DATA PRESENTATION, ANALYSIS AND RESULTS
5.6 TESTING OF RESEARCH HYPOTHESES
5.6.5 Mediated indirect effects of Targeted Procurement strategies on SMC development through relationship quality
Research Objective Five sought to determine the indirect influence of Targeted Procurement strategies on SMC development as required to test Hypotheses 5a and 5b.
Hypothesis 5a: Supply chain relationship quality mediates the relationship between Targeted Procurement strategies and social indicators of construction SMC development.
Hypothesis 5b: Supply chain relationship quality mediates the relationship between Targeted Procurement strategies and economic indicators of construction SMC development.
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Mediated MR was performed by introducing relationship quality (continuous mediator variable – M) into the MR models in equations 5.2 to 5.10 to assess the indirect effect – c' of X on Y; and c - c' is the measure of the indirect effect (Imai et al. 2010). To establish that M completely mediates the X-Y relationship, c' should be (a) < c, and (b) equal to zero or not significant (Baron and Kenny, 1986; Frazier et al., 2004; James and Brett, 1984). The main effects mediation model of Equation 5.1 accounting for X and M is:
log [ Pr (Y = j | x) ] = j + j X+ j M [5.17]
It is assumed that the relationships in Equation 5.17 are causal and that M is causally located between X and Y; and that X causes M which in turn causes Y (Hayes, 2013) as depicted in the conceptual model in Figure 3.4.
The hypotheses tested in the section were aimed at developing a mediated causal model, hence, the variables of Targeted Procurement strategies were the predictor variables while variables of the two categories of SMC development were the response variables, and supply chain relationship quality was the mediator variable. Multiple MR models were first used to examine the combined effect of the six Targeted Procurement strategies and relationship quality on individual SMC development indicators, while mediated MR models were used to examine the mediated indirect effect of individual Targeted Procurement strategy on SMC development indicators, with relationship quality as the mediator. The mediation analysis procedure attempted to isolate the main effects of Targeted Procurement strategies on SMC development, and to assess whether the predictive effect of each Targeted Procurement strategy on SMC development indicators was mediated by relationship quality. Given that most contemporary scholars of mediation analysis (for example, Hayes, 2009; MacKinnon, 2008; Rucker et al., 2011; Shrout and Bolger, 2002; Zhao et al., 2010) agree that lack of correlation does not disprove causation, and correlation is neither a necessary nor a sufficient condition of causality, mediation tests is expected to be performed for all Targeted Procurement strategy – SMC development relationships irrespective of prior evidence of correlation (Hayes, 2013).
5.6.5.1 Mediated indirect effect of Targeted Procurement strategies on social indicators of SMC development through relationship quality
Hypotheses 5a states that: Relationship quality mediates the relationship between Targeted Procurement strategies and social indicators of construction SMC development. The following mediated MR models (and further mediated multiple MR models with individual Targeted Procurement strategies and relationship quality as independent variables) were fitted to
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statistically test the hypothesis and examine whether relationship quality completely mediates the Targeted Procurement strategy – SMC development relationship (see equations 5.18 to 5.22). See Appendix C5 for a typical result of mediated regression analysis conducted.
log [Pr (SDSDE = j | x)] =
j + TSPRE*+ TSTEQ* + TSARO + TSMSU* + TSUNB + TSTPM* + RQ* [5.18]
log [Pr (SDITE = j | x)] =
j + TSPRE*+ TSTEQ* + TSARO* + TSMSU + TSUNB + TSTPM* + RQ* [5.19]
log [Pr (SDSTR = j | x)] =
j + TSPRE*+ TSTEQ* + TSARO + TSMSU* + TSUNB + TSTPM* + RQ* [5.20]
log [Pr (SDACR = j | x)] =
j + TSPRE+ TSTEQ* + TSARO + TSMSU + TSUNB + TSTPM + RQ* [5.21]
log [Pr (SDJVP = j | x)] =
j + TSPRE*+ TSTEQ* + TSARO* + TSMSU* + TSUNB + TSTPM* + RQ* [5.22]
*. Correlations significant at p < .01; p < .05
Table 5.18 presents results from multiple MR models that include the relationship quality variable (c') which is compared to the model without the relationship quality variable (c). The log likelihood ratio test from the MLR models was first assessed for overall model fit among the Targeted Procurement strategies, relationship quality and social indicators of SMC development.
