Pergamon Journal of Management 27 (2001) 409 – 429 Strategic fit in transitional economies: The case of China’s electronics industry Bryan A. Lukas,a, J. Justin Tan,b, G. Tomas M. Hult,c,* a Department of Management, University of Melbourne, Faculty of Economics and Commerce, Victoria 3010, Australia b Epply College of Business Administration, Creighton University Omaha, NE 68178 c Department of Marketing and Supply Chain Management, Eli Broad Graduate School of Management, Michigan State University, East Lansing, MI 48824-1122 USA Received 21 May 1999; received in revised form 3 May 2000; accepted 31 July 2000 Abstract The strategic fit paradigm, originally derived from observations in market-based economies, asserts that an appropriate match between environment and strategy has significant and positive implications for business performance. Based on a random sample of Chinese electronics firms, this study systematically examines the applicability of the strategic fit paradigm in China’s centrally planned economy in transition. Results indicate that while environment-strategy coalignment is evident, coalignment only improves performance under certain environmental conditions. © 2001 Elsevier Science Inc. All rights reserved. 1. Introduction Strategic fit is a revered theoretical paradigm in theories of organizational adaptation (Zajac, Kraatz & Bresser, 2000) and is omnipresent in strategic marketing (e.g., Gatignon & Xuereb, 1997; Hurley & Hult, 1998; Slater & Narver, 1994) and strategic management (e.g., Miller & Friesen, 1983; Venkatraman & Prescott, 1990). Essentially, the contention is that environment and strategy interact in a dynamic coalignment process (Miller, 1988) and the resulting fit between strategy and its environmental context has positive implications for * Corresponding author. Tel.: 11-517-353-4336; fax: 11-517-432-1112. E-mail address: hult@msu.edu (G.T. Hult). 0149-2063/01/$ – see front matter © 2001 Elsevier Science Inc. All rights reserved. PII: S 0 1 4 9 - 2 0 6 3 ( 0 1 ) 0 0 1 0 1 - 5 410 B.A. Lukas et al. / Journal of Management 27 (2001) 409 – 429 performance (Venkatraman & Prescott, 1990). Although derived almost exclusively from observations in western economies, where the focal firm operates in a stable, market-based economy, the strategic fit paradigm is promoted as a universal strategic framework. For the most part, emerging market economies, such as developing countries in Asia, Latin America, Africa, and the Middle East, as well as transitional economies in the former Soviet Union and China, have been excluded from strategic fit research. This is partly because of their recent economic and political underperformance or isolation and partly because of strategy research’s distaste for replication. As a result, the strategy discipline cannot be sure of the paradigm’s universal applicability, which in turn limits theory building. We suggest that transitional economies are a particularly challenging testing ground for performance concepts centered on environment-strategy linkages. These economies, as the label ‘transitional’ suggests, are in a prolonged state of transformation involving a shift from centrally planned economies to free-market economies. Typically, the process is characterized by a restructuring of administrative frameworks, redistribution of property rights, and shifting discretion over resources allocations—to highlight but a few of the involved changes. As ‘economies in flux’, transitional economies lend themselves especially well to exploring boundary conditions for strategic fit— one of the strategy discipline’s most fundamental premises. The purpose of this study is to examine the western strategic fit paradigm in the context of a transitional economy. The study was conducted in the Peoples’ Republic of China. We chose China because it is the world’s largest economy in transition and involved in far-reaching economic changes. The recent Fourth Plenum of the 15th Central Committee of the Communist Party has defined the major objectives and guidelines for China’s transition until 2010, which shows the extent of transformation underway. In the document, Decision on Major Issues Concerning the Reform and Development of State-Owned Enterprises, the objectives are “basically completing strategic readjustment and restructuring, creating a more rational layout and structure for the state-owned economy, establishing a modern corporate system, improving economic performance, [as well as] strengthening the capability of scientific and technological development, market competition, and risk-taking” (Wang, 2000, p. 9). 2. Research framework According to Venkatraman and Prescott (1990, p. 1), the fundamental question strategic fit research must answer, ultimately, is whether a firm “that aligns its strategic resource deployment to the specific requirements of its environmental context (i.e., achieve an acceptable level of environment-strategy coalignment) perform[s] significantly better than a business unit that does not achieve the requisite match.” We address this question systematically in the context of a transitional economy by means of a two-step approach. After specifying the theoretical constructs, we establish first that environment-strategy coalignment is likely to occur in transitional economies and specify the nature of the alignment in a sequence of hypotheses. Then, by drawing from the strategic fit paradigm and its perfor- B.A. Lukas et al. / Journal of Management 27 (2001) 409 – 429 411 mance implications, we expand on these hypothesized alignments and move to specify their performance impact in a corresponding sequence of hypotheses. 