Examining different perspectives of fit among environment, organizational slack, and strategy in Taiwan semiconductor industry

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1 Journal of Business Administration and Management Sciences Research Vol. 4(4), pp , May, 2015 Available online at ISSN Apex Journal International Full Length Research Examining different perspectives of fit among environment, organizational slack, and strategy in Taiwan semiconductor industry Chin-Shien Lin 1 and Van-Thac Dang 2 * 1 Department of Business Administration, College of Management, National Chung Hsing University, Taichung, Taiwan (R.O.C). 2 Department of Business Administration, College of Management, National Chung Hsing University, Taichung, Taiwan (R.O.C). Accepted 24 April, 2015; Published 20 May, 2015 This study conducts an empirical analysis of different perspectives of strategic fit. Using a sample of 122 firms, this study examines simultaneously the external fit, internal fit, and overall fit among environment, organizational slack and strategy in the context of Taiwan semiconductor industry. Empirical analysis shows some interesting results: (1) Low cost strategy is best fit with environmental dynamism and organizational slack to enhance firm performance, (2) Differentiation strategy fits well with environmental munificence to enhance firm performance, and (3) There are some ideal firms exist in Taiwan semiconductor industry. Findings of this study provide both theoretical and practical implications. Key words: Strategic fit, strategictypology, environmental munificence, environmental dynamism, organizational slack, firm performance. INTRODUCTION One of fundamental questions in the field of strategic management research is to indentify how firms achieve competitive advantages and have differences in profitability. According to classical SWOT framework, a firm has superior performance due to an emphasis on formulating competitive strategy through both internal analyses of firm s strengths and weaknesses and external analyses of opportunities and threats (Andrews 1971; Hofer and Schendel 1978). While this framework has remained a predominant position in the field of strategic management for many decades, industrial organization (IO) paradigm has tended to focus primarily on the notion that industry structure has a strong influence in determining the firm s competitive advantage *Corresponding author. wilsondang1005@gmail.com and performance. Industry factors such as entry barriers, market concentration, competitive intensity, and growth define the competitive environment, which affects the firm s profitability (Porter 1980, 1985; Scherer and Ross 1990). One of the disadvantages of industrial organization paradigm is that the role of firm behavior is largely ignored in traditional IO research. In contrast, resource-based approach mainly focuses on resource endowments, firm s resources that are rare, valuable, imperfectly imitable and non-substitutable are major sources of firm s sustained competitive advantage (Barney 1991; Wernerfelt 1984). While most research on sources of competitive advantage has focused either on analyzing external environment and industry structure or internal resources, all these models share a common underlying assumption that factors including firm s strategy, organizational resources and external environment must somehow fit together if the firm is to perform

2 Lin and Dang 085 well (Drazin and Van de Ven 1985, Venkatramanand Prescott1990). Strategic fit has played an important role in the field of strategic management and organizational research (Buttermann, Germain, and Iyer 2008). In its central concept, a firm s strategy is defined in terms of its fit with environmental conditions and organizational capabilities and resources (Drazin and Van de Ven 1985; Venkatraman 1989). Although researchers have indicated that strategic fit makes a great contribution to the potential capability of an organization and exerts a significant positive impact on firm performance, however strategic fit has received a little attention in the last decade. A lack of correspondence between the concept and statistical methodology makes difficulties for researchers to determine the strategic fit and its effects on performance (Bergeron, Raymond, and Rivard 2001; Venkatraman 1989). Theoretically, some scholars have made great efforts to clarify the correspondence between concept and statistical testing in strategic fit. According to Van de Ven and Drazin (1985), fit can be identified as selection, interaction, and systems approaches; whereas six different perspectives of fit are proposed by Venkatraman (1989), these are fit as moderation, mediation, matching, gestalts, covariation, and profile deviation. Although the works of these scholars offer a definitional clarity to the link between theory building and theory testing in strategic fit, however empirical studies simultaneously