Companies and organizations must make decisions concerning capital budgeting. Capital budgeting is a decision-making process that determines whether a firm should purchase equipment to be used on a long-term basis. The initial investment in the equipment is predicted to be returned through revenue gained by the use of the equipment over its lifetime. However, there is inherent risk associated with these investment decisions. Therefore, potential purchasers must decide whether the risk involved with investing in the equipment is justified. This dissertation addresses risk-based technology assessment for capital equipment acquisition decisions in small firms. Technology assessment, here, is concerned with understanding the uncertainty associated with assessing the value predicted in the capital budgeting process. When analyzing the risk for a given technology, we assign a probability law to its net present value. Our primary research contribution is providing an analytical framework together with a computational strategy to support capital equipment budgeting in firms where the value of candidate technologies can represent nearly all the firm’s value. Since small firms typically have limited budgets, spending for technology is always a difficult budgeting decision. The organization’s administration must decide which, if any, among the available technologies will be best for their operation. The process for acquiring technology in many small firms can be filled with challenges. Most important among them is that capital budgeting is typically a “one-off” decision. These decisions are difficult since the candidate technologies may not have operational data available. Thus, decision makers need some means to predict how the proposed technology (e.g., equipment or machinery) will be used. Hence, firms should follow techniques and procedures based on appropriate normative principles and well-established theory. Senior company executives and/or governance boards are often authorized to approve capital equipment purchases. However, these company leaders may not have adequate expertise in the operations of candidate technologies or may lack the understanding necessary to determine how new technologies may impact other company operations. Appropriate financial evaluation measures and selection criteria that incorporate risk are critical to making sound, quantitative acquisition decisions. The research reported here offers an analytical framework for comparing different technology alternatives in capital budgeting decisions. Comparison is based on the expected net present value and the risk (i.e., probability law on net present value) associated with each decision alternative. To this end, the operational characteristics of each technology alternative are connected to their potential revenue and cost streams. The framework is embedded within a computational architecture that can be customized to account for operations and technologies in specific application scenarios. One major barrier addressed by this research is overcoming the fact that new technologies typically have no historical operational data. Therefore, characterizing the uncertainty of operations (e.g., distribution of the equipment lifetime) can be very difficult. Discrete- event simulation is used to generate potential revenue and cost estimates. We demonstrate the tractability and practicality of the analytical framework and computational architecture via a healthcare technology assessment decision. Data extracted from a published journal article detailing a hospital’s technology assessment decision are used to find the risk of the medical technology using the computational architecture developed. Widely-available, no-cost software tools are employed. Results of the health care example suggest that the financial analysis in the original technology assessment was in- adequate and simplistic. Small firms may find this research particularly beneficial because potential investments can be a significant portion of a small firm’s value.

The application of the 1984 Education Policy of the Division of World Outreach (DWO) of the United Church of Canada was examined in its "loosely coupled" context that is characterized by a consultative relationship based on trust in overseas partners and confidence in their choices of goals and objectives. Egon Guba's model of policy analysis, its policy-definition driven research process, and its categories of policy-in-intention and policy-in-implementation usefully assisted the appraisal of logical congruence between the goals and objectives stated in the 13 guidelines of the education policy (a policy-in-intention) and those stated in documents from a sample of supported programs (policies-in-implementation). In addition, the DWO's program-based budget practice was assessed for any relevant use of the Planning Programming Budgeting System (PBBS). Finally, a logical congruence between Guba's model and the PBBS model was probed for a possible synthesis. / Documents from 1981, 1985, and 1989 from the sample of five programs from five regions in three continents were reviewed. (Abstract shortened by UMI.)

Accurate client budget estimates are critical to the initial decision-to-build process for the highway construction projects. This decision-to-build point in a project's development is seen as the international standard for measuring any subsequent cost estimate inaccuracies involved (National Audit Office/Department of Transport, 1992; World Bank, 1994; Nijkamp and Ubbels, 1999), with accuracy being defined as the difference between the initial project estimate at the decision-to-build stage and the real, accounted project cost determined at the time of project completion. Expressed as a percentage of estimated cost, this is often termed cost escalation, cost overrun or cost growth, and occurs as a result of many factors, some of which are related to each other, but all are associated with forms of risks. The analysis of these risks is often a necessary step for the improvement of any given estimating system and can be used to diagnose trouble spots and to pinpoint areas where project estimating accuracy improvement might be obtained. In this research, highway projects in Queensland, Australia that have suffered significant cost overrun are analysed. The research seeks to address the gap in the knowledgebase as to why highway projects overrun their costs. It focuses on understanding how client projects budgets go wrong, when dealing with project risk. The foundation for this research is drawn from the post-mortem analysis of highway projects, each costing in excess of A$1m and whose final total expenditure exceeded budget by 10% or greater. The research identifies client risk variables which have contributed to significant cost overrun and then uses factor analysis and also expert elicitation, using nominal group technique, to establish groups of importance ranked client risks. Stepwise multivariate regression analysis is then used to investigate any correlation of these risks, along with project attributes such as highway project type, indexed project cost, geographic location and project delivery method to the percentage of cost overrun. The research results indicates a correlation between the reciprocal of project budget size and percentage cost overrun that can be useful in clients determining more realistic decision-to build highway budget estimates when taking into account project size in relation to economy of scale.

Thesis (Ph. D.)--Ohio University, June, 2004. / Includes bibliographical references (p. 176-180).

Thesis (Ph. D.)--University of Oregon, 2002. / Typescript. Includes vita and abstract. Includes bibliographical references (leaves 241-249). Also available for download via the World Wide Web; free to University of Oregon users.

Thesis (Ph. D.)--University of Wisconsin--Madison, 1969. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

Dissertation (Ph.D.)--Auburn University, / Abstract. Includes bibliographic references (p.145-155).

Thesis (M.Soc.Sc.)--University of Hong Kong, 1986. / Also available in print.

Thesis (M.S. in Management) Naval Postgraduate School, December 1996. / Thesis advisor(s): Larry R. Jones, Jerry L. McCaffery. "December 1996." Includes bibliographical references (p. 103-106). Also available online.

Thesis (Ph.D.)--Ohio University, June, 2004. / Title from PDF t.p. Includes bibliographical references (p. 176-180)