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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
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
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
This study investigates how well prominent behavioral theories from social psychology explain green purchasing behavior (GPB). I assess three prominent theories in terms of their suitability for GPB research, their attractiveness to GPB empiricists, and the strength of their empirical evidence when applied to GPB. First, a qualitative assessment of the Theory of Planned Behavior (TPB), Norm Activation Theory (NAT), and Value-Belief-Norm Theory (VBN) is conducted to evaluate a) how well the phenomenon and concepts in each theory match the characteristics of pro-environmental behavior and b) how well the assumptions made in each theory match common assumptions made in purchasing theory. Second, a quantitative assessment of these three theories is conducted in which r2 values and methodological parameters (e.g., sample size) are collected from a sample of 21 empirical studies on GPB to evaluate the accuracy and generalize-ability of empirical evidence. In the qualitative assessment, the results show each theory has its advantages and disadvantages. The results also provide a theoretically-grounded roadmap for modifying each theory to be more suitable for GPB research. In the quantitative assessment, the TPB outperforms the other two theories in every aspect taken into consideration. It proves to 1) create the most accurate models 2) be supported by the most generalize-able empirical evidence and 3) be the most attractive theory to empiricists. Although the TPB establishes itself as the best foundational theory for an empiricist to start from, it's clear that a more comprehensive model is needed to achieve consistent results and improve our understanding of GPB. NAT and the Theory of Interpersonal Behavior (TIB) offer pathways to extend the TPB. The TIB seems particularly apt for this endeavor, while VBN does not appear to have much to offer. Overall, the TPB has already proven to hold a relatively high predictive value. But with the state of ecosystem services continuing to decline on a global scale, it's important for models of GPB to become more accurate and reliable. Better models have the capacity to help marketing professionals, product developers, and policy makers develop strategies for encouraging consumers to buy green products.
ContributorsRedd, Thomas Christopher (Author) / Dooley, Kevin (Thesis advisor) / Basile, George (Committee member) / Darnall, Nicole (Committee member) / Arizona State University (Publisher)
Created2012
Description
Image understanding has been playing an increasingly crucial role in vision applications. Sparse models form an important component in image understanding, since the statistics of natural images reveal the presence of sparse structure. Sparse methods lead to parsimonious models, in addition to being efficient for large scale learning. In sparse modeling, data is represented as a sparse linear combination of atoms from a "dictionary" matrix. This dissertation focuses on understanding different aspects of sparse learning, thereby enhancing the use of sparse methods by incorporating tools from machine learning. With the growing need to adapt models for large scale data, it is important to design dictionaries that can model the entire data space and not just the samples considered. By exploiting the relation of dictionary learning to 1-D subspace clustering, a multilevel dictionary learning algorithm is developed, and it is shown to outperform conventional sparse models in compressed recovery, and image denoising. Theoretical aspects of learning such as algorithmic stability and generalization are considered, and ensemble learning is incorporated for effective large scale learning. In addition to building strategies for efficiently implementing 1-D subspace clustering, a discriminative clustering approach is designed to estimate the unknown mixing process in blind source separation. By exploiting the non-linear relation between the image descriptors, and allowing the use of multiple features, sparse methods can be made more effective in recognition problems. The idea of multiple kernel sparse representations is developed, and algorithms for learning dictionaries in the feature space are presented. Using object recognition experiments on standard datasets it is shown that the proposed approaches outperform other sparse coding-based recognition frameworks. Furthermore, a segmentation technique based on multiple kernel sparse representations is developed, and successfully applied for automated brain tumor identification. Using sparse codes to define the relation between data samples can lead to a more robust graph embedding for unsupervised clustering. By performing discriminative embedding using sparse coding-based graphs, an algorithm for measuring the glomerular number in kidney MRI images is developed. Finally, approaches to build dictionaries for local sparse coding of image descriptors are presented, and applied to object recognition and image retrieval.
ContributorsJayaraman Thiagarajan, Jayaraman (Author) / Spanias, Andreas (Thesis advisor) / Frakes, David (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
In order to be competitive in the hotel market, more and more hotels have proposed various types of "wow" services to inform customers' impressions of the hotel in a positive way. Many customers consider these services excellent, and they often exceed their expectations. However, some "wow" services only generate the effect of amazement instead of meeting customers' needs and wants. Applying the notion of the Zone of Tolerance (ZOT: the range between customers' desired and adequate levels of service expectations) to the unique services provided by the Hotel Royal Chiao Hsi Spa in Taiwan, this research study explores hotel customers' service expectations and perceived service quality while revealing the relationship between service quality, satisfaction, and future behavioral intentions. The findings indicate that the ZOT indeed exists in customers' service expectations through the significant difference between the desired and adequate levels of expectations. In addition, findings indicate that customers have diverse tolerance zones toward different hotel services regarding the perceived level of essentialness. Ultimately, the findings specify that customers' perceived service quality has a direct effect on both customer satisfaction and future behavioral intentions.
ContributorsChiu, Chien-Fen (Author) / Lee, Woojin (Thesis advisor) / Larsen, Dale (Committee member) / Tyrrell, Timothy (Committee member) / Kim, Yushim (Committee member) / Arizona State University (Publisher)
Created2013