The research adopted 10 financial figures decomposed from Du Pont identifiers to represent the resource bundles in the automobile industry. The financial variables were applied to run PCA analysis and common latent factors were derived to represent the latent factors prevailing around the automobile industry in each period. These key latent factors also addressed the resource combinations which further shaped the characteristic of the competitive advantages required by the industry (Tang & Liou, 2010).
The main purpose of applying principal component analysis (PCA) was to reduce the variables numbers. PCA not only drew new synthetic variables underlying the original resource bundles, but also kept the contents of the original information. Secondly, these new variables were mutually independent and the independent characteristics offset the issue of multicollinearity in the analysis of financial index.
The KMO values of 10 financial variables before and after financial crisis were 0.51 and 0.56 accordingly. Both KMO values were larger than 0.5, and the values entailed the appropriateness of the variables for principal component analysis.
The research applied the criteria suggested by Zateman and Burger (1975) for judging the significance of factor analysis: the eigenvalue of latent factors is larger than 1, the accumulative explanation variance is larger than 40% and the factor loadings after varimax rotation are considerably significant if they are larger than 0.3. According to the table of rotated factor pattern before financial crisis, four factors were selected because their eigenvalues were larger than 1 and the accumulative explained variation of four factors were up to 65%. Likewise, three factors were derived after financial crisis based on the same criteria, but its accumulative explained variation was slightly lower, which was 54%.
The nomination for the latent factors was mainly affected by the loadings of the rotated factor pattern derived from varimax method. In this research, if the loadings of the variables were larger than 0.5, the variables were considered to be significant to that factor and the factor would be given a name depending on the interactive influence of these variables. In next paragraph, the research further elaborated the reasoning about the nomination of each factor before and after the financial crisis.
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Table 4 Rotated factor pattern before financial crisis
Before financial crisis:
(1) Administration Process Efficiency
Factor 1 was influenced strongly by inventory turnover rate (INTO), ratio of cost of goods sold over sales (C_S) and ratio of sales, general and administration cost over sales (SGA_S). The results implied the critical influence of the firm’s ability to manage the cost and inventory in the economic downturn. Three indices all together represented the ability of squeezing the profits during the business operation process and the squeezing ability referred to reduce wastes of any resources used during the administration process, in term of human resource, time, space, material and facilities. Factor 1 was therefore named the capability of managing Administration Process Efficiency.
(2) Assets Utilization Arrangement
Factor 2 was dominated by account payable turnover rate (APTO), account receivable turnover rate (ARTO), fixed assets turnover rate (FATO) and ratio of
depreciation cost over sales (DA_S). These variables were related to assets and production efficiency and therefore factor 2 was named as capability of Assets Utilization
Arrangement.
DA_S and factor 2 were negatively related. The higher the DA_S was the less influence of the factor 2 was. If the firms invested more in facilities or equipment, its higher DA_S lowered the value of Assets Utilization Arrangement. On the contrary, the FATO and factor2 were positively related, and therefore if FATO was higher, the value of factor 2 would also be higher. APTO and ARTO here were viewed as kinds of relationship assets.
Administration Factor1 Factor2 Factor3 Factor4
APTO 0.23697 0.65725 0.18488 0.07611
ARTO 0.27532 0.52249 0.03309 0.19021
IN_TO 0.6096 0.19237 0.12367 0.1346
FATO -0.06696 0.83999 0.02515 0.02813
RD_S -0.25704 0.1536 -0.05104 0.79714
SGA_S -0.83907 0.1162 -0.09571 0.29156
C_S 0.85315 0.14395 -0.20136 -0.14201
DA_S 0.1857 -0.63847 0.07904 0.50575
TAX_S -0.22408 0.07706 0.79425 -0.25451 INT_S -0.2271 -0.07474 -0.73771 -0.18249 Eigenvalue 2.14229 1.92454 1.28481 1.15535 Accumulative
Explained
Variance 21.42% 40.67% 53.52% 65.07%
Financial index
Resource Configuration
Rotated Factor Pattern Before Financial Crisis
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The relationship assets controlled the ability to bargain over suppliers or customers.
Therefore, if the APTO and ARTO were higher, the value of Assets Utilization Arrangement factor was higher as well.