examining different approaches of fit in specific context have been rare (Bergeron et al., 2001). It appears that requirements for more empirical confirmation of the concept and statistical tests of different perspective of fit are necessary (Buttermann et al., 2008). A number of prior studies on strategic fit have focused attention either on external fit between environment and strategy (Covin and Slevin 1989; Hambrick 1983; Luo and Park 2001) or internal fit between strategy and resources (Black and Boal 1994; Shortell and Zajac 1990; Zajac, Kraatz, and Bresser 2000). For example, Prescott (1986) found that environment as measured by characteristics of market structure and strategy fit together in influencing performance. Venkatraman and Prescott (1990) indicated that various dimensions of strategy fit with various dimensions of environment to be positively related to firms performance. And Miller (1992) also found that internal fit among variables of structure and process to be positively related to performance. However, a very few of previous studies have examined simultaneously external fit and internal fit among environment, strategy and resources at a specific point in time in a given industry (Bergeron et al., 2001). Moreover, the role of organizational slack has also largely been ignored in the previous strategic fit literature (Kraatz and Zajac 2001). Singh (1986) suggested that organizational slack can serve as environmental buffers and/or facilitators. When a firm faces highly complex and uncertainty in the environment, managers with more organizational slack will have more capability to respond quickly to the environmental changes (Bourgeois 1981), these managers can also use organizational slack to enhance innovation and help organization survive in a turbulent environment (Nelson and Winter 1982). Accordingly, organizational slack has substantive implication for managers in formulating and executing strategy and dealing with external environmental changes (Kraatz and Zajac 2001). Thus, it is necessary to examine simultaneously external fit and internal fit as well as overall fit among environment, organizational slack, and strategy in a specific industry context to fill the gap in the strategic fit literature. In addition, Taiwan is one of the largest economies in the Asia-Pacific region. During decades of development, semiconductor has been one of the most industries that have greatest contributions to the Taiwan economy. According to market statistics from Taiwan Institute of Economic Research, Taiwan has been one of the world s largest suppliers of semiconductors for the last few decades. Recent years, Taiwan has ranked number one in both foundry service and IC packaging and testing services in the world. Besides, IC design industry has captured the number two position in the world. For instance, Taiwan s foundry service and IC packaging account for over 50 percent of the world s market share with US$21.5 billion and US$16.0 billion revenue in 2011, respectively; IC design also reaches revenue of US$15.1 billion in Due to semiconductor industry plays an important role in Taiwan economy and it occupies a dominant position in the world s semiconductor markets, it is attractive for us to understand the environment, resources, and strategy of firms in this industry. The objective of this study is to conduct an empirical analysis of different perspectives of strategic fit. Using a sample of 122 firms, the present study examines simultaneously the external fit and internal fit as well as overall fit among environment, organizational slack and strategy in the context of Taiwan semiconductor industry. First, this study provides empirical results to clarifying the correspondence between concept and statistical testing of different perspectives of fit. Second, the results of this study offer evidences illustrating the important role of organizational slack in fitting with strategy and external environmental conditions. And third, the alignment relationships among environment, organizational slack, and strategy offer in-depth understanding of Taiwan semiconductor industry. This study proceeds by reviewing different perspectives of strategic fit, followed by the methodology and discussion of the results and conclusions. DIFFERENT PERSPECTIVES OF STRATEGIC FIT To clarify the correspondence between a particular

