System Dynamics Integrative Modeling and Simulation for Mobile Telephony Innovation Diffusion: A Study of Emerging Indian Telecom Market

System Dynamics Integrative Modeling and Simulation for Mobile Telephony Innovation Diffusion: A Study of Emerging Indian Telecom Market

Sanjay Bhushan (Dayalbagh University, India)
Copyright: © 2012 |Pages: 38
DOI: 10.4018/ijsda.2012070103
OnDemand PDF Download:
No Current Special Offers


In the recent times, India has emerged as one of the fastest growing telecom markets in the world and witnessed a telecommunication revolution brought about by a collaboration of government, industry, and the scientific community. It has truly been a success story of indigenous technology development and effective diffusion management of mobile telephony services. In the present paper, a system dynamics integrated model of Indian telecommunication sector (Mobile Telephony) has been calibrated to demonstrate the nature of interactions among system variables and the resultant outcome which assume degrees of importance at different stages of the diffusion/adoption process in the Indian telecom sector. The work done here proves how the application of system dynamics modeling and simulation coupled with soft computational neural networking can improve the holistic understanding of the dynamic structural complexities and forces of telecom diffusion. Simulation results show the potential of system dynamics as a promising tool to capture and predict the structural behavior of innovation diffusion process.
Article Preview

1. Review Of Literature And Conceptual Framework

The innovation diffusion model suggested by various researchers concerns to how innovations are spread. In this respect, diffusion is claimed to be the process through which an innovation spreads via communication channels over time among the members of a social system (Rogers, 2003; Stoneman, 1995, 2002). Drawing from the diffusion of innovation theory, we normally come to infer that the new technology pursues a diffusion path illustrated by a logistic curve and it illustrates emergent behavior and feedback when aggregates of individual behavior scale up to a similar behavior on a system level (Rogers, 1994, 2003) (Figure 1).

Figure 1.

Roger’s standard diffusion process diagram


Past Studies and Problem Identification

Many researches in the past in the area of innovation adoption and prediction have attempted to model the various structural aspects of innovation diffusion processes and properties of the product life cycle curve namely, the Bass Model, Gompertz Curve, the Pearl Curve, the Mansfield Model, the Blackman Model, the Fisher-Pry Model, the NSRL Model, the Non-Uniform Influence (NUI) Model, the Sharif-Kabir Model, the Weibull Distribution Model (or Sharif-Islam Model), and the Horsky Model. These models are now used widely for demonstrating and explaining technological changes and diffusion processes of new ideas (Tingyan, 1990).

Complete Article List

Search this Journal:
Volume 11: 5 Issues (2022)
Volume 10: 4 Issues (2021)
Volume 9: 4 Issues (2020)
Volume 8: 4 Issues (2019)
Volume 7: 4 Issues (2018)
Volume 6: 4 Issues (2017)
Volume 5: 4 Issues (2016)
Volume 4: 4 Issues (2015)
Volume 3: 4 Issues (2014)
Volume 2: 4 Issues (2013)
Volume 1: 4 Issues (2012)
View Complete Journal Contents Listing