<p>The financial well-being of individuals depends on the appropriateness of their investment decisions. As the world is moving from defined benefit (DB) pension plans to defined contribution (DC) pension plans, the burden of making the right investment decisions has now shifted to employees. One implicit assumption about this global trend is that plan participants are capable of making sound decisions themselves. However, several studies have demonstrated that people often fall prey to various psychological biases and make flawed investment decisions.</p> <p>The use of computers in support of decision-making is not new. For several decades, the finance and investment sectors have extensively used different computing methods and technologies collectively known as decision support systems (DSS) to support investors. However, the type of support provided by such DSS has primarily been quantitative in nature. As such, existing investment DSS do not assist decision makers in overcoming the impact of their psychological biases, which have been shown to play a critical role in investment decision-making. The overall objective of this research is to investigate the potential for building a human-centered investment DSS that can provide qualitative support to investors.</p> <p>In this research, a theoretical framework of investment decision-making is proposed by using the psychological concepts of beliefs, preferences and attitudes. Major investment-related biases are identified and a taxonomy is suggested to classify them as cognitive, affective, and conative.</p> <p>An empirical study involving 119 subjects was conducted to verify the impact of cognitive biases in investment decision-making and to assess the effectiveness of decision aids in lowering the impact of such biases on the ability of investors to make sound investment decisions. In this study, feedback and graphical aids were provided as cognitive support in six investment decision-making tasks involving framing, representativeness, and ambiguity biases. A large majority of subjects exhibited the influence of these biases in their investment decision-making. Cognitive support was found to significantly improve asset allocation decisions for most subjects. Demographic variables collected during the experiment enabled several analyses leading to some additional interesting observations. Findings from this study indicate the usefulness of personalization in investment DSS.</p> <p>This research culminated with a vision toward the development of a human-centered investment DSS that may provide qualitative support to its users. Different philosophical inquiring systems were described as potential debiasing strategies for the proposed DSS. An architecture was suggested for implementing such a DSS with a detailed example illustrating the feasibility of the proposed system. The dissertation concludes with an outline of potential contributions of this study and directions for future research in this area.</p> / Doctor of Philosophy (PhD)

<p>A polling system is a cyclic queueing model with multiple customer classes and a single server. Each customer class has its own queue (station). After the server switches to a station, it serves customers waiting at that station according to a specified service regime, e.g., exhaustive, gated or globally gated. It then moves to the next station, following a strict cyclic order. These models have several application areas including computer and communication networks and multi-item production systems. For example, a Local Area Network (LAN) can be modeled as a polling system by defining the central processing unit as the server and the data transmission requests from each terminal as customers. Similarly, a multi-item production system can be modeled as a polling system by considering the flexible machining cell as the server and each product type as a different customer class. In most systems that polling models are used to represent, the server requires time to switch and/or setup before it may start serving a different customer class. These processes (switching/setup) may take considerable amounts of time, and when that happens, it is undesirable to setup for a product type if there are no (or only a few) jobs of that type in the system. Therefore, a server scheduling policy that ignores system state information can easily lead to suboptimal performance.</p> <p>Whereas most previous studies on polling models have assumed that the server behaves independently of the state of the system, we discuss two kinds of state-dependent server scheduling rules: i) the threshold setups model, and ii) the threshold start-up model. In the former model, the server does not setup (and does not serve any customers) at a station at which it finds less than a critical number of waiting customers, called the setup threshold. In the latter model, the server starts idling each time the system becomes empty, and it stays idle until arrivals to the system reach a critical number, called the start-up threshold. The server then resumes service from the station where it had stopped. Our analysis makes it possible to compare system performance under these state-dependent server scheduling rules and state-independent rules.</p> <p>In this dissertation the following results are achieved. We develop an exact analysis for the one-threshold setup model with two stations, and an efficient approximation for the same model with any number of stations. For the general threshold setups model, we construct a numerical solution technique which is near-exact for calculating queue length distributions and station mean waiting times. The threshold start-up model is analyzed in detail, and mathematically exact expressions for man station waiting times are obtained for both exhaustive and globally gated service regimes. For each model, the extension to the gated service regime is also discussed.</p> / Doctor of Philosophy (PhD)