3 086 J. Bus. Admin. Manage. Sci. Res X (Strategy) Z (Environment/ Slack) Y (Performance) X*Z(Interaction) Figure 1. Fit as moderation Z (Strategy) X (Environment/ Slack) Y (Performance) Figure 2. Fit as mediation. concept and its subsequent testing scheme in strategic fit, based on the degree of specificity of the functional form of fit-based relationship, choice of anchoring the specification of fit-based relationships and number of variables in the fit equation, Venkatraman (1989) theoretically developed a conceptual framework that indentifies six perspectives of fits; these are fit as moderation, fit as mediation, fit as matching, fit as gestalts, fit as covariation, and fit as profile deviation. Each of these perspectives is discussed as following. Fit as moderation In moderation perspective, fit is conceptualized as the interaction between two variables. The influence of a predictor variable on a response variable is dependent on the level of the moderating variable. According to this perspective, the interaction between strategy and environment as well as between strategy and organizational slack will have implication on firm s performance (as illustrated in Figure 1). When this perspective is adopted, a moderated regression analysis (MRA) is used to test the form of moderation: If the unstandardized coefficient a 3 differs significantly from zero, the moderation effect is supported, representing the effect of fit between the predictor and moderator variables on the response variable (Venkatraman 1989). Fit as mediation In mediation perspective, fit is conceptualized as the existence of a significant intervening mechanism of a mediator between a predictor variable and a response variable. According to this perspective, strategy is an intervening variable between environment/organizational slack and firm s performance (Figure 2). Test of fit as mediation is usually performed by a regression model framework proposed by Barron and Kenny (1986):

4 Lin and Dang 087 High Fit1 Fit2 Environment Slack Low Fit4 Fit3 Low Strategy High Figure 3. Fit as matching. First, the predictor variable must affect the response variable in the first equation; second, the predictor variable must affect the mediator in the second equation; and third, the mediator must affect the response variable in the third equation. Partial mediation holds if the effect of the predictor variable on the response variable is less in the third equation than in the first equation, and perfect mediation holds if the predictor variable has no effect when the mediator is controlled in the third equation. Fit as matching In matching perspective, fit is defined as a match between two related variables, and a measure of fit between two variables is developed independent of any performance anchor (Venkatraman [1989, 430]). According to this perspective, a fit exists when strategy matches environment/organizational slack (Figure 3). One commonly used method for testing the matching relationships is the deviation score analysis. This method states that the absolute difference between the standardized scores of two variables indicates a lack of fit, and the performance implication of fit can be tested by examining the effect of this absolute deviation score on performance: If the coefficient a 3 is significantly different from zero, then the performance effect of fit is supported (Venkatraman 1989). The three first perspectives of fit (moderation, mediation, and matching) are referred to as reductionistic perspective, because they deal with bivariate coalignment among the individual dimensions that represent the constructs of environment, organizational slack and strategy (Venkatraman and Prescott 1990). Although this perspective has the ability to isolate precisely specified theoretical links and impacts, however the use of pairwise coalignment between individual dimensions limits the ability to capture coalignment (Butternann et al., 2008). Other approaches of fit (gestalts, covariation, and profile deviation) are referred to as holistic perspective. This is a broader concept of fit between several characteristics of many constructs of environment, organizational slack and strategy. One of the most advantages of this perspective is its ability to deal with complex and interrelated nature of linkages (Butternann et al., 2008).These holistic perspectives of fit are discussed as follows. Fit as gestalts This perspective defines fit as the degree of internal congruence among a set of theoretical attributes. A difference between previous perspectives of fit and fit as gestalts is that Instead of looking at a few variables or at linear associations among such variables we should be trying to find frequently recurring clusters of attributes or gestalts (Miller [1981, 5]). As illustrated in Figure 4, adopting this perspective, we should look simultaneously at the patterns among environment, organizational slack, and strategy variables. One of appropriate analytical analyses to fit as gestalts is the cluster analysis or q- factor analysis (Venkatraman, 1989). After classifying variables into different clusters, one should identify high and low performance between clusters. According to the concept of equifinality suggested by Hambrick (1984) and Miller and Friesen (1984), fit as gestalts are supported if performance is not significantly different among clusters. Fit as covariation Venkatraman(1989) defined fit as covariation as a pattern of covariation or internal consistency among a set of underlying theoretically related variables (p. 435). According to this perspective, many variables of environment, organizational slack and strategy will fit together to influence performance (Figure 5). In terms of

