Innovation in Data-Rich Environments


Special issue of Journal of Product Innovation Management, Edited by Neeraj Bharadwaj and Charles H. Noble; Deadline 1 Feb 2016

Special Issue of Journal of Product Innovation Management:
“Innovation in Data-Rich Environments”

Guest Editors

Neeraj Bharadwaj, The University of Tennessee (
Charles H. Noble, The University of Tennessee (

Motivation and Overview

Proclamations abound that we have entered the era of data-rich environments. This is supported in reports suggesting that 90 percent of the data that exists in the world today was created in the last two years1 and that the amount of data present in the “digital universe” will double every year to reach 40,000 exabytes by 2020.2 As we hope to explore with this special issue, this shift towards a data-rich environment has significant implications for successful new product development and innovation efforts.

The Big Data sets that are ubiquitous in today’s data-rich environments possess three characteristics: Volume, Velocity, and Variety, or the “3Vs”.3 The 3V’s have important implications for innovation, and present a host of interesting challenges.

The sheer magnitude of data (Volume) taxes the “bounded rationality” of managers (e.g., “We are drowning in data!”). In an era of customer-driven and open innovation, attempting to process this much raw material is problematic and requires research on proper approaches to encouraging, processing, and sifting this data for high quality ideas. It also requires extending research on factors affecting the flow of information among collaborators in complex new product alliances4 and when users are involved in creating and designing products.5

The rate at which new data is being generated (Velocity) requires firms to be adept at translating data to information to insights to solutions faster than their rivals. The implications for new product and service development process are substantial and new challenges in fast-tracked new product development arise that need to be better understood. For instance, the rate at which the data is being generated poses challenges to listening to the “voice of the customer” across many mediums and forums.6 Firms will need to develop new methodologies to capture and translate these insights and convert them into new products and services at an accelerating pace.7,8 As shorter product life cycles become the new norm, firms will need to develop and launch new products continually in order to remain viable.9 And, the need to improve the rate at which firms learn may also have implications regarding the rate at which firms unlearn.10,11

The types of data being created range from the traditional structured, numeric information to unstructured text (e.g., web chatter on social media; reviews on e-tailers such as Amazon), images on social media, GPS signals from cell phones, readings from sensors, asynchronous feedback on crowdfunding platforms, and customer-driven innovation platforms (e.g., Quirky, Threadless). This (Variety) poses challenges in terms of methodologies to analyze and interpret these diverse data in feeding innovation and new product development processes.

The impact of the 3Vs on innovation and new product development requires rigorous and relevant inquiry. A listing of potential research areas follows.

We expect that this special issue will result in seminal contributions to our understanding of the broad connections between data-rich environments, innovation, and new product development (NPD). Potential research topics for this special issue include the following, many of which connect directly to the recently published (2014) JPIM research priorities.12, 13 These topics are only intended to be thought-starters, and other perspectives are highly encouraged.

  • Innovating in a Data-Rich Environment
    • How can companies improve their analytical capabilities to make better NPD decisions and implement them within NPD teams?
    • How can new product ideas emerge in data-rich environments? What is the appropriate role of experimentation?
    • What is the role of social media in generating new product and service ideas? How can it be used as a complement to traditional idea generation methods?
    • How can firms leverage the use of digital/social/mobile data in NPD?
    • What new analytical methods can be developed to generate better new product ideas from unstructured data (e.g., social media)?
    • What methods can be used to make real-time NPD decisions in a data-rich environment?
  • Corporate Innovation
    • How can the large amount of data now available help improve technology forecasting?
    • How can companies balance the apparent contradiction between increasingly massive data challenges and constantly decreasing corporate attention spans?
  • Service Innovation
    • What are the key characteristics of a systematic process for service innovation in data-rich environments?
    • How can goods-based companies use data to be more strategic in their service infusion strategies?
  • Collaboration
    • What can large amounts of data add to small scale (internal) and broader external innovation collaborations?
    • How is an effective balance achieved between externally-generated large-scale data and more internallyfocused “collaboratories” and innovation centers?
  • Success & Failure
    • As a learning tool, how can the availability of large datasets (e.g., A.C. Nielsen Consumer Panel Data and Retail Scanner Data) improve the chances of innovation success and minimize the chances of failure?
  • Globalization
    • What are the challenges encountered in incorporating large amounts of globally-generated data into the innovation process?
  • Open Innovation
    • In an Open Innovation environment, are unstructured and structured data equally useful?
    • Are ideas such as “crowdsourcing” still to
    • narrowly-bounded to take advantage of the potential of Big Data?
    • What is the effect of crowdsourcing on the organization and management of NPD? Are the traditional tasks, roles, and responsibilities challenged and how?
    • What is the “dark side” (e.g., information overload, loss of control, winner takes all, intellectual property issues) of crowdsourcing?
    • How can firms ‘qualify’ crowdsourcers? How can/should they evaluate and assess what crowdsourcers do?
  • Talent
    • How does the nature of the ideal innovation-driving senior management team change in a world where large amounts of data must be understood and acted upon?
    • How does the composition of the NPD team need to change in a data-rich environment?
  • Process
    • How can large-scale data best be incorporated as just one element of a successful new product development and innovation process?
    • Are different types of data (e.g., unstructured vs. structured; or text vs. images) more suitable for different types of innovation?

