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

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Table 2.17 Inter-item Correlations SIQ CustNPD CF CCC IM AssBrand RP. Sales. Fair. BFV SIQ 1.00 CustNPD 0.50 1.00 CF 0.52 0.35 1.00 CCC -0.39 -0.35 -0.38 1.00 IM -0.40 -0.37 -0.33 0.54 1.00 AssBrand 0.34 0.39 0.26 -0.43 -0.51 1.00 RP 0.49 0.31 0.26 -0.25 -0.32 0.33 1.00 Sales 0.44 0.43 0.40 -0.40 -0.49 0.50 0.45 1.00 Fair 0.31 0.34 0.40 -0.28 -0.43 0.44 0.36 0.58 1.00 BFV 0.38 0.38 0.38 -0.44 -0.37 0.45 0.26 0.36 0.34 1.00 *Please look at Table 2.18 for interpretation of the abbreviation. 58
2.5 Measurement Model For creating our measurement model, we followed the steps used by Hair et al. (2006). We (1) specified, (2) identified, (3) estimated, (4) evaluated, and (5) respecified the model. We used AMOS version 17 for our analysis. The four variables in the measurement model are supplier responsiveness, idiosyncratic relational risk, satisfaction, and supplier brand value. As buyer firm value is represented by a single observed variable it is not included in the measurement model as AMOS 17 cannot handle a latent construct measured by a single observed item. Although there is a standard procedure of fixing the regression weight to 1 of the latent variable for single item factor, but as it is an artificial procedure recognized by many scholars as being based on an unrealistic assumption (Hair et al., 2006, p. 599). Therefore, in order not to compromise our results we did not include it in the measurement model. We had a total of 48 items and we used 38 items of our survey to create our measurement model; specifically, the survey consisted of 12 items that measured supplier responsiveness, 14 items that addressed IdRR, 8 items that focused on buyer satisfaction, 3 items to assess supplier brand value, and 1 item to confirm buyer firm value. We employed AMOS 17 to draw covariations between all of the latent constructs and observed items. We assessed our model by using several criteria, namely the magnitude of each items’ error variance estimate, item cross-loadings indicated by large modification indices, and significant residual covariation (Kohli et al., 1993). Our initial model, which was entitled MOD1, was unidentifiable. Using modification indices, we deleted items to improve the model’s fit and achieve identification. Finally, we achieved an excellent model fit with 10 items following the instructions of AMOS modification indices to improve the fit statistics. The questions representing the 10 items are provided in Table 2.18. 59
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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
Page 8 and 9: 5.1 RESEARCH FINDINGS 114 5.2 OVERA
Page 10 and 11: List of Tables Table 2.1 Representa
Page 12 and 13: PREFACE Oscar Wilde once said, “I
Page 14 and 15: Chapter 1 Introduction to the Thesi
Page 16 and 17: Figure 1.1 Mckinsey Survey Results
Page 18 and 19: 1.4 Scope PhD thesis This thesis fo
Page 20 and 21: Netherlands. We used the maximum li
Page 22 and 23: 2.1 Introduction A central tenant o
Page 24 and 25: only provide an assessment of the s
Page 26 and 27: Table 2.1 highlights several promin
Page 28 and 29: other hand, they later argue that m
Page 30 and 31: Therefore, there is a convergence i
Page 32 and 33: usiness performance, firms need to
Page 34 and 35: means is that the more better a str
Page 36 and 37: Supplier responsiveness or market o
Page 38 and 39: Naryandas, 1995). Further, idiosync
Page 40 and 41: They might also include the ease wi
Page 42 and 43: they ask about intimate supplier in
Page 44 and 45: Table 2.2 Modified Items of the MAR
Page 46: Table 2.2 Modified Items of the MAR
Page 49 and 50: market intelligence, offering flexi
Page 51 and 52: We considered several scales before
Page 53 and 54: Table 2.7 Component Matrix Buyer Sa
Page 55 and 56: een monitored to make sure they gav
Page 57: satisfaction and strategic supplier
Page 61 and 62: In the third step, we used maximum
Page 63 and 64: incremental fit index (IFI) is one
Page 65 and 66: ange of 100-150 (Hair et al., 2006,
Page 67 and 68: indicator that has a link to custom
Page 69 and 70: and strengths and their stock marke
Page 71 and 72: accompanies any innovation, accordi
Page 73 and 74: important finding because it provid
Page 75 and 76: 3.1 Introduction The Netherlands, d
Page 77 and 78: possibility that firm size may infl
Page 79 and 80: Figure 3.1 The Netherlands and Unit
Page 81 and 82: comprehensive agreement on each oth
Page 83 and 84: Why do we pay more for an orange wi
Page 85 and 86: constructs, for cross-cultural fact
Page 87 and 88: will discuss the SEM analysis of th
Page 89 and 90: BFV Hypothesis 4 IdRR->SBV Hypothes
Page 91 and 92: products. Overall, the effects of s
Page 93 and 94: more diverse comparison. China is a
Page 95 and 96: Chapter 4 The Quest for Performance
Page 97 and 98: insurance premiums. Hence, perceptu
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Page 101 and 102: should reinforce objective measures
Page 103 and 104: Table 4.2 Standardized Regression W
Page 105 and 106: Table 4.5 SEM Results Construct Dir
Page 107 and 108: 4.7 Discussion We start this discus
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Jaowrski and Kohli (1993) provided
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Using the findings of our current s
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iii) iv) Indeed, there may not be m
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uyer firm performance through a ser
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specifically highlighted the import
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5.5 Practical Implications From a m
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Summary in English This dissertatio
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is that, when developing marketing
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isk by 0.98 standard deviations. Su
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collectivist culture and a Confucia
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Overwegende dat Verhoef en Leeflang
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van de waarde te communiceren dat d
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erkennen dat dit niet altijd een mo
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ij de vraag of cultureel risico voo
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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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