2.1. Theoretical constructs The strategic fit paradigm generally considers organizational environment as an exogenous construct over which firms have limited control. Consequently, organizational environment is thought to determine the context of strategy formulation. Two prominent environmental views prevail in the marketing and management disciplines—the information processing view and the resource dependence view. Both views offer a unique perspective of the environment and can be considered complementary. The first—the information processing view— conceives the environment as a source of information (e.g., Duncan, 1972; Hult, 1998; Hult et al., 2000; Tung, 1979). The second view is that of resource dependence, which suggests that the environment is a repository of scarce resources sought after by competing firms (e.g., March & Simon, 1958; Ruekert, Walker & Roering, 1985). Since Burns and Stalker (1961), several environmental dimensions have been identified that coincide with the information processing and resource dependence views of the environment. Environmental complexity, which can be understood as the predictability of competitive environment, and environmental dynamism, which refers to the change in competitive environment, have been associated with the information uncertainty view (Lawrence & Lorsch, 1967; Thompson, 1967). Environmental hostility, which can be interpreted as the impact of competitive environment on the firm, has been linked to the resource dependence view (Aldrich, 1979; Pfeffer & Salancik, 1978). Variations of complexity, dynamism, and hostility continue to be fundamental environmental dimensions in the marketing literature (e.g., Gatignon & Xuereb, 1997; Jaworski & Kohli, 1993; Slater & Narver, 1994) and management literature (e.g., Dess & Beard, 1984; Miller & Friesen, 1978; Homburg, Krohmer & Workman, 1999). Both these views, when combined with the associated environmental dimensions, provide a framework for the environmental challenges influencing strategic choice in transitional economies. The chief factor in the environment-strategy link over which management has direct control is strategy— defined as a patterned stream of decisions which focus on resource allocations in an attempt to reach a market position consistent with a firm’s environment (cf. Mintzberg, 1973). There are three primary views of strategy alignment: the situation specific view, the universal view, and the contingency view (Hambrick & Lei, 1985). The situation specific view of strategic fit is based on the perspective that two identical environmental settings never occur and, consequently, every strategy is unique. The universal view is based on the premise that universal business strategies exist and apply in all environmental settings. Finally, the contingency view suggests that certain environmental profiles correspond with certain strategic profiles. A number of scholars have made a convincing case suggesting that research concerned with the performance impacts of environment-strategy coalignment can make its greatest contribution by adopting the contingency view (e.g., Hambrick, 1983; Hambrick & Lei, 1985; Miller, 1987; Pinder & Moore, 1979; Mintzberg, 1979). Accordingly, we adopt the contingency view in this study. The comparative approach to strategy conceptualization is frequently used with the 412 B.A. Lukas et al. / Journal of Management 27 (2001) 409 – 429 contingency view and is aimed at identifying the key dimensions of the strategy construct as they relate to the environment (cf. Miller & Friesen, 1984). The appeal of this approach is that it allows the researcher to view variations found across strategy descriptions in a fine-grained manner by observing differences along each underlying dimension, or subset of dimensions (cf. Venkatraman, 1989). Based on Venkatraman (1989) and consistent with the comparative approach to strategy conceptualization, we argue that strategy is best specified as a multifaceted construct consisting of different orientations. Two principal orientations stand out repeatedly in strategy conceptualizations and operationalizations thereof. The first is best characterized as prospective—manifested in terms of a firm’s emphasis on risk-taking as well as on searching for new products, new brands, and new market trends. The second is best characterized as protective—manifested in terms of a firm’s emphasis on analytical problem solving as well as on preserving ownership of products, technologies, and markets (cf. Venkatraman, 1989). Among the more prominent management studies to accommodate these orientations is Dess and Davis’ (1984) work in which entrepreneurial and innovation behaviors are clearly differentiated from optimization and conservation behaviors in an attempt to operationalize Porter’s (1980) generic strategies. A similar differentiation is made by Conant, Mokwa, and Varadarajan (1990) and Lukas (1999) in their operationalization of Miles and Snow’s (1978) strategic archetypes. 2.2. Hypotheses 2.2.1. Environment-strategy coalignment The strategic fit paradigm is built on the premise that for a certain set of environmental conditions, a preferred strategic response exists (cf. Harvey, 1982). We propose that this relationship is evident in formally planned economies in transition. Our reasoning is derived from a small but growing body of research in transitional economies. Studies of strategic choices made by Chinese managers suggest that they could favor more risky strategic decisions (i.e., adopt a prospective strategic orientation) over security-oriented strategic decisions if they perceive their environments as stable (Adler, Brahm & Graham, 1992; Chong, Cragin & Scherling, 1983; Lai & Lam, 1986). On the other hand, environmental uncertainty in transitional economies, followed by the lifting of state-instituted price controls, could cause firms to emphasize defensive and safeguarding strategic behaviors, such as collusive pricing behaviors (i.e., adopt a protective strategic orientation). Taken together, this preliminary research in transitional economies suggests that a decrease (or increase) in environmental uncertainty is likely to be met with protective-oriented strategic behavior (or prospective-oriented strategic behavior). On the basis that environmental uncertainty is best understood in terms of complexity, dynamism, and hostility (Dess & Beard, 1984; Mintzberg, 1979; Thompson, 1967; Tung, 1979), the following hypotheses are offered: H1a: The less complex the environment, the more prospective the strategic orientation. H1b: The more complex the environment, the more protective the strategic orientation. H2a: The less dynamic the environment, the more prospective the strategic orientation. H2b: The more dynamic the environment, the more protective the strategic orientation. B.A. Lukas et al. / Journal of Management 27 (2001) 409 – 429 413 H3a: The less hostile the environment, the more prospective the strategic orientation. H3b: The more hostile the environment, the more protective the strategic orientation. 