The present paper on the firm’s performance link of contract manufacturers in the electronic hardware industry characterized by rapid technological change and time-based competition provides us an opportunity to functionally decompose the essence of dynamic capabilities (Teece et al., 1997;
Eisenhardt & Martin, 2000) based on the competence-based management (Collis & Montgomery, 1997; Penrose, 1959; Sanchez et al., 1996). Different from the traditional perspective of OEM suppliers, who is mainly providing manufacturing capacity at competitive cost position, the contract electronic manufacturers in the computer industry globally through making competence investment and business reconfigurations will conduct in an optimal way. Constantly upgrading their competence and building effort proves to be essential for their reconfigurations and vital to their performance.
Depending upon the trade off of business configurations for strategic flexibility or operating efficiency, managing a balanced business scope to pursue superior performance constitutes an important piece of the jigsaw puzzle of dynamic capabilities. Concurring with Penrose’s notion (1959) of balanced growth, our research implies that not only by combining efforts of competence building to business reconfigurations but with leveraging existing competence can a contract manufacturer pursue the optimal productive opportunities that its resources can render and create a self-reinforcing cycle for sustainable growth.
The present paper contains several deficiencies due to exploratory nature of the research design and the limitations of some measurements. First of all, our use of research and development ratio (RDR) and equipment investment ratio (EIR) as the key measurement for a contractual manufacturer’seffortin building competencemay tend to underestimatethedifficulty and complexity of a resource renewal process. Future research could consider the other input factors of capability, such as marketing capability, to be more exploratory.
Secondly, the present elaborates that firms expand and stretch the business scope for pursuit of strategic flexibility and reduction of risk. Therefore, future research may include the construct about the dimension of the flexibility or risk for further verifying the inference from current study.
Thirdly, the current research may suffer all limitations associated with single country and single industry analysis. As outsourcing is a dominant mode of operation in many industries, such as shoemaking and clothing, future research can expand our conceptualization to other industries.
Watching closely at industry heterogeneity may provide more insights regarding different styles of dynamic capability development within the context of industry supply chains and may render more verification for the logic of business growth presented in this study. Furthermore, future research that applies the logic of present study may prove to be fruitful in identifying both opportunities and limitations for competence building and business reconfigurations, which will provide a significant implication for a firm’s growth strategy.
Product
aStandardized estimates of the path coefficients are shown.
*p < 0.05，** p <0.01，*** p <0.001
Table 1
Variables and Measurements
Construct Variable Description
Product Competence
Buildingi, t RDRi, t-1= R&D Expenditurei, t-1/ Total Sales Revenuei, t-1 Competence
Building
Process Competence Buildingi, t
EIRi, t = (Machinery equipment i,t - Machinery equipment
i,t-1) / Machinery equipmenti,t×100％
Product Scope Diversityi, t
PDIVi, t= 1－Σ( Pi,t/ Total Sales Revenuei, t)2
Where as Pi,t refers to the announced sales of i product in t year
Customer Scope Diversityi, t
CDIVi, t= 1－Σ( Ci,,t/ Total Sales Revenuei, t)2
Where as C i,t refers to the announced sales of i customer higher than 10% in t year
Business Configuration
Vertical Integrationi, t VADi, t = (Total Sales Revenuei, t –Overheadi, t –Material costi, t)/ Total Sales Revenuei, t
Interest and Tax+ Depreciationi, t- Non-operating Incomei, t）/（Total Asseti, t- Else Asseti, t–Long Term Investmenti, t）×100％
Table 2
Correlations Matrix and Descriptive Statistics for Constructa
Variable Mean
Standard
Deviation ROIC RDR EIR VAD CDIV PDIV
Returns on invested
(VAD) 0.390 0.229 0.095** 0.140** - 0.145** 1.000
Customer scope diversity
(CDIV) 0.901 0.126 - 0.038 0.114** 0.003 0.155** 1.000
Product scope diversity
(PDIV) 0.418 0.235 0.009 0.152** - 0.009 0.174** 0.178** 1.000
aStandardized estimates of the coefficients are shown and based on 6 year average (1997-2002).
N = 756 (126 × 6)
*p<0.05；**p<0.01；***p<0.001; all tests are two-tailed.
Table 3
Standardized Regression Coefficients for Path Estimates
Hypothesis Path Relations β S.E. C.R. p-Value
Hypothesis 1 Product Competence Building Product Scope Diversity
1.171*** 0.319 3.667 0.000
Hypothesis 2 Product Competence Building Customer Scope Diversity
0.434** 0.172 2.529 0.011
Hypothesis 3 Product Competence Building Vertical Integration
1.126*** 0.308 3.657 0.000
Hypothesis 4 Process Competence Building Product Scope Diversity
0.008 0.036 0.212 0.832
Hypothesis 5 Process Competence Building Customer Scope Diversity
0.001 0.019 0.055 0.956
Hypothesis 6 Process Competence Building Vertical Integration
-0.144*** 0.034 - 4.202 0.000
Hypothesis 7 Product Scope Diversity Customer Scope Diversity
0.079*** 0.020 4.038 0.000
Hypothesis 8 Customer Scope Diversity Vertical Integration
0.221*** 0.066 3.350 0.001
Hypothesis 9 Vertical Integration Product Scope Diversity
0.143*** 0.038 3.791 0.000
Hypothesis 10 Product Scope Diversity Return on Invested
Capital - 0.001 0.013 - 0.044 0.965
Hypothesis 11 Customer Scope Diversity Return on Invested Capital
- 0.040 0.025 - 1.642 0.101
Hypothesis 12 Vertical Integration Return on Invested Capital
0.054*** 0.014 3.931 0.000
Hypothesis 13 Product Competence Building Return on Invested Capital
- 0.034 0.117 - 0.292 0.770
Hypothesis 14 Process Competence Building Return on Invested Capital
*：C.R. < 1.96；using a significance level of 0.05, critical ratios that exceed 1.96 would be called significant.
1.β：Standardized Coefficient 2. S.E.：Standard Error ofβ 3. C.R.：Critical Ratio（=β/ S.E.）
4. p-Value：Statistical Significance of the Test
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