5 088 J. Bus. Admin. Manage. Sci. Res Strategy I II ideal profile. Next, misalignment in the study sample (80 percent of the sample) or deviation from ideal profile is computed by using the Euclidean distance in an n- dimensional space. These misalign scores must be negatively related to performance in the study sample (Venkatraman 1989). III Slack METHODOLOGY Sample data Environment Figure 4. Fit as gestalts. Data used in this study is obtained from Taiwan Economic Journal Database (TEJ). Data consist of 199 firms from semiconductor industry in A final completed sample data consists of 122 firms. Environment Measures Strategy Slack Figure 5. Fit as covariation. Fit Performance analytical issues, Venkatraman (1989) suggested that a second order factor analysis would be an appropriate testing technique for fit as covariation. If all coefficients are significantly contributed to the second order factor (fit), and the coefficient of this factor is significantly related to performance, then fit as covariation is supported. Fit as profile deviation Fit as profile deviation is defined as the degree of adherence to a specified profile. This ideal strategy profile is assumed to exist, and deviation from this profile implies a negative effect on performance. According to this perspective, we would expect an ideal profile of environment, organizational slack and strategy, deviation from this ideal point should result in a lower performance (Table 1). When fit as profile deviation is adopted, the first step is to calculate a calibration sample, which is a top 10 percent of the highest performers selected from a larger sample in terms of dependent variable. The bottom 10 percent was removed to avoid a skewed sample downward. Then, the standardized mean scores along environment, organizational slack, and strategy variables in the calibration sample are calculated to specify the Environmental variables Consistent with previous literature, environment is measured by munificence and dynamism (Dess and Beard 1984). Environmental munificence represents the level of dependence of resources on the environment while environmental dynamism represents the level of uncertainty a company faces (Lumpkin and Dess 2001). Environmental munificence is measured by averaging the regression coefficients on the industry s net sales and operating income over a five-year period. Besides, the average standard errors of regression coefficient of the above mentioned function is used as a proxy for the environmental dynamism (Boyd 1990; Keats and Hitt 1988). These two dimensions, though derived from the same database, are independent of and will not influence each other (Heeley, King, and Covin 2006). Strategy variables According to prior strategic literature, two fundamental types of strategy are used to describe strategy in this study: low cost and differentiation (Porter 1980, 1985). Low cost is measured by employee productivity, which is calculated by using the value added divided by number of employees (Hambrick 1983). Differentiation strategy is measured by the average standard deviation of marketing differentiation and technology differentiation. Marketing differentiation is measured by advertising intensity, which is calculated using advertising expenses divided by sales. Technology differentiation is measured by research and development density, which is calculated using R&D expenses divided by sales (Spanos, Zaralis, and Lioukas 2004). Measurement of strategy variables in this study is in accord with previous strategic literature (Geletkanycz and Hambrick 1997; Kotha and Nair 1995).

6 Lin and Dang 089 Table 1. Fit as profile deviation. Strategy Dimensions Importance Standardized Scale for Measuring the Dimensions X 1 b X 2 b 2 Xs 1 Xc 1 X 3 b 3 X 4 b 4 X 5 b 5 Xs 6 X 6 b 6 Xc 6 Xs: denotes the values in the study sample. Xc: denotes the values in the calibration sample. Organizational slack Three types of organizational slack have been classified and used widely in literature (Bourgeois and Singh 1983), these are available slack, recoverable slack, and potential slack. Consistent with previous studies (Bourgeois and Singh 1983; Cheng and Kesner 1997; Voss, Sirdeshmukh and Voss 2008), available slack is measured using the ratio of current assets to the current liabilities, which indicates unabsorbed resources. Recoverable slack is measured by the firm s selling and general administrative expenses divided by sales, which represents resources that have been absorbed into certain organizational activities but may be recovered. Potential slack is measured by using the equity to debt ratio, which indicates the capacity of the organization to obtain external resources. This study uses the average standardized scores