Thesis advisor: James Lubben / It has been shown that the effect of social networks and social support systems on health may be greater among immigrants who live away from their homeland and their natural social support systems. Despite the significance of social support systems, relevant research on minorities in the U.S. is limited. For this reason, the current cross-cultural study examined the pathways underlying different formations of social networks (kin vs. non-kin) and social support systems (emotional vs. instrumental), which affect depression symptoms and perceived general health among older Korean immigrants and non-Hispanic Caucasians in the United States. This analysis is based on the secondary data from the "Korean-American Elderly: Social Supports and Long-Term Care" study conducted in 1994. The data (n=424) were collected from non-Hispanic White Americans (n=201) and Korean immigrant elders (n=223) aged 65 and older residing in Southern California. Structural equation modeling (SEM) was used to test the proposed conceptual model designed to explain the direct and indirect relationships between social networks and social support on health outcomes. Empirical evidence from this study indicated different effect of one's social networks and social support on health by race/ethnicity. The result indicated that both kin and non-kin networks provided social support for Korean immigrants. Moreover, among social network measures, non-kin networks had a greater effect on social support for older Korean immigrants compared to kin networks. For non-Hispanic Caucasians, on the other hand, only kin network had a significant effect on social support systems. In addition, receiving instrumental support lowered the perceived general health among older non-Hispanic Caucasians. The work discussed in this paper pointed to the need to recognize the role of culture in assessing the effect of one's social networks and social support systems on health. This paper highlighted the characteristics of those older Korean-American and older non-Hispanic Caucasians who are most, as well as, least likely to benefit from social networks and social support systems. By utilizing the existing social networks and social support of diverse populations, we can improve overall health outcomes and serve the elderly community better. / Thesis (PhD) — Boston College, 2013. / Submitted to: Boston College. Graduate School of Social Work. / Discipline: Social Work.

