Figure 1: Mediation model path diagram Reference for mediation paths: Fairchild and MacKinnon (2009), A General Model for Testing Mediation and Moderation Effects. Prevention Science, 10:87-99. In step one of mediation testing, the dependent variable Y was regressed on the independent variable X and the standardized regression coefficient (beta for path c) examined to determine the size and direction of the relationship and checked for significance. This beta for path c was significantly different from zero and therefore in step two, the mediator M was regressed on the independent variable X to estimate the standardized beta regression coefficient for path a, which was examined to determine the size and direction of the relationship and was significantly different from zero. In step three, Y was regressed on M to determine the beta coefficient for path b, which was significant. In step four, the dependent variable Y was regressed on X while controlling the effect of M on Y, by performing a hierarchical regression analysis that placed M and X in successive independent variable boxes in the SPSS program. If both coefficients for paths a, and b are significant, then M mediates the relationship between X and Y and cl is assessed to check the link strength (Fairchild and MacKinnon, 2009; Bennett, 2000; Shaver, 2005; Sharma et al, 1981). Findings Correlations Service quality emerged as the feature with the most profound positive influence on other quality drivers and on satisfaction and intention to recommend. Service quality had influence on product quality (r = 0.46, p< 0.05) and moderate correlations with complaints handling and ease of doing business as well. It had a positive non-significant correlation with product price. The implication of this is that Business customers are likely to associate good service with superior quality of product and associated processes. Figure 2 highlights the correlations between the level of service performance and the other attributes. Figure 2: Correlation Coefficients- service quality and other parameters ** p< 0.01, * p< 0.05, ns: not significant, N= 81, Source: Primary Data
Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change dimension0 1 .131a .017 .013 .74965 .017 4.391 1 251 .037 a. Predictors: (Constant), Days of the month Source: Research data The coefficient of determination (R2) in table equals 0.017. This shows that days of the month explain 1.7 percent of the total variation of Daily return of stock index leaving only 98.3 percent unexplained. The P- value of 0.037 implies that the model of Daily return of stock index is not significant at the 5 percent significance level. Table 7: ANOVA Model Sum of Squares Df Mean Square F Sig. 1 Regression 2.468 1 2.468 4.391 .037a Residual 141.057 251 .562 Total 143.525 252 a. Predictors: (Constant), Days of the month b. Dependent Variable: Daily return of stock index The P- value of 0.37 implies that there is no linear regression relationship between days of the month and the Daily return of stock index at 5 percent significance level. Table 8: Year 2005 Model summary Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change dimension0 1 .015a .000 -.004 .54664 .000 .057 1 246 .811 a. Predictors: (Constant), Days of the month The coefficient of determination (R2) equals 0.000. This shows that days of the month do not explain any variation of Daily return of stock index but determined by other factors. The P- value of 0.811 implies that the model of Daily return of stock index is not significant at the 5 percent significance level. Table 9: ANOVA Model Sum of Squares df Mean Square F Sig. 1 Regression .017 1 .017 .057 .811a Residual 73.507 246 .299
Total 73.524 247 a. Predictors: (Constant), Days of the month b. Dependent Variable: Daily return of stock index Source: Research data The P- value of 0.811 implies that there is no linear regression relationship between days of the month and the Daily return of stock index at 5 percent significance level. Table 10: Year 2006 Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change dimension0 1 .000a .000 -.004 20.17648 .000 .000 1 244 .999 a. Predictors: (Constant), Days of the month Source: Research data The coefficient of determination (R2) equals 0.000. This shows that days of the month do not explain any variation of Daily return of the stock index. The P- value of 0.999 implies that the model of Daily return of stock index is insignificant at the 5 percent significance level. Table 11: ANOVA Model Sum of Squares df Mean Square F Sig. 1 Regression .001 1 .001 .000 .999a Residual 99330.000 244 407.090 Total 99330.002 245 a. Predictors: (Constant), Days of the month b. Dependent Variable: Daily return of stock index Source: Research data Table 12: Year 2007 Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change dimension0 1 .040a .002 -.002 1.01766 .002 .398 1 246 .529 a. Predictors: (Constant), Days of the month Source: Research data The coefficient of determination (R2) equals 0.002. This shows that days of the month only explain 0.2% of the total variation of Daily return of the stock index leaving 99.8% unexplained. The P- value of 0.529 implies that the model of Daily
