Thesis (Ph. D.)--Rutgers University, 2008. / "Graduate Program in Operations Research." Includes bibliographical references (p. 83-88).

There is limited engagement with research based evidence among senior managers within the NHS, and a failure to consistently integrate research findings into the decision making process. While much is known about the decision making and information behaviour of clinical staff and policy makers, there is little knowledge of this for senior non-clinical managers within the NHS. There is also a lack of clarity on how best to facilitate and integrate research evidence into the decision making process and a lack of research regarding the context of non-clinical healthcare managers working in the NHS. This study addresses these shortcomings through in-depth analysis in a case study approach. Data was collected through semi-structured interview, questionnaire and observed scenario work. This data was analysed to increase knowledge and understanding of the current information behaviour and decision making practices of non-medical senior management staff working within the NHS in England. Several key barriers to research utilisation were identified in the analysis of the data. These barriers included insufficient information literacy skills in the cohort, shortcomings with the published research papers, a culture which was focused on achieving politically set targets, and a lack of defined processes to decision making. To address these barriers an embedded librarian and a SharePoint based knowledge management system were implemented and evaluated. Evaluation of these interventions concluded that an embedded librarian was effective in increasing and supporting evidence informed decision making. This provides a practical example of an effective service development which should be considered for implementation across the NHS and wider healthcare community. The study also recommends that, to increase use of their output, researchers need to include more explicit information on the implementation and financial elements of their findings rather than a narrow focus on the intervention outcome. In addition, findings showed the target driven culture of the NHS create an environment that stifles evidence informed decision making. To address this barrier the study recommends that the NHS adopts methods of quality assurance and metrics which place an emphasis on measures of process. This study contributes to theory by exploring the information behaviour of a specific group which have been overlooked in previous research, and contributes new understanding of mechanisms for knowledge translation and interactions between the research based evidence and decision making processes in the context of NHS non-clinical management.

Investment in Research and Development (R&D) is necessary for innovation, allowing an organization to maintain a competitive edge. The U.S. Federal Government invests billions of dollars, primarily in basic research technologies to help fill the pipeline for other organizations to take the technology into commercialization. However, it is not about just investing in innovation, it is about converting that research into application. A cursory review of the research proposal evaluation criteria suggests that there is little to no emphasis placed on the transfer of research results. This effort is motivated by a need to move research into application. One segment that is facing technology challenges is the energy sector. Historically, the electric grid has been stable and predictable; therefore, there were no immediate drivers to innovate. However, an aging infrastructure, integration of renewable energy, and aggressive energy efficiency targets are motivating the need for research and to put promising results into application. Many technologies exist or are in development but the rate at which they are being adopted is slow. The goal of this research is to develop a decision model that can be used to identify the technology transfer potential of a research proposal. An organization can use the model to select the proposals whose research outcomes are more likely to move into application. The model begins to close the chasm between research and application -- otherwise known as the "valley of death." A comprehensive literature review was conducted to understand when the idea of technology application or transfer should begin. Next, the attributes that are necessary for successful technology transfer were identified. The emphasis of successful technology transfer occurs when there is a productive relationship between the researchers and the technology recipient. A hierarchical decision model, along with desirability curves, was used to understand the complexities of the researcher and recipient relationship, specific to technology transfer. In this research, the evaluation criteria of several research organizations were assessed to understand the extent to which the success attributes that were identified in literature were considered when reviewing research proposals. While some of the organizations included a few of the success attributes, none of the organizations considered all of the attributes. In addition, none of the organizations quantified the value of the success attributes. The effectiveness of the model relies extensively on expert judgments to complete the model validation and quantification. Subject matter experts ranging from senior executives with extensive experience in technology transfer to principal research investigators from national labs, universities, utilities, and non-profit research organizations were used to ensure a comprehensive and cross-functional validation and quantification of the decision model. The quantified model was validated using a case study involving demand response (DR) technology proposals in the Pacific Northwest. The DR technologies were selected based on their potential to solve some of the region's most prevalent issues. In addition, several sensitivity scenarios were developed to test the model's response to extreme case scenarios, impact of perturbations in expert responses, and if it can be applied to other than demand response technologies. In other words, is the model technology agnostic? In addition, the flexibility of the model to be used as a tool for communicating which success attributes in a research proposal are deficient and need strengthening and how improvements would increase the overall technology transfer score were assessed. The low scoring success attributes in the case study proposals (e.g. project meetings, etc.) were clearly identified as the areas to be improved for increasing the technology transfer score. As a communication tool, the model could help a research organization identify areas they could bolster to improve their overall technology transfer score. Similarly, the technology recipient could use the results to identify areas that need to be reinforced, as the research is ongoing. The research objective is to develop a decision model resulting in a technology transfer score that can be used to assess the technology transfer potential of a research proposal. The technology transfer score can be used by an organization in the development of a research portfolio. An organization's growth, in a highly competitive global market, hinges on superior R&D performance and the ability to apply the results. The energy sector is no different. While there is sufficient research being done to address the issues facing the utility industry, the rate at which technologies are adopted is lagging. The technology transfer score has the potential to increase the success of crossing the chasm to successful application by helping an organization make informed and deliberate decisions about their research portfolio.