When skills development was entered as the dependent variable in Model 5.18 (c'), the results show that the final model significantly and positively predicted skills development [likelihood ratio 2(108) = 191.430, p < .01]. In other words, together, the Targeted Procurement strategies and relationship quality accounted for a statistically significant amount of variance in skills development acquired by the SMCs, meaning that the model fits the data. Moreover, the model accounted for approximately 73% of the variance in the outcome (R2 = .727), which is an improvement of the model over and above Model 5.2 (c). This means that the introduction of relationship quality increased the predictive effect and improved overall model fit.
Model 5.19 (c') tested the degree to which application of innovation and technology (I&T) was predicted by Targeted Procurement strategies and relationship quality, which was statistically significant and positive [2(108) = 176.204; p < .001] with a predictive effect of approximately 69% (R2 = .694), which is also an improvement of the model over and above Model 5.3 (c). Model 5.20 (c') showed that Targeted Procurement strategies and relationship quality significantly and
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positively predicted skills transfer [2(108) = 166.623; p < .001] with a predictive effect of approximately 68% (R2 = .682), which is an improvement of the model over and above Model 5.4 (c). Exhibiting a predictive effect of approximately 64% (R2 = .641), Model 5.21 (c') also indicated that Targeted Procurement strategies and relationship quality significantly and positively predicted advancement on the cidb RoC [2(108) = 153.407; p < .01], which was an improvement of the model over and above Model 5.5 (c). The predictive effect of Model 5.22 (c') (R2 = .675) implies that Targeted Procurement strategies and relationship quality accounted for approximately 68%
of the variance in predicting JV partnerships which was also statistically significant and positive [2(96) = 142.685; p < .01], and an improvement of the model over and above Model 5.6 (c). It emerged that all the relationships were statistically significant and positive, thus supporting Hypothesis 5a.
Table 5.18 further presented results from individual mediated MLR models that include the relationship quality variable (c') which is compared to the model without the relationship quality variable (c). Mediation analysis was performed only on models that were significant (p < .05) in Model c. When skills development was entered as the dependent variable, results from Model c show that accelerated rotation was a statistically significant predictor of skills development [Wald χ2(16) = 31.564, p < .05]. The mediation analysis results (c') show that accelerated rotation was no longer a statistically significant predictor of skills development after controlling for the mediator – relationship quality [χ2(16) = 23.713, p > .05], which is consistent with complete mediation. Moreover, approximately 40% of the variance in skills development was accounted for by accelerated rotation and relationship quality (R2 = .401). On the other hand, the mediation analysis for preferencing [χ2(16) = 31.107, p < .05, R2 = .437] and third-party management [χ2(16)
= 29.636, p < .05, R2 = .421] Targeted Procurement strategies on skills development show that the Targeted Procurement strategies retained a statistically significant predictive effect on skills development, which is consistent with partial mediation.
When application of I&T was entered as the dependent variable, results from Model c show that accelerated rotation was a statistically significant predictor of application of I&T [χ2(16) = 27.501, p < .05]. However, the mediation analysis results (c') show that accelerated rotation was no longer a statistically significant predictor of application of I&T after controlling for the mediator – relationship quality [χ2(16) = 19.582, p > .05], which is consistent with complete mediation.
Moreover, approximately 40% of the variance in application of I&T was accounted for by
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accelerated rotation and relationship quality (R2 = .396). Similar complete mediation results in Model c' were obtained for preferencing [χ2(16) = 22.698, p > .05, R2 = .417], tendering equity [χ2(16) = 24.991, p > .05, R2 = .408], and unbundling of contracts [χ2(16) = 25.809, p > .05, R2 = .419].
For skills transfer, results from Model c show that tendering equity was a statistically significant predictor of skills transfer [χ2(16) = 31.332, p < .05]. However, the mediation analysis results (c') show that tendering equity was no longer a statistically significant predictor of skills transfer after controlling for the mediator – relationship quality [χ2(16) = 19.430, p > .05], which is consistent with complete mediation. Moreover, approximately 33% of the variance in skills transfer was accounted for by tendering equity and relationship quality (R2 = .396). On the other hand, the mediation analysis for accelerated rotation [χ2(16) = 38.244, p < .05, R2 = .404] and unbundling of contracts [χ2(16) = 27.155, p < .05, R2 = .360] Targeted Procurement strategies on skills transfer show that the Targeted Procurement strategies retained a statistically significant predictive effect on skills transfer, which is consistent with partial mediation.