by Tso Tze Kwong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1990. / Bibliography: leaves [121]-[123]. / Acknowledgments / Abstract --- p.A-1 / Table of Figures --- p.F-1 / Chapter Chapter 1. --- Group Factors and Their Impacts / Chapter 1.1 --- Introduction --- p.1-1 / Chapter 1.2 --- Why Group --- p.1-3 / Chapter 1.2.1 --- Effectiveness --- p.1-3 / Chapter 1.2.2 --- Efficiency --- p.1-4 / Chapter 1.2.3 --- Knowledge --- p.1-4 / Chapter 1.2.4 --- Social Bias Reduction --- p.1-5 / Chapter 1.2.5 --- Commitment --- p.1-5 / Chapter 1.2.6 --- Communications --- p.1-5 / Chapter 1.3 --- Quality of Decision --- p.1-6 / Chapter 1.4 --- Risk Taking --- p.1-8 / Chapter 1.5 --- Social Factors --- p.1-8 / Chapter 1.6 --- Problems on Groups --- p.1-9 / Chapter Chapter 2. --- Group Decision Support Systems / Chapter 2.1 --- Introduction --- p.2-1 / Chapter 2.2 --- Group Decision Making / Chapter 2.2.1 --- Definition of Decision-making Group --- p.2-2 / Chapter 2.2.2 --- An Information-Exchange View --- p.2-2 / Chapter 2.2.3 --- Group Interaction --- p.2-3 / Chapter 2.2.4 --- Group Decision Making Process --- p.2-4 / Chapter 2.2.5 --- Group Decision Making Process Model TC-l --- p.2-7 / Chapter 2.3 --- Group Decision Support System --- p.2-9 / Chapter 2.3.1 --- Current Research Trend --- p.2-9 / Chapter 2.3.2 --- Definition of GDSSs --- p.2-10 / Chapter 2.3.3 --- Comparisons of Major Features of GDSSs in Practice --- p.2-13 / Chapter 2.3.4 --- The GDSS Software Models / Chapter 2.3.4.1 --- The Software Components --- p.2-19 / Chapter 2.3.4.2 --- Mapping Group Decision Making Concepts into GDSS Model --- p.2-23 / Chapter Chapter 3. --- The GDSS Design / Chapter 3.1 --- Introduction --- p.3-1 / Chapter 3.2 --- System Overall Objectives --- p.3-2 / Chapter 3.3 --- The Assumptions --- p.3-2 / Chapter 3.4 --- System Scope / Chapter 3.4.1 --- Design Scope --- p.3-3 / Chapter 3.5 --- Objectives / Chapter 3.5.1 --- User's Perspective --- p.3-4 / Chapter 3.5.2 --- System's Perspective --- p.3-5 / Chapter 3.5.3 --- Decision Support Perspective --- p.3-7 / Chapter 3.6 --- The Conceptual Design of Our GDSS --- p.3-8 / Chapter 3.6.1 --- The Information Exchange Subsystem --- p.3-8 / Chapter 3.6.2 --- The Decision Making Subsystem --- p.3-10 / Chapter 3.6.3 --- The Communications Framework of The System --- p.3-12 / Chapter 3.7 --- The Physical Design of The System / Chapter 3.7.1 --- The Network Structure --- p.3-14 / Chapter 3.7.2 --- The Communications Flow --- p.3-16 / Chapter 3.7.3 --- The Overall System Structure / Chapter 3.7.3.1 --- The Setup Module Its Functions and Components --- p.3-17 / Chapter 3.7.3.2 --- The Monitor Module Its Functions and Logic --- p.3-19 / Chapter 3.7.3.3 --- The Private Module Its Functions and Logic --- p.3-22 / Chapter 3.7.3.4 --- The Common Module Its Functions and Logic --- p.3-24 / Chapter 3.7.4 --- The System Overall Control Logic --- p.3-26 / Chapter 3.8 --- Aids in Group Decision Making / Chapter 3.8.1 --- The Nominal Group Technique --- p.3-29 / Chapter 3.8.2 --- Decision Tree --- p.3-30 / Chapter 3.8.3 --- Multi-Attribute Utility Technique (MAU) --- p.3-32 / Chapter 3.8.4 --- Adjusted Multi-Attribute Utility Model --- p.3-35 / Chapter 3.8.5 --- Compromise Rules / Chapter a. --- Simple Majority --- p.3-38 / Chapter b. --- Borda Rule --- p.3-39 / Chapter c. --- Weighting --- p.3-40 / Chapter 3.9 --- The Information-Exchange Phase --- p.3-41 / Chapter 3.10 --- The Decision Making Phase / Chapter I --- Factors to Consider --- p.3-41 / Chapter II --- The Solution of Finalizing / Chapter Chapter 4. --- The Implementation of GDSS / Chapter 4.1 --- Introduction --- p.4-1 / Chapter 4.2 --- The Mechanism of Exchanging Information --- p.4-1 / Chapter 4.3 --- The Implementation of NGT --- p.4-2 / Chapter 4.4 --- The Forming of The Decision Structure --- p.4-3 / Chapter 4.5 --- The Finalizing of Node Details --- p.4-9 / Chapter 4.6 --- Methods in Evaluating A Final Choice --- p.4-12 / Chapter Chapter 5. --- A Practical Application / Chapter 5.1 --- Introduction --- p.5-1 / Chapter 5.2 --- Background --- p.5-1 / Chapter 5.3 --- Objective --- p.5-2 / Chapter 5.4 --- Decision Analysis Rationale --- p.5-3 / Chapter 5.5 --- The Decision Tree --- p.5-4 / Chapter 5.6 --- Decision Making Process --- p.5-8 / Chapter 5.7 --- The Feedback on Use of The System --- p.5-10 / Chapter Chapter 6. --- Conclusion / Chapter 6.1 --- Introduction --- p.6-1 / Chapter 6.2 --- System Feedback --- p.6-2 / Chapter 6.3 --- The Practical Means of The System --- p.6-5 / Chapter 6.4 --- The Limitation of The System --- p.6-6 / Chapter 6.5 --- The Future Perspective of The System --- p.6-6 / References --- p.ref-1

