Date of Award
5-2015
Degree Name
Doctor of Philosophy
Department
Industrial and Manufacturing Engineering
First Advisor
Dr. Larry Mallak
Second Advisor
Dr. David Lyth
Third Advisor
Dr. Brooks Applegate
Fourth Advisor
Dr. Diana Prieto
Abstract
What attributes of products are most associated with product success from the perspective of the consumer and how is this association affected by the technology level of the product offering? Creating successful new products is an imperative for long term survival for businesses. Past research has focused on the perspectives of manufacturers to understand product attributes that comprise the construct described as product advantage. This study investigated the components of product advantage from the perspective of the consumer and the significance of each component in explaining variance in product success.
A methodology and two classification toolsets were developed for the collection of data from an e-commerce retailer and tested for their reliability compared to manually coded values. Over 2.6 million opinion phrases were collected, parsed, and classified using the developed methodology. The dataset generated from this collection and classification activity was used in regression modeling to test several hypotheses to better understand the components of product advantage from the perspective of the consumer.
The measure of consumer sentiment expressed in reviews related to product advantage components showed significant correlation with product success. Evaluation of interactions among product advantage components was conducted using hierarchical regression modeling and the results were analyzed for significance by product group. Assessment of variable significance in the collection of regression models provided evidence of statistically significant differences among certain product advantage components in high technology and low technology products.
This research provides a methodology and toolsets for research of product advantage from the perspective of the consumer using large scale unstructured datasets. It also provides an extension of existing product advantage theory to the realm of consumer sentiment. Both contributions provide a model for using unstructured consumer reviews both for further research studies as well as managerial insight into attributes associated with product performance.
Access Setting
Dissertation-Open Access
Recommended Citation
Akerman, Ashley Nolen, "Modeling New Product Success from Component Measures of Product Advantage: A Model Utilizing Automated Text Classification and Sentiment Analysis" (2015). Dissertations. 510.
https://scholarworks.wmich.edu/dissertations/510
Comments
5th Advisor: Dr. Ala Al-Fuqaha