When advancement on cidb RoC was entered as the dependent variable, results from Model c show that tendering equity was a statistically significant predictor of advancement on cidb RoC [χ2(16) = 27.501, p < .05]. The mediation analysis results (c') show that tendering equity retained a statistically significant predictive effect on advancement on cidb RoC [χ2(16) = 19.582, p > .05], which is consistent with partial mediation. Moreover, 34% of the variance in advancement on cidb RoC was accounted for by tendering equity and relationship quality (R2 = .340).
For JV partnerships, results from Model c show that preferencing was a statistically significant predictor of JV partnerships [χ2(16) = 31.332, p < .05]. However, the mediation analysis results (c') show that preferencing was no longer a statistically significant predictor of JV partnerships after controlling for the mediator – relationship quality [χ2(16) = 19.430, p > .05], which is consistent with complete mediation. Moreover, approximately 33% of the variance in JV partnerships was accounted for by preferencing and relationship quality (R2 = .396). On the other hand, the mediation analysis for tendering equity [χ2(16) = 38.244, p < .05, R2 = .404] and accelerated rotation [χ2(16) = 27.155, p < .05, R2 = .360] Targeted Procurement strategies on JV partnerships show that the Targeted Procurement strategies retained a statistically significant predictive effect on JV partnerships, which is consistent with partial mediation. It emerged that
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seven the relationships were completely mediated while the other seven were partially mediated, thus further supporting Hypothesis 5a.
5.6.5.2 Mediated indirect effect of Targeted Procurement strategies on economic indicators of SMC development through relationship quality
Hypotheses 5b states that: Relationship quality mediates the relationship between Targeted Procurement strategies and economic indicators of construction SMC development. The following mediated MR models (and further mediated MR models with individual Targeted Procurement strategies and relationship quality as independent variables) were fitted to statistically test the hypothesis and examine whether relationship quality completely mediates the Targeted Procurement strategy – SMC development relationship (see equations 5.23 to 5.26). See Appendix C5 for a typical result of mediated regression analysis conducted.
log [Pr (EDTUR = j | x)] =
j + TSPRE*+ TSTEQ* + TSARO* + TSMSU + TSUNB + TSTPM + RQ [5.23]
log [Pr (EDAST = j | x)] =
j + TSPRE+ TSTEQ + TSARO + TSMSU + TSUNB + TSTPM* + RQ [5.24]
log [Pr (EDEMP = j | x)] =
j + TSPRE+ TSTEQ* + TSARO* + TSMSU + TSUNB + TSTPM + RQ [5.25]
log [Pr (EDPRO = j | x)] =
j + TSPRE+ TSTEQ* + TSARO + TSMSU + TSUNB + TSTPM + RQ [5.26]
*. Correlations significant at p < .01; p < .05
Table 5.19 presents results from multiple MLR models that include the relationship quality variable (c') which is compared to the model without the relationship quality variable (c). The log likelihood ratio test from the MLR models was first assessed for overall model fit among the Targeted Procurement strategies, relationship quality and economic indicators of SMC development. When company turnover was entered as the dependent variable in Model 5.23 (c'), the results show that the final model significantly and positively predicted turnover [likelihood ratio 2(27) = 46.465, p < .05]. In other words, together, the Targeted Procurement strategies and relationship quality accounted for a statistically significant amount of variance in company turnover, meaning that the model fits the data. Moreover, the model accounted for approximately 39% of the variance in the outcome (R2 = .389), which is an improvement of the model over and above Model 5.7 (c). This means that the introduction of relationship quality increased the predictive effect and improved overall model fit.
