Beyond the data fads: consequences of big data to contemporary innovation and technology management

Guest edition of Technovation | The International Journal of Technological Innovation, Entrepreneurship and Technology Management. 

Call for Papers

Deadline for submission:

Starting: June 1, 2021

Closing: January 31, 2022

Expected time of publication: Spring 2023

For more information visit Call for Papers

The emerging data-driven economy concerns all of us at the individual, organizational, national, and global levels. There is an increasing amount of data being generated and stored every day creating numerous business or innovation opportunities (Luo et al., 2021; Sestino et al., 2020; Sun et al., 2020; Weiner et al., 2020), but also raising many challenges concerning security, data privacy, data ownership, requiring new approaches to control the flows of the data and extract the value from the data. Prior research explored the advantages of big data, contingencies, and antecedents of dealing with large data sets, implementation hurdles related to big data, exhibited cases of how organizations can leverage data in different sectors; approaches for data management and analysis to help organizations convert available data into valuable information and knowledge that helps them achieve their objectives (Akhtar et al., 2019; Günther et al. 2017; Johnson et al., 2019; Philip Chen et Zhang 2014; Gandomi et Haider 2015; Sheng et al., 2017; Trabucchi et al., 2018).

Despite recorded successful cases or roadblocks on the use of big data, prior research has largely focused on the supply side of the “right” data sets or data as a “ready-to-use” resource (Abbasi et al., 2016; Brynjolfsson et al., 2011; Chandy et al., 2017; George et al, 2014; Del Vecchio et al., 2018; Sorescu, 2017) and only a few recent papers discuss how to enable value creation from data, shift from data considered as a “ready-to-use” resource (e.g. open data) to the co-design of data-based products and services, and impacts that data-driven practices induce on current business practices and organizations (Côrte-Real et al., 2020; Sumbal et al., 2019; Urbinati et al., 2019). Furthermore, we lack insights on how advances in big data, machine learning, data management impact innovation and technology management fields in general.

This call for papers invites scholars to focus their efforts on understanding the consequences of big data to contemporary innovation and technology management. In particular, we invite research papers that explore the role of innovation management, information system, design, and co-design practices in data enriched environment; data-driven services, and organizations. We invite conceptual and empirical submissions drawing on a range of theoretical perspectives and diverse methodologies. We expect papers to advance our theoretical understanding of value creation from data, consequences of big data to organizations, processes and innovation and technology management practices.

For this Special Issue, we envision articles possibly related to (but not at all limited to) any of the following questions:

1. Understanding data-driven design and management practices and co-design processes of value creation from data

What are the different strategies to create value from data?What are the strategies of data management that different organizations have to put in place to leverage data efficiently?How to characterize the new value creation logics in a data-based environment?What business models do organizations use across the multiplicity of actors in the data value chain? And consequences of their implementation?
2. Processes to study or conduct data-driven innovation

What are the methods and processes that organizations have to put in place to design data-based products and services?How to overcome unavoidable design and decision-making biases when dealing with data?How innovation, technology management, and information systems fields are changing with the increasing implementation of data-driven practices? Or do they need to change?
3. Organizational aspects of data usage in practice

Which organizational forms do support value creation from data in a better way?Which actors and competencies are required to enable value creation from data?What are the relevant ecosystems for designing data-based services?How organizations transform with data-informed decision-making?What are the negative and positive consequences of data-driven practices?
We invite submissions that are either conceptual or empirical. No specific theoretical or methodological approaches are preferred. High quality conceptual, qualitative, quantitative and mixed methods submissions are welcome.

Only original unpublished manuscripts can be submitted, according to the 'Guide for Authors' published on the Technovation website.

Please mention the name of the Special Issue in your cover letter. All manuscripts will be peer-reviewed following the established policies and procedures of the journal. The final papers will be selected for publication depending on the results of the peer-review process and the reviews of the Guest Editors.

BIO of guest editors

Olga Kokshagina is an Assistant Professor and Vice Chancellor’s Research Fellow in the Graduate School of Business and Law at RMIT University. She is affiliated with the Centre for Management Science at Mines ParisTech PSL Research University, France. Her work is related to several areas: innovation and technology management, strategic management of design, open & radical innovation, data-driven innovation. She is a co-director of RMIT W+SN network and a member of the French Digitial Council (CNNUM). She holds several patents and her work was published in the leading technology and innovation management journals including Research Policy, Technological Forecasting and Social Change, Journal of Business and Industrial Marketing, and R&D Management among others. Olga has led scientific coordination activities of ISPIM (2014 - 2018) where she had to manage the review process and scientific panel for ISPIM events. She currently researches socio-economic benefits of data in healthcare organizations.

Pascal Le Masson is a Professor of Management at Mines ParisTech PSL Research University, Chair of Design Theory and Methods for Innovation. He is also the Deputy Director of the Centre for Management Science (Centre de Gestion Scientifique). An engineer by training (Mines ParisTech, 1996), Pascal Le Masson holds a PhD in Management Science from Mines ParisTech (2001) and a Habilitation from East-Paris University (2008). Along with Eswaran Subrahmanian (Carnegie Mellon University), he is the Chairman of the Design Theory Special Interest Group of the Design Society. He was the Chairman of the Innovation SIG of the European Academy of Management and is in the Editorial board of European Management Review.