<p>A value is the concept of the desirable. If those who develop information systems and those who use information systems have different values, the value gap can lead to serious consequences. Two methods are developed and tested in this thesis to measure the value systems of developers, users and other participants in the information system development process: one based on a personal value questionaire (PVQ) and the other based on multi-attribute value theory (MAVT).</p> <p>A framework for information systems (IS) relevant values was developed to enumerate and organize the values to be measured into three groups: economic, technical and socio-political-psychological.</p> <p>There were 86 values enumerated, which were used in the PVQ method to survey 132 system developers and 47 47 system users in 13 firms. Developers and users were found to have similar values with respect to economic and technological items but sharply differing values in the socio-political-psychological group.</p> <p>A condensed set of items consisting of two economic, five technological and four socio-political items, were organized in a hierarchical struture, for the MAVT method. Preference conditions were assessed and values were measured using the rating scale approach. Three subjects were interviewed in depth; two produced additive multi-attribute value functions, while one produced a multiplicative function</p> / Doctor of Philosophy (PhD)

<p>Several methods have been proposed and tested for calculating lower bounds on the objective function of facilities problems. These methods contribute to the efficiency of iterative solution methods by allowing the user to terminate the computation process when the objective function comes within a predetermined fraction of the optimal solution. Two of the existing bounding methods have been presented only for single facility location models with Euclidean (straight-line) distances. One of these methods uses the dual of the single facility location model to compute a lower bound. This thesis introduces a method for generating a feasible dual solution from any primal solution by means of a projection matrix. The projection matrix method is applied to single and multi-facility models. The second bounding method, which involves the solution of a rectilinear distance model to obtain a lower bound, is extended in this thesis to include a generalized, distance function and the multi-facility situation. Computation results for the two new bounding methods are compared with several existing bounding methods. These results should aid practitioners in selecting an appropriate bounding method for an iterative solution method to a facilities location problem.</p> / Doctor of Philosophy (PhD)

<p>In this dissertation, we look at two families of distance predicting functions, the ℓkpθ norm family and the block norm family, that can be used for modelling actual distances. To compare the distance-predicting accuracy of the two families, an empirical study is conducted. Two types of regions, which are large geographic regions and urban centres, are used in the study. In the large geographic regions, actual distances between cities in each region are modelled, whereas in the urban centres, actual distances within a city are modelled. To evaluate the accuracy of the different norms, two goodness-of-fit criteria are employed. For the block norms, new procedures that determine global solutions for the criteria are developed. Normality assumptions regarding the individual terms of the criteria are examined since these terms are used to formulate statistical tests that are sensitive to departures from normality. A new criterion for evaluating the accuracy of a distance predicting function is developed. This criterion, unlike the other two criteria, is impartial both to short actual distances and to long actual distances. The criterion is used to evaluate the accuracy of the ℓkpθ norm for the geographic regions that were used in the empirical study. A statistical analysis of the errors from this new criterion leads to the development of confidence intervals for unknown distances in any geographic region. The results from this thesis will help an analyst select an appropriate distance predicting function to model actual distances in any application. Further, a new measure for evaluating distance predicting functions may be considered by an analyst, and confidence intervals for unknown distances can be easily constructed.</p> / Doctor of Philosophy (PhD)

Management of Social Security Administration (SSA) has been challenged by the phenomena of overpayments with its Supplemental Security Income (SSI) program. Oversight authorities of SSA had expressed concerns about the overpayment abuse caused by parents as representative payees for disabled minor children. It was important to address this problem because the amount of overpayments prior to 1998 had caused integrity issues for management of SSA and increased federal debt from SSI claimants. The purpose of this study was to examine how management of SSA and the SSI program had responded to the concerns of the oversight authorities. Theoretical foundations for this study were based on systems concepts and theories as basic rationale for the examination of feedback from systems operation and for organizational learning about the phenomena of overpayments. This study employed a case study methodology using an approach that examined the SSI program and its operational procedures. The questions for this study involved inquiries into processes and procedures that were developed for improving payment accuracy of SSI claims. Data analysis was performed by comparing the change in overpayment amounts to the implementation of different technological and operational procedures for the SSI program over time using time series analysis. The results of this study suggested that technology that allows sharing communications between federal and state governmental organizations improved overpayment detection and collection for the SSI program. This sharing provided positive social change that could have implications for improved operational efficiency in many other social service programs in the United States.

