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Description
Scholars have written much about home and meaning, yet they have said little about the professionally furnished model home viewed as a cultural artifact. Nor is there literature addressing how the home building industry uses these spaces to promote images of family life to increase sales. This research notes that not only do the structure, design, and layout of the model home formulate cultural identity but also the furnishings and materials within. Together, the model home and carefully selected artifacts placed therein help to express specific chosen lifestyles as that the home builder determines. This thesis considers the model home as constructed as well as builder's publications, descriptions, and advertisements. The research recognizes the many facets of merchandising, consumerism, and commercialism influencing the design and architecture of the suburban home. Historians of visual and cultural studies often investigate these issues as separate components. By contrast, this thesis offers an integrated framework of inquiry, drawing upon such disciplines as cultural history, anthropology, and material culture. The research methodology employs two forms of content analysis - image and text. The study analyzes 36 model homes built in Phoenix, Arizona, during the period 1955-1956. The thesis explores how the builder sends a message, i.e. images, ideals, and aspirations, to the potential home buyer through the design and decoration of the model home. It then speculates how the home buyer responds to those messages. The symbiotic relationship between the sender and receiver, together, tells a story about the Phoenix lifestyle and the domestic ideals of the 1950s. Builders sent messages surrounding convenience, spaciousness, added luxury, and indoor-outdoor living to a growing and discriminating home buying market.
ContributorsGolab, Coreen R (Author) / Brandt, Beverly K. (Thesis advisor) / Bernardi, Jose (Committee member) / Schleif, Corine (Committee member) / Arizona State University (Publisher)
Created2013
Description
The objective of this dissertation is to empirically analyze the results of the retail location decision making process and how chain networks evolve given their value platform. It employs one of the largest cross-sectional databases of retailers ever assembled, including 50 US retail chains and over 70,000 store locations. Three closely related articles, which develop new theory explaining location deployment and behaviors of retailers, are presented. The first article, "Regionalism in US Retailing," presents a comprehensive spatial analysis of the domestic patterns of retailers. Geographic Information Systems (GIS) and statistics examine the degree to which the chains are deployed regionally versus nationally. Regional bias is found to be associated with store counts, small market deployment, and the location of the founding store, but not the age of the chain. Chains that started in smaller markets deploy more stores in other small markets and vice versa for chains that started in larger markets. The second article, "The Location Types of US Retailers," is an inductive analysis of the types of locations chosen by the retailers. Retail locations are classified into types using cluster analysis on situational and trade area data at the geographical scale of the individual stores. A total of twelve distinct location types were identified. A second cluster analysis groups together the chains with the most similar location profiles. Retailers within the same retail business often chose similar types of locations and were placed in the same clusters. Retailers generally restrict their deployment to one of three overall strategies including metropolitan, large retail areas, or market size variety. The third article, "Modeling Retail Chain Expansion and Maturity through Wave Analysis: Theory and Application to Walmart and Target," presents a theory of retail chain expansion and maturity whereby retailers expand in waves with alternating periods of faster and slower growth. Walmart diffused gradually from Arkansas and Target grew from the coasts inward. They were similar, however, in that after expanding into an area they reached a point of saturation and opened fewer stores, then moved on to other areas, only to revisit the earlier areas for new stores.
ContributorsJoseph, Lawrence (Author) / Kuby, Michael (Thesis advisor) / Matthews, Richard (Committee member) / Ó Huallacháin, Breandán (Committee member) / Kumar, Ajith (Committee member) / Arizona State University (Publisher)
Created2013
Description
Sparsity has become an important modeling tool in areas such as genetics, signal and audio processing, medical image processing, etc. Via the penalization of l-1 norm based regularization, the structured sparse learning algorithms can produce highly accurate models while imposing various predefined structures on the data, such as feature groups or graphs. In this thesis, I first propose to solve a sparse learning model with a general group structure, where the predefined groups may overlap with each other. Then, I present three real world applications which can benefit from the group structured sparse learning technique. In the first application, I study the Alzheimer's Disease diagnosis problem using multi-modality neuroimaging data. In this dataset, not every subject has all data sources available, exhibiting an unique and challenging block-wise missing pattern. In the second application, I study the automatic annotation and retrieval of fruit-fly gene expression pattern images. Combined with the spatial information, sparse learning techniques can be used to construct effective representation of the expression images. In the third application, I present a new computational approach to annotate developmental stage for Drosophila embryos in the gene expression images. In addition, it provides a stage score that enables one to more finely annotate each embryo so that they are divided into early and late periods of development within standard stage demarcations. Stage scores help us to illuminate global gene activities and changes much better, and more refined stage annotations improve our ability to better interpret results when expression pattern matches are discovered between genes.