of these three organizational slacks as a proxy of organizational slack. EMPIRICAL RESULTS Descriptive statistics Table 2 presents the means, standard deviations, and Pearson correlation for all variables used in this study. An examination of the correlation matrix indicates that neither low cost nor differentiation strategy is significantly related to environmental variables and organizational slack. However, low cost strategy is significantly positive related to firm performance (r=0.08, P<0.01), differentiation strategy is significantly negative related to firm performance (r=-0.30, P<0.01). Additionally, both environmental munificence and dynamism are not significantly related to organizational slack and firm performance. Also, organizational slack is not significantly related to firm performance. Empirical results of fit among environment, organizational slack, and strategy are examined further in the following section. Financial performance In this study, three variables are used to measure company financial performance: return on asset (ROA), return on equity (ROE), and return on sales (ROS). Followed Venkatraman and Ramanujam (1986), these three variables are standardized and averaged to calculate a composite measure of firm performance. The higher the value is, the better a firm s business performance (Raymond and Bergeron 2008; Zajac et al., 2000). Control variables To control the potential confounding effects of other variables on firm performance, this study controls for the following three variables: company age as calculated by counting the number of years since a company s establishment, scale of business operation as measured by natural logarithm of sales, and prior financial performance (Godfrey and Hill 1995; Jacobson 1990; Kothaand Nair 1995). Fit as moderation This perspective states that the impact of strategy on performance is dependent on environment and slack resources, thus our statistical interest is in the effects of interactions between strategy and environment as well as between strategy and organizational slack on performance. Table 3 shows the results of external fit between environment and strategy and their effect on performance. First, control variables were entered into model 1, next low cost and differentiation strategies were entered into model 2, then environmental munificence and dynamism were entered into model 3, and finally the interactive terms between environment and strategy variables were entered into model 4. Results in model 4 of Table 3 indicate that only interactive term between low cost strategy and environmental dynamism is significantly positive related to performance (β=0.91, p<0.001), other interactive terms between environment and strategy variables are not statistically significant. Similarly, results of internal fit between strategy and organizational slack and their effects on performance are shown in Table 4. As indicated in model 4 of Table 4, the interaction term

7 090 J. Bus. Admin. Manage. Sci. Res Table 2. Means, standard deviations, and Pearson correlation matrix. Means S.D Firm age Firm size Prior Performance ** Low cost strategy * 0.34** Differentiation strategy ** ** Environmentalmunificence Environmental dynamism Organizational slack Firm performance ** 0.08** -0.30** Table 3. Results of fit as moderation: External fit between environment and strategy and their effects on firm performance. Model 1 Model 2 Model 3 Model 4 Firm age * * Firm size *** *** *** Prior performance 0.39*** 0.09* 0.09* 0.08 Low cost 0.79*** 0.79*** Differentiation *** *** *** Munificence * * Dynamism Low cost* Munificence 0.04 Low cost* Dynamism 0.91*** Differentiation*Munificence 0.01 Differentiation*Dynamism 0.15 R F value 7.091*** *** *** *** R F change 7.091*** *** *** between low cost strategy and organizational slack is significantly positive related to firm performance (β=0.87, p<0.001), interaction term between differentiation strategy and organizational slack is not significantly related to performance. Thus, in fit as moderation perspective, only external fit between low cost strategy and environmental dynamism, and internal fit between low cost strategy and organizational

8 Lin and Dang 091 Table 4. Results of fit as moderation: Internal fit between strategy and organizational slackand their effects on firm performance. Model 1 Model 2 Model 3 Model 4 Firm age * * * Firm size *** *** Prior performance 0.39*** 0.09* 0.10* 0.15*** Low cost 0.79*** 0.79*** Differentiation *** *** *** Slack *** Low cost*slack 0.87*** Differentiation*Slack 0.02 R F value 7.091*** *** *** *** R F change 7.091*** *** *** Table 5. Results of fit as mediation: External fit between environment and strategy and their effects on firm performance. Panel A Model 1 Model 2 Model 3 Model 4 Model 5 (Per) (Low cost) (Per) (Differentiation) (Per) Firm age * Firm size *** Prior performance 0.39*** 0.32*** 0.14** * 0.35*** Munificence Low cost 0.78*** Differentiation ** R F value 5.620*** 4.683*** **** *** 6.269*** slack is supported. This result indicates that Taiwan semiconductor firms implement low cost strategy can effectively deal with environmental uncertainty and exploit slack resources to enhance their performance. Fit as mediation According to the mediation perspective of fit, environment and organizational slack will influence performance through the mediating effect of strategy. Statistical testing is operated by the method of Baron and Kenny (1986). Table 5 panels A and B present the mediating role of strategic variables on the relationship between environmental variables and firm performance. As shown in Table 5, neither environmental munificence nor dynamism affects performance through low cost and differentiation strategies, representing no mediating effect of strategy on the relationship between environment and performance. Similarly, Table 6 shows the results of mediating effect of strategy on the relationship between organizational slack and performance. The results indicate that both low cost and differentiation strategies do not mediate the relationship between organizational slack and firm performance. Therefore, in mediation perspective of fit, neither external fit between environment and strategy nor internal fit between strategy and organizational slack is supported. Fit as matching In matching approach, fit can be tested with reference to performance (Venkatraman 1989). According to deviation score method, absolute differences between the standardized scores of environment and strategy as well as between strategy and organizational slack are computed to indicate the lack of fit. If these difference scores are high, they represent a higher mismatch between variables and must lead to decreasing performance. Tables 7 and 8 show the results of this matching approach of fit. As shown in Table 7, only the

9 092 J. Bus. Admin. Manage. Sci. Res Table 6. Results of fit as mediation: Internal fit between strategy and organizational slack and their effects on firm performance. Model 1 (Per) Model 2 (Low cost) Model 3 (Per) Model 4 (Differentiation) Model 5 (Per) Firm age Firm size *** Prior performance 0.39*** 0.32*** 0.14** * 0.35*** Slack Low cost 0.78*** Differentiation ** R F value 5.274*** 4.784*** *** *** 3.776** Table 7. Results of fit as matching: External fit between environment and strategy and their effects on firm performance. Model 1 Model 2 Firm age Firm size ** Prior performance 0.39*** 0.05 Munificence Low cost Munificence Differentiation * Dynamism Low cost 4.13 Dynamism Differentiation R F value 7.091*** *** Table 8. Results of fit as matching: Internal fit between strategy and organizational slack and their effects on firm performance. Model 1 Model 2 Firm age Firm size * Prior performance 0.39*** 0.15* Slack Low cost *** Slack Differentiation R F value 7.091*** *** absolute difference scores between environmental munificence and differentiation strategy are significantly related to performance (β=-0.17, p<0.05). However, the absolute difference scores between other environmental variables and strategic variables are not significantly related to firm performance. Besides, results in Table 8 show that only the absolute difference scores between low cost strategy and organizational slack are significantly and positively related to performance (β=- 0.66, p<0.001). Thus, in fit as matching perspective, external fit between environmental munificence and differentiation strategy, and internal fit between low cost strategy and organizational slack are supported. Fit as gestalts This perspective states that fit is the degree of internal congruence among a set of theoretical attributes. Thus, clusters or gestalts of these attributes should be

10 Lin and Dang 093 Table 9. Results of fit as gestalts: Fit between environment, organizational slack, strategy, and their effects on firm performance. Penal A. Clusters solution Stage no. No. of clusters Coefficient Increase in heterogeneity to next stage % % % % Penal B. ANOVA for variables between clusters Variables Clusters 1(n=51) 2(n=63) 3(n=8) F(ANOVA) Munificence (p=0.042) Dynamism (p=0.612) Low cost (p=0.900) Differentiation (p=0.000) Slack (p=0.550) Penal C. ANOVA for performance between clusters Clusters Performance Multiple comparisons Mean S.D 1(n=51) 2(n=48) 3(n=23) 1(n=51) (n=48) P= (n=23) P=0.012 P= F=3.360, p=0.038 determined by a cluster analysis. Table 9 shows the results of gestalts approach of fit. Environment, organizational slack, and strategy are used as clustering variables, and Ward s method and Euclidean distance are applied in hierarchical cluster analysis. First, as shown in panel A, based on the increase in heterogeneity to the next stage (Hair, Black, Babin, and Anderson 2010), a three clusters solution was remained with numbers of observations in each cluster are 51, 63 and 8, respectively. Next, means of each attribute of environment, organizational