Thesis (Ph. D.)--University of Tennessee, 2007. / Title from title page screen (viewed on Oct. 24, 2007). Thesis advisor: Ernest R. Cadotte. Vita. Includes bibliographical references.

Thesis (M.M.S.) - Carleton University, 2003. / Includes bibliographical references (p. 61-63). Also available in electronic format on the Internet.

Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.

Thesis (Ph. D.)--University of Wisconsin, 1967. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliography.

Thesis (Ph.D.)--University of Iowa, 2006. / Supervisors: Dhananjay Nayakankuppam, Baba Shiv. Includes bibliographical references (leaves 57-61).

Thesis (M.A.)--East Carolina University, 2006. / Presented to the faculty of the Department of Geography. Advisor: Derek Alderman. Includes bibliographical references (leaves 114-119). Also available via the World Wide Web. Adobe reader required.

published_or_final_version / Business Administration / Master / Master of Business Administration

by Chi-heng Chan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1996. / Includes bibliographical references (leaves 70-71). / Chapter 1 --- Introduction --- p.4 / Chapter 1.1 --- Existing models on purchase intentions --- p.4 / Chapter 1.2 --- Objective --- p.7 / Chapter 2 --- Modelling --- p.9 / Chapter 2.1 --- Beta Distribution --- p.9 / Chapter 2.2 --- Beta-binomial Distribution --- p.16 / Chapter 2.3 --- A new model without the binomial assumption --- p.17 / Chapter 2.3.1 --- Difficulties of the binomial assumption --- p.17 / Chapter 2.3.2 --- A new measure of stated purchase intentions --- p.18 / Chapter 2.3.3 --- Modelling the True purchase intention --- p.19 / Chapter 3 --- Methodology --- p.21 / Chapter 3.1 --- Two Approaches of Estimation --- p.21 / Chapter 3.1.1 --- Least Square Approach --- p.21 / Chapter 3.1.2 --- Maximum Likelihood Approach --- p.23 / Chapter 3.2 --- Estimation Procedures --- p.24 / Chapter 3.2.1 --- Least Square Estimation --- p.26 / Chapter 3.2.2 --- Maximum Likelihood Estimation --- p.27 / Chapter 3.2.3 --- Difference between DUNLSF and DUMPOL --- p.27 / Chapter 3.2.4 --- Evaluation of the Two approaches --- p.28 / Chapter 3.3 --- Results --- p.28 / Chapter 3.4 --- Simulation Study --- p.37 / Chapter 3.4.1 --- Procedure --- p.37 / Chapter 3.4.2 --- Simulation Results --- p.37 / Chapter 3.4.3 --- Evaluation of performance --- p.41 / Chapter 4 --- An Example --- p.42 / Chapter 4.1 --- Review on Usage of survey --- p.42 / Chapter 4.2 --- The Survey --- p.43 / Chapter 4.2.1 --- Details of the survey --- p.44 / Chapter 4.2.2 --- Results and Findings --- p.45 / Chapter 4.2.3 --- Interpretation --- p.47 / Chapter 5 --- Discussions and Conclusions --- p.51 / Chapter 5.1 --- Discussions --- p.51 / Chapter 5.2 --- Further Implications --- p.52 / Chapter 5.3 --- Conclusion --- p.54 / Chapter A --- Beta Distribution --- p.55 / Chapter B --- Programmes of estimation --- p.57 / Chapter B.l --- Minimization of (??) by the algorithm DUNLSF --- p.57 / Chapter B.2 --- Minimization of (??) by the algorithm DUMPOL --- p.60 / Chapter C --- Programmes of simulation --- p.63 / Chapter C.1 --- Simulation 1 (Refer to P.39) --- p.63 / Chapter C.2 --- Simulation 2 (Refer to P.40) --- p.67 / Chapter C.3 --- Simulation 3 (Refer to P.41) --- p.67 / Chapter D --- Programmes for figure drawing --- p.68 / Bibliography --- p.69

Graduation date: 1994

Marketers and advertisers have long searched for new and more powerful ways to measure the effectiveness of advertising. One data source that has proven useful is consumers’ moment-to-moment affective responses to advertisements. The first essay of my dissertation examines consumers’ moment-to-moment evaluations of advertisements and presents an application of principal components analysis that allows researchers to understand divergence in consumer response and link this divergence to specific elements of the ad’s storyline. While traditional research has focused on the aggregate peak, final moment and linear trend of consumers’ affect traces in predicting overall evaluations of the advertisement, this application provides better predictions of holdout evaluations. Additionally, I find these traditional measures do not provide insight into consumers’ credibility assessments of the advertisement and illustrate that these evaluations are determined much earlier in the advertisement. The second essay of my dissertation examines how important consumer characteristics (receiver factors), such as prior brand attitude and product category involvement, impact consumers’ moment-to-moment affective responses to advertisements. I also examine how these consumer characteristics moderate the relationship between consumers’ affect traces and important downstream variables such as attitude toward the ad, attitude toward the brand and likelihood to purchase the product. I demonstrate that consumers form biased evaluations based on their prior brand attitude and category involvement and illustrate how advertisers can reduce these biases resulting in greater attitude change in consumers less positively predisposed to the product. / text