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Thesis (Ph.D.)--City University of Hong Kong, 2009. / "Submitted to Department of Information Systems in partial fulfillment of the requirements for the degree of Doctor of Philosophy." Includes bibliographical references (leaves 107-127)

Thesis (Ph. D.)--University of Texas at Austin, 2000. / Vita. Includes bibliographical references (leaves 184-196). Available also in a digital version from Dissertation Abstracts.

Chan, Ka Yan Elisa. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 99-106). / Abstracts in English and Chinese. / ABSTRACT (ENGLISH) --- p.ii / ABSTRACT (CHINESE) --- p.iv / ACKNOWLEDGEMENTS --- p.vi / TABLE OF CONTENTS --- p.viii / LIST OF TABLES --- p.xi / LIST OF FIGURES --- p.xii / LIST OF APPENDICE --- p.xii / Chapter CHAPTER ONE --- INTRODUCTION --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.2 --- Overview of Research Objectives --- p.2 / Chapter 1.3 --- Outline of the Current Study --- p.2 / Chapter CHAPTER TWO --- LITERATURE REVIEW --- p.4 / Chapter 2.1 --- Defining “Experience´ح in Consumption --- p.4 / Chapter 2.2 --- Experience-rich Consumption --- p.7 / Chapter 2.3 --- Consumer Value --- p.8 / Chapter 2.4 --- Models and Scales of Consumer Value --- p.11 / Chapter 2.4.1 --- Typology of Consumer Value by Holbrook (1999) --- p.11 / Chapter 2.4.2 --- The Theory of Consumption Value --- p.12 / Chapter 2.4.3 --- Consumer Perceived Value --- p.16 / Chapter 2.4.4 --- Experiential Value Scale (EVS) --- p.17 / Chapter 2.5 --- Summary of Literature Review --- p.19 / Chapter CHAPTER THREE --- OBJECTIVES OF THE CURRENT STUDY --- p.22 / Chapter 3.1 --- First Objective of this Research --- p.22 / Chapter 3.2 --- Second Objective of this Research --- p.22 / Chapter 3.3 --- Third Objective of this Research --- p.23 / Chapter CHAPTER FOUR --- CONCEPTUALIZATION OF EXPERIENTIAL VALUE --- p.25 / Chapter 4.1 --- Exploring the Components of Experience --- p.25 / Chapter 4.1.1 --- Psychology Literature: Everyday Life Experience --- p.25 / Chapter 4.1.2 --- Stimulus-Organism-Response Framework --- p.26 / Chapter 4.2 --- Dimensionality of Experiential Value --- p.27 / Chapter 4.2.1 --- Emotional Value --- p.28 / Chapter 4.2.2 --- Intellectual Value --- p.30 / Chapter 4.3 --- Dimensional Relation Between Emotional and Intellectual Value --- p.33 / Chapter CHAPTER FIVE --- DEVELOPING THE EXPERIENTIAL VALUE SCALE --- p.35 / Chapter 5.1 --- Study 1: Item Generation and Selection --- p.35 / Chapter 5.1.1 --- Literature Review --- p.35 / Chapter 5.1.2 --- Focus Group --- p.37 / Chapter 5.2 --- Study 2: Item Reduction and Dimensionality of the Scale --- p.38 / Chapter 5.2.1 --- Scale Purification with Exploratory Factor Analysis --- p.39 / Chapter 5.2.2 --- Initial Confirmatory Factor Analysis --- p.42 / Chapter 5.2.3 --- Scale Reliability and Validity --- p.42 / Chapter 5.3 --- Study 3: Convergent and Discriminant Validity Analysis --- p.43 / Chapter 5.3.1 --- Method --- p.43 / Chapter 5.3.2 --- Results --- p.44 / Chapter CHAPTER SIX --- CONCEPTUAL MODEL TESTING --- p.50 / Chapter 6.1 --- Study 4: The Antecedents and Consequents of Experiential Value --- p.51 / Chapter 6.1.1 --- Subjective Well-Being --- p.52 / Chapter 6.1.2 --- Method --- p.53 / Chapter 6.1.2.1 --- Dependent Measures --- p.54 / Chapter 6.1.3 --- Results and Discussion --- p.54 / Chapter 6.1.3.1 --- Discriminant Validity of Constructs --- p.54 / Chapter 6.1.3.2 --- Overall Model Results --- p.55 / Chapter 6.1.3.3 --- Equivalence Across Religious Group --- p.56 / Chapter 6.2 --- Study 5: Using Experiential Value to Predict Consumer Behavior and the Moderating Effect of Experience Context --- p.57 / Chapter 6.2.1 --- "Experiential Values, Consumer Trust, and Loyalty" --- p.57 / Chapter 6.2.2 --- The Moderating Effect of Shopping Context --- p.59 / Chapter 6.2.3 --- Method --- p.61 / Chapter 6.2.3.1 --- Dependent Measures --- p.61 / Chapter 6.2.4 --- Results and Discussion --- p.62 / Chapter 6.2.4.1 --- Discriminant Validity of Constructs --- p.62 / Chapter 6.2.4.2 --- Overall Model Results --- p.63 / Chapter 6.2.4.3 --- Moderation Model Results --- p.63 / Chapter CHAPTER SEVEN --- GENERAL DISCUSSION AND MANAGERIAL IMPLICATION --- p.65 / Chapter 7.1 --- Theoretical Contribution --- p.66 / Chapter 7.2 --- Managerial Implication --- p.68 / Chapter 7.3 --- Limitations and Future Research --- p.71 / REFERENCES --- p.99

