Date of Award
Summer 8-2020
Document Type
Dissertation
Degree Name
Doctor of Business Administration (DBA)
Department
Computer Information Systems
Abstract
The big data phenomenon has transformed every area of life and business. Businesses today rely on the volume, velocity, and variety (3Vs) of data available today in product design, advertisement, sales, and post-sale follow up activities. Communication between the firm and the consumer is personalized using data collected on the consumer to match the consumer’s location, time, and needs. Some marketers argue that this has birth a new era of marketing; transformative marketing, in which the firm’s ability to deliver value and to acquire and maintain long-run competitive advantage determined by the firm’s data resources. In other words, data are the currency of the transformative marketing era. This sentiment is pervasive and has led to massive investments in data in recent years.
This dissertation puts forward a classification of consumer big data to aid the firm extract value out of big data despite the 3Vs. The classification also demonstrates how value in a transformative marketing era does not have to be created at the expense of the consumer, but with the consumer. Five conceptual dichotomies are put forward in essay two that are more comprehensive than any other classification of data available in the research.
Finally, the third essay investigates how the big data phenomenon affects consumer freedom and emotions. Most people agree that freedom is a fundamental human right, and that business practices should respect consumer freedom. However, research on consumer freedom is scant. Two experiments investigate how the characteristics of data collected on consumers affects consumer perception of decision freedom and satisfaction with value propositions. With the big data phenomenon has come a push toward algorithmic decision making. Consumer’s anxiety toward algorithmic decision making is investigated along with the satisfaction derived from decisions made by third parties that collect data on consumers.
Recommended Citation
Ellis, Anuashine Chefor, "" (2020). Dissertation. 867.
https://digitalcommons.latech.edu/dissertations/867