(3) External Relationship
The ratio of tax over sales (TAX_S) and the ratio of interests over sales (INT_S) demonstrated significant influence on the factor 3. Tax was considered as the investment from the government and interests was the cost of utilizing capital borrowed from the creditors. Both the creditors and government involved with the profits outflow of the company. In this point of view, both parties were regarded as external investors for the company. If the INT_S was higher, it implied that the firms owned substantial debt which further suggested the firm’s strong relationship with creditors, such as banks. Higher interest expense was likely to indirectly reduce the tax expense because of the effect of tax shield.
On the other hand, if the firm was taxed significantly, it indicated the firm owned strong relationship with government, and the firm might have more influence to acquire supports from the government. With regard to above reasoning, the factor 3 was named as factor of managing External Relationship.
(4) Technology Exploration
Because only the loadings of RD_S was significantly larger than 0.5, it was not easy to give a name for the factor 4. However, the factor explained significantly 11% of variance for the sample data and RD_S variable played an important role in this research. Therefore, the research boldly named factor 4 as the capability of Technology Exploration.
In the economic downturn, most companies paid attention on the budget control and cost-down arrangement in respect of the decrease of sales. If the company insisted on investing notably in the R&D, it implied the company highly empathized on strengthening its technology and developing innovations. On the contrary, it also implied that technology innovations of the automobile related industry demanded intensive investment in terms of money, efforts and time. Thus, the investment cannot be easily cut off even during the economic downturn. More terrifying reality was that the remarkable investments could not guarantee abundant returns.
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Table 5 Rotated factor pattern after financial crisis
After financial crisis:
(1) Ambidextrous Value Creation
The loadings of RD_S, SGA_S, C_S and TAX_S were distinguished in the factor 1.
In the business upturn, the eminent advantages of the industry indicated that the most firms allocated more resource on R&D and SGA_S. Substantial portion in R&D activities indicated firms’ distributing more resource in exploration activities. On the other hand, higher ratio in SGA_S indicated firms’ allocating more resource in the activities which were assisted to make the best use of current resources. Firms that managed both exploration and exploitation ability to create values entailed their Ambidexterity and therefore factor 1 was named as the capability of Ambidextrous Value Creation.
The capability of Ambidextrous Value Creation was crucial to profit creation. The lower the C_S was, the higher the margin would be. In addition, Tax cost was one kind of inevitable byproducts of high margins. If the cost of SGA, RD, and TAX were high and C_S was lower, the capability of Ambidextrous Value Creation would be strong as well.
(2) Asset Utilization Arrangement
Factor 2 after financial crisis distinguished similar variables to those in factor 2 before financial crisis, which were APRO, ARTO, FATO and DA_S. Therefore, factors 2 here was also named as the capability of Assets Utilization Arrangement. However, of four variables in this period, DA_S and ARTO were more manifest than those in previous period.
In other words, it suggested the firms placed more resource on managing sufficient and qualified production facilities in the economic upturn.
Ambidexterous
APTO -0.01567 0.50043 -0.48645
ARTO -0.21759 0.6514 -0.1603
IN_TO -0.409 -0.02503 -0.52987
FATO 0.32147 0.6388 -0.04027
RD_S 0.5433 -0.18989 -0.10491
SGA_S 0.79565 0.17533 0.33337
C_S -0.80824 0.02619 -0.19091
DA_S 0.04893 -0.70131 -0.19655
TAX_S 0.58767 0.06375 -0.32296
INT_S -0.0957 0.00628 0.79798
Eigenvalue 2.25660 1.64687 1.48301 Accumulative
Explained
Variance 22.57% 39.03% 53.86%
Financial index
Resource Configuration
Rotated Factor Pattern After Financial Crisis
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(3) Relationship Leverage
INTO and INT_S were manifest among the variables in factor 3. The eminence of these two variables entailed the strong demand of cash flow. INT_S highlighted the
relationship with banks and creditors. Higher INT_S illustrated that the firms required more free cash flow from creditors at the cost of interests. If the INTO ratio was higher, it
indicated that the company managed inventory well and kept inventory in a reasonable level and it’s working capital was not sunk in the stock. One key factors of maintaining excellent management of inventory was the firm’s efficient and effective cooperation with suppliers.
The efficiency and effectiveness were built from solid foundation such as mutually trust relationship.
Therefore, factor 3 was named as the capability of Relationship Leverage. If the score of factor 3 was higher and positive, it indicated the firm might perform poor logistic efficiency and bore higher interest burden. On the contrary if score was lower and negative, it indicated that firm enjoyed the benefits of the logistic efficiency and leveraged its strong relationship over suppliers.