Below chart represented the analytical process of the research.
Figure 1 Analytical process of the research
3.3 Research methods
Below listed the methods applied in the research:
(1) Descriptive Statistics of DuPont identifiers financial index (2) Principal component analysis
(3) Cluster analysis (4) Discriminant Analysis (5) Cross tabulation (6) ANOVA test
This research aimed to compare the strategic groups’ changes before and after the financial crisis. Financial index derived from Du Pont identifiers were manipulated as basic variables and the descriptive statistics was calculated to summarize the sample data.
Next, the research ran principal component analysis to simplify the financial variables into latent factors which were deployed to represent the hidden resources configuration of firms in the industry.
In the next step, the research adapted these latent factors to run cluster analysis and grouped firms in each period. Meanwhile, the research followed up applying the
discriminant analysis to verify the effectiveness of the grouping.
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After finalizing the clusters, cross tabulation comparison was used to address the characteristics of clusters and ANOVA test was applied to examine the financial performance (ROIC EBIT_S) of each cluster.
In the end, the research further analyzed the strategy swift of the firms before and after financial crisis and resulted in nine types of strategy transition for automobile industry.
The transitions were analyzed to explain how firms adjusted the resource allocation before and after the financial crisis and how the performance they ended in. Thus, ANOVA test was applied to further test the financial performance of the type of strategy transitions.
In the research, the ratio of SGA expense over sales was assumed to represent the exploitation advantages of an organization and the ratio of RD expense over sales was used to address the exploration advantage. The performance index of an organization was represented by the ratio of ROIC and EBIT_S. The companies whose SGA_S and RD_S were both higher than industry average were defined as ambidextrous organizations. If either value of a firm was lower than industry average, the firm was considered as not with ambidexterity.
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Research results
4.1 Samples
The research boundary was limited in the companies which had 6 years averaged financial ratio dated from 2007 to 2012 and the companies was classified in Motor Vehicles
& Passenger Car Bodies(SIC:3711) and Motor Vehicle Parts & Accessories(SIC:3714). The financial data was collected from S&P COMPUSTAE Database.
Financial crisis happened in 2008, and 2009 was the first year which directly reflected the impact of the crisis. Therefore, the year of 2009 was set to distinguish the influence of financial crisis. The data from 2007 to 2009 was averaged to address the sample data of before financial crisis and 3-year averaged financial data from 2010 to 2012
represented those of after financial crisis. The sample data were selected according to below criteria:
(1) Delete any observations with missing data in the key financial variables before or after financial crisis. Total reserved 224 companies.
(2) Delete outliers of the financial variables except ROIC, EBIT_S. 20 companies were deleted and 204 companies were retained in the end.
The average ROIC of sample firms was 2.09% in 2007, and declined to -1.54% in 2009 due to the influence of financial crisis in 2008. The ROIC bounced back to 2.36% in 2010 and was slightly back to previously level at 2.20% in 2011 and then slightly decreased to 1.40% in 2012.
Table 2 ROIC trend in automobile industry from 2007 to 2012
Figure 2 ROIC trend of sample firms in automobile industry
YEAR 2007 2008 2009 2010 2011 2012
ROIC 2.09% -0.15% -1.54% 2.36% 2.20% 1.40%
ROIC trend of sample firms
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Below was the table of the descriptive statistics about the sample data.
Table 3 Descriptive statistics for two periods
Minimum Maximum Mean Std Dev Skewness Kurtosis APTO 0.456334 5.978800 1.611732 0.775276 2.236987 8.126107 ARTO 0.343462 4.865435 1.634947 0.796379 1.605120 3.307816 IN_TO 0.533216 18.300864 2.588031 1.844152 3.999138 27.132425
FATO 0.181518 11.871128 0.989420 0.998379 7.121541 70.793372 RD_S 0.000120 0.264509 0.023786 0.027080 4.016403 30.337842 SGA_S 0.017411 0.523806 0.133771 0.075649 1.965756 5.529227
C_S 0.383728 0.944949 0.754277 0.105558 -1.111824 1.423548 DA_S 0.009064 0.171309 0.052241 0.026055 1.022283 1.806838 TAX_S -0.032442 0.066404 0.012956 0.013380 0.849990 3.686791 INT_S 0.000101 0.102123 0.015876 0.018058 2.190470 5.748143 ROIC -0.929686 0.101172 0.000473 0.075195 -9.650275 116.539563 EBIT_S -0.208466 0.277233 0.046492 0.061804 0.270172 3.382356
Minimum Maximum Mean Std Dev Skewness Kurtosis APTO 0.329183 4.702004 1.589853 0.680875 1.385129 3.258824 ARTO 0.417299 6.870428 1.687321 0.891722 2.417409 9.007654 IN_TO 0.445537 14.299453 2.677018 1.684262 2.753834 12.966212
FATO 0.256117 20.703295 1.152849 1.576884 9.954238 118.629542 RD_S 0.000008 0.306932 0.025102 0.029679 4.685629 39.571378 SGA_S 0.014330 0.628494 0.129338 0.073320 2.552909 11.675033 C_S 0.429701 1.141985 0.755649 0.097755 -0.717878 1.951202 DA_S 0.007404 0.146757 0.043907 0.020637 1.252487 3.317061 TAX_S -0.074847 0.062572 0.014610 0.014085 -0.944484 9.008510 INT_S 0.000094 0.118185 0.013371 0.017729 3.085047 12.163686 ROIC -0.136847 0.095475 0.018916 0.028109 -1.361721 6.707181 EBIT_S -0.388816 0.255073 0.057733 0.062545 -2.665716 19.493425
Descriptive Statistics before financial crisis
Descriptive Statistics after financial crisis Variable
Variable
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4.2 Principal component analysis results
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.