2.2.2. Performance impact of environment-strategy coalignment The core proposition of the strategic fit paradigm is that environment-strategy coalignment has positive implications for performance (Ginsberg & Venkatraman, 1985; Venkatraman & Prescott, 1990; Miles & Snow, 1994; Zajac et al., 2000). This proposition is rooted in the belief that performance suffers for firms whose strategic allocation of organizational resources is at odds (i.e., not in alignment) with the corresponding environmental context. This profound and intuitively appealing proposition has served as the theoretical and conceptual foundation for numerous studies concerned with performance implications of matching strategy with environment (Anderson & Zeithaml, 1984; Bourgeois, 1980; Hambrick, 1988; Hofer, 1975; Hitt, Ireland & Stadter, 1982; Jauch, Osborn & Glueck, 1980; Prescott, 1986). Empirical studies set in transitional economies clearly point to a pattern of environmentstrategy coalignment induced by market deregulation—as specified in H1, H2, and H3. Consequently, it is both reasonable and appropriate, based on the strategic fit paradigm, to argue that the environment-strategy coalignment pattern specified in these hypotheses will have positive implications for firm performance in transitional economies. Accordingly, we systematically extend our first three hypotheses to specify the following performance outcomes: H4a: The less complex the environment, the more performance-effective the prospective-oriented strategy. H4b: The more complex the environment, the more performance-effective the protective-oriented strategy. H5a: The less dynamic the environment, the more performance-effective the prospective-oriented strategy. H5b: The more dynamic the environment, the more performance-effective the protective-oriented strategy. H6a: The less hostile the environment, the more performance-effective the prospectiveoriented strategy. H6b: The more hostile the environment, the more performance-effective the protectiveoriented strategy. 3. Research methodology 3.1. Data collection Because large-sample, firm-level data are difficult to collect in China, early investigations have relied on case studies (e.g., Boisot & Child, 1988, 1990; Child & Lu, 1990; Nee, 1992). Although highly insightful for theory development, case studies are less useful when the objective of the research is theory testing. In this study, we rely on quantitative analysis of data from a relatively large sample of Chinese electronics firms. We selected the electronics 414 B.A. Lukas et al. / Journal of Management 27 (2001) 409 – 429 industry because it is one of the ‘strategic pillar’ industries identified by the Chinese government to improve performance and it faces substantial environmental turbulence from both deregulation and increased international competition. Tariff and nontariff protections from the government have been largely removed since early stages of economic reform. A large number of multinational corporations have invested in China and provided much needed capital and technology. As a result, this industry has become competitive and a major exporter of low to medium priced electronic products. Because publicly available information at the firm level is not available, we utilized a mail survey to undertake the study. The questionnaire was first reviewed and revised by expert colleagues, and then subjected to back-translation to ensure validity in a cross-cultural setting (cf. Adler et al., 1992). Three hundred and sixty Chinese electronics firms were randomly selected. Our research contact in China drew the sample from the Chinese government’s registry of electronics firms using computer generated random numbers. The selected firms were engaged in the manufacturing, assembly, and service of electronics products. Unfortunately, information concerning the type of technology used was not obtained. Nonetheless, because most Chinese electronics firms rely on standardized global production technology, we do not expect any China-specific technology differences among the firms to affect the responses. The sample included joint ventures between local firms and foreign investors, as well as enterprises controlled by state or township governments and private entrepreneurs. These sample characteristics reflect adequately the current structure of the Chinese electronics industry, thus, ensuring that the sample provides the structural variations necessary to account for the different environmental characteristics faced by Chinese firms. First, 30 firms were randomly selected from our sample. Questionnaires were then sent to each firm’s president and the director in charge of business planning to determine the extent to which we could rely on subjective, single-source firm data from China. Among those questionnaires returned, we received 12 pairs of responses and checked inter-rater consistency. The correlation between the two respondents from each firm was very high, indicating that each individual was able to provide an accurate assessment of the firm. We also asked the respondents to report decisions they considered ‘strategic’. Results displayed good correspondence (correlations range from 0.88 to 0.91) between the respondents’ perception of what strategic decisions are and the strategy measures in the questionnaire. After this initial test, the questionnaire was sent to the presidents of the remaining 330 firms in the sample. We chose the president because we learned through numerous personal interviews during the conceptualization of this study that the ‘President Responsibility System’, implemented since the economic reform, has given them— even those in stateowned and collective-owned enterprises— unprecedented decision-making prerogatives and, consequently, always involves them in major strategic decisions. This decision authority, which would more likely