by Cheung Wai-Lam, William. / Thesis (M.B.A.)--Chinese University of Hong Kong, 1992. / Includes bibliographical references (leaves 93-96). / Chapter chapter 1: --- introduction / Chapter 1.1 --- INTRODUCTION --- p.1 / Chapter 1.2 --- OBJECTIVES AND SCOPE --- p.2 / Chapter 1.3 --- STRUCTURE OF REPORT --- p.3 / Chapter 1.4 --- CHAPTER SUMMARY --- p.5 / Chapter chapter 2: --- decision support systems (dss) / Chapter 2.1 --- THE DECISION MAKING PROCESS --- p.6 / Chapter 2.2 --- DEFINITION OF DSS --- p.8 / Chapter 2.3 --- STRUCTURE OF DSS --- p.12 / Chapter 2.3.1 --- Users --- p.12 / Chapter 2.3.2 --- Database --- p.12 / Chapter 2.3.3 --- Model Base --- p.14 / Chapter 2.4 --- CHAPTER SUMMARY --- p.15 / Chapter chapter 3: --- dss for stock evaluation / Chapter 3.1 --- STOCK VALUATION: CAPM vs APT --- p.16 / Chapter 3.2 --- DSS FOR STOCK INVESTMENT --- p.21 / Chapter 3.3 --- THE PROPOSED STOCK EVALUATION DSS --- p.23 / Chapter 3.4 --- CHAPTER SUMMARY --- p.26 / Chapter chapter 4: --- analyticheerarchy process (ahp) / Chapter 4.1 --- WHAT IS AHP --- p.27 / Chapter 4.2 --- AN EXAMPLE: PLANT LOCATION SELECTION --- p.27 / Chapter 4.3 --- COMPUTATION PROCESS OF AHP / Chapter 4.3.1 --- Notations --- p.35 / Chapter 4.3.2 --- Principal Eigenvector --- p.35 / Chapter 4.3.3 --- Eigenvalue --- p.36 / Chapter 4.3.4 --- Consistency Ratio --- p.36 / Chapter 4.4 --- CHAPTER SUMMARY --- p.37 / Chapter chapter 5: --- an ahp model for stock evaluation / Chapter 5.1 --- ALTERNATIVES FOR STOCK EVALUATION --- p.39 / Chapter 5.2 --- THE AHP MODEL FOR STOCK SELECTION --- p.41 / Chapter 5.3 --- EXPLANATIONS AND JUSTIFICATIONS FOR PROPOSED HIERARCHY --- p.43 / Chapter 5.3.1 --- Level1 --- p.45 / Chapter 5.3.2 --- Level2 --- p.45 / Chapter 5.3.3 --- Level3 --- p.46 / Chapter 5.3.4 --- Level4 --- p.48 / Chapter 5.3.5 --- Level5 --- p.49 / Chapter 5.3.6 --- Level6 --- p.60 / Chapter 5.4 --- CHAPTER SUMMARY --- p.61 / Chapter chapter 6: --- the development of iris: a prototype / Chapter 6.1 --- SYSTEM FLOWCHART --- p.63 / Chapter 6.2 --- PROGRAM SPECIFICATION --- p.65 / Chapter 6.2.1 --- File Maintenance Module --- p.65 / Chapter 6.2.2 --- Hierarchy Setup --- p.65 / Chapter 6.2.3 --- Eigenvector Computation --- p.67 / Chapter 6.2.4 --- Overall Weight Computation --- p.67 / Chapter 6.3 --- PROTOTYPE OPERATION --- p.67 / Chapter 6.4 --- CHAPTER SUMMARY --- p.79 / Chapter chapter 7: --- user evaluationof model and prototype / Chapter 7.1 --- METHODOLOGY OF EVALUATION --- p.82 / Chapter 7.1.1 --- Participants --- p.82 / Chapter 7.1.2 --- Stock Candidates --- p.83 / Chapter 7.1.3 --- Stock Data --- p.83 / Chapter 7.1.4 --- Process of Model and Prototype Evaluation --- p.84 / Chapter 7.2 --- FINDINGS --- p.85 / Chapter 7.2.1 --- Structure the Stock Evaluation Process --- p.86 / Chapter 7.2.2 --- Time-consuming --- p.87 / Chapter 7.2.3 --- The Consistency Ratio --- p.87 / Chapter 7.2.4 --- Reconsideration of Factors --- p.87 / Chapter 7.2.5 --- Precise Amount Available --- p.88 / Chapter 7.2.6 --- Users Forced to Considered All Factors --- p.88 / Chapter 7.3 --- CONCLUSION OF EVALUATION --- p.89 / Chapter 7.4 --- CHAPTER SUMMARY --- p.90 / Chapter chapter 8: --- summary and conclusion / Chapter 8.1 --- REPORT SUMMARY --- p.91 / Chapter 8.2 --- CONCLUSION --- p.91 / references --- p.93 / appendix --- p.97

by Liu Shu Cheung, Jimmy. / Thesis (M.Ph.)--Chinese University of Hong Kong, 1988. / Bibliography: leaves 61-64.

