The majority of the research conducted on consumer choice phenomena focuses on how choices are made and the processes that lead up to those choices. While these are essential aspects within the breadth of choice knowledge that exists today, little research has been conducted on the options that are rejected during this process. Thus, the overarching goal of this dissertation is gain an understanding of consumer choice processes and outcomes through the lens of a nearly chosen alternative. Specifically, this dissertation investigates how the decision process can cause a close second option to be rejected when the chosen option is found to be unavailable.As a means of achieving these goals, I first demonstrate the phenomenon that consumers do not always select a close second option when the first choice option is unavailable, contrary to the prediction of economic rationality. Next, I propose that the decision process itself, specifically the use of a tie-breaking attribute to differentiate between close options, triggers a choice outcome that does not include the original second choice option, but rather an alternative that possesses this tie-breaking attribute. Finally, I examine the implications that the preference reversal phenomenon described above has for retailers and manufacturers.My original interest in this phenomenon stems from anecdotal evidence provided by a variety of informants. Although this evidence helped me to recognize the prevalence of rejected second choice options, experimental design is used to investigate this phenomenon and the boundary conditions that confine this effect. Consequently, my dissertation consists of 6 experiments. Experiment 1 and a pilot study establish the effect and investigate the theoretical process that account for my findings. Experiments 2 through 4 rule out alternative explanations and add support towards the existence and prevalence of the effect. Finally, Experiments 5 and 6 explore the impact of these results for improving the performance of marketing managers. It is my belief that incorporating the dynamic effects of the second-most preferred option may ultimately lead to more accurate and sophisticated prediction of buyer choices, more effective retailing and personal selling strategies, and more profitable management of product line portfolios.

Draft and proof of an algorithm computing incremental changes within a labeled layouted concept lattice upon insertion or removal of an attribute column in the underlying formal context. Furthermore some implementational details and mathematical background knowledge are presented. / Entwurf und Beweis eines Algorithmus zur Berechnung inkrementeller Änderungen in einem beschrifteten dargestellten Begriffsverband beim Einfügen oder Entfernen einer Merkmalsspalte im zugrundeliegenden formalen Kontext. Weiterhin sind einige Details zur Implementation sowie zum mathematischen Hintergrundwissen dargestellt.

Encouraging the use of computer algorithms by developing new algorithms and introducing uncommonly known algorithms for use on environmental science problems is a significant contribution, as it provides knowledge discovery tools to extract new aspects of results and draw new insights, additional to those from general statistical methods. Conducting analysis with appropriately chosen methods, in terms of quality of performance and results, computation time, flexibility and applicability to data of various natures, will help decision making in the policy development and management process for environmental studies. This thesis has three fundamental aims and motivations. Firstly, to develop a flexibly applicable attribute selection method, Tree Node Selection (TNS), and a decision tree assessment tool, Tree Node Selection for assessing decision tree structure (TNS-A), both of which use decision trees pre-generated by the widely used C4.5 decision tree algorithm as their information source, to identify important attributes from data. TNS helps the cost effective and efficient data collection and policy making process by selecting fewer, but important, attributes, and TNS-A provides a tool to assess the decision tree structure to extract information on the relationship of attributes and decisions. Secondly, to introduce the use of new, theoretical or unknown computer algorithms, such as the K-Maximum Subarray Algorithm (K-MSA) and Ant-Miner, by adjusting and maximizing their applicability and practicality to assess environmental science problems to bring new insights. Additionally, the unique advanced statistical and mathematical method, Singular Spectrum Analysis (SSA), is demonstrated as a data pre-processing method to help improve C4.5 results on noisy measurements. Thirdly, to promote, encourage and motivate environmental scientists to use ideas and methods developed in this thesis. The methods were tested with benchmark data and various real environmental science problems: sea container contamination, the Weed Risk Assessment model and weed spatial analysis for New Zealand Biosecurity, air pollution, climate and health, and defoliation imagery. The outcome of this thesis will be to introduce the concept and technique of data mining, a process of knowledge discovery from databases, to environmental science researchers in New Zealand and overseas by collaborating on future research to achieve, together with future policy and management, to maintain and sustain a healthy environment to live in.