reside with the entire top management team in western firms, is further enhanced by the fact that most Chinese management structures have remained highly centralized around the president’s office, limiting the control function of director boards and other organizational control structures that may be in place. These circumstances make presidents of Chinese firms highly competent key respondents. In the questionnaire, the presidents were guaranteed absolute anonymity. Adler, Campbell, and Laurent (1989) report B.A. Lukas et al. / Journal of Management 27 (2001) 409 – 429 415 that under the condition of anonymity, Chinese managers are more willing and more likely to provide accurate information. Two hundred and one returned questionnaires were used in the present study. To assess nonresponse bias, we divided the questionnaires into four subgroups of equal size and compared the first and last groups on the means of the study variables (Armstrong & Overton, 1977). We found no significant differences between early and late respondents for any of the variables. Accordingly, we rule out the possibility of systematic nonresponse bias. 3.2. Measures Environmental uncertainty, strategic orientation, and firm performance are concepts originally developed for capitalist and western economies. A number of studies show that these concepts also apply to, and can be interpreted by respondents in, formally planned economies in transition (e.g., Adler et al., 1992; Luo, 1999). Nonetheless, to ensure that all Chinese respondents understood the issues involved, the study constructs were defined at the beginning of each measurement section (see Appendix) and the corresponding scale items were worded in simple terms. Further, to reduce the possibility of common method and source variance, which could result in spurious results distorting the significance of the findings, different response formats for each measurement construct were used. Environmental uncertainty was measured along three dimensions— complexity, dynamism, and hostility. The complexity scale assessed the predictability of competition, technology, regulation, and international developments. The dynamism scale measured the change observed in customers, technology, regulation, and suppliers. Finally, the hostility scale assessed the impact of customers, economy, socio-cultural requirements, and international developments on the respondents’ firms (cf. Jauch et al., 1980; Khandwalla, 1977). Each scale consisted of four items and used a seven-point response format (see Appendix). Strategic Orientation was measured along four dimensions—risk, proactiveness, analysis, and defensiveness. Although more dimensions of strategic orientation exist in the literature (e.g., Venkatraman, 1989), the four dimensions selected covered a broad range of strategy aspects applicable to the Chinese environment. The risk scale measured a firm’s propensity to make high-risk investments, make bold decisions despite the uncertainty of their outcomes, and approve new projects with ‘blanket’ approval rather than on a ‘stage-by-stage’ basis. The proactiveness scale measured a firm’s desire to constantly introduce new brands and products, move proactively to try to capitalize on ambiguity in government regulation, and respond to product-market opportunities quickly. The analysis scale measured a firm’s willingness to emphasize planning techniques and information systems, evaluate decision consequences thoroughly and obtain alternatives, and seek demonstrably promising opportunities. The defensiveness scale measured a firm’s desire to use cost control systems for monitoring performance, constantly modify manufacturing technology to achieve efficiency, and follow government regulations. Each scale consisted of three measurement items and used a seven-point response format (see Appendix). Because risk and proactiveness represent complementary aspects of a prospective orientation (cf. Venkatraman, 1989), they were combined to form a single measure of prospective orientation. Because analysis and defen- 416 B.A. Lukas et al. / Journal of Management 27 (2001) 409 – 429 siveness represent complementary aspects of a protective orientation (cf. Venkatraman, 1989), they were combined to form a single measure of protective orientation. Performance was assessed with five questions devised to determine a firm’s performance relative to close competitors. Pretesting indicated that ‘close competitors’ were typically understood by Chinese managers to be those who focused on the same markets with similar products. Managers were asked to identify their firm’s relative performance based on after-tax return on total assets, after-tax return on total sales, total sales growth, overall firm performance and success, and competitive position. Because the Chinese government uses these performance indicators to evaluate firm performance in all industries on an annual basis, Chinese managers are familiar with them. A five-point response format was used (see Appendix). 3.3. Psychometric properties of measures After collecting the data, the measures were subjected to a purification process involving a series of reliability and validity assessments. The psychometric properties of the eight constructs were evaluated by employing the method of confirmatory factor analysis (CFA) via the use of LISREL (Jöreskog & Sörbom, 1996; Jöreskog, Sörbom, Du Toit & Du Toit, 1999). The model fits were evaluated using the DELTA2 index, the relative noncentrality index (RNI), the comparative fit index (CFI), the Tucker–Lewis index (TLI), and the root mean square error or approximation index (RMSEA). These fit indices have been shown as the most stable in confirmatory factor analysis and structural equation modeling (Gerbing & Anderson, 1992; Hu & Bentler, 1999). The specific items were evaluated based on the item’s error variance, modification index (,3.84), and residual covariation (, 2.58 ) (Anderson & Gerbing, 1988; Jöreskog & Sörbom, 1996; Jöreskog et al., 1999). Utilizing these criteria, the confirmatory factor model resulted in a moderate but acceptable fit to the data (DELTA2 5 0.80, RNI 5 0.80, CFI 5 0.80, TLI 5 0.80, RMSEA 5 0.10, x2 5 1061.15, df. 