Impact Score 6.43
Data-driven innovation (DDI) has been regarded as the fastest emerging driver of transformational product development opportunities in digital markets (Davenport & Kudyba, 2016; Delen & Demirkan, 2013). In recent years, digital giants Amazon, Alibaba, Tencent, Google, Apple, and Facebook are enjoying stronger competitive advantages from DDI (Akter and Wamba, 2016). It is fuelled by the advancement in information and communication technologies (ICT), strong data management and analytics capabilities, robust data governance, application of smart machines (Ransbotham and Kiron, 2017), growth of investment in big data and AI initiatives, building a data culture accompanied with organizational alignment and cultural compliance (Duan et al., 2018). Examples of new product developments using DDI is evidenced by Facebook’s “People You May Know” to connect people based on mutual friends, work, education information, and other factors or, LinkedIn’s “Jobs You May Be Interested In” and “Groups You May Like”(Davenport, 2013).
Given the exponential growth in information and communication technology (ICT) such as artificial intelligence, blockchain, cloud computing and the internet of things (IoT), vast amounts of data are stored on global storage data centers (Waller and Fawcett, 2013; Wang et al., 2018). Such a big amount of data can enable the proliferation of digital firms embracing data-driven innovation such as, introducing new products or upgrading existing product lines (Dubey et al., 2019). The business value of DDI is evidenced by Amazon as it increased its sales revenue by more than 30% through its big data-driven recommendation engine, Capital One increased its retention rate by 87%, Marriott enjoyed 8% more revenue through revenue optimization, and Progressive enhanced its market capitalization of over $19 billion by using real-time information, products and rate comparisons (Akter et al., 2019).
The big data literature identifies data and analytics at the heart of this new wave of digital product development which conceptualizes big data analytics (BDA) as the analytical capability to collect, process, analyze and interpret large datasets to extract out insights relevant for effective decision making and operational performance (Akter et al., 2016; Wamba et al., 2017). Although BDA can dramatically accelerate innovation, value and productivity, the extant research is limited to traditional information products (Moenaert and Souder, 1990; Littler et al., 1995; Meyer and Zack, 1996; Von Hippel, 1998; Browning et al., 2002; Nambisan, 2003; Kim et al., 2006) with little advancement in this emerging field. The challenges of DDI is identified as data driven innovation culture in an organisation, talent capability, technological sophistication, management capability, data privacy and security, commercialization, business model synchronisation etc. These challenges are decisive in how firms embrace DDI in their new product development decisions. Akter et al. (2019) claimed that deriving value from DDI is a multi-step process that runs from idea generation to commercialisation. In this context, researchers can be greatly benefited by extending IS theories such as, IS success theory, IT capability theory or expectation-confirmation theory. DDI research can also benefit from classic management theories, such as the resource-based view theory (RBV; Barney, 1991), knowledge-based view theory (KBV; Grant, 1996), and dynamic capability theory (DC; Helfat and Peteraf, 2009). de Camargo Fiorini et al. (2018) report that data and analytics driven innovation can also be explored by applying, for instance, actor network theory, agency theory, contingency theory, diffusion of innovation theory, game theory, ecological modernization theory, institutional theory, knowledge management theory, social capital theory, social exchange theory, stakeholder theory or, transaction cost theory.
As fostered by IJIM, there is a greater potential of articulating the challenges and opportunities of DDI in digital markets through this special issue. DDI renders innovative applications with strategic benefits derived from data analytics to enhance specific organizational performances and decision making process. A holistic picture of data driven new product developments for the digital economy will help organisations prepare for this new innovation paradigm.
The Special Section of IJIM is focused on research papers which make new contributions to innovation theory, innovation methodology and empirical results on DDI, new product development and relevant business models for digital markets. The special issue welcomes high quality/high-impact full research papers, state-of-the-art developments building upon core IS or interdisciplinary theories.
This special issue encourages submissions from PACIS 2020 conference participants that will take place on June 20-24, 2020 in Dubai, UAE, and is open to the broader academic ICT community. The general theme for the special issue is “Data-driven innovation: The future of new product development in the digital markets”. The PACIS 2020 conference papers submitted to this Special Issue must make an additional contribution to the existing corpus of knowledge that can be found in IJIM papers, and stipulate a clear contribution.