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4.3 Cluster analysis results
In order to avoid the multicollinearity issue, the research adopted the latent factors derived from previously PCA as new variables and ran cluster analysis independently for both periods. The research deployed two methods and Euclidean distance for the cluster analysis. Firstly the research ran several cluster analysis to compare the results of the clustering. According to rules of thumb to decide the better number of clusters, the larger CCC and Pseudo F statistic the better, the optimal cluster number for both two periods was inferred to be three in this research based on table 6.
Next, the wards method was applied to derive the centroid of each cluster. Wards method maximized the variance among groups but minimized the variance within groups.
Therefore its centroids were most appropriate to be the seeds for K-means analysis. After assigning the seeds, the research followed up applying K-means method to calculate the Euclidean distance between each firm and seeds and to dispatch sample firms into different groups based on the distance.
Table 6 Criteria of optimal number of clusters
Below table presented the average scores of each factor of each cluster in two periods. The nomination for the clusters was determined on the features of each clusters. In order to picture out the underlying characteristics of the groups, the research also compared categorical information for each cluster and aimed to give an appropriate name for each cluster. The list of the subgroups was shown in appendix 1.
Table 7 Averaged factor scores in each cluster before crisis
Cluster Pseudo F A Rsquare CCC Cluster YEAR A Rsquare CCC
2 27.87 19.56 -6.29 2 43.20 25.75 -5.32
3 35.32 34.57 -6.26 3 47.76 45.47 -7.98
4 31.39 46.75 -10.62 4 51.03 61.41 -12.03
5 38.64 56.93 -10.60 5 45.27 67.09 -13.09
Before Financial Crisis After Financial Crisis Criteria of selecting optimal numbers of clusters
Process
Efficiency Assets
Utilization Technology Exploration
103 75 26
A1 A2 A3
Factor1 Administration Process
Efficiency 0.558223 -0.297808 -1.352358 Factor2Assets Utilization
Arrangement -0.258562 0.408263 -0.153381 Factor3 External
Relationship 0.361814 -0.336624 -0.462312 Factor4 Technology
Exploration 0.287631 -0.834550 1.267892 Cluster
Before Financial Crisis
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Figure 3 Comparison of factor scores mean among clusters before crisis
Before financial crisis:
(1) Cluster A1: Process Efficiency Group
More than half of the firms were classified into this group. The capability of managing Administration Process Efficiency and the capability of External relationship illustrated strong influence on the cluster. While further evaluating the variables mean of each cluster, it was found that this group had the highest C_S ratio but still maintained certain level of margins. The contrast indicated that there had some forces other than products helping the firms of the group to create margins. The higher Administration Process Efficiency factor scores projected that the group enjoyed the competitive advantage in managing administration process efficiency and prevented any unnecessary of resource wastes. However, in the economic downturn, the efficient advantage cannot guarantee positive ROIC. Even though the group preserved certain level of operation margin, its low margin was offset by the high portion of DA expenses and TAX costs.
(2) Cluster A2: Assets Utilization Group
The assets utilization group here was composed by smaller size of companies. The cluster showed strong capability of utilizing assets. The group was featured by the highest FATO rate, and lowest DA expense. Moreover, the group was the only cluster who
maintained the highest operation margins and enjoyed positive ROIC even in the economic downturn. The group had higher C_S but maintained most profitable margins. In sum, the group was currently harvesting its previous investment and hard efforts from the point of its lower DA_S ratio and R&D cost.
(3) Cluster A3: Technology Exploration Group
This group was classified according to its eminent capability of technology exploration. In figure 3, the group (A3) presented the highest SGA_S and RD_S among three groups, and the two high values indicated that the group was trying to balance resource in exploration and exploitation activities. This group was composed by the larger firms
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among all 3 groups because its employee size was the largest. Its INT_S was remarkable high but TAX_S and the C_S was the lowest. The substantial investment in the RD and SGA seemed significantly offset the group’s profits because it had the lowest ROIC and EBIT_S.
