An Architecture Real Options Complexity-Based Valuation Methodology (ARC-VM) is developed for use to aid in the acquisition of military systems-of-systems (SoS). ARC-VM is suitable for acquisition-level decision making, where there is a stated desire for more informed tradeoffs between cost, schedule, and performance during the early phases of design. First, a framework is introduced to measure architecture complexity as it directly relates to military SoS. Development of the framework draws upon a diverse set of disciplines, including Complexity Science, software architecting, measurement theory, and utility theory. Next, a Real Options based valuation strategy is developed using techniques established for financial stock options that have recently been adapted for use in business and engineering decisions. The derived complexity measure provides architects with an objective measure of complexity that focuses on relevant complex system attributes. These attributes are related to the organization and distribution of SoS functionality and the sharing and processing of resources. The use of Real Options provides the necessary conceptual and visual framework to quantifiably and traceably combine measured architecture complexity, time-valued performance levels, as well as programmatic risks and uncertainties. An example suppression of enemy air defenses (SEAD) capability demonstrates the development and utility of the resulting architecture complexity&Real Options based valuation methodology. Different portfolios of candidate system types are used to generate an array of architecture alternatives that are then evaluated using an engagement model. This performance data is combined with both measured architecture complexity and programmatic data to assign an acquisition value to each alternative. This proves useful when selecting alternatives most likely to meet current and future capability needs.

Thesis (Ph. D.)--University of Missouri-Columbia, 2000. / Typescript. Vita. Includes bibliographical references (leaves 164-175). Also available on the Internet.

Thesis supervisor: Michael S. Lewis-Beck. Thesis supervisor: David Redlawsk. Includes bibliographic references (p. 187-195).

Thesis (Ph. D.)--University of Missouri-Columbia, 2000. / Typescript. Vita. Includes bibliographical references (leaves 164-175). Also available on the Internet.

The main objective of this study is to develop requisite models for information and communication technology (ACT) adoption and diffusion in the banking industry in Thailand. The research, combining two study areas of multiple criteria decision-making (MCDM) and system dynamics (SD), is conducted using two research methodologies: system development and a case study of the Siam Commercial Bank PCL in Thailand.The study shows how to combine the two decision-making tools of MCDM and system dynamics effectively. The requisite group models of ICT adoption and diffusion provide ways to select the most preferred technology and to allow forward planning to diffuse the adopted technology more effectively. With an embedded decision support tool, decision-makers are able to apply the models with their available information, intuition, knowledge, and experience to improve their decision-making and enhance their learning.Initially, the research revealed that the Siam Commercial Bank currently employs various types of information and communication technologies (ICT) to facilitate work processes, fulfil customers' requirements, and retain its competitive advantage. However, the bank still confronts problems relating to technology adoption and diffusion.A requisite group model of ICT adoption was developed using MCDM as a decision making tool. The model illustrated how to select the most preferable technological alternative that fulfilled the mission of the bank. Results from the MCDM analysis revealed that the preferred technology was Extranet banking followed by a data warehouse. The requisite group model of ICT diffusion was further developed using the system dynamics approach in order to enhance understanding of system behaviour of the selected technology and then provide ways to diffuse it more effectively. The model analyses were divided into three sub-models of information ++ / and communication technologies (ICT), a data warehouse, and Extranet banking.The generic model of ICT can be applied to any particular technology. Results revealed that the pattern of technology diffusion follows the S curve and the dominant variables that may impact on technology diffusion are training, a backlog of problems, and market potential. Furthermore economic returns are obtained only after spending substantially on technological investment. Thus, it is necessary to balance between technological investment and economic returns. The model of diffusion of a data warehouse was developed highlighting the necessity of quality and quantity of knowledge workers. Therefore, training support is an important factor to diffuse this technology. On the other hand, the model of diffusion of Extranet banking revealed that the success of this technology comes from the acceptance by customers. Thus, perceived relative advantages, positive features of the technology and promotional advertising should be taken into consideration. The S curve pattern of technology diffusion is also confirmed by the two technologies.The policy for technology adoption involves the selection of technology, which best fits with identified criteria. The policy analyses of the three technologies confirm that the core important policies that increase technology diffusion and economic gains are increasing positive features of technology, decreasing perceived complexity, increased perceived relative advantages and increasing co-operation between IT people and users. If technology is to support the work performance in an organisation, training support is the dominant policy, whereas if technology facilitates customers directly, marketing strategy such as promotional advertising is vital.The study implied that the banking industry in Thailand is able to use ICT as levers for competitive advantage. ++ / However, technological investment in each bank differs depending on size, objectives and readiness in terms of capital and human resources.All the findings have implications for the bank and could be applied to other banks and general policy makers in various business enterprises.

Thesis (M.A.)--Naval Postgraduate School, 2008. / Description based on title page of source document ( viewed on April 28, 2008). Includes bibliographical references (p. 137-143).

Dissertation (Ph.D.)--Auburn University, 2008. / Abstract. Vita. Includes bibliographic references (p.176-181).

Thesis (Ph. D.)--University of Missouri-Columbia, 1996. / Typescript. Vita. Includes bibliographical references (leaves 237-256). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 1996. / Typescript. Vita. Includes bibliographical references (leaves 237-256). Also available on the Internet.

Thesis (Ph. D.)--Virginia Polytechnic Institute and State University, 1991. / Vita. Abstract. Includes bibliographical references (leaves 204-214). Also available via the Internet