Data-driven innovation: The future of new product development in digital markets

International Journal of Information Management

Editor: Yogesh Dwivedi

Call for Papers (Special Section @IJIM)

Theme: Data-driven innovation: The future of new product development in digital markets

Short Title SI: Data-Driven Innovation

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:

·       The role IS/IT in data-driven innovation (DDI)

·       DDI culture

·       data-driven new product development stages

·       Innovation capabilities/resources for new product development in digital markets

·       Privacy and security challenges of DDI

·       Business model implications of data driven-new products

·       Data governance strategies for DDI

·       R&D challenges for DDI

·       Commercialisation challenges for data-driven new products


Important Dates

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

Expected Publication: TBA


Submission Guidelines

All submissions have to be prepared according to the Guide for Authors as published in the Journal website at:

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:

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.

Guest Editors

Shahriar Akter*

Sydney Business School, University of Wollongong

Wollongong, Australia


Kathy Shen
University of Wollongong in Dubai
Dubai, UAE

Katina Michael
School of Computing and Information Technology
University of Wollongong, Australia

John D’Ambra
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.

NFC Innovation Award

Who: NFC Forum

What: NFC Innovation Awards

Theme: Recognizing Breakthrough Products and Start-ups

When: Nominations due January 11, 2017; winners will be announced March 14, 2017

Where: (Awards reception) Green Valley Ranch Resort, Las Vegas, Nevada



Questions: Please contact Lisa Gundlach at


NFC Innovation Program Timeline:

Submission Entry Period: December 5, 2016 – January 11, 2017

Judging Period: January 18 – February 14

Semi-finalists Announced: February 15

Award Ceremony - Winners Announced: March 14


About the NFC Innovation Awards

The NFC Innovation Awards were created to recognize breakthrough products and start-ups and to raise awareness and exposure of the outstanding implementations of NFC in a wide range of industries. The award program is open to member and non-member companies; systems integrators, agencies and media companies may also submit nominations on behalf of their clients. There is no fee to enter.

Winners will be chosen based on innovation, benefits, results, user experience and NFC Impact. The deadline for submissions is Wednesday, January 11, 2017.

Winners will be announced at an awards reception to be held in Las Vegas at the Green Valley Ranch Resort on Tuesday, March 14 (co-located with NFC Forum member meeting).

IEEE Potentials on "Unintended Consequences" (Pringle, Michael & Michael)

IEEE Potentials is seeking contributions to a special issue guest edited by Ramona Pringle+, Katina Michael* and MG Michael*. The theme of the issue is: “Unintended Consequences: the Paradox of Technological Potential”.

We are looking for critical reviews and analyses, case examples, commentaries, interviews, opinion pieces, stories, projections and science fiction narratives from researchers, futurists, practitioners and storytellers, examining the hidden implications of our ever-digital lives.

While we are open to predictive scenarios of what the near future will bring, we are also looking for contemporary analysis as well. After all, we are living at a time where the line between science fiction and reality is blurring: our relationships are mediated, our memories are archived, and our identities are public documents. What are the implications of rapidly advancing technology on government (e.g. military drones), organizations (e.g. data analytics), and our personal lives (e.g. wearables)?

With all great innovation comes responsibility; an inevitable dark side, and with the exponential growth of technology, the window within which we can examine the ethics and consequences of our adoption of new technologies becomes increasingly narrow. Instead of fear mongering, how do we adjust our course, as a society, before it is too late? We are looking for disruptive perspectives, and articles that present solutions and blueprints, while questioning the status quo. These may take the form of precautionary tales, scenario-based planning and action, assessment impacts and response, design principles, standards, regulations, and laws, organisational policies and approaches to corporate social responsibility, externality fines and penalties for breaches, advocacy, and the formation of specialised global NGOs.

IEEE Potentials is interested in manuscripts that deal with theory, practical applications, or new research. They can be tutorial in nature.

Submissions may consist of either full articles or shorter, opinion-oriented essays. When submitting an article, please remember:

     All manuscripts should be written at the level of the student audience.

     Articles without equations are preferred; however, a minimum of equations is acceptable.

     List no more than 12 references at the end of your manuscript. No embedded reference numbers should be included in the text. If you need to attribute the source of key points or quotes, state names in the text and give the full reference at the end.

     Limit figures to ten or fewer, and include captions for each.

     Articles should be approximately 1,500–4,000 words in length; essays should be 900–1,000 words.

     Include an individual e-mail address and a brief biography of four to six lines for each author.

All submitted manuscripts are evaluated by the IEEE Potentials reviewer team and graded in accordance with the above guidelines. Articles may be required to go through multiple revisions depending on reviewers’ grades and comments.



CFP distribution: 30 November 2015

Expression of interest (abstract submission): 8 January 2016

Feedback to authors: 15 January 2016

Final paper submission: 15 March 2016

Proof back to authors: 15 April 2016

Publication Date: July/August 2016 (vol. 35, no. 4)


Guest Editors

+Ramona Pringle is an Assistant Professor at the RTA School of Media at Ryerson University.

*Katina Michael is an Associate Professor in the Faculty of Engineering and Information Sciences at the University of Wollongong.

*MG Michael is an honorary Associate Professor in the School of Computing and Information Sciences at the University of Wollongong.