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ASU Electronic Theses and Dissertations


This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.


The recent changes in the software markets gave users an unprecedented number of alternatives for any given task. In such a competitive environment, it is imperative to understand what drives user behavior. To that end, the research presented in this dissertation, tries to uncover the impact of business strategies often used in the software markets. The dissertation is organized into three distinct studies into user choice and post choice use of software. First using social judgment theory as foundation, zero price strategies effects on user choice is investigated, with respect to product features, consumer characteristics, and context effects. Second, role …

Contributors
Kanat, Irfan Emrah, Santanam, Raghu, Vinze, Ajay, et al.
Created Date
2016

By collecting and analyzing more than two million tweets, U.S. House Representatives’ voting records in 111th and 113th Congress, and data from other resources I study several aspects of adoption and use of Twitter by Representatives. In the first chapter, I study the overall impact of Twitter use by Representatives on their political orientation and their political alignment with their constituents. The findings show that Representatives who adopted Twitter moved closer to their constituents in terms of political orientation. By using supervised machine learning and text mining techniques, I shift the focus to synthesizing the actual content shared by Representatives …

Contributors
Mosuavi, Seyedreza, Gu, Bin, Vinze, Ajay S., et al.
Created Date
2016