archives@tulane.edu / Multicultural psychologists have discussed the growing concern over ecological validity in understanding children’s mental health: insufficient participatory efforts to incorporate marginalized perspectives have led to a subsequent lack of inclusive, culturally sensitive definitions of support and support systems. The phenomenology around experiences, beliefs and perceptions is an integral component in describing support and support systems, where the composition and content of support systems depend on context and culture. Using archival qualitative research data from focus groups and ecomaps conducted with elementary school-aged children across 9 of the 14 international sites in the Promoting Psychological Well¬-Being Globally (PPWBG) project (Nastasi & Borja, 2016), the present research used children’s voices in identifying contextually-relevant sources of support, as well as patterns across 9 of the 14 sites. The present research was a part of a multi-method study, serving as a secondary analysis to triangulate (across two methods) previously analyzed data elicited from the focus groups (Borja et al., 2016) and ecomap tool (Borja et al., 2017). The goal of triangulation was to examine for consistent themes related to support and enhance the credibility of the combined tools’ ability to elicit data around support. The triangulation yielded 33 codes related to systems of support for psychological well-being¬. A thematic analysis condensed the codes into 5 broad themes: Financial/Material Support, Emotional Support, Social Support, Recreational Activities and Academic Support. These themes were salient across the nine sites. Ultimately, the five themes were either identified as being possibly global or context-specific, which may be useful in future research to inform practice and instrument development. / 1 / Sydney A. Wing

An Engineering Decision Support System, EDSS, was developed using Bayesian mathematics which incorporates knowledge and confidence components observed in alternative-criterion pair decision making. The separation of knowledge and confidence has been previously unaccounted for in decision-making methods. EDSS provides decision support to individuals or teams which must make choices between alternatives using alternative-criterion pair evaluations. Further, EDSS was instanciated into computer software. The EDSS decision support system was statistically tested using two variables, mechanical experience of the participants and the use of a decision method, at two different levels and in a replicated factorial experiment. The experiment consisted of teams of subjects solving a simple mechanical design problem. Data from the experiment was collected for eighteen different metrics in four categories. This research reports on each of eighteen metrics using the hypothesis that the use of EDSS will show improvements in, or positive impact on, the following four categories: the decision making productivity of idea processing, the decision-making process, the perception of the decisions made by the decision makers, and the ease of use of a computer decision support tool. Statistical results of the experiment showed that EDSS successfully matched ad-hoc and Pugh's decision matrix performance for sixteen of the eighteen metrics and statistically exceeded the remaining two. These two metrics are, the conduction of more evaluations of alternative-criterion pairs ,and increased problem understanding. This research also shows that a new alternative-criterion pair evaluation method has been successfully created that provides for: - A separation of knowledge and confidence in the Belief Model of decision making. - Decision results without complete evaluation of all alternative-criterion pairs. - Aggregation of preferences from team members. - A convenient means for investigating decision improvements. / Graduation date: 1997

Thesis (Ph.D.)--University of Delaware, 2006. / Principal faculty advisors: Sandra M. Carberry and Keith S. Decker, Dept. of Computer & Information Sciences. Includes bibliographical references.

Thesis (Ph.D.)--Kent State University, 2005. / Title from PDF t.p. (viewed Feb. 23, 2005). Advisor: Acar, William. Available online via OhioLINK's ETD Center. Keywords: strategy making; causal mapping; multi-agent systems; knowledge management; dialectical inquiry Includes bibliographical references (p. 111-116).

Thesis Erasmus University Rotterdam. / ook verschenen in gedrukte versie. With bibliogr., with a summary in Dutch.

Thesis (M. Phil.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 163-177).