Thesis (Ph. D.)--Ohio State University, 2008. / Title from first page of PDF file. Includes bibliographical references (p. 175-192).

Thesis (MComm)--University of Stellenbosch, 2008. / Bibliography. Also available via the Internet.

Focusing on consumer trust in science, this dissertation explores the societal and ecological factors that can influence consumer’s science denial tendency, and also explores how to leverage consumers’ input with crowdsourcing to rate scientific article veracity and hence create a trustworthy media environment. In the first chapter, I find that lower religious diversity in a region, or an individual’s experience, predicts lower religious tolerance and greater science denial. The belief that my religion trumps other religions precipitates the attitude that it trumps science too. I find supporting evidence from seven studies using U.S. mobile location data, census data, worldwide archival data, national surveys conducted in different countries with participants from different religious groups, and experiments. In the second chapter, I propose a novel crowdsourcing method to leverage the input of general consumers into the fact-checking efforts. I validate the use of similarity judgments to facilitate unbiased consumer responses and prove that asking lay consumers to rate the similarity between scientist-rated and unrated articles provide an unbiased and efficient way to scale up veracity ratings of scientific articles. In order to increase consumer trust in science, I argue that policy makers should emphasize religious integration and heterogeneity in communities. In order to build a better news environment with more trustworthy scientific information, I argue that news companies, news platforms, and third-party fact-checkers can engage general consumers’ input by asking the right questions to get unbiased and reliable responses.

This research examines the confluence of consumers’ use of social media to share information with the ever-present need for innovative research that yields insight into consumers’ economic decisions. Social media networks have become ubiquitous in the new millennium. These networks, including, among others: Facebook, Twitter, Blog, and Reddit, are brimming with conversations on an expansive array of topics between people, private and public organizations, governments and global institutions. Preliminary findings from initial research confirms the existence of online conversations and posts related to matters of personal finance and consumers’ economic outlook. Meanwhile, the Consumer Confidence Index (CCI) continues to make headline news. The issue of consumer confidence (or sentiment) in anticipating future economic activity generates significant interest from major players in the news media industry, who scrutinize its every detail and report its implications for key players in the economy. Though the CCI originated in the United States in 1946, variants of the survey are now used to track and measure consumer confidence in nations worldwide. In light of the fact that the CCI is a quantified representation of consumer sentiments, it is possible that the level of confidence consumers have in the economy could be deduced by tracking the sentiments or opinions they express in social media posts. Systematic study of these posts could then be transformed into insights that could improve the accuracy of an index like the CCI. Herein lies the focus of the current research—to analyze the attributes of data from social media posts, in order to assess their capacity to generate insights that are novel and/or complementary to traditional CCI methods. The link between data gained from social media and the survey-based CCI is perhaps not an obvious one. But our research will use a data extraction tool called NetBase Insight Workbench to mine data from the social media networks and then apply natural language processing to analyze the social media content. Also, KH Coder software will be used to perform a set of statistical analyses on samples of social media posts to examine the co-occurrence and clustering of words. The findings will be used to expose the strengths and weaknesses of the data and to assess the validity and cohesion of the NetBase data extraction tool and its suitability for future research. In conclusion, our research findings support the analysis of opinions expressed in social media posts as a complement to traditional survey-based CCI approaches. Our findings also identified a key weakness with regards to the degree of ‘noisiness’ of the data. Although this could be attributed to the ‘modeling’ error of the data mining tool, there is room for improvement in the area of association—of discerning the context and intention of posts in online conversations.

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Using data from the Michigan Consumer Survey, I explore alternatives for constructing the Index of Consumer Sentiment (ICS) to improve its forecasting power regarding consumption and its components. Questions which seemed to matter in the past are no longer good predictors. For more recent sample periods, expectations of automobile purchases, unemployment, and current economic situations are more important than categories selected previously. An alternative index is constructed accordingly. Applying different techniques suggested in the literature, the new index significantly outperforms the ICS in both in-sample and out-of-sample tests. Furthermore, the new index also produces more accurate results when forecasting recessions.