing, 75(4), 136-154. https://doi.org/10.1509/jmkg.75.4.136

MIT Sloan Management Review. (2020). Why culture is the greatest barrier to data success. https://sloanreview.mit.edu/article/why-culture-is-the-greatest-barrier-to-data-success/

Naor, M., Jones, J. S., Bernardes, E. S., Goldstein, S. M., & Schroeder, R. (2014). The culture-effectiveness link in manufacturing. Journal of World Business, 49(3), 321-331. https://doi.org/10.1016/j.jwb.2013.06.003

Oliveira, M. I. S., & Lóscio, B. F. (2018). What is a data ecosystem? In Proceedings of the 19th Annual International Conference on Digital Government Research, 74-82. ACM. https://doi.org/10.1145/3209281.3209335

Otto, B., Geisler, S., & Gelhaar, J. (2019). Data ecosystems: Conceptual foundations and recommendations. ISST-Report. https://doi.org/10.13140/RG.2.2.17561.83048

Ramalho, T. S., Tarraco, E. L., Yokomizo, C. A., & Bernardes, R. C. (2019). Analysis of innovation in strategic projects. Revista de Gestão, 26(4), 409-428. https://doi.org/10.1108/rege-01-2019-0016

Rantanen, M. M., Hyrynsalmi, S., & Hyrynsalmi, S. M. (2019). Towards ethical data ecosystems: A literature study. 2019 IEEE International Conference on Engineering, Technology and Innovation, 1-9. https://doi.org/10.1109/ICE.2019.8792599

Sambhara, C. (2020). Information management challenges and the adverse consequences of using reverse auctions. Information & Management, 57(8), 103363. https://doi.org/10.1016/j.im.2020.103363

Salerno, F. F., & Maçada, A. C. G. (2024). The impact of data quality orchestration in data ecosystems. MCIS 2024 Proceedings, Paper 10. https://aisel.aisnet.org/mcis2024/10

Schlegel, D., Wallner, J., Monauni, M., & Kraus, P. (2023). Data-driven culture: A transformational framework. Proceedings of the International Conference on Information Systems. https://aisel.aisnet.org/icis2023/gov_strategy/gov_strategy/7

Vafaei-Zadeh, A., Hanifah, H., & Ramayah, T. (2024). Data-driven culture and competitive advantage: A resource-based perspective. Journal of Strategic Information Systems, 33(1), 102-125. https://doi.org/10.1016/j.jsis.2023.101789

Wade, M., & Hulland, J. (2004). The Resource-Based View and information systems research. MIS Quarterly, 28(1), 107-142. https://doi.org/10.5555/2017307.2017316

Wang, P. (2021). Connecting the parts with the whole: Toward an information ecology theory. MIS Quarterly, 45(1), 345-377. https://doi.org/10.25300/MISQ/2021/16021

Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS Quarterly, 26(2), xiii-xxiii. https://www.jstor.org/stable/4132319.

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Published

2025-07-22

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Article

How to Cite

Data-driven culture and orchestrated data ecosystems: a conceptual model based on the resource-based view. (2025). REGE Revista De Gestão, 32(2). https://doi.org/10.1108/REGE-12-2024-0184