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 and b are significant, then M mediates the dependent variable Y was regressed on the relationship between X and Y and is cl independent variable X and the assessed to check the link strength standardized regression coefficient (beta (Fairchild and MacKinnon, 2009; Bennett, for path c) examined to determine the size 2000; Shaver, 2005; Sharma 1981). et al, and direction of the relationship and Findings checked for significance. This beta for Correlations path c was significantly different from Service quality emerged as the feature zero and therefore in step two, the with the most profound positive influence mediator M was regressed on the on other quality drivers and on satisfaction independent variable X to estimate the and intention to recommend. Service standardized beta regression coefficient for quality had influence on product quality (r path a, which was examined to determine = 0.46, p< 0.05) and moderate correlations the size and direction of the relationship with complaints handling and ease of and was significantly different from zero. doing business as well. It had a positive In step three, Y was regressed on M to non-significant correlation with product determine the beta coefficient for path b, price. The implication of this is that which was significant. In step four, the Business customers are likely to associate dependent variable Y was regressed on X good service with superior quality of while controlling the effect of M on Y, by product and associated processes. Figure 2 performing a hierarchical regression highlights the correlations between the analysis that placed M and X in successive level of service performance and the other independent variable boxes in the SPSS attributes. program. If both coefficients for paths a, Figure 2: Correlation Coefficients- service quality and other parameters ** p< 0.01, * p< 0.05, ns: not significant, N= 81, Source: Primary Data
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
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