U.S. private entities considering adoption of International Standards for Small- and Medium-sized Entities (IFRS for SMEs) need to understand how the new standards will modify financial reporting. However, there has been no determination of the significance of the financial statement impact of changing from United States Generally Accepted Accounting Principles (U.S. GAAP) to IFRS for SMEs. Without this knowledge, private entities in the United States will not be able to make an informed decision as to the benefits or consequences of adopting IFRS for SMEs. Based on stakeholder theory, this study sought to determine how adoption of IFRS for SMEs would affect the financial reporting of U.S. private entities. Using identified reporting differences between the 2 sets of standards, hypothetical 2010 IFRS for SMEs' financial statements were prepared for 3 participating entities. Analysis of variation between the hypothetical IFRS for SMEs' financial statements and the original U.S. GAAP financial statements provided a means to determine the financial reporting impact of IFRS for SMEs' adoption. In each of the 3 case studies, adoption of IFRS for SMEs did not significantly influence the financial reporting of U.S private entities, indicating that the communication of financial information would be fundamentally the same using the simplified IFRS for SMEs or the more complex U.S. GAAP. The results of this study suggest that IFRS for SMEs should be considered an acceptable set of standards for the preparation of quality financial statements by U.S. private entities. This study positively contributes to social change by providing new knowledge to assist private companies in the evaluation of the adoption of IFRS for SMEs; such knowledge could, in turn, reduce financial reporting costs and improve the SMEs' economic conditions.

Local retail jewelry leaders of Saudi Arabian (S.A.) small to medium enterprises (SMEs) have struggled to survive through declining profits and increasing business foreclosures, thus threatening the sustainability of the Saudi retail sector and the Saudi economy. A globalization strategy to enhance profitability for jewelry retail SMEs in S.A. is needed, given the limited options for improving profitability. Despite this acknowledged need, leaders in S.A. have refrained from such a strategy because they lack knowledge of economic attraction features to target in the globalization process. The purpose of this quantitative correlational study using discriminant analysis was to examine specific countries' economic attraction features in the historical globalization strategy of a leading U.S. global jewelry company that could facilitate the implementation of a successful globalization strategy for a local Saudi jewelry SME retail company. The study addressed the effects of 6 independent predictor variables of 25 target countries' economic attractions on the dependent grouping variable, which distinguished among 3 order-of-entry groups according to the U.S. company's date of entry in each country between 1972 and 2009. Results indicated that except for the Hofstede index, no other variable had a significant role in the classification of the target countries. Because there was a scarcity of research on this topic, the study is beneficial for its theoretical and academic value, and may be practical for the derivative benefits of catalyzing business growth by empowering leaders of local, successful luxury brands in S.A. to implement their own globalization expansion process and increase employment in the Middle East.

Managers at automotive manufacturers are seeking ways to reduce energy consumption, costs, carbon emissions, and waste from production processes. Researchers and practitioners perceive energy efficiency as the least expensive and most effective way to deal with issues related to climate change, but adoption of energy efficiency measures has been slow among industrial facilities. The topic of this research study was the decision-making process for energy efficiency projects in the U.S. automotive manufacturing industry. Flaws in this decision-making processes are preventing changes that can dramatically reduce energy usage, cost, and pollution. The study was grounded in the theories of energy management, organizational learning, systems thinking, and strategic management. Data is from open-ended question interviews and questionnaires of 21 decision makers in automotive manufacturing companies in the United States about their perception and experiences regarding the decision-making process for energy efficiency projects. The data were coded to identify themes. The findings indicated that organizational leaders with responsibility over energy management should include energy management standards and frameworks such as ISO 50001, Six Sigma DMAIC, and Energy Star as guidelines for selecting energy efficiency projects. Decision makers may find these results useful in improving their decision-making processes for evaluating energy efficiency projects. This research has the potential to promote positive social change in the automotive industry by reducing energy consumption and business costs, and it could benefit communities by reducing pollution through increasing energy efficiency in the automotive manufacturing industries.

Universities must retain satisfied employees to enhance productivity and reduce turnover. Leadership represents one of the fundamental factors in job satisfaction. The purpose of this correlational study was to examine the relationship between perceived academic administrator leadership styles and the satisfaction of faculty members. The independent variables were the transformational, transactional, and passive/avoidant leadership styles of academic administrators as evaluated by faculty members. The dependent variable was job satisfaction of full-time faculty members. The Multifactor Leadership Questionnaire was used to identify the leadership style of an administrator as perceived by faculty members. Spector's Job Satisfaction Survey was used to assess a faculty member's level of job satisfaction. One hundred four participants from a state university in Florida completed the online survey. A logistic regression model was developed, and the statistically significant correlations indicated that (a) faculty members who identified transformational leadership as dominant had increased job satisfaction, (b) faculty members who identified transactional leadership as dominant had increased job satisfaction, and (c) faculty members who identified passive/avoidant leadership as dominant had decreased job satisfaction. Based on a 95% significance level, there was a significant relationship between the 3 leadership styles and job satisfaction. Using this model, academic leaders can take further action by refining their leadership styles on the basis of their faculty members' indicated preferences. The study results may contribute to social change by making academic administrators aware of effective leadership models that promote higher job satisfaction among faculty in universities.