Innovation & Strategy Tracks > Track 21: Data-Driven Innovation and Economic Development

Track Chairs:

• Prof. Glenn Parry, EPSRC Centre for Decentralised Digital Economy [DECaDE], Surrey Business School, Guildford, UK
• Prof. Jens-Henrik Söldner, Ansbach University of Applied Sciences, Ansbach, Germany

In view of the ongoing digital transformation of business and society, numerous scholars talk about a new industrial revolution (Dalenogare et al., 2018; Yin et al., 2018) whose effects go beyond the impact that information technology has had so far (Cimini et al., 2019; Porter and Heppelmann, 2014). Ubiquitous, interconnected digital devices allow for a new form of digital innovation (Yoo et al., 2010), which enables new forms of business models and collaboration in economic ecosystems (Zott & Amit, 2017; Ibarra et al., 2018; Nambisan et al., 2019).

This phenomenon can be considered to be data driven. First and foremost, more data are available to support industrial processes, inform decision making and identify new opportunities for value creation, e.g. for sustainable, circular economies (Gölzer & Fritzsche, 2017; Raut et al., 2019). Second, new forms of data processing and storage can be implemented, enabling cloud computing, distributed ledger technologies, smart services, and other solution designs that play a leading role in the digital transformation (Adams et al., 2017, 2018; Boukhris & Fritzsche, 2019). Third, data themselves become a resource for new business activities as a tradeable good and organizational asset, leading to new designs of ecosystems facilitated by online platforms (Sorescou, 2017; Kenney & Zysman, 2020).

We welcome contributions that discuss aspects of data-driven innovation and ecosystem design on different strategic and operational levels. Case studies from industry concerning the implementation or impact of particular innovations are welcomed, as are broader explorations of the phenomenon, including conceptual works on data-driven value creation and ecosystems.

Potential topics include, but are not limited to:

• Business model innovation with big data: opportunities and challenges
• Technical, legal, educational and otherwise societal prerequisites for successful implementations of data-driven businesses and ecosystems
• Data-driven innovation in different industries: single or comparative studies
• Identifying and assessing digital assets
• The worldwide data economy and digital sovereignty of single nations; self-determined actions of individuals and organizations in ecosystems
• Data as a driving force for smart services and artificial intelligence applications
• Data-driven innovation and trust
• Governmental use of data: identity, surveillance, taxation, healthcare etc.
• Cloud business activities
• Data-driven business, short-term and long-term expectations of growth

References

Adams, R., Parry, G., Godsiff, P., & Ward, P. (2017). The future of money and further applications of the blockchain. Strategic Change, 26(5), 417-422.

Adams, R., Kewell, B., & Parry, G. (2018). Blockchain for good? Digital ledger technology and sustainable development goals. Handbook of sustainability and social science research, 127-140.

Boukhris, A., & Fritzsche, A. (2019). What is smart about services? Breaking the bond between the smart product and the service. In Proceedings of the 27th European Conference on Information Systems. ECIS 2019.

Cimini, C., Pezzotta, G., Pinto, R., & Cavalieri, S. (2019). Industry 4.0 Technologies Impacts in the Manufacturing and Supply Chain Landscape: An Overview. In Borangiu, T., Trentesaux, D., Thomas, A., & Cavalieri, S. (Eds.), Studies in Computational Intelligence. Service Orientation in Holonic and Multi-Agent Manufacturing (Vol. 803, pp. 109-120). Cham: Springer International Publishing.

Dalenogare, L. S., Benitez, G. B., Ayala, N. F., & Frank, A. G. (2018). The Expected Contribution of Industry 4.0 Technologies for Industrial Performance. International Journal of Production Economics, 204, 383-394

Gölzer, P., & Fritzsche, A. (2017). Data-driven operations management: organisational implications of the digital transformation in industrial practice. Production Planning & Control, 28(16), 1332-1343.

Ibarra, D., Ganzarain, J., & Igartua, J. I. (2018). Business model innovation through Industry 4.0: A review. Procedia manufacturing, 22, 4-10.

Kenney, M., & Zysman, J. (2020). The platform economy: restructuring the space of capitalist accumulation. Cambridge journal of regions, economy and society, 13(1), 55-76.

Nambisan, S., Wright, M., & Feldman, M. (2019). The digital transformation of innovation and entrepreneurship: Progress, challenges and key themes. Research Policy, 48(8), 103773.

Raut, R. D., Mangla, S. K., Narwane, V. S., Gardas, B. B., Priyadarshinee, P., & Narkhede, B. E. (2019). Linking big data analytics and operational sustainability practices for sustainable business management. Journal of cleaner production, 224, 10-24.

Sorescu, A. (2017). Data‐driven business model innovation. Journal of Product Innovation Management, 34(5), 691-696.

Yin, Y., Stecke, K. E., & Li, D. (2018). The Evolution of Production Systems from Industry 2.0 through Industry 4.0. International Journal of Production Research, 56(1-2), 848–861.

Yoo, Y., Henfridsson, O., & Lyytinen, K. (2010). Research commentary—the new organizing logic of digital innovation: an agenda for information systems research. Information systems research, 21(4), 724-735.

Zott, C., & Amit, R. (2017). Business model innovation: How to create value in a digital world. NIM Marketing Intelligence Review, 9(1), 18-23.