This thesis is centered around the problem of attribute learning -- using the joint effort of humans and machines to describe objects, e.g., determining that a piece of music is "soothing," that the bird in an image "has a red beak", or that Ernest Hemingway is an "Nobel Prize winning author." In this thesis, we present new methods for solving the attribute-learning problem using the joint effort of the crowd and machines via human computation games. When creating a human computation system, typically two design objectives need to be simultaneously satisfied. The first objective is human-centric -- the task prescribed by the system must be intuitive, appealing and easy to accomplish for human workers. The second objective is task-centric -- the system must actually perform the task at hand. These two goals are often at odds with each other, especially in the casual game setting. This thesis shows that human computation games can accomplish both the human-centric and task-centric objectives, if we first design for humans, then devise machine learning algorithms to work around the limitations of human workers and complement their abilities in order to jointly accomplish the task of learning attributes. We demonstrate the effectiveness of our approach in three concrete problem settings: music tagging, bird image classification and noun phrase categorization. Contributions of this thesis include a framework for attribute learning, two new game mechanisms, experiments showing the effectiveness of the hybrid human and machine computation approach for learning attributes in vocabulary-rich settings and under the constraints of knowledge limitations, as well as deployed games played by tens of thousands of people, generating large datasets for machine learning.

This thesis describes the design and implementation of the IPE, an interactive proof editor for first-order intuitionistic predicate calculus, developed at the University of Edinburgh during 1983-1986, by the author together with John Cartmell and Tatsuya Hagino. The IPE uses an attribute grammar to maintain the state of its proof tree as a context-sensitive structure. The interface allows free movement through the proof structure, and encourages a "proof-byexperimentation" approach, since no proof step is irrevocable. We describe how the IPE's proof rules can be derived from natural deduction rules for first-order intuitionistic logic, how these proof rules are encoded as an attribute grammar, and how the interface is constructed on top of the grammar. Further facilities for the manipulation of the IPE's proof structures are presented, including a notion of IPE-tactic for their automatic construction. We also describe an extension of the IPE to enable the construction and use of simply-structured collections of axioms and results, the main provision here being an interactive "theory browser" which looks for facts which match a selected problem.

Encouraging the use of computer algorithms by developing new algorithms and introducing uncommonly known algorithms for use on environmental science problems is a significant contribution, as it provides knowledge discovery tools to extract new aspects of results and draw new insights, additional to those from general statistical methods. Conducting analysis with appropriately chosen methods, in terms of quality of performance and results, computation time, flexibility and applicability to data of various natures, will help decision making in the policy development and management process for environmental studies. This thesis has three fundamental aims and motivations. Firstly, to develop a flexibly applicable attribute selection method, Tree Node Selection (TNS), and a decision tree assessment tool, Tree Node Selection for assessing decision tree structure (TNS-A), both of which use decision trees pre-generated by the widely used C4.5 decision tree algorithm as their information source, to identify important attributes from data. TNS helps the cost effective and efficient data collection and policy making process by selecting fewer, but important, attributes, and TNS-A provides a tool to assess the decision tree structure to extract information on the relationship of attributes and decisions. Secondly, to introduce the use of new, theoretical or unknown computer algorithms, such as the K-Maximum Subarray Algorithm (K-MSA) and Ant-Miner, by adjusting and maximizing their applicability and practicality to assess environmental science problems to bring new insights. Additionally, the unique advanced statistical and mathematical method, Singular Spectrum Analysis (SSA), is demonstrated as a data pre-processing method to help improve C4.5 results on noisy measurements. Thirdly, to promote, encourage and motivate environmental scientists to use ideas and methods developed in this thesis. The methods were tested with benchmark data and various real environmental science problems: sea container contamination, the Weed Risk Assessment model and weed spatial analysis for New Zealand Biosecurity, air pollution, climate and health, and defoliation imagery. The outcome of this thesis will be to introduce the concept and technique of data mining, a process of knowledge discovery from databases, to environmental science researchers in New Zealand and overseas by collaborating on future research to achieve, together with future policy and management, to maintain and sustain a healthy environment to live in.