ContributorsYuan, Lei (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Xue, Guoliang (Committee member) / Kumar, Sudhir (Committee member) / Arizona State University (Publisher)
Created2013
Description
This work presents two complementary studies that propose heuristic methods to capture characteristics of data using the ensemble learning method of random forest. The first study is motivated by the problem in education of determining teacher effectiveness in student achievement. Value-added models (VAMs), constructed as linear mixed models, use students’ test scores as outcome variables and teachers’ contributions as random effects to ascribe changes in student performance to the teachers who have taught them. The VAMs teacher score is the empirical best linear unbiased predictor (EBLUP). This approach is limited by the adequacy of the assumed model specification with respect to the unknown underlying model. In that regard, this study proposes alternative ways to rank teacher effects that are not dependent on a given model by introducing two variable importance measures (VIMs), the node-proportion and the covariate-proportion. These VIMs are novel because they take into account the final configuration of the terminal nodes in the constitutive trees in a random forest. In a simulation study, under a variety of conditions, true rankings of teacher effects are compared with estimated rankings obtained using three sources: the newly proposed VIMs, existing VIMs, and EBLUPs from the assumed linear model specification. The newly proposed VIMs outperform all others in various scenarios where the model was misspecified. The second study develops two novel interaction measures. These measures could be used within but are not restricted to the VAM framework. The distribution-based measure is constructed to identify interactions in a general setting where a model specification is not assumed in advance. In turn, the mean-based measure is built to estimate interactions when the model specification is assumed to be linear. Both measures are unique in their construction; they take into account not only the outcome values, but also the internal structure of the trees in a random forest. In a separate simulation study, under a variety of conditions, the proposed measures are found to identify and estimate second-order interactions.
ContributorsValdivia, Arturo (Author) / Eubank, Randall (Thesis advisor) / Young, Dennis (Committee member) / Reiser, Mark R. (Committee member) / Kao, Ming-Hung (Committee member) / Broatch, Jennifer (Committee member) / Arizona State University (Publisher)
Created2013
Description
Consumers search before making virtually any purchase. The notion that consumers engage in costly search is well-understood to have deep implications for market performance. However to date, no theoretical model allows for the observation that consumers often purchase more than a single product in an individual shopping occasion. Clothing, food, books, and music are but four important examples of goods that are purchased many items at a time. I develop a modeling approach that accounts for multi-purchase occasions in a structural way. My model shows that as preference for variety increases, so does the size of the consideration set. Search models that ignore preference for variety are, therefore, likely to under-predict the number of products searched. It is generally thought that lower search costs increase retail competition which pushes prices and assortments down. However, I show that there is an optimal number of products to offer depending on the intensity of consumer search costs. Consumers with high search costs prefer to shop at a store with a large assortment of goods and purchase multiple products, even if the prices that firm charges is higher than competing firms' prices. On the other hand, consumers with low search costs tend to purchase fewer goods and shop at the stores that have lower prices, as long as the store has a reasonable assortment offering. The implications for market performance are dramatic and pervasive. In particular, the misspecification of demand model in which search is important and/or multiple discreteness is observed will produce biased parameter estimates leading to erroneous managerial conclusions.
ContributorsAllender, William Jacob (Author) / Richards, Timothy J. (Thesis advisor) / Park, Sungho (Committee member) / Hamilton, Stephen (Committee member) / Arizona State University (Publisher)
Created2013
Description
I compare the effect of anonymous social network ratings (Yelp.com) and peer group recommendations on restaurant demand. I conduct a two-stage choice experiment in which restaurant visits in the first stage are informed by online social network reviews from Yelp.com, and visits in the second stage by peer network reviews. I find that anonymous reviewers have a stronger effect on restaurant preference than peers. I also compare the power of negative reviews with that of positive reviews. I found that negative reviews are more powerful compared to the positive reviews on restaurant preference. More generally, I find that in an environment of high attribute uncertainty, information gained from anonymous experts through social media is likely to be more influential than information obtained from peers.
ContributorsTiwari, Ashutosh (Author) / Richards, Timothy J. (Thesis advisor) / Qiu, Yueming (Committee member) / Grebitus, Carola (Committee member) / Arizona State University (Publisher)
Created2013
Description
Schennach (2007) has shown that the Empirical Likelihood (EL) estimator may not be asymptotically normal when a misspecified model is estimated. This problem occurs because the empirical probabilities of individual observations are restricted to be positive. I find that even the EL estimator computed without the restriction can fail to be asymptotically normal for misspecified models if the sample moments weighted by unrestricted empirical probabilities do not have finite population moments. As a remedy for this problem, I propose a group of alternative estimators which I refer to as modified EL (MEL) estimators. For correctly specified models, these estimators have the same higher order asymptotic properties as the EL estimator. The MEL estimators are obtained by the Generalized Method of Moments (GMM) applied to an exactly identified model. The simulation results provide promising evidence for these estimators. In the second chapter, I introduce an alternative group of estimators to the Generalized Empirical Likelihood (GEL) family. The new group is constructed by employing demeaned moment functions in the objective function while using the original moment functions in the constraints. This designation modifies the higher-order properties of estimators. I refer to these new estimators as Demeaned Generalized Empirical Likelihood (DGEL) estimators. Although Newey and Smith (2004) show that the EL estimator in the GEL family has fewer sources of bias and is higher-order efficient after bias-correction, the demeaned exponential tilting (DET) estimator in the DGEL group has those superior properties. In addition, if data are symmetrically distributed, every estimator in the DGEL family shares the same higher-order properties as the best member.