slack, and strategy are compared cross three clusters. As indicated in panel B, only environmental munificence and differentiation strategy are significantly different across clusters (F=2.555, P<0.05; F= , P<0.001). However, other attributes of environment, organizational slack, and strategy do not significantly differ among clusters. Finally, means of performance among three clusters are compared in panel C. The results show that performance appears to be significantly different between cluster 1 and cluster 3 as well as between cluster 2 and cluster 3. However, means of performance between cluster 1 and cluster 2 are not significantly different. These results do not provide evidence for the notion of equifinality as stated by Hambrick (1984) and Miller and Friesen (1984). Therefore, in fit as gestalt perspective, the congruence between environment, organizational slack, and strategy is not supported. Fit as covariation This perspective of fit defines fit as a pattern of covariation among a set of related variables. Fit as covariation can be tested by structural equation modeling, the latent construct represents the coalignment and can be obtained from observed variables of environment, organizational slack, and strategy; the effect of this coalignment construct on performance can also be estimated by the path analysis. As shown in Figure 7, results indicate that environmental munificence is significantly and negatively contributed to the coalignment construct (β=-0.01, p<0.05), low cost strategy is significantly positive contributed to the coalignment construct (β=0.46, p<0.001), and differentiation strategy is also significantly negative contributed to the coalignment construct (β=-0.21, p<0.001). However, environmental dynamism and organizational slack do not significantly contribute to the coalignment construct, and also the effect of this coalignment construct on performance is not statistically significant. Thus, in covariation approach of fit, the

11 094 J. Bus. Admin. Manage. Sci. Res Munificence * Dynamism Low cost 0.46 *** Fit 1.72 Performance *** Differentiation Slack Figure 7. Results of fit as covariation: Fit among environment, organizational slack, strategy, and their effects on firm performance Table 10. Results of fit as profile deviation: Relationship between misalignment and firm performance. Model1 Model2 Firm age Firm size Prior performance 0.34*** 0.31** MISALIGN -0.47*** R F value 4.528*** *** R F change 4.528*** *** Xc: calibration sample: n=12, standardized means scores (ideal points) are 0.28 for environmental munificence, 0.02 for environmental dynamism, 0.41 for low cost strategy, 0.08 for differentiation strategy, and 0.05 for organizational slacks. coalignment among environment, organizational slack and strategy, and their effects on performance are not supported. Fit as profile deviation This approach of fit defines fit as the degree of adherence to a specified profile, and deviation from this ideal point should result in a lower performance. As stated earlier, according to Venkatraman and Prescott (1990), the top 10 percent of the sample firms in terms of performance were selected as a calibration sample (n=12), the ideal profile was obtained by calculating standardized mean scores along all variables of environment, organizational slack, and strategy in this calibration sample. The bottom 10 percent of the sample firms were then removed so as not to skew the sample downward. The remaining 80 percent of sample firms (n=98) were used to compute the Euclidean distance to the ideal point along variables of environment, organizational slack, and strategy; misalignment or deviation from the ideal profile is assumed to be significantly negative related to performance (Venkatraman, 1989). Table 10 shows the results of regression analysis for fit as profile deviation. Effects of three control variables on performance are controlled in model 1, misalignment scores among environment, organizational slack, and strategy on firm performance are shown in model 2. The results indicate that misalignment scores are significantly

12 Lin and Dang 095 and negatively related to firm performance (β=-0.47, p<0.001), representing profile deviation perspective of fit is supported. DISCUSSION Six different perspectives of fit have been simultaneously examined in this study. Empirical analysis shows the results of different perspectives of fit. First, in moderation perspective of fit, external fit between low cost strategy and environmental dynamism as well as internal fit between low cost strategy and organizational slack are found to be supported. This finding indicates that when Taiwan semiconductor firms implement low cost strategy, they can deal effectively with environmental dynamism and exploit organizational slack to enhance their performance. This can be explained by the fact that most of Taiwan semiconductor firms are manufacturers, and almost their conventional