Thesis (M.S.)--University of Missouri-Columbia, 2007. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on November 9, 2007) Includes bibliographical references.

abstract: This dissertation is focused on building scalable Attribute Based Security Systems (ABSS), including efficient and privacy-preserving attribute based encryption schemes and applications to group communications and cloud computing. First of all, a Constant Ciphertext Policy Attribute Based Encryption (CCP-ABE) is proposed. Existing Attribute Based Encryption (ABE) schemes usually incur large, linearly increasing ciphertext. The proposed CCP-ABE dramatically reduces the ciphertext to small, constant size. This is the first existing ABE scheme that achieves constant ciphertext size. Also, the proposed CCP-ABE scheme is fully collusion-resistant such that users can not combine their attributes to elevate their decryption capacity. Next step, efficient ABE schemes are applied to construct optimal group communication schemes and broadcast encryption schemes. An attribute based Optimal Group Key (OGK) management scheme that attains communication-storage optimality without collusion vulnerability is presented. Then, a novel broadcast encryption model: Attribute Based Broadcast Encryption (ABBE) is introduced, which exploits the many-to-many nature of attributes to dramatically reduce the storage complexity from linear to logarithm and enable expressive attribute based access policies. The privacy issues are also considered and addressed in ABSS. Firstly, a hidden policy based ABE schemes is proposed to protect receivers' privacy by hiding the access policy. Secondly,a new concept: Gradual Identity Exposure (GIE) is introduced to address the restrictions of hidden policy based ABE schemes. GIE's approach is to reveal the receivers' information gradually by allowing ciphertext recipients to decrypt the message using their possessed attributes one-by-one. If the receiver does not possess one attribute in this procedure, the rest of attributes are still hidden. Compared to hidden-policy based solutions, GIE provides significant performance improvement in terms of reducing both computation and communication overhead. Last but not least, ABSS are incorporated into the mobile cloud computing scenarios. In the proposed secure mobile cloud data management framework, the light weight mobile devices can securely outsource expensive ABE operations and data storage to untrusted cloud service providers. The reported scheme includes two components: (1) a Cloud-Assisted Attribute-Based Encryption/Decryption (CA-ABE) scheme and (2) An Attribute-Based Data Storage (ABDS) scheme that achieves information theoretical optimality. / Dissertation/Thesis / Ph.D. Computer Science 2011

The thesis "Attribute in Spanish language" is aimed at the analysis of the word patterns that are used to build an attribute. The target of the thesis is to define attributes as a part of a sentence and describe every particular kind of them. This analysis is based on the study of linguistic materials coming from both, Czech and Spanish sources. First, the theoretical part will be focused on the unification of the terminology. It will also be attempted to connect these two very different language systems in one. Further, the attribute will be described in detail from various formal points of view. In the practical part the findins from the theoretical part will be applied to analyze some text taken from Spanish periodicals. The results of analysis will be stated in the third part of the thesis, conclusion. The graphical illustration of the chosen results is would also be presented in the conclusion. Résumé in Spanish is also part of this thesis.

A cross-sectional design was utilized to analyze data from 762 U.S. beef consumers surveyed in May 2015. The objective of the survey was to obtain an understanding of how consumers in the Millennial, X, and Boom Generations value beef credence labels with regard to level of importance and willingness to pay (WTP). The survey was created using LimeSurvey, and pilot tested at the following three universities: Southern Illinois University, Iowa State University, and Tarleton State University prior to submitting it to the C & T Marketing group across the United States in May 2015. The survey also included the following components: generational differences in beef consumption, other animal protein source consumption and sources of information utilized regarding beef. Demographics collected were used to separate respondents into the following generational categories: Millennial (18-33 years old), X (34-54 years old), and Boom (55-72 years old), and consumers represented 42 states of the U.S. plus the District of Columbia. Twelve credence labels were statistically different (P < 0.05) in their levels of importance based on generation cohort. Specifically, some credence labels significantly important to the Boom generation compared to X or Millennial generation were: Raised in the USA (P < 0.001), Product of the USA (P < 0.001) and Raised without Antibiotics (P < 0.001). However, Millennials reported higher averages (P < 0.001) in their WTP for credence attributes that contained the word “organic” in some way. Even though these labels showed significance, results indicated that respondents would be willing to pay below the current market value ($10.39) for a 12 ounce Choice Beef Ribeye Steak. Furthermore, Millennials are consuming the most beef at home among the three generations with consumption at more than two to four times per week. Beef is consumed more often that poultry, pork and seafood in a restaurant. Overall, beef consumers are primarily using online resources to obtain beef information, however; consumers still value information gathered from peer interaction, beef farmers, and butchers. Beef consumers from different generations have varying opinions on what beef credence labels are important to them, and what price they are willing to pay for those labels. However, this study would suggest organic beef is important to beef consumers, but they are not willing to pay for that particular credence label.