5 349) (Hair, Anderson, Tatham, and Black, 1998). The parameter estimates (factor loadings) were all significant (t-values ranged from 8.09 to 16.72, p , .01) and ranged from 0.70 to 0.88 for risk, 0.76 to 0.92 for proactiveness, 0.79 to 0.89 for analysis, 0.83 to 0.90 for defensiveness, 0.71 to 0.89 for complexity, 0.63 to 0.85 for dynamism, 0.61 to 0.77 for hostility, and 0.57 to 0.97 for performance. Next, we assessed the reliability of the measures. Within the CFA setting, composite reliability is calculated using the procedures outlined by Fornell and Larcker (1981), based on the work by Werts, Linn, and Jöreskog (1974). The formula specifies that: CRh 5 (Slgi)2/[(Slgi)21(Sei)], where CRh 5 composite reliability for scale h, lyi 5 standardized loading for scale item gi, and ei 5 measurement error for scale item gi. We also examined the parameter estimates and their associated t-values, and assessed the average variance extracted for each construct (e.g., Anderson & Gerbing 1988). The reliabilities for risk (composite reliability 5 0.80), proactiveness (0.83), analysis (0.83), defensiveness (0.86), complexity (0.85), dynamism (0.79), hostility (0.75), and performance (0.84) exceed the commonly accepted reliability cutoff of 0.70, indicating excellent levels of composite reliability for the eight constructs in the sample (Jöreskog & Sörbom, 1996; Jöreskog et al., 1999). Similarly, the average variances extracted (explained variances) of the eight con- B.A. Lukas et al. / Journal of Management 27 (2001) 409 – 429 417 structs were good, with risk (average variance extracted of 57.33%), proactiveness (61.33%), analysis (62.67%), defensiveness (67.00%), complexity (57.25%), dynamism (48.75%), hostility (43.25%), and performance (53.40%) all having average variances extracted above 40%. Discriminant validity was assessed using the procedure recommended by Anderson (1987) and Bagozzi and Phillips (1982). This entails analyzing all possible pairs of constructs in a series of two-factor CFA models using LISREL. Each model was run twice— once constraining the phi coefficient (f) to unity and once freeing this parameter. A x2 (x2) difference test was then performed on the nested models to assess if the x2 values were significantly lower for the unconstrained models (Anderson and Gerbing, 1988). The critical value (Dx2(1).3.84) was exceeded in all 28 pairwise cases (in total 56 CFAs were analyzed). The lowest Dx2 were found between the risk and defensiveness scales. Analyzing these two scales simultaneously, the unconstrained model (U) resulted in a x2 5 35.53, df. 5 8, while the constrained model (C) resulted in a x2 5 82.12, df. 5 9. As such, Dx2(1) 5 46.59, when comparing the U and C models. This is significantly above the critical value of Dx2(1).3.84. All other combinations resulted in higher Dx2(1) than between the risk and defensiveness scales. Thus, overall, the eight measurement scales can be considered reliable, valid, and stable in the context of this study. 3.4. Hypotheses analysis Multiple regression analysis and hierarchical regression analysis with interactions were used to test the hypotheses—an analysis approach that is widely used in coalignment research (Venkatraman & Prescott, 1990). First, multiple regression analysis was used to examine how environmental uncertainty and strategic orientation aligns—as specified in H1, H2, and H3. Then, hierarchical regression analysis with interactions was used to examine how alignment between environmental uncertainty and strategic orientation improves firm performance—as specified in H4, H5, and H6. The independent variables were entered into the hierarchical regression model in the first step and the interaction terms were entered in a second step. The independent variables were mean-centered to reduce the potential effects of collinearity associated with interactive regression modeling. Next, slope analyses were conducted to improve understanding of the significant interaction effects. These procedures allow for analysis of significant interactions at different levels of the continuous moderator variables without creating categorical versions. They have been adopted by studies with a contingency focus in both the management literature (e.g., Simerly & Li, 2000) and marketing literature (e.g., Baker & Sinkula, 1999). Following recommendations by Aiken and West (1991), a series of simple slopes were derived by entering different values for the significant moderating variables into the interactive regression model. Usually, one standard deviation above and below the mean value are entered as the high and low values (e.g., Baker & Sinkula, 1999), but two or more standard deviations above and below the mean value can be entered for a richer analysis of the significant interactions (cf. Simerly & Li, 2000). Given the strong contingency focus of the present study, regressions were conducted at very high (two standard deviations above), high (one 418 Variables 1. Performance 2. Prospective Orientation (a) 3. Protective Orientation (b) 4. Complexity (c) 5. Dynamism (d) 6. Hostility (e) 7. a 3 c 8. a 3 d 9. a 3 e 10. b 3 c 11. b 3 d 12. b 3 e Mean s.d. 1 3.103 .992 1.000 4.189 .761 .146* 2 3 4 5 6 7 8 9 10 11 12 1.000 4.337 .773 .316*** 2.715*** 1.000 5.455 5.138 4.142 22.758 21.333 17.170 23.931 22.506 18.141 .835 .784 .733 4.915 4.410 4.034 6.198 5.929 4.993 * p # .05 (two-tailed). ** p # .01 (two-tailed). *** p # .001 (two-tailed). .315*** 2.210** .253*** 1.000 .439*** 2.264*** .279*** .471*** 1.000 .387*** 2.197** .261*** .363*** .126 1.000 .089 .698*** 2.455*** .543*** .121 .076 1.000 .196** .667*** 2.412*** .189** .530***2.075 .718*** 1.000 .155* .645*** 2.379*** .093 2.106 .611*** .613*** .485*** 1.000 .388*** 2.630*** .839*** .734*** .459*** .382***2.022 2.198** 2.223** 1.000 .468*** 2.618*** .825*** .434*** .768*** .242***2.235*** .035 2.311*** .824*** 1.000 .441*** 2.591*** .799*** .382*** .240*** .782***2.257*** 2.332*** .121 .759*** .662*** 1.000 B.A. Lukas et al. / Journal of Management 27 (2001) 409 – 429 Table 1 Descriptive statistics and correlations B.A. Lukas et al. / Journal of Management 27 (2001) 409 – 429 419 Table 2 Results of multiple regression analysis Independent Variables Prospective Orientation b S.E. b t b Protective Orientation S.E. b t Constant Complexity Dynamism Hostility R2 Adjusted R 2 F 4.175 2.051 2.218 2.166 .107 .092 7.328*** .054 .078 .076 .078 2.055 2.226 2.159 77.681*** 2.659 22.850** 22.127* 4.370 .075 .185 .220 .130 .116 9.167*** .052 .076 .074 .076 .083 .195 .215 83.801*** .999 2.491** 2.914** * p # .05 (one-tailed). ** p # .01 (one-tailed). *** p # .001 (one-tailed). standard deviation above), low (one standard deviation below) and very low (two standard deviations below) levels of environmental uncertainty. 