business models are OEM and ODM, low cost is one of the primarily critical strategies for these firms to respond effectively to external conditions and exploit intangible resources to obtain higher performance (Hobday 2001). When firms face high complexity and uncertainty in the environment, they tend to be internally focused and control oriented, through effectively utilizing slack resources and emphasizing internal efficiency, firms can secure a stable niche in the market and obtain competitive advantages (Hambrick 1983). Second, in matching perspective of fit, only external fit between environmental munificence and differentiation strategy is supported, representing Taiwan semiconductor firms implement differentiation strategy will fit well with environmental munificence and enhance firm performance. This result implies that when abundance of resources is available in the environment, firms will have more opportunities to acquire those external resources to provide for their innovative behaviors, thus creating competitive advantages and firm performance (Irwin, Hoffman and Geiger 1998). Besides, only internal fit between low cost strategy and organizational slack in fit as matching approach is found to be supported, this result is similar to the result found in fit as moderation perspective. One possible explanation is that when these Taiwan semiconductor firms implement low cost strategy, they can effectively use organizational slack to expand their capacity, thus scales of economy can lead to cost efficiency and enhance firm performance. Third, in profile deviation perspective of fit, the alignment among environment, organizational slack, and strategy is found to be supported. This finding implies an interesting phenomenon in Taiwan semiconductor industry, namely, some ideal firms exist in this industry. These model firms have higher performance than other firms in the same industry because they have a high degree of fit among environment, organizational slack, and strategy. These ideal firms may be the model role and driver of Taiwan semiconductor industry. Finally, in mediation, gestalts and covariation perspectives of fit, the results reveal no evidence of fit among environment, organizational slack, and strategy. Thus, fit as mediation, gestalts, and covariation perspectives are not supported. Conclusions In this paper, we have empirically tested six different perspectives of fit and simultaneously examined external fit between environment and strategy, and internal fit between organizational slack and strategy, and their impacts on firm performance in Taiwan semiconductor industry. Findings of this study provide comprehensive understanding of the meanings of different types of fit. Previous researchers often choose an available and convenient method to determine the relationships among environment, resources, and strategy. This is because of a lack of correspondence between concepts and statistical testing schemes. Consistent with the claim of previous theoretical research (Drazin and Van de Ven 1985; Venkatraman 1989), the empirical findings of this study confirm that each perspective of fit is appropriate for different theoretical questions and corresponds to different testing methods. Researchers should base on their research questions to select a single suitable concept and testing approach or consider multiple perspectives to determine strategic fit. Moreover, empirical results of this study indicate the important role of organizational slack (intangible resources) in influencing firm s strategic behaviors and in dealing with environmental conditions to enhance firm performance. Additionally, this study reveals interesting findings about Taiwan semiconductor industry: When Taiwan semiconductor firms implement low cost strategy, they can obtain a better fit with environmental dynamism and organizational slack to gain higher performance. Moreover, when these firms implement differentiation strategy, they can also achieve better fit with environmental munificence, thus enhance their performance. Furthermore, there exist some ideal firms in Taiwan semiconductor industry. These firms are better in terms of performance because they formulate and perform strategy that is best fit with environment and slack resources. These firms may be the model role and driver for Taiwan semiconductor industry. Thus, other firms in the same industry should learn from these ideal firms the way they plan and implement business policy and how they build and use intangible resources to increase their performance. Finally, some limitations should be acknowledged in this study. Sample of this study is a cross-sectional data and restricted to only one single industry, and discussion of fit in this paper has focused only on static manner. Future research can gather longitudinal data from different industries to specify and test dynamic concept of

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