A Measurement System for Science and Engineering Research Center Performance Evaluation

Gibson, Elizabeth

doi:10.15760/etd.3276

Cited by 5 publications

(29 citation statements)

References 20 publications

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“…As noted by Kocaoglu [250], and as per precedent established by other studies [245], [263], [264], [266], [268], the inconsistency level should not be higher than 10%, in order to be taken as acceptable. Should the inconsistency level exceed the 10% mark, a more careful consideration should be taken, e.g., the most inconsistent experts should be asked to repeat the judgments, and in extreme cases the most inconsistent judgments could be deleted from the analysis [268]. Where: ̅ = mean of the normalized relative value of the variable i for the j th orientation = normalized relative value of the variable i for the j th orientation in n factorial orientations…”

Section: Inconsistency Analysismentioning

confidence: 85%

“…Chan argues that inconsistencies in experts' judgments are common in AHP-based studies [266]. Following the same reasoning, Gibson states that one should expect inconsistency to occur when experts face multiple decisions and have to judge items [268].…”

Section: Inconsistency Analysismentioning

confidence: 99%

“…Since dealing with a large number of experts augments the process complexity exponentially, it has been argued that the maximum amount of experts per panel should be 12 [260]. Leveraging the work done in past dissertations [245], [263], [264], [266]- [268], it is safe to say that expert panels composed of six to twelve experts each are reliable and at the same time manageable.…”

Section: Rd Step -Experts Panel Formationmentioning

confidence: 99%

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A Scoring Model to Assess Organizations' Technology Transfer Capabilities: The Case of a Power Utility in the Northwest USA

Lavoie¹

2000

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This research intends to advance knowledge in the technology management field, most importantly in the study of organizations that develop technologies inhouse and wish to enhance their technology transfer performance while maintaining adherence between R&D activities and overall business strategies. The objective was to build a multi-criteria decision-making model capable of producing a technology transfer score, which can be used by practitioners in order to assess and later improve their organizations' technology transfer capabilities-ultimately aiming to improve technology development as a whole. The model was applied to a major power utility organization in the Pacific Northwest of the United States. The introduction brings initial and basic information on the topic, along with the problem statement-this chapter is aimed at situating the reader on the boundaries of the topic while highlighting its importance within the technology management field of study. The second chapter is the literature review. It brings general and specific information on technology transfer, as well as its complexities, gaps, relationship with other fields and the characteristics of this topic within the energy realm. It also tries to shed a light on how the alignment between R&D and business strategy is perceived by the literature, discussing some of the methods used and its shortcomings. Additionally, the literature review brings an analysis that builds the argument in favor of a continuous technology transfer process, and tries to show how it would be helpful in vi TABLE OF CONTENTS

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Section: Inconsistency Analysismentioning

confidence: 85%

Section: Inconsistency Analysismentioning

confidence: 99%

Section: Rd Step -Experts Panel Formationmentioning

confidence: 99%

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A Scoring Model to Assess Organizations' Technology Transfer Capabilities: The Case of a Power Utility in the Northwest USA

Lavoie¹

2000

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“…Complex decision-making problems need strong expert panels when quantitative data are not available [240] [250]. Figure 12 shows how experts are selected to validate the decision model content and construct; the process is adapted and developed from Chitu (2004) [250].…”

Section: Expert Selection Proceduresmentioning

confidence: 99%

“…A good model or framework is usually tested by qualified experts [301]. Dissemination of the final model is essential [240]. Therefore, the survey instrument (see Appendix C-2) was designed and distributed to several experts with experience in the field of construction management and project delivery.…”

Section: Criterion-related Validitymentioning

confidence: 99%

Evaluating Project Assessment Techniques for High-Profile Transportation Projects Development and Delivery: Case of State Departments of Transportation (DOTs) in the United States

Khalifa¹

2000

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Time delays and cost overruns in construction projects are generally due to factors such as inappropriate planning, design errors, unexpected site conditions, inadvisable tools selection, change scope, weather conditions, lack of resources, and other project changes.Time delays and cost overruns are of concern to most project managers, owners, and governments. These elements of time and cost are two of the critical defects that impact the construction project delivery. These defects can lead to project failures and to various negative issues like increasing in disagreements among the project team, the contractor, suppliers, and the owner.State Departments of Transportation (DOTs) in the U.S. continue to spend heavily on roads, highways, and bridges construction, as well as development, maintenance, and expansion. This continued financial commitment reflects decades of commitment to improve the transportation service for safer and better use by the general public. Despite the notable efforts from most of the states to develop the transportation infrastructure, budget restrictions and lack of funds are some of the major challenges faced by DOTs.Highway and bridge infrastructure has a high potential growth in the U.S. construction market. Well-organized highway and bridge project investment decision-making becomes increasingly crucial in the transportation sector. In this research, transportation is specified to highway and bridge projects, and it focused on high-profile projects. These projects are ii defined as high-cost projects, and are associated with higher project delivery risk (typically $100 -$500 million).All states are working with their state transportation plan, listing the projects based on each state priorities and population growth. Proper planning leads to the right decision regarding selecting the best alternative within budget, and it must reflect certain core principles, including a comprehensive analysis. To facilitate such a decision process, decision makers need a trusted decision model that considers all important options and impacts. By using a decision model, decision-making will not be subjectively influenced to favor one option or group. The decision model becomes the primary tool for selecting the best option, based on its structure levels, perspectives, sub-criteria, and experts' input.Recently, there is an apparent need for a decision model to help DOTs evaluating their options. Effective project delivery assessment tools, techniques, or practices are strongly needed to improve transportation construction projects' performance.The research objective is to develop a comprehensive decision model that can be used by project managers and their teams to choose the most effective project assessment technique for measuring the success of performance and outcomes related to the delivery of transportation projects. This research was focused on the assessment techniques that are used in the development phase within the transportation project lifecycle phases.To this end, the research identified ...

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Development of a Readiness Assessment Model for Evaluating Big Data Projects: Case Study of Smart City in Oregon, USA

Barham¹

2000

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Big data projects are facing an alarmingly high percentage of failure, with severe consequences related to the cost of this type of projects, the waste of resources and efforts in doing it, and the competitive disadvantage caused by the lack of big data analytics capabilities in comparison with competitors. Furthermore, there is a decent amount of research on the main challenges facing big data projects. However, there is a lack of research on how to leverage on this knowledge to evaluate an organization's readiness for a big data project, more specifically, how to systematically assess an organization's current status against known reasons that might cause a big data project to fail. Hence, identifying shortcomings that need to be addressed before the project start, to reduce vi TABLE OF CONTENTS

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A Measurement System for Science and Engineering Research Center Performance Evaluation

Cited by 5 publications

References 20 publications

A Scoring Model to Assess Organizations' Technology Transfer Capabilities: The Case of a Power Utility in the Northwest USA

A Scoring Model to Assess Organizations' Technology Transfer Capabilities: The Case of a Power Utility in the Northwest USA

Evaluating Project Assessment Techniques for High-Profile Transportation Projects Development and Delivery: Case of State Departments of Transportation (DOTs) in the United States

Development of a Readiness Assessment Model for Evaluating Big Data Projects: Case Study of Smart City in Oregon, USA

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