Practicing CPA, vol. 20 no. 12, December 1996

https://egrove.olemiss.edu/aicpa_news/2683/thumbnail.jp

Employee Volunteer and Employer Benefits From Business-Education Partnerships as Perceived by Employee Volunteers

The U.S. is losing global competitiveness in its institutions, higher education, and the casino industry. An industry’s competitiveness depends on its ability to produce a highly skilled workforce, and higher education plays a key role in preparing students with skills critical to workplace success. Business and education entities form partnerships to use employee volunteerism (EV) as a student skill gaps solution and as a corporate social responsibility (CSR) strategy. Currently, education entities lack a systematic approach to measure and communicate the benefits of EV to their business partners. Without accountability, education entities may risk the long-term support of business partners. Seven research objectives were established for this study to determine employee volunteer and employer benefits from business-education partnerships (BEPs), as perceived by employee volunteers (EVs). The study used a cross-sectional, descriptive nonexperimental, ex post facto research design and a 30-question researcher-designed survey instrument to collect descriptive quantitative and qualitative data in a mixed mode of online and paper survey distribution. The study population was a finite population of 106 employee volunteers (EVs) of iPASS®, the BEP between Mississippi casino industry partners and The University of Southern Mississippi. Data was analyzed using the Phillips ROI Methodology Chain of Impact Logic Model™ levels of evaluation. Study results revealed majority of the employee volunteers are college graduates, between 30-49 years old, holding entry to mid-level management positions. Majority of the EVs have no prior work experience in other jurisdictions and averaged 14.5 years of industry experience. Employee volunteers primarily served as face-to-face presenters but iPASS® roles are trending towards online guest presentations and volunteers are taking on more diverse roles and activities. Employee volunteers spent more time annually in adjunct instruction and the least time in career placement networking. About half of the EVs participate in iPASS® because they were approached by Southern Miss and one out of three were approached by their employer. The volunteers perceived EV in iPASS® as worthwhile investment for their employers and themselves. The knowledge, skills, and abilities (KSAs) gained and most applied to EV jobs are communication, leadership and interpersonal. Over half of the EVs perceived volunteerism in iPASS® most positively influences corporate image in the local community, employer attractiveness to potential employees, corporate image in the industry and corporate image to the Mississippi Gaming Commission. Employee volunteers perceived employer attractiveness to potential employees to be most directly linked to EV in iPASS®. An ROI forecast is recommended based on the job contribution of improved KSAs to address limitations of no access to financial and proprietary data. The study recommends forming a taskforce to identify missed opportunities, and to establish a formal evaluation plan and reporting standards to develop EV into a competitive CSR strategy for business partners. Recommendations for research include replicating the study to measure employee volunteerism in other gaming jurisdictions, in hospitality and tourism, and other undergraduate programs for comparison study purposes

An investigation into inductive parameter learning in complex hierarchical knowledge structures representing clinical expertise

This dissertation investigates the very important and current problem of modelling human expertise. This is an apparent issue in any computer system emulating human decision making. It is prominent in Clinical Decision Support Systems (CDSS) due to the complexity of the induction process and the vast number of parameters in most cases. Other issues such as human error and missing or incomplete data present further challenges. In this thesis, the Galatean Risk Screening Tool (GRiST) is used as an example of modelling clinical expertise and parameter elicitation. The tool is a mental health clinical record management system with a top layer of decision support capabilities. It is currently being deployed by several NHS mental health trusts across the UK. The aim of the research is to investigate the problem of parameter elicitation by inducing them from real clinical data rather than from the human experts who provided the decision model. The induced parameters provide an insight into both the data relationships and how experts make decisions themselves. The outcomes help further understand human decision making and, in particular, help GRiST provide more accurate emulations of risk judgements. Although the algorithms and methods presented in this dissertation are applied to GRiST, they can be adopted for other human knowledge engineering domains

Cost-effective asset management planning for the sustainable future of rural irrigation systems

Irrigation systems in developing countries often have low performance and poor sustainability. The asset management planning (AMP) model that has been developed in this project enables Water User Associations (WUAs) in rural Indonesia to manage the assets of a transferred irrigation system in an improved cost-effective way towards sustainability goals. The AMP was developed by building on existing processes and including elements of internationally developed assest management sytems and also with upgraded physical and management processes

An investigation into inductive parameter learning in complex hierarchical knowledge structures representing clinical expertise

This dissertation investigates the very important and current problem of modelling human expertise. This is an apparent issue in any computer system emulating human decision making. It is prominent in Clinical Decision Support Systems (CDSS) due to the complexity of the induction process and the vast number of parameters in most cases. Other issues such as human error and missing or incomplete data present further challenges. In this thesis, the Galatean Risk Screening Tool (GRiST) is used as an example of modelling clinical expertise and parameter elicitation. The tool is a mental health clinical record management system with a top layer of decision support capabilities. It is currently being deployed by several NHS mental health trusts across the UK. The aim of the research is to investigate the problem of parameter elicitation by inducing them from real clinical data rather than from the human experts who provided the decision model. The induced parameters provide an insight into both the data relationships and how experts make decisions themselves. The outcomes help further understand human decision making and, in particular, help GRiST provide more accurate emulations of risk judgements. Although the algorithms and methods presented in this dissertation are applied to GRiST, they can be adopted for other human knowledge engineering domains.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

An investigation into inductive parameter learning in complex hierarchical knowledge structures representing clinical expertise

This dissertation investigates the very important and current problem of modelling human expertise. This is an apparent issue in any computer system emulating human decision making. It is prominent in Clinical Decision Support Systems (CDSS) due to the complexity of the induction process and the vast number of parameters in most cases. Other issues such as human error and missing or incomplete data present further challenges. In this thesis, the Galatean Risk Screening Tool (GRiST) is used as an example of modelling clinical expertise and parameter elicitation. The tool is a mental health clinical record management system with a top layer of decision support capabilities. It is currently being deployed by several NHS mental health trusts across the UK. The aim of the research is to investigate the problem of parameter elicitation by inducing them from real clinical data rather than from the human experts who provided the decision model. The induced parameters provide an insight into both the data relationships and how experts make decisions themselves. The outcomes help further understand human decision making and, in particular, help GRiST provide more accurate emulations of risk judgements. Although the algorithms and methods presented in this dissertation are applied to GRiST, they can be adopted for other human knowledge engineering domains.EThOS - Electronic Theses Online ServiceGBUnited Kingdo