ContributorsXiang, Jin (Author) / Ahn, Seung (Thesis advisor) / Wahal, Sunil (Thesis advisor) / Bharath, Sreedhar (Committee member) / Mehra, Rajnish (Committee member) / Tserlukevich, Yuri (Committee member) / Arizona State University (Publisher)
Created2013
Description
There have been multiple calls for research on consumers' responses to social issues, regulatory changes, and corporate behavior. Thus, this dissertation proposes and tests a conceptual framework of parents' responses to government regulations and corporate social responsibility (CSR) that address juvenile obesity. This research builds on Attribution Theory to examine the impact of government regulations and CSR on consumers' attitudes and their subsequent behavior. Three pilot studies and three main experiments were conducted; a between-subjects and randomized experimental design being used to capture the effects of regulations and corporate actions on product satisfaction, company evaluations, and behavioral intentions, while examining the mediating role of attributions of responsibility for a negative product outcome. This research has implications for policy makers and marketing practitioners and scholars. This is the first study to offer a new perspective, based on attributions of blame, to explain the mechanism that drives consumers' responses to government regulations. Considering numerous calls for government actions that address childhood obesity, it is important to understand how and why consumers respond to such regulations. The results illustrated that certain policies may have unintended consequences due to unexpected attributions of blame for unhealthy products. Only recently have researchers tried to address the psychological mechanism through which CSR has an impact on consumers' attitudes and behavior. To date, few studies have investigated attributions as a mediating variable in the transfer of CSR associations on consumer responses. Nonetheless, this is the first study that concentrates on attributions of responsibility, per se, to explain the impact of CSR on company evaluations. This dissertation extends previous research, where locus, stability, and controllability mediated the relationship between CSR and attributions of blame; the degree of blame being consequential to brand evaluations. The current results suggest that attributions of responsibility, per se, mediate the impact of CSR on company evaluations. Additionally, attributions of blame are measured as the degree to which consumers take personal responsibility for a negative product outcome. This highlights a new role of the CSR construct, as a moderator of consumers' self-serving bias, a fundamental psychological response that has been neglected in the marketing literature.
ContributorsDumitrescu, Claudia (Author) / Shaw Hughner, Renée (Thesis advisor) / Schmitz, Troy G. (Committee member) / Seperich, George (Committee member) / Shultz, Ii, Clifford J. (Committee member) / Arizona State University (Publisher)
Created2013
Description
The lack of food safety in a grower's produce presents the grower with two risks; (1) that an item will need to be recalled from the market, incurring substantial costs and damaging brand equity and (2) that the entire market for the commodity becomes impaired as consumers associate all produce as being risky to eat. Nowhere is this more prevalent than in the leafy green industry, where recalls are relatively frequent and there has been one massive E. coli outbreak that rocked the industry in 2006. The purpose of this thesis is to examine insurance policies that protect growers from these risks. In doing this, a discussion of current recall insurance policies is presented. Further, actuarially fair premiums for catastrophic revenue insurance policies are priced through a contingent claims framework. The results suggest that spinach industry revenue can be insured for $0.02 per carton. Given the current costs of leafy green industry food safety initiatives, growers may be willing to pay for such an insurance policy.
ContributorsPagaran, Jeremy (Author) / Manfredo, Mark R. (Thesis advisor) / Richards, Timothy J. (Thesis advisor) / Nganje, William (Committee member) / Arizona State University (Publisher)
Created2013
Description
I show that firms' ability to adjust variable capital in response to productivity shocks has important implications for the interpretation of the widely documented investment-cash flow sensitivities. The variable capital adjustment is sufficient for firms to capture small variations in profitability, but when the revision in profitability is relatively large, limited substitutability between the factors of production may call for fixed capital investment. Hence, firms with lower substitutability are more likely to invest in both factors together and have larger sensitivities of fixed capital investment to cash flow. By building a frictionless capital markets model that allows firms to optimize over fixed capital and inventories as substitutable factors, I establish the significance of the substitutability channel in explaining cross-sectional differences in cash flow sensitivities. Moreover, incorporating variable capital into firms' investment decisions helps explain the sharp decrease in cash flow sensitivities over the past decades. Empirical evidence confirms the model's predictions.
ContributorsKim, Kirak (Author) / Bates, Thomas (Thesis advisor) / Babenko, Ilona (Thesis advisor) / Hertzel, Michael (Committee member) / Tserlukevich, Yuri (Committee member) / Arizona State University (Publisher)
Created2013