4. Research results Table 1 shows the means, standard deviations, and correlation matrix for the variables of the study. The correlation values for the independent variables remain below the critical level of 0.90 specified by Hair et al. (1998) as a first indication of problematic collinearity. Table 2 shows the multiple regression results for H1, H2, and H3. Because the hypotheses are directional, one-tailed tests of significance are reported. H1a predicts that the less complex the environment, the more prospective the strategic orientation. Results indicate a nonsignificant relationship between complexity and prospective orientation. Thus, H1a is not supported. H1b predicts that the more complex the environment, the more protective the strategic orientation. Results indicate a nonsignificant relationship between complexity and protective orientation. Therefore, H1b is not supported. H2a predicts that the less dynamic the environment, the more prospective the strategic orientation. Results indicate a significant negative relationship between dynamism and prospective orientation (b 5 20.218, t 5 22.850, p # .01), which supports H2a. H2b predicts that the more dynamic the environment, the more protective the strategic orientation. Results indicate a significant positive relationship between dynamism and protective orientation (b 5 0.185, t 5 2.491, p # .01), which supports H2b. H3a predicts that the less hostile the environment, the more prospective the strategic orientation. Results indicate a significant negative relationship between hostility and prospective orientation (b 5 20.166, t 5 22.127, p # .05), thus supporting H3a. H3b predicts that the more hostile the environment, the more protective the strategic orientation. Results indicate a significant positive relationship between hostility and protective orientation (b 5 0.220, t 5 2.914, p # .01), thus supporting H3b. Table 3 shows the hierarchical regression results with interactions for H4, H5, and H6. As a set, the interaction terms explain a significant portion of the variance in firm performance 420 B.A. Lukas et al. / Journal of Management 27 (2001) 409 – 429 Table 3 Results of multiple regression analysis with interactions: performance of strategic orientation Independent Variables Performance Step 1 b Constant Prospective Orientation (a) Protective Orientation (b) Complexity (c) Dynamism (d) Hostility (e) a3c a3d a3e b3c b3d b3e R2 Adjusted R 2 F Change in R 2 Change in F 3.103 .225 .323 .012 .483 .417 .329 .311 17.884*** Step 2 S.E. b t b S.E. b t .061 .119 .123 .088 .088 .090 .175 .246 .011 .388 .310 51.284*** 1.896* 2.638*** .144 5.496*** 4.652*** 3.083 .292 .375 .018 .548 .427 .406 2.659 2.338 .285 2.631 2.132 .378 .339 9.735*** .049 2.280*‡ .066 .124 .124 .095 .091 .092 .208 .221 .202 .242 .249 .234 .226 .286 .016 .440 .318 .246 2.401 2.187 .140 2.313 2.064 46.745*** 2.349** 3.030** .198 5.994*** 4.657*** 1.948* 22.978** 21.673* 1.176 22.533** 2.564 *‡ p # .05 (two-tailed). * p # .05 (one-tailed). ** p # .01 (one-tailed). *** p # .001 (one-tailed). (change in R2 5 0.049, p # .05). In the following, one-tailed tests of significance are reported, because the hypotheses are directional. H4a predicts that the less complex the environment, the more performance-effective the prospective strategic orientation. Results indicate that complexity has a significant positive interaction with prospective orientation on firm performance (b 5 0.406, t 5 1.948, p # .05), which is the opposite direction to that hypothesized. Thus, H4a is not supported. The follow-up slope analysis (slope analyses are not reported in table format because of space constraints) indicates that the relationship between prospective orientation and firm performance is not significant when complexity remains below the mean value at low and very low levels. However, it is significant and positive at high levels of complexity (b 5 0.631, t 5 2.616, p # .01), and is significant, positive, and stronger at very high levels of complexity (b 5 0.970, t 5 2.414, p # .01). H4b predicts that the more complex the environment, the more performance-effective the protective strategic orientation. Results indicate no significant interaction between protective orientation and complexity on firm performance. Therefore, H4b is not supported. Because the main effect of protective orientation on firm performance is significant and positive (b 5 0.375, t 5 3.030, p # .01), the nonsignificant interaction suggests that protective-oriented strategies positively influence firm performance regardless of environmental complexity. H5a predicts that the less dynamic the environment, the more performance-effective the prospective strategic orientation. Results indicate that dynamism has a significant negative interaction with prospective orientation on firm performance (b 5 20.659, t 5 22.978, p # B.A. Lukas et al. / Journal of Management 27 (2001) 409 – 429 421 .01), which supports H5a. The follow-up slope analysis shows that the relationship between prospective orientation and firm performance is significant and positive when dynamism is very low (b 5 1.325, t 5 3.473, p # .001), is significant and positive, but weaker, when dynamism reaches a moderately low level (b 5 0.808, t 5 3.601, p # .001), and is not significant when dynamism exceeds mean value to reach high and very high levels. H5b predicts that the more dynamic the environment, the more performance-effective the protective strategic orientation. Results indicate that complexity has a significant negative interaction with protective orientation on firm performance (b 5 20.631, t 5 22.533, p # .01), which is the opposite direction to that hypothesized. Therefore, H5b is not supported. The follow-up slope analysis shows that the relationship between protective orientation and firm performance is significant and positive when dynamism is very low (b 5 1.364, t 5 3.253, p # .001), is significant and positive, but weaker, when dynamism reaches a moderately low level (b 5 0.870, t 5 3.628, p # .001), and is not significant when dynamism exceeds mean value to reach high and very high levels. H6a predicts that the less hostile the environment, the more performance-effective the prospective strategic orientation. Results indicate that hostility has a significant negative interaction with prospective orientation on firm performance (b 5 20.338, t 5 21.673, p # .05), which supports H6a. The follow-up slope analysis shows that the relationship between prospective orientation and firm performance is significant and positive when hostility is very low (b 5 0.788, t 5 2.281, p # .05), is significant and positive, but weaker, when hostility reaches a moderately low level (b 5 0.540, t 5 2.534, p # .01), and is not significant when hostility exceeds mean value to reach high and very high levels. Finally, H6b predicts that the more hostile the environment, the more performanceeffective the protective strategic orientation. Results indicate no significant interaction between protective orientation and hostility on firm performance. Hence, H6b is not supported. Given the significant positive main effect of protective orientation on firm performance (b 5 0.375, t 5 3.030, p # .01), the nonsignificant interaction indicates that protective-oriented strategies positively influence firm performance regardless of environmental hostility. 5. Discussion This study examined environment-strategy coalignment and the performance implications in an industry-specific context of China’s transitional economy. Adopting a reductionistic perspective of coalignment, six hypotheses were developed. Three were based on the assumption that environment-strategy coalignment can be understood in terms of pair-wise coalignment among the individual dimensions, or dimension sets, that represent the two constructs. The other three were based on the assumption that performance implications of environment-strategy coalignment can be understood in terms of performance impact of the before mentioned pair-wise coalignment. The discussion of the analysis is organized around the study findings and the study limitations. 422 B.A. Lukas et al. / Journal of Management 27 (2001) 409 – 429 5.1. Study findings Two major findings emerged from our study in the Chinese electronics industry. The first finding is that environment-strategy coalignment is evident in China’s transitional economy. As expected, a strategic orientation is more prospective when a firm experiences low levels of environmental dynamism and hostility— conditions that characterize predictable (i.e., less uncertain) markets. Moreover, as predicted, a strategic orientation is more protective when a firm is confronted with high levels of environmental dynamism and hostility— conditions that characterize uncertain markets. The second finding is that environment-strategy coalignment has mixed performance implications in China’s transitional economy. As predicted, a prospective strategic orientation is more performance-effective at lower levels of environmental dynamism and hostility. However, contrary to predictions, a prospective strategic orientation has no impact on firm performance at lower levels of environmental complexity. Also contrary to predictions, the impact of a protective strategic orientation on firm performance is not contingent on the level of environmental complexity and hostility, and has no impact on firm performance at higher levels of environmental dynamism. Taken together, these findings show that prospective and protective stances are viable strategic orientations in China’s transitional economy and are influenced by environmental uncertainty. The findings also show that the importance of environment-strategy coalignment for firm performance is contingent on the level of environmental uncertainty encountered. Overall, our findings confirm the expected direction of a firm’s strategic orientation in response to environmental uncertainty. However, a firm’s performance does not necessarily improve when its strategic orientation matches the level of environmental uncertainty encountered. This is not consistent with the strategic fit paradigm, which asserts that an appropriate match between environment and strategy has significant and positive implications for performance (e.g., Ginsberg & Venkatraman, 1985; Venkatraman & Prescott, 1990; Miles & Snow, 1994; Zajac et al., 2000). This suggests that the strategic fit paradigm, as it is currently conceptualized in the strategy literature, is overly general and, therefore, could incur overly optimistic performance predictions when applied to a centrally planned economy in transition. 5.2. Study limitations Our results must be qualified in at least two ways. One limitation is that perceptual measures of strategic fit were used instead of objective measures, which could bias the findings. However, perceptual measures of strategic fit have some tradition in strategy research. Lawrence and Lorsch (1967) and Pfeffer and Salancik (1978) do not favor ‘objective’ measures, such as archival measures established by third parties, because strategy making is a function of perceived environment—and only factors that firms perceive can be considered in the strategy formulation process. Perceptual measures of firm performance were also used. In this case, we had no choice because financial and accounting data are not made available to the Chinese public. Fortunately, firms are able to compare their relative performance with close competitors using key indicators supplied by the Chinese govern- B.A. Lukas et al. / Journal of Management 27 (2001) 409 – 429 423 ment. This way, the Chinese government encourages ‘socialist style’ competition, which is intended to develop ‘friendly’ competition among firms. Nonetheless, while theoretical considerations justify