Essays on supplier responsiveness and buyer firm value - Nyenrode ...

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3.2.3 Normality of Data Table 3.3 presents the results for the normality of the distribution of the variables in our model based on the Dutch data. As recommended by Baumgartner and Homburg (1996), we first conducted tests of normality on our data. We found that two variables in our data SIQ and RP did exhibit skewness. The criteria we used to assess for skewness and kurtosis was whether the skewness or kurtosis ratio to its respective standard error was less than 2 (Bachman, 2004, p. 74). We transformed SIQ and RP variables by taking their square root computed by SPSS 17, under the compute new variable tab. The result was that the new computed variables SIQ and RP were normally distributed. Table 3.3 Kurtosis and Skewness N Mean Std. Dev Skewness Kurtosis Stat Stat Stat Stat Std. Error Ratio 1** Stat Std. Error Ratio 2** SIQ 92 2.27 0.77 0.51 0.25 2.04 0.92 0.50 1.86 CustNPD 92 2.35 0.84 0.38 0.25 1.52 0.20 0.50 0.41 CF 92 2.49 0.76 -0.04 0.25 -0.15 -0.31 0.50 -0.62 RP 92 2.11 0.91 0.77 0.25 3.08 0.85 0.50 1.71 Fairness 92 2.30 0.77 0.46 0.25 1.84 0.87 0.50 1.75 SALES 92 2.30 0.82 0.35 0.25 1.39 0.35 0.50 0.69 IM 92 3.53 0.88 -0.20 0.25 -0.79 0.28 0.50 0.56 CCC 92 3.51 0.83 -0.15 0.25 -0.61 0.67 0.50 1.35 AssBrand 92 2.27 0.80 0.26 0.25 1.02 0.40 0.50 0.80 BV 92 2.33 0.70 0.01 0.25 0.05 -0.22 0.50 -0.44 SIQ (***) 92 1.48 0.26 -0.08 0.25 -0.33 0.26 0.50 0.51 RP (***) 92 1.41 0.39 0.22 0.25 0.88 -0.39 0.48 -0.62 ***SIQ and RP. SIQ and RP are transformed by taking the square root of SIQ and RP in order to correct for the skewness of both variables. **The Ratio 1 column represents the skewness statistic to its standard error ratio. The ratio 2 column represents the kurtosis statistic to its standard error ratio. Each ratio value should be less than 2 in absolute value. 3.2.4 SEM We estimated two SEM models in our analysis for comparison with the model in chapter 2. The reason we estimated two models is because of the rule that a 10 respondents are required per measured item in a SEM model (Hair et al., 2006, p. 604). Our sample size of 92 respondents and 10 observed variables did not let us meet that ratio. Our second model in this chapter labeled Model 2 consists of the variables supplier responsiveness, IdRR, supplier brand value, and buyer firm value. Our third model in this chapter labeled Model 3 consists of the variables supplier responsiveness, buyer satisfaction, and buyer firm value. For a large model with 10 observed variables we normally should have about 150-200 respondents and recommended of 10 respondents per item (Hair et al, 2006, p. 604). The two models (2 and 3) fulfill the criteria of 10 respondents per item. In the rest of this section we 86
will discuss the SEM analysis of these two models. We present the models’ goodness of fit statistics and hypotheses results in Table 3.4. 3.2.4.1 Model 1 Model 1 is a replica of the model in Chapter 2. However, as it does not fulfill the criteria of 10 respondents per variable we have tested it in two smaller models called models 2 and 3. Therefore we do no discuss Model 1 in further detail in this chapter. For details of the fit statistics and hypotheses tests please refer to Table 3.4 of this chapter. 3.2.4.2 Model 2 We have one exogenous construct, which is supplier responsiveness. We have three endogenous constructs, IdRR, supplier brand value, and buyer firm value. We then tested for unidimensionality. Thus the current model has a total of 8 items. Moreover, our results indicated that our factors load on more than one factor, and therefore, unidimensionality is not a concern in the current study. In the next stage of our research we determined the input matrix type and estimated the proposed model. We chose the covariance matrix because it is a true test of theory (Hair et al., 2006 p.603). The correlation matrix is not suited for theory testing as it only represents the pattern of relationships. We used the maximum likelihood estimation method and our sample size of 92 was slightly below the recommended 100-150 (Hair et al., 2006, p 605). In AMOS, we also selected covariance supplied and to be analyzed as the maximum likelihood option, and we selected the option random permutations. Further, we estimated our structural model in AMOS. Model identification is measured by the difference between the number of distinct sample moments (36) and the distinct parameters to be estimated (16). The resulting degrees of freedom are 20, indicating that the model has been identified. We achieved a good model fit and identification with 9 items. Our model exceeds the minimum GFI limit of > 0.9 with a value of 0.945, and this is better than the Chapter 2 model with 0.939. The RMSEA values are acceptable between .05 and .08, with an upper threshold limit of 0.1(Hair et al., 2006, p.634). By this standard, our current model is good with a RMSEA of 0.040, and it is better than Chapter 2 structural model’s RMSEA of 0.072 3 . The second category of fit indices are incremental fit measures. Our model has a Tucker- Lewis index (TLI) value of 0.981 and is more than the minimum acceptable level of 0.9, whereas the model of Chapter 2 had a value of 0.930. The last incremental fit index we use is the comparative fit index (CFI) and it is specifically meant for samples with small sizes (Hair et al., 2006). We surpass the minimum level of 0.9 with our model score of 0.987, slightly better than the Chapter 2 value of 0.953. Moreover, the model has a χ 2 /degrees of freedom ratio of 1.14 and that is considered good. Since model 2 meets the criteria of the goodness of fit measures we accept the measurement model. The fit statistics for model 2 overall were very good. Hypothesis 1 is significant. It indicates also that supplier responsiveness is indeed an antecedent to IdRR and that both the constructs are negatively related. The β= -0.993, and this is similar to the β= -0.980 in the model in Chapter 2. This indicates a strong negative effect of supplier responsiveness on IdRR. This finding supports our hypothesis that supplier responsiveness negatively influences IdRR, 3 Please look at Table 3.4 for Chapter 2 structural model fit indices. 87
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ISBN 978-90-8980-055-8 © M. Abrahi
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Examination Committee Supervisor: P
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2.3 DEVELOPMENT OF THE CONCEPTUAL M
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5.1 RESEARCH FINDINGS 114 5.2 OVERA
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List of Tables Table 2.1 Representa
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PREFACE Oscar Wilde once said, “I
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Chapter 1 Introduction to the Thesi
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Figure 1.1 Mckinsey Survey Results
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1.4 Scope PhD thesis This thesis fo
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Netherlands. We used the maximum li
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2.1 Introduction A central tenant o
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only provide an assessment of the s
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Table 2.1 highlights several promin
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other hand, they later argue that m
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Therefore, there is a convergence i
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usiness performance, firms need to
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means is that the more better a str
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Businessweek. (2012). S&P/BusinessW
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-industry-09012011.html Grewal, R.,
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Langerak, F. (2001). Effects of mar
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Quelch, J. (2007). How to brand an
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Verhoef, P. C., & Leeflang, P. S. H
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1. The name of the company where yo
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9.The supplier seems to have good i
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17. The supplier takes a very long
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25. If the supplier were to find ou
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33.Does your strategic supplier als
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41.To what degree do those specific
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Supplier Brand Value 51.How likely
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Appendix B Scatter Plot Diagrams fo
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