Analysis and Results. The analysis of the results of the evaluations of the component programmes has been conducted by examining the series of common themes and issues: The Intervention Logic: setting out the relationships is inevitably complex in a Programme with so many distinct elements but helps to establish their shape and to identify the main elements. The Governance of the CIP: The common governance features are reviewed in the form of the various committee structures, but also the ‘softer’ processes whereby the substance of policy is articulated and developed. While acknowledging that the formalities are carried out efficiently and effectively, steps are proposed, stemming from the discussion, to strengthen governance structures further and enable potential synergies to be identified and acted on. These are restated in the Evaluations’ recommendations. The main themes of the Programme: the measures in pursuit of the competitiveness objective are judged to be relevant and at appropriate levels. Furthermore, the Programme has been able to demonstrate an important degree of flexibility in responding to emerging issues, such as the growing importance of the management and protection of Intellectual Property Rights. In the current Programme there has been a greater focus than in earlier programmes on where EU level instruments, such as the Financial Instruments and the Enterprise Europe Network, could add the greatest value to support provided at national or more local level and this focus has benefitted SMEs. Innovation is one of the central themes in the CIP addressing a significant problem for the EU, in that Europe is not short of good ideas but has a relatively poor record in exploiting them commercially. The respective contributions of the different elements of the CIP are reviewed briefly – Europe INNOVA, PRO INNO Europe, eco-innovation and the contributions under the IEE and ICT- PSP programmes. The various evaluation reports indicate valuable, pioneering work being undertaken on strengthening the dissemination and adoption of innovatory ideas and technologies, but they also suggest that these processes should build on recent experience, project management improvements, such as a greater focus on the identification of results, should be consolidated and synergies exploited to a much greater extent both within the Programme and together with the other main programmes, especially the Structural Funds. Finally, the issue of the relative concentration on energy in the de...
Analysis and Results. Because of the limited nature of the dependent variable, I estimate all models using probit. I employ robust standard errors to control for correlated errors. Model results for the “Chamber Median Models” are presented in Tables 3.5, 3.7, 3.8, and 3.9. Results for the “Party Median Models” are presented in Tables 3.10 and 3.12. It should be noted that the variable measuring the sponsor’s membership in the majority party in his chamber is dropped from the models. Membership in the chamber minority predicts xxxx failure perfectly. That is, in the sample, there are no bills sponsored by members of the chamber minority that are successful. I cannot estimate an effect of this variable on xxxx success but the total absence of successful bills sponsored by minority members offers persuasive evidence supporting Hypothesis 6. When estimating interactive models, the ideal approach is to include all interactive CHAPTER 3. BEGINNING AT THE BEGINNING Table 3.4: Descriptive Statistics Variable Minimum Maximum Mean Standard Deviation DV Xxxx Presented to President 0 1 0.04 0.19 Distance between Congress and Supreme Court 0.01 0.40 0.17 0.13 Median Distance between Congress and Status Quo 0.00 0.33 0.11 0.06 Model Distance between Court and Status Quo 0.04 0.48 0.20 0.09 Distance between Sponsor and Chamber Median 0.00 2.94 0.76 0.51 Distance between Congress and Supreme Court 0.48 0.86 0.60 0.12 Party Potentially Constrained Congress 0 1 0.61 0.49 Median Distance between Congress and Status Quo 0.01 1.35 0.49 0.44 Model Distance between Court and Status Quo 0.03 0.90 0.48 0.20 Distance between Sponsor and Party Median 0.00 2.55 0.41 0.41 Sponsor is Member of Majority Party 0 1 0.86 0.35 Sponsor is Member of Referring Committee 0 1 0.81 0.40 All Models Sponsor is Chair of Judiciary Committee 0 1 0.15 0.35 Proportion of Originating Chamber Co–Sponsoring 0.00 0.67 0.09 0.13 Media Coverage of Xxxx Topic 0.00 3.04 2.07 0.49 President Supports 0 1 0.04 0.19 President Opposes 0 1 0.05 0.21 67 n = 390 terms and all constitutive components of each interaction term (Brambor, Clark & Golder 2006). Accordingly, I first attempt to estimate a full model that includes eight strategic variables and all control variables. The results of this model are presented in Table 3.5. I employ one–tailed tests of significance since all of my hypotheses predict directional ef- fects. The model offers little support for the separation of powers hypotheses—only one of the primary independent variable...
Analysis and Results. As already briefly described in chapter 4.1 the main objective of analysis is the assessment of the following fundamental questions: Concepts and Definitions
Analysis and Results. 4.1 General Profiles of the Firms Interviewed In the previous chapter I had illustrated the general profile of METUTECH, in terms of its firms and sectors. It was also explained that the data selection process was managed carefully, with cluster sampling, in order to get the most representative sample feasible (Chapter 3 - Table 4). Therefore, parallel to the real distribution of sectors in the METUTECH, the sectoral diversification of the firms interviewed is as in Figure 12. As it can be seen in that figure, 48.84% of the firms interviewed are “Software & ICT” firms, it is followed by “Other” cluster which I formed to include all other sectors such as telecommunication, nanotechnology and so on, where firms compose a less-than-10% percentage by themselves, with totally a 27.91% percentage and the second most important sector by itself, which corresponds to 13,95% percent of the firms interviewed, “Electronics”. The rest is the “Design” cluster, which is just around 10% (9,30%). This distribution of the sectors seemed to be important both in terms of having a representative sample in getting the general overview of METUTECH, and also in terms of looking deep into each cluster to figure out whether these sectors which have large number of firms in the technopolis have different attitudes towards of cooperation and other related subjects (four constituents: R&D and Innovation, Support, Sectoral Diversification, METUTECH Infrastructure). Figure 12. Percentages of the Firms Interviewed related to Their Sectoral Distribution First, I want to present some quantitative statistics related to all firms that I interviewed. I believe that these statistics will be beneficial, before I examine the relationship between opinions and perspectives of every sector cluster firms related to four constituents and cooperation for giving me the information about the general profile. I obtained these statistics from the quantitative questions from the “Firm Evaluation Form” Part 1. In other words, I prepared the “physical profile and statistics of the firms interviewed” based on their own answers. The general distribution graphics of these statistical is prepared with Microsoft Office - Excel 2013 program. Only 6 of the 43 firms which is interviewed in terms of trust anxieties and time constraints (the reasons explained “Chapter 3 - Methodology and Data Collection”), are in “large-scaled” group. I think in making this situation, especially, sub-departments (such as R&D departmen...
Analysis and Results. 3.1.1 The analysis that should be done on each sample is a base test for basic fertilizer recommendations: A. ph level;