Table 8 Averaged factor scores in each cluster after crisis
Figure 4 Comparison of factor scores mean among clusters after crisis
After financial crisis:
(1) Cluster B1: Process Efficiency Group
Half of the firms was classified into this group which demonstrated competitive advantage in managing Process Efficiency. More than 80% of the members were notably overlapped with those was in cluster A1. Despite the group had the highest C_S, it had the lowest SGA cost and highest INTO rate. The group enjoyed a moderate ROIC rate. These data implied that the group managed lower product margin but they succeeded in exerting control over process efficiency to squeeze profits.
(2) Cluster B2: Ambidexterity Prone Group
This group was composed by large size firms and presented potential to generate the highest ROIC and profit margins, even though its administrative efficiency and asset
utilization were not salient among groups. The notable RD_S and SGA_S depicted the features of ambidextrous organizations. The group illustrated lowest C_S ratio and highest EBIT_S.
Process
Efficiency Ambidexterity
Prone Assets
Utilization
104 53 47
B1 B2 B3
Factor1 Ambidextrous
Value Creation -0.377905 1.079662 -0.381277 Factor2 Assets Utilization
Arrangement -0.326341 -0.286999 1.045754 Factor3 Relationship
Leverage -0.509585 0.264011 0.829879 Cluster
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(3) Cluster B3: Assets Utilization Group
The assets utilization group here was composed by smaller size companies. The capability of Assets Management was standing out to feature the group. The group presented the highest FATO, ARTO and INT_S but the lowest RD_S, DA_S and TAX_S.
Obviously, the group presented the poorer performance due to its lowest EBIT_S and ROIC.
The group was inferred to enjoy the benefits of its previous investment due to its lowest DA_S ratio and high FATO ratio.
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4.4 Discriminant analysis results
In order to verify the effectiveness of the cluster results, the research applied discriminant analysis to test the classification of clusters. The classification was further examine by hit ratio and cross validation hit ratio.
Before Financial Crisis
According to Table 9, total four factors showed statistically significance to
distinguish the clusters. The eigenvalues of the two canonical functions were larger than 1, which were 1.6734 and 1.167. The canonical correlation were larger than 0.6 and the accumulative explained variance was larger than 75%. Based on the rule of thumb, the two canonical functions were both tested statistically significant.
Table 9 Results of distriminant analysis before crisis.
Table 10 revealed that the canonical function Can1 distinguished cluster 2(A2) from the others mainly according to assets utilization and technology exploration. Can2 was used to set apart cluster 3(A3) from others based on impacts of administration process efficiency and technology exploration.
Table 10 Results of discriminant canonical function before crisis
Factor1 1 0.762 0.7966 0.4251 0.7395 74.32 <.0001
Factor2 1 0.9542 0.3835 0.0985 0.1093 10.98 <.0001
Factor3 1 0.9343 0.45 0.1357 0.157 15.77 <.0001
Factor4 1 0.7069 0.8684 0.5052 1.021 102.61 <.0001
Statistic Value F Value Num DF Den DF Pr > F Wilks'
Lambda 0.1726125 69.64 8 396 <.0001
Function Canonical
CorrelationEigenvalue Proportion Cumulative Pr > F
1 0.79117 1.6734 0.5891 0.5891 <.0001
2 0.733847 1.167 0.4109 1 <.0001
S=2 M=0.5 N=98
Discriminant Function
Between Standard Deviation
Multivariate Statistics and F Approximations R-Square /
(1-RSq) Before Financial Crisis Univariate Test Statistics F Statistics, Num DF=2, Den DF=201
Variable R-Square F Value Pr > F
Total
Numbers CLUSTER Can1 Can2
103 1 1.123731867 0.496884816
75 2 -1.648783751 0.286251954
26 3 0.304399961 -2.794155103
Variable Can1 Can2
Administration Process Efficiency Factor1 0.544098586 0.839341905 Assets Utilization Arrangement Factor2 -0.612948267 0.058259135 External Relationship Factor3 0.587092593 0.383534128 Technology Exploration Factor4 0.815169973 -0.61628797
Before Financial Crisis
Pooled Within-Class Standardized Canonical Coefficients Class Means on Canonical Variables
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Moreover as shown in Table 11, among 103 firms in Process Efficiency Group, 99 firms were classified correctly; 69 of 75 firms in Assets Management Group were classified correctly; and 25 of 26 firms in Technology Exploration Groups were classified correctly.
Moreover as shown in Table 11, among 103 firms in Process Efficiency Group, 99 firms were classified correctly; 69 of 75 firms in Assets Management Group were classified correctly; and 25 of 26 firms in Technology Exploration Groups were classified correctly.