The proposed Special Section addresses the following topics, and others related to the DDI more generally:
Manuscript submission deadline: 31-Nov-2020
Notification of Review: 30-Mar-2021
Revision due: 31-Jun-2021
Notification of 2nd Review: 1-Aug-2021
2nd Revision [if needed] due: 1-Sep-2021
Notification of Final Acceptance: 30-Sep-2021
All submissions have to be prepared according to the Guide for Authors as published in the Journal website at: https://www.elsevier.com/journals/international-journal-of-information-management/0268-4012/guide-for-authors
Authors should select “SI: Data-Driven Innovation”, from the “Choose Article Type” pull- down menu during the submission process. All contributions must not have been previously published or be under consideration for publication elsewhere. Link for submission of manuscript is: https://www.evise.com/evise/jrnl/IJIM
A submission based on one or more papers that appeared elsewhere has to comprise major value-added extensions over what appeared previously (at least 50% new material). Authors are requested to attach to the submitted paper their relevant, previously published articles and a summary document explaining the enhancements made in the journal version.
All submitted papers will undergo a rigorous peer-review process that will consider programmatic relevance, scientific quality, significance, originality, style and clarity.
The acceptance process will focus on papers that address original contributions in the form of theoretical, empirical and case research, which lead to new perspectives on data-driven innovation. Papers must be grounded on the body of scholarly works in this area (exemplified by some of the references below) but yet discover new frontiers so that collectively, the Special Section will serve communities of researchers and practitioners as an archival repository of the state of the art in data-driven innovation.
Sydney Business School, University of Wollongong
University of Wollongong in Dubai
School of Computing and Information Technology
University of Wollongong, Australia
School of Information Systems
University of New South Wales, Australia
* Managing editor
Akter, S., Bandara, R., Hani, U., Fosso Wamba, S., Foropon, C., Papadopoulos, T., 2019. Analytics-based decision-making for service systems: A qualitative study and agenda for future research. International Journal of Information Management 48, 85-95.
Akter, S., Wamba, S.F., 2016. Big data analytics in E-commerce: a systematic review and agenda for future research. Electronic Markets, 1-22.
Akter, S., Wamba, S.F., Gunasekaran, A., Dubey, R., Childe, S.J., 2016. How to improve firm performance using big data analytics capability and business strategy alignment? International Journal of Production Economics 182, 113-131.
Barney, J., 1991. Firm resources and sustained competitive advantage. Journal of management 17, 99-120.
Davenport, T.H., 2013. Analytics 3.0. Harvard Business Review 91, 64-72.
de Camargo Fiorini, P., Roman Pais Seles, B.M., Chiappetta Jabbour, C.J., Barberio Mariano, E., de Sousa Jabbour, A.B.L., 2018. Management theory and big data literature: From a review to a research agenda. International Journal of Information Management 43, 112-129.
Duan, Y., Cao, G., Edwards, J.S., 2018. Understanding the impact of business analytics on innovation (In press). European Journal of Operational Research.
Dubey, R., Gunasekaran, A., Childe, S.J., Blome, C., Papadopoulos, T., 2019. Big Data and Predictive Analytics and Manufacturing Performance: Integrating Institutional Theory, Resource‐Based View and Big Data Culture. British Journal of Management 30, 341-361.
Grant, R.M., 1996. Prospering in dynamically-competitive environments: Organizational capability as knowledge integration. Organization Science 7, 375-387.
Helfat, C.E., Peteraf, M.A., 2009. Understanding dynamic capabilities: progress along a developmental path. Strategic Organization 7, 91-102.
Ransbotham, S., Kiron, D., 2017. Analytics as a Source of Business Innovation. MIT Sloan Management Review 58, n/a-0.
Waller, M.A., Fawcett, S.E., 2013. Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. Journal of Business Logistics 34, 77-84.
Wamba, S.F., Gunasekaran, A., Akter, S., Ren, S.J.-f., Dubey, R., Childe, S.J., 2017. Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research 70, 356-365.
Wang, Y., Kung, L., Byrd, T.A., 2018. Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change 126, 3-13.