Review Process and Timeline

Call for papers announcement: February 1, 2015
Submission due date: February 1, 2016
First round decisions: May 1, 2016
Research workshop/conference (at The University of Tennessee)* June, 2016
*For authors who are invited to submit a revision
Revision due date: November 1, 2016
Second round decisions: February 1, 2017
Final editorial decision March 31, 2017

Submission Process

Submissions to the special issue should be sent electronically through the JPIM ScholarOne system ( Authors need to clearly indicate in their submission information and letter that their manuscript is for the Special Issue on “Innovation in Data-Rich Environments.” All submissions will be subject to the standard double blind review process followed by JPIM. All manuscripts must be original, unpublished works that are not concurrently under review for publication elsewhere. All submissions should conform to the JPIM manuscript submission guidelines available at:

Questions about this special issue may be directed to either of the guest editors at their email addresses provided above.


1. IBM (2013). What is Big Data?.

2. EMC (2015). The Digital Universe.

3. McAfee, A. and Brynjolfsson, E. (2012). Big Data: The Management Revolution. Harvard Business Review, 90(10): 60-68.

4. Rindfleisch, A and Moorman, C. (2001). The Acquisition and Utilization of Information in New Product Alliances: A Strength-of-Ties Perspective. Journal of Marketing 65(2): 1-18.

5. Schreier, M., Fuchs, C., and Dahl, D.W. (2012). The Innovation Effect of User Design: Exploring Consumers’ Innovation Perceptions of Firms Selling Products Designed by Users. Journal of Marketing 76(5): 18-32.

6. Griffin, A. and Hauser, J.R. (1993). The Voice of the Customer. Marketing Science 12(1): 1-27.

7. Du, R.Y., Hu, Y., and Damangir, S. (2015). Leveraging Trends in Online Searches for Product Features in Market Response Modeling. Journal of Marketing 79 (1): 29-43.

8. Tirunillai, S., and Tellis, G.J. (2014). Mining Marketing Meaning from Online Chatter: Analysis of Big Data Using Latent Dirichlet Allocation. Journal of Marketing Research 51 (4): 463-479.

9. D’aveni, R.A., Dagnino, G.B., and Smith, K.G. (2010). The Age of Temporary Advantage. Strategic Management Journal 31 (13): 1371-85.

10.Atuahene-Gima, K. (2005). Resolving the Capability-Rigidity Paradox in New Product Innovation. Journal of Marketing 69(4): 61-83.

11.Akgun, A.E. Lynn, G.S., and Byrne, J.C. (2006). Antecedents and Consequences of Unlearning in New Product Development Teams. Journal of Product Innovation Management 23(1): 73-88.

12.Biemans W. and Langerak, F. (2015). More Research Priorities. Journal of Product Innovation Management 32(1): 2-3.

13.Barczak G. (2014). JPIM Research Priorities. Journal of Product Innovation Management 31(4): 640-641.