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Table 5.18: Mediated regression analysis of Targeted Procurement strategies, relationship quality and social indicators of SMC development
Model c Model c'
Model Dependent Variable Predictor Variable χ2 df Sig. R2Nag χ2 df Sig. R2Nag Model fit Mediation 5.18 Skills development All TPS + RQ 151.198 96 .000** .594 191.430 108 .000** .727 Yes
TSPRE(a) 39.728 16 .001** .194 31.107 16 .013* .437 Partial
RQ – – – – 58.236 12 .000**
TSARO 31.564 16 .011* .155 23.713 16 .096 .401 Complete
RQ – – – – 56.031 12 .000**
TSTPM(a) 33.997 16 .005** .167 29.636 16 .020* .421 Partial
RQ – – – – 58.386 12 .000**
5.19 Application of innovation
& technology
All TPS + RQ 137.012 96 .004** .556 176.204 108 .000** .694 Yes
TSPRE(a) 36.503 16 .002** .179 22.698 16 .122 .417 Complete
RQ – – – – 60.354 12 .000**
TSTEQ(a) 35.263 16 .004** .167 24.991 16 .070 .408 Complete
RQ – – – – 61.620 12 .000**
TSARO(a) 27.501 16 .036* .136 19.582 16 .240 .396 Complete
RQ – – – – 59.191 12 .000**
TSUNB 30.664 16 .015* .148 25.809 16 .057 .419 Complete
RQ – – – – 64.181 12 .000**
5.20 Skills transfer All TPS + RQ 132.967 96 .007** .553 166.623 108 .000** .682 Yes
TSTEQ(a) 31.332 16 .012* .150 19.430 16 .247 .332 Complete
RQ – – – – 43.468 12 .000**
TSARO 42.894 16 .000** .206 38.244 16 .001** .404 Partial
RQ – – – – 40.414 12 .000**
TSUNB 31.685 16 .011* .153 27.155 16 .040* .360 Partial
RQ – – – – 47.169 12 .000**
Advancement on cidb RoC All TPS + RQ 133.151 96 .007** .546 153.407 108 .003** .641 Yes
TSTEQ(a) 37.065 16 .002** .173 31.265 16 .012* .340 Partial
RQ – – – – 37.335 12 .000**
5.22 JV partnerships All TPS + RQ 142.685 96 .001** .605 151.023 108 .004** .675 Yes
TSPRE(a) 29.731 16 .019* .157 25.097 16 .068 .269 Complete
RQ – – – – 23.225 12 .026*
TSTEQ(a) 33.109 16 .007** .169 26.928 16 .042* .268 Partial
RQ – – – – 21.611 12 .042*
TSARO(a) 28.059 16 .031* .147 29.623 16 .020* .289 Partial
RQ – – – – 23.442 12 .024*
**. p < .01; *. p < .05; (a). Correlations significant at p < .01; p < .0
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Model 5.24 (c') tested the degree to which application of company assets (plant and equipment) was predicted by Targeted Procurement strategies and relationship quality, which was statistically significant and positive [2(27) = 43.497; p < .05] with a predictive effect of 42% (R2 = .420), which is also an improvement of the model over and above Model 5.8 (c). Model 5.25 (c') showed that Targeted Procurement strategies and relationship quality significantly and positively predicted number of employees [2(27) = 52.279; p < .01] with a predictive effect of approximately 50%
(R2 = .501), which is an improvement of the model over and above Model 5.9 (c). The predictive effect of Model 5.26 (c') (R2 = .400) implies that Targeted Procurement strategies and relationship quality accounted for 40% of the variance in predicting company profit, however it was not statistically significant [2(27) = 36.002; p > .05. It emerged that three out of four relationships were statistically significant and positive, thus partially supporting Hypothesis 5a.
Table 5.19 further presented results from individual mediated MR models that include the relationship quality variable (c') which is compared to the model without the relationship quality variable (c). Mediation analysis was performed only on models that were significant (p < .05) in Model c. When company turnover was entered as the dependent variable, results from Model c show that accelerated rotation was a statistically significant predictor of company turnover [Wald χ2(4) = 11.084, p < .05]. However, the mediation analysis results (c') show that accelerated rotation retained a statistically significant predictive effect on company turnover after controlling for the mediator – relationship quality [χ2(4) = 10.279, p > .05], which is consistent with partial mediation.
Moreover, approximately 13% of the variance in company turnover was accounted for by accelerated rotation and relationship quality (R2 = .132).
For company assets, results from Model c show that third-party management was a statistically significant predictor of company assets [χ2(4) = 10.586, p < .05]. However, the mediation analysis results (c') show that third-party management was no longer a statistically significant predictor of company assets after controlling for the mediator – relationship quality [χ2(4) = 7.377, p > .05], which is consistent with complete mediation. Moreover, approximately 16% of the variance in company assets was accounted for by third-party management and relationship quality (R2 = .162).
When company profit was entered as the dependent variable, results from Model c show that mandatory subcontracting was a statistically significant predictor of company profit [χ2(4) = 11.773, p < .05]. However, the mediation analysis results (c') show that mandatory subcontracting was no longer a statistically significant predictor of company profit after controlling for the
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mediator – relationship quality [χ2(4) = 5.279, p > .05], which is consistent with complete mediation. Moreover, approximately 12% of the variance in company profit was accounted for by mandatory subcontracting and relationship quality (R2 = .118). It emerged that two relationships were completely mediated while one was partially mediated, thus lending some support to Hypothesis 5a.