Jianxi Luo is a tenured Associate Professor with Singapore University of Technology and Design (SUTD), Director of Data-Driven Innovation Lab (, and former Director of SUTD Technology Entrepreneurship Program. Prof. Luo holds a PhD in Engineering Systems (Technology Management and Policy track) and S.M. degree in Technology and Policy from Massachusetts Institute of Technology, and M.S. and B.E. degrees in Mechanical Engineering from Tsinghua University. He was Chair of INFORMS Technology Innovation Management & Entrepreneurship Section (2015-2016). He is on the editorial boards of Design Science (Associate Editor), Research in Engineering Design, IEEE Transactions on Engineering Management, among other journals. His research fuses design science, network science and data science to advance artificial intelligence for design and innovation.

The guest editor’s welcome informal enquiries and can be contacted on:

We invite interested authors to send extended abstracts (by email) by August 31, 2021. Extended abstracts be shorter than 500 words (with max 1 figure/diagram) and provide sufficient details on research questions, methods, data, (anticipated) findings for the co-editors to evaluate suitability
Decisions on acceptance of abstracts and invitations to the paper development mini conference by September 30, 2021
A virtual paper development and feedback mini conference hosted by RMIT university, Melbourne and Mines ParisTech, Paris will be organized on October 30, 2021

Full paper submission is expected by January 30, 2022


Abbasi, A., Suprateek S., et Roger H. L. C. 2016. Big Data Research in Information Systems: Toward an Inclusive Research Agenda. Journal of the Association for Information Systems 17: 3.

Akhtar, P., Khan, Z., Rao‐Nicholson, R., & Zhang, M. (2019). Building relationship innovation in global collaborative partnerships: big data analytics and traditional organizational powers. R&D Management 49, 7–20.

Berger, C., Möslein, K., Piller, F., & Reichwald, R. (2005). Co‐designing modes of cooperation at the customer interface: learning from exploratory research. European Management Review 2(1), 70-87.

Brynjolfsson, E., Hitt, L. M., & Kim, H. H. (2011). Strength in numbers: How does data-driven decision making affect firm performance? Available at SSRN 1819486

Chandy, R., Magda H., & Prokriti M. (2017). Big Data for Good: Insights from Emerging Markets. Journal of Product Innovation Management 34 (5): 703‑13.

Côrte-Real, N., Ruivo, P., Oliveira, T. (2020) Leveraging internet of things and big data analytics initiatives in European and American firms: Is data quality a way to extract business value? Information & Management. 57(1).

Del Vecchio, P., Di Minin A., Petruzzelli A. M., Panniello U., & Pirri S. (2018) Big Data for Open Innovation in SMEs and Large Corporations: Trends, Opportunities, and Challenges. Creativity and Innovation Management 27 (1): 6‑22.

Gandomi, A., and Murtaza H. (2015). Beyond the hype: Big data concepts, methods, and analytics. International journal of information management 35.2: 137-144.

George, G., Haas R. M, & Pentland A. (2014). Big Data and Management. Academy of Management Journal 57 (2): 321‑26.

Günther, W. A.E, Mohammad H. R. M, Huysman M., & Feldberg M. (2017). Debating Big Data: A Literature Review on Realizing Value from Big Data. The Journal of Strategic Information Systems 26 (3): 191‑209.

Johnson, S. L., Gray, P., & Sarker, S. (2019). Revisiting IS research practice in the era of big data. Information and Organization, 29(1), 41-56.

Luo, J., Sarica, S., & Wood, K. (2021) Guiding Data-Driven Design Ideation by Knowledge Distance. Knowledge-Based Systems, 218, 106873.

Philip Chen, C. L., & Zhang, C-Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences 275 (august): 314‑47.

Sestino, A., Prete, M. I., Piper, L., & Guido, G. (2020). Internet of Things and Big Data as enablers for business digitalization strategies. Technovation, 102173.

Sheng, J., Amankwah-Amoah, J., & Wang, X. (2017). A multidisciplinary perspective of big data in management research. International Journal of Production Economics, 191, 97-112.

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

Sumbal, M.S., Tsui, E., Irfan, I., Shujahat, M., Mosconi, E., & Ali, M., (2019). Value creation through big data application process management: the case of the oil and gas industry. Journal of Knowledge Management 23, 1566–1585.

Sun, S., Hall, D. J., & Cegielski, C. G. (2020). Organizational intention to adopt big data in the B2B context: An integrated view. Industrial Marketing Management, 86, 109-121.

Trabucchi, D., Buganza T., Dell’Era C., & Pellizzoni E. (2018). Exploring the Inbound and Outbound Strategies Enabled by User Generated Big Data: Evidence from Leading Smartphone Applications. Creativity and Innovation Management 27 (1): 42‑55.

Urbinati, A., Bogers, M., Chiesa, V., & Frattini, F. (2019). Creating and capturing value from Big Data: A multiple-case study analysis of provider companies. Technovation 84/85, 21–36.

Wiener, M., Saunders, C., & Marabelli, M. (2020). Big-data business models: A critical literature review and multiperspective research framework. Journal of Information Technology 35(1), 66-91.

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