The behaviors assessed during teaching practice were categorized into six attributes. These are lesson preparation, lesson presentation, lesson development, classroom management, summary/conclusion and personal factors. There is no significant difference in the performance of students in both modes of learning in Preparation of the lesson (p=.618), Presentation of the lesson (p=.508), Classroom management (p=.141) and summary and conclusions (p=606). However, on-campus performed significantly higher in lesson development (p=.000) while ODE performed significantly higher in personal factors (p=.036. Table 1.6: Output for the Mean performance in each attribute Teaching practice attributes Mode of learning N Mean Std. deviation Std. Error Mean t df Sig. Overall TP mean performance Distance learning On-campus learning 50 131 69.88 71.73 4.77 4.60 .675 .401 -2.392 179 .018 Preparation Distance learning On-campus learning 50 131 11.54 11.37 1.88 2.17 .265 .189 .499 179 .618 Presentation Distance learning On-campus learning 50 131 10.74 10.60 1.12 1.37 .159 .120 .664 179 .508 Lesson Development Distance learning On-campus learning 50 131 26.16 28.34 3.06 2.35 .433 .205 -5.128 179 .000 Classroom management Distance learning On-campus learning 50 131 6.72 6.96 .97 .99 .137 .086 -1.480 179 .141 Summary and conclusion Distance learning On-campus learning 50 131 6.60 6.69 .78 1.20 .111 .105 -.519 179 .606 Personal factors Distance learning On-campus learning 50 131 8.10 7.82 .93 .75 .132 .066 2.115 179 .036
Formation (GFCF) Tanzania 640 953 1813 1229 1706 Tanzania’s FDI as % of GFCF 15.3 - 24.6 15.6 17 Source: UNCTAD (2013). Table : FDI stock in Kenya and Tanzania (selected years, in US$ million) Item 1995 2009 2010 2011 2012 Kenya 732 2104 2282 2617 2876 Kenya’s FDI stock as % of GDP 6.3 - 7.1 7.7 7.0 Tanzania 620 8066 8762 9278 10984 Tanzania’s FDI stock as % of GDP 10.2 - 37.1 38.1 38.2 Source: UNCTAD (2013). UNCTAD (2005) had argued that Kenya’s inability to attract FDI is a result of corruption, poor governance, inconsistencies in economic policies and structural reforms, deteriorating public service and poor infrastructure all of which are being addressed. Despite massive efforts by the government to implement reforms such as trade reforms, the country continues to loose its competitiveness for FDI to Uganda and Tanzania. However, Sims (2013) indicates that inflows of FDI to Kenya could match those of Tanzania and Uganda beginning 2014 aided by opportunities created by the discovery of oil deposits in Turkana. The FDI inflows are projected to average US $ 1.3 billion annually for the period 2013-2018 , placing it at par with Tanzania and Uganda who had over the years attracted more investors due to their vast natural resources such as gas , oil and other minerals. The UNCTAD (2011) figures show that Uganda and Tanzania have overtaken Kenya in terms of growth rates due to their rising FDI inflows , but Kenya is still the regional business leader ,is it that FDI is a key factor in driving economic growth or are other factors equally important ?. Some of these shortcomings have been recognized by the government and it has sought to amend the Investment Promotion Act by making investment certificates optional for all investors .The special incentives remain conditional upon holding a certificate, though the minimum capital requirement to qualify for one would be lowered to US$ 100,000 for foreign investors and US $ 13,000 for national investors. From the year 2000, the Kenya government has implemented a number of initiatives to improve both economic performance and stimulate foreign direct investments. The government joined the Free Trade Area of the Common Market
United Nations Conference on Trade and Development (1998), “World Investment Report“, New York, United Nations Publication United Nations Conference on Trade and Development (2005), “An Investment Guide to Kenya”, Opportunities and Conditions, New York, United Nations Publication. World Bank (1996), “World Debt Tables: External Finance for Developing Countries”, Vol. 1 (Analysis and Summary Tables) Washington D.C., The World Bank World Bank (2000), “World Business Environment Survey”, Washington D.C., World Bank. World bank (2012). Connecting to Compete: Trade Logistics in the Global Economy, Washington D.C. World Investment Report (2006), “FDI from Developing and Transition Economies Implications for Development”, U.N.