Document Type


Date of Degree

Summer 2014

Degree Name

PhD (Doctor of Philosophy)

Degree In

Business Administration

First Advisor

Gruca, Thomas S.

Second Advisor

Russell, Gary J.

First Committee Member

Gruca, Thomas S.

Second Committee Member

Russell, Gary J.

Third Committee Member

Nayakankuppam, Dhananjay

Fourth Committee Member

Goins, Sheila T.

Fifth Committee Member

Lee, Sanghak


The Bass model has been used extensively and globally to forecast the first purchases of new products. It has been named by INFORMS as one of the top 10 most influential papers published in the 50-year history of Management Science. Most models for the diffusion of innovation are deeply rooted in the work of Bass (1969). His work provides a framework to model the underlying process of innovation adaption among first-time customers.

Potential customers may be connected to one another in some sort of network. Prior research has shown that the structure of a network affects adoption patterns (Dover et al. 2012; Hill et al. 2006; Katona and Sarvary 2008; Katona et al. 2011; Newman et al. 2006; Shaikh et al. 2010; Van den Bulte and Joshi 2007). One approach to addressing this issue is to incorporate network information into the original Bass model. The focus of this study is to explore how to incorporate network information and other micro-level data into the Bass model.

First, I prove that the Bass Model assumes all potential customers are linked to all other customers. Through simulations of individual adoptions and connections among individuals using a Random Network , I show that the estimate of q in the Bass Model is biased downward in the original Bass model. I find that biases in the Bass Model depend on the structure of the network. I relax the assumption of the fully connected network by proposing a Network-Based Bass model (NBB), which incorporates the network structure into the traditional Bass model. Using the proposed model (NBB), I am able to recover the true parameters.

To test the generalizability and to enhance the applicability of my NBB model, I tested my NBB model on the various network types with sampled data from the population network. I showed that my NBB model is robust across different types of networks, and it is efficient in terms of sample size. With a small fraction of data from the population, it accurately recovered the true parameters. Therefore, the NBB model can be used when we do not have complete network information.

The last essay is the first attempt to incorporate heterogeneous peer influence into the NBB model, based on individuals' preference structures. Besides the significant extension of the NBB (Bass) Model, incorporating high-quality data on individual behavior into the model leads to new findings on individuals' adoption behaviors, and thus expands our knowledge of the diffusion process.


Bass model, Diffusion of Innovation, Heterogeneous Social Influence, Network Sampling, Social Influence, Social Network


viii, 117 pages


Includes bibliographical references (pages 113-117).


Copyright 2014 Tae-Hyung Pyo