Strategy for Launching Data-Intensive Services: A Tipping
Point for Market Adoption
Sarah Mostafavi
Department of Industrial and Systems Engineering
Virginia Tech
7054 Haycock Road, Room 433, Falls Church, VA 22043 USA
sarahmb@vt.edu
Tel. 703-538-8434
Navid Ghaffarzadegan
Department of Industrial and Systems Engineering, Virginia Tech, VA, USA.
navidg@vt.edu
7054 Haycock Road, Room 430, Falls Church, VA 22043 USA
sarahmb@vt.edu
Tel. 703-538-8434
Hyunjung Kim*
Department of Management
College of Business
California State University, Chico.
400 West 1*. St. Chico, C
hkim18,
Tel. 530-898-5936
Fax. 530-898-5501
*Corresponding Author
Strategy for Launching Data-Intensive Services:
A Tipping Point for Market Adoption
Abstract
New advancements in information technology and the availability of large datasets have provided
new opportunities for industries to offer customized services that highly rely on big data (Brown, Chui, &
Manyika, 2011). Referred as data-intensive services, the process of value generation in these enterprises
relies on continuous data gathering, and the end product is in the form of data/information (Davenport &
Kudyba, 2016). Previous strategic management theories of service industries are yet to explain success
and failure of these data-intensive services, and we offer a dynamic theory to fill the gap.
Based on a major case study from the IT industry, we develop a system dynamics model of
market adoption of data-intensive services. In our model, the classic Bass Diffusion Model (1969) is
modified to reflect the unique value chain of data-intensive services where data as raw material derive
from the service adopters. The model also captures the interplay between firm’s analytical capabilities
and the value of adoption.
We use the model to show that diffusion success is sensitive to the launching mode, specifically
regarding the initial value of adopter population, data volume, and analytical capabilities of data-intensive
services. We also show that there is a tipping point where small changes in initial conditions result in
significant outcome differences. In contrast to the common belief, a high volume of initial adopters may
negatively influence market adoption of data-intensive services in the long run.
Bass, F. M. (1969). A new product growth for model durables. M it science, 15(5),
215-227.
Brown, B., Chui, M., & Manyika, J. (2011). Are you ready for the era of ‘big data’. McKinsey Quarterly,
4(1), 24-35.
Davenport, T. H., & Kudyba, S. (2016). Designing and developing analytics-based data products. MIT
Sloan Management Review, 58(1), 83.