the use of perceptual measures of strategic fit—and the practical difficulties of collecting data in China determine our performance assessment approach— ‘objective’ data could provide an interesting alternative data platform for analysis. Another limitation is that strategic fit was operationalized in terms of bivariate alignments of environmental and strategic dimensions which could result in ‘logical typing’ error— meaningless relationships between individual interaction components and a criterion variable when the sum of individual components does not represent the whole (Bateson, 1979; Venkatraman & Prescott, 1990). However, alternative operationalizations of coalignment as holistic manifestations are not without substantial pitfalls. Holistic approaches to coalignment research, delineated in detail by Venkatraman and Prescott (1990), specify strategic fit as ‘profile deviation’, which states that strategic fit is the degree to which strategic orientation adheres to an ‘ideal profile’ for a given environment. Unfortunately, these schemes are fundamentally dependent on the development of an ‘ideal profile’, a process that represents a serious theoretical and empirical challenge to researchers. Nonetheless, a holistic approach reduces the possibility of ‘logical typing’ error and could provide a more systemic view of strategic fit in China than our more traditional, disaggregated approach. 6. Conclusion Based on research in free-market economies, strategic fit is a generally accepted paradigm in the strategy literature, but few studies have verified this proposition in other types of economies such as centrally planned economies in transition. While strategic fit research should continue in established market economies, understanding the performance impact of environment-strategy coalignment in different environmental circumstances, especially transitional contexts, is most important to exploring boundary conditions of the theoretical paradigm. Our findings show that the premise of environment-strategy coalignment applies to China’s transition economy. However, the performance effects appear to be more dynamic than existing discussions of strategic fit would warrant. This suggests that the strategic fit paradigm should be refined before it is relied on in transitional economies. Two issues stand out that require investigation in future studies before more specific recommendations can be made. The first issue relates to how the theoretical proposition of strategic fit applies across different centrally planned economies in transition. China, as well as other transitional economies in Eastern Europe and former Soviet republics, inherited the same centralized planning system from the former Soviet Union. Many of the idiosyncrasies that emerged directly from this socialist tradition are quite enduring. However, even though the government remains an influential player, economic reform in the last two decades has resulted in a multitude of structures in which different organizational types coexist and compete. These transitional economies present a wide array of opportunities and challenges that matter to organizational researchers as well as practitioners. Replication and extension 424 B.A. Lukas et al. / Journal of Management 27 (2001) 409 – 429 of our study in other formally planned economies will help verify the need to refine our understanding of strategic fit in transitional economies. The second issue relates to how additional variables not considered in the present study, such as organizational ownership and size, affect the concept of strategic fit in transitional economies. Traditionally, coalignment studies have opted to focus exclusively on the fit issue without incorporating ownership or size in their models (e.g., Venkatraman & Prescott, 1990; Jauch et al., 1980; Prescott, 1986). As these variables change, however, so does the number of decision-makers and stakeholders, thus potentially affecting direction and magnitude of strategic orientation. Apart from including ownership and size as control variables in the environment-strategy relationship, a deeper understanding of their moderating effects may become critical when recalibrating the link between performance and environment-strategy coalignment for application in transitional economies. Regarding ownership, for example, it could be that privately owned Chinese enterprises are more likely to adopt a prospective orientation than state-owned Chinese enterprises to achieve superior performance. This is because entrepreneurial incentives might be stronger for this ownership type than for state-owned enterprises, which still must meet government-specified production targets with selected factor resources. In conclusion, we hope that our study will serve to span the boundary between where the strategy discipline has been and where it needs to go to further develop strategic fit theory. Acknowledgments This research was supported by a fellowship from the Chiang Ching-kuo Foundation for International Scholarly Exchange and grants from both the Chinese University of Hong Kong and University of Melbourne. The authors thank the Editor K. Michele Kacmar, Gregory J. Whitwell, and three anonymous reviewers for their helpful suggestions on drafts of this article. Bryan A. Lukas is Director of the Masters of Applied Commerce Program and Associate Professor of Marketing in the Department of Management at the University of Melbourne, Australia. His research interests are product development, marketing strategy, and market orientation. He has published in Journal of the Academy of Marketing Science, Journal of Business Research, Industrial Marketing Management, and Journal of Market-Focused Management, among others. Justin Tan is a Professor of Entrepreneurship at Creighton University and a Senior Research Fellow with the Ivey Business School at the University of Western Ontario. His research areas include organizational environment and strategy in transitional economies, joint venture management, cross-cultural management, and entrepreneurship. B.A. Lukas et al. / Journal of Management 27 (2001) 409 – 429 425 G. Tomas M. 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