Several limitations of this study can be addressed in future research. First, this study relies on cross-sectional data. Future research may consider validating the findings of this research using a time-series data. Second, the study only explored three antecedents of KAM capabilities. Other antecedents such as environmental uncertainty, competitive intensity or partner goal congruence may prove valuable as potential additional drivers of KAM capabilities. Further, this study conceptualizes KAM capabilities including coordination and reconfiguration. Other dimensions of KAM capabilities such as standardization or global integration deserve further research attention. In terms of the impact of cultural different on KAM, this study explores the
influence of cultural distance based on Hofstede’s conceptualization. As key account managers increasingly develop global mindsets in terms of managing their key customer relationship, other cultural dimensions such as manager’s cultural sensitivity deserve consideration. Finally, this study only focuses on one supplier IT resources: IT advancement. Other types of IT resources could influence a firm’s KAM capabilities as well.
For example, IT integration and IT flexibility, among others, are possible IT-related resources that could have the effects on KAM capabilities. Future research should incorporate these in exploring the developing KAM strategies as well as firm performance implications.
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Figure 1: Conceptual framework
KAM Capability
Moderator:
- Cultural Distance - IT Advancement Supplier
Dependence Customer Orientation
Trust
Market Performance H1
H2
H3
H4
H5
Table 1: Demographic characteristics of the respondents firms (n=246)
Product Distribution Percentage Sales Revenue
Communication Products 12.1% NT100M-NT500M (US $3M) 19.6%
Systems 6.3% NT501-NT1B 15.8%
Computer Peripherals 14.2% NT1B-NT5B 39.2%
Optoelectronics 14.6% NT5B-NT10B 7.5%
Semiconductors 15.8% More than NT10B (US $300M) 17.9%
Computer Components 37.1%
Employee Numbers
Less than 100 20.0%
100-199 21.7%
200-250 26.7%
251-999 13.3%
1000-4999 14.6%
5000-9999 1.7%
10000 and above 2.1%
Table 2: Measuresa and composite reliabilities
Constructs and measures λ Composite
reliability
Trust : adapted from Doney and Cannon (1997) .85
We believe the information that our international customer provides us. .78 Our international customer is genuinely concerned that our business succeeds. .91 When making important decisions, our international customer considers our welfare
as well as his own. .73
Dependence: adopted from Lusch and Brown (1996) .84
Our company is strongly dependent on this major international customer. .65 It would be very difficult for our company to replace sales and profits realized from
this major international customer. .87
Our major international customer would be costly to replace. .86
Customer orientation: adapted from Li and Calantone (1998) .89 We use multiple methods to gather information about our international customer’s
products, services, and strategies. .81
We frequently collect information about the international customer’s operations that are relevant to our business (e.g. purchasing, marketing, R&D). .95 We continually review the likely effects of changes in the business environment that may affect our international customer management practices. .81 We regularly collect information about our competitor’s product, services and
strategies. .66
International key account management capability: adapted from Shi et al. (2005, 2009) .92 We make great efforts to coordinate our service, product and supply chain activities
for this international customer. .64
Our international customer support team located across multiple countries work
together well to sever this international customer. .62 We can redeploy the strategic resources serving this international customer in global
market faster than our competitors can. .92
We can realign our organizational processes with respect to this international
customer’s changing needs ahead of competition. .97
We can modify our products or services to accommodate the global account’s needs
ahead of competition. .96
Market performance: adapted from Venkatraman and Ramanujam’s (1986) .84
Increased sales growth. .88
Increased market share. .80
Increased profitability. .71
IT advancement: adopted from Wu et al (2006) .95 Our company uses the most advanced IT for supply chain management. .90
Our IT for supply chain management is always state-of-the-art technology. .91 Relative to our competitors, our IT for supply chain management is more advanced. .91 My company is always first to use new IT for supply chain management in our
industry. .89
In our industry my company is regarded as an IT leader for supply chain
management. .88
CFA model goodness of fit indices:
Chi-square: 566.469 on 215 d.f.
NNFI: .907 CFI: .921 RMSEA: .082
a All items were measured using a 7-point Likert scale ranging from 1=strongly disagree to 7=strongly agree
Table 3: Intercorrelations and shared variances of measures (n=246)
F1 F2 F3 F4 F5 F6
Customer orientation (F1) .66 .00 .21 .28 .18 .11
Dependence (F2) .06 .64 .00 .02 .03 .00
Trust (F3) .46 .13 .66 .19 .21 .02
International KAM Capability (F4) .53 .13 .44 .70 .19 .05
Market Performance (F5) .43 .16 .46 .44 .64 .02
IT Advancement (F6) .33 -.05 .15 .23 .14 .81
Note: The correlations are in the lower triangle of the matrix. Shared variances are in the upper triangle of the matrix. The average variance extracted is in the diagonals.
Table 4: Assessment of common method bias
Model χ2 df p CFI NNFI SRMR
M1: Null model 4713.02 253 .000 n/a n/a n/a M2: Trait only model 566.47 215 .000 .921 .907 .059 M3: Method only model 3053.69 230 .000 .367 .304 .193 M4: Trait and method model 365.19 192 .000 .961 .949 .056
Model Comparison ∆χ2 ∆df p Conclusion
Testing for the presence of trait factors
M1-M2 4146.55 38 <.01 M1>M2
M3-M4 2688.5 38 <.01 M3>M4
Testing for the presence of a method factor
M1-M3 1659.33 23 <.01 M1>M3
M2-M4 201.28 23 <.01 M2>M4
Figure 2: Results of model estimation
KAM Capability Supplier
Dependence Customer Orientation
Trust
Market Performance .495**
.079
.333**
.453**
Fit Indexes:
χ2 = 399.85 on 145 d.f.
NNFI = .905 CFI = .920 RMSEA = .085
* Significant at 5%
** Significant at 1%
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