This thesis examines three research questions under the framework of Regulatory Focus Theory (Higgins, 1997, 1998). These research questions are organized into three essays. The first essay examines the malleability of regulatory construction of goals. I demonstrate that regulatory construction of a goal is subject to goal distance—the perceived discrepancy between current and desired end state. When goal distance is large, the goal is more likely to be construed as a promotion-focused goal; when goal distance is small, the goal is more likely to be construed as a prevention-focused goal. This effect is mediated by the intensity of anticipated affect (pleasure of goal attainment versus pain of goal failure). The second essay examines a fit between sustainability and a prevention focus. I demonstrate that sustainability claims activate prevention concerns in consumers. Consumers make prevention-focused inferences about products of sustainable companies. Finally, regulatory fit between a sustainable product and prevention-focused product claims leads to enhanced product evaluations. The third essay examines the influence of regulatory focus in sequentially presented choice sets. I demonstrate that regulatory focus influences evaluations of equivalent sequentially presented choice alternatives, the amount of search and choice of option form a sequential set. Prevention-focused individuals defer favorable evaluations until choice options presented later in the sequential set. They perform more search compared to promotion-focused individuals and select an option encountered later in the sequence. Theoretical contributions and practical implications of these essays are discussed.

Existing literature on consumer behavior has argued that an individual’s regulatory orientation interacts with message framing. If there is a match between regulatory orientation (promotion versus prevention) and message framing, this results in positive attitudes toward a given advertisement. Conversely, if there is a mismatch, the effect is opposite, i.e., attitudes toward that advertisement are less positive and less favorable. This research extends the term of compatibility by examining how regulatory focus moderates the impact of two aspects of message framing (attribute framing and risky choice framing) on customer perceptions. It also examines whether regulatory fit is created when there are interactions between individuals’ regulatory orientation and message framing and how the fit changes customer perceptions about a message. Specifically, this dissertation provides answers to the following questions: (1) does regulatory fit take place when regulatory focus is compatible with two aspects of message framing (attribute and risky choice)?; (2) does regulatory fit take place when one aspect of message framing (attribute) is compatible with the other (risky choice)?; and (3) how do customer perceptions change if customers experience regulatory fit? The results show that the effects of utilitarian attributes and national brands are dominating and that both promotion- and prevention-oriented customers have higher perceptions of these attributes and brands. The findings of this study have both theoretical and practical implications. Theoretically, this study should enhance our understanding of regulatory focus theory. Practically, the results should provide marketers with more insights into the correlation between message framing and regulatory orientation and the effect of fit on message persuasion, enabling them to develop more effective marketing strategies.

In recent time particular interest has been drawn to the inference of gene regulatory networks from microarray gene expression data. But despite major improvements with data based methods, the network reconstruction from expression data alone still presents a computationally complex (NP-hard) problem. In this work it is incorporated additional information – regulatory motifs from yeast, when inferring a gene regulatory network for mouse genes. It was put forward the hypothesis that regulatory patterns analogous to these motifs are present in the set of mouse genes and can be identified by comparing yeast and mouse genes in terms of sequence similarity or Gene Ontology (The Gene Ontology Consortium 2000) annotations. In order to examine this hypothesis, small permutations of genes with high similarity to such yeast gene regulatory motifs were first tested against simple data-driven regulatory networks by means of consistency with the expression data. And secondly, using the best scored interactions provided by these permutations it were then inferred networks for the whole set of mouse genes. The results showed that individual permutations of genes with a high similarity to a given yeast motif did not perform better than low scored motifs and that complete networks, which were inferred from regulatory interactions provided by permutations, did also neither show any noticeable improvement over the corresponding data-driven network nor a high consistency with the expression data at all. It was therefore found that the hypothesis failed, i.e. neither the use of sequence similarity nor searching for identical functional annotations between mouse and yeast genes allowed to identify sets of genes that showed a high consistency with the expression data or would have allowed for an improved gene regulatory network inference.

<p>In recent time particular interest has been drawn to the inference of gene regulatory networks from microarray gene expression data. But despite major improvements with data based methods, the network reconstruction from expression data alone still presents a computationally complex (NP-hard) problem. In this work it is incorporated additional information – regulatory motifs from yeast, when inferring a gene regulatory network for mouse genes. It was put forward the hypothesis that regulatory patterns analogous to these motifs are present in the set of mouse genes and can be identified by comparing yeast and mouse genes in terms of sequence similarity or Gene Ontology (The Gene Ontology Consortium 2000) annotations.</p><p>In order to examine this hypothesis, small permutations of genes with high similarity to such yeast gene regulatory motifs were first tested against simple data-driven regulatory networks by means of consistency with the expression data. And secondly, using the best scored interactions provided by these permutations it were then inferred networks for the whole set of mouse genes.</p><p>The results showed that individual permutations of genes with a high similarity to a given yeast motif did not perform better than low scored motifs and that complete networks, which were inferred from regulatory interactions provided by permutations, did also neither show any noticeable improvement over the corresponding data-driven network nor a high consistency with the expression data at all.</p><p>It was therefore found that the hypothesis failed, i.e. neither the use of sequence similarity nor searching for identical functional annotations between mouse and yeast genes allowed to identify sets of genes that showed a high consistency with the expression data or would have allowed for an improved gene regulatory network inference.</p>

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This dissertation examines inspector discretion and industry compliance in the street-level implementation of building codes. In particular, this study examines the effects that agency-level, individual-level, and environmental variables have on the choice of inspectors to exercise discretion. Unique to this study is the examination of policy congruence between building departments and street-level inspectors as a predictor of industry compliance with regulatory policy. In addition, the various effects of building department enforcement philosophies, departmental capacity for enforced compliance, and environmental variables are considered. The findings indicate that regulatory policy implementation and impact are complex phenomena. There is no single, best predictor for determining what influences inspector behavior and industry compliance. Rather, this study shows that it is a multiplicity of factors, in concert, that shape regulatory outputs and outcomes. From this dissertation we can learn lessons that can be applied to other policy areas to create a better understanding of inspector discretion, improve industry compliance with regulations, and achieve more effective street-level implementation and understanding of policy impact.

Thesis (Ph.D.)--Boston University / PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you. / DNA sequencing techniques have evolved to the point where one can sequence millions of bases per minute, while our capacity to use this information has been left behind. One particularly notorious example is in the area of gene regulatory networks. A molecular study of gene regulation proceeds one protein at a time, requiring bench scientists months of work purifying transcription factors and performing DNA footprinting studies. Massive scale options like ChIP-Seq and microarrays are a step up, but still require considerable resources in terms of manpower and materials. While computational biologists have developed methods to predict protein function from sequence, gene locations from sequence, and even metabolic networks from sequence, the space of regulatory network reconstruction from sequence remains virtually untouched. Part of the reason comes from the fact that the components of a regulatory interaction, such as transcription factors and binding sites, are difficult to detect. The other, more prominent reason, is that there exists no "recognition code" to determine which transcription factors will bind which sites. I've created a pipeline to reconstruct regulatory networks starting from an unannotated complete genomic sequence for a prokaryotic organism. The pipeline predicts necessary information, such as gene locations and transcription factor sequences, using custom tools and third party software. The core step is to determine the likelihood of interaction between a TF and a binding site using a black box style recognition code developed by applying machine learning methods to databases of prokaryotic regulatory interactions. I show how one can use this pipeline to reconstruct the virtually unknown regulatory network of Bacillus anthracis. / 2031-01-01