Application of Generic Strategies and Models
Ting, C. (2010). Corporate competitive strategies in a transitional manufacturing industry: An empirical study. Management Decision, 48(6), 976–995. doi: 10.1108/00251741011053497 (ProQuest Document ID: 578010952)
Introduction
With the liberalization of international trade and financial markets, an increasingly interconnected global economy has been emerging ([19] Dicken, 2007). Nowadays, companies are facing more radical changes than ever before to which they must adapt to survive and prosper ([32] Gereffi, 2001). These changes have been widely felt across many sectors of industry and commerce, including the US textile industry (e.g. [4] Anson et al., 2003; [42] Kilduff, 2005).
In the past two decades, the US textile industry has been experiencing a fundamental transition similar to those unfolding in many other US manufacturing sectors ([14] Chi, 2009). As pillar of industrialization, it has been at the forefront of globalization in terms of confronting international competition, and has seen the emergence of large retail groups exercising control over the product agenda while seeking out lowest cost sources of supply ([33] Gereffi and Memedovic, 2003). Competitive pressures have steadily escalated as a result of continued international trade liberalization, including the phase-out of textile and apparel quotas under the World Trade Organization (WTO), the creation of the North American Free Trade Area (NAFTA), and the growing number of US bilateral preferential/free trade agreements (P/FTAs) ([2] Amponsah and Boadu, 2002; [59] Taplin, 2003). Against this backdrop, the industry as a whole has experienced a sharp downturn since 1997 ([42] Kilduff, 2005).
Table I [Figure omitted. See Article Image.] exhibits the US mill fiber consumption by end-use destination in 1992, 1997, 2002, and 2007 respectively. The drastic contraction in apparel and home textile productions since 1997 and the downward trend in carpet production in recent years are evident while fiber consumption in technical type products has remained much more resilient. This situation has been further reinforced by a wave of technological innovation over the last few years that has advanced process and product technologies, and diversified the numbers and applications of technical textile products ([12] Chang and Kilduff, 2002). [15] Chi et al. (2005) estimated that the value of technical textile shipments in the USA was around $20 billion in 2002, accounting for some 33 percent of total value of shipments by the US textile industry. The total workforce in this sector of the industry increased slightly between 1997 and 2007, reaching some 230 thousand in the latter year ([64] United States Department of Labor, 2009). This contrasts sharply with the apparent decline of overall textile and apparel employment over the same time period. [58] Smith (2001) indicated that the US technical textile sector has established strong position in the domestic market and the rapid growth of international markets creates even broader opportunities for this sector. The definition and scope of technical textiles are provided in the Appendix. As competition continues to escalate across traditional textile manufacturing sectors, many US apparel-related and household end-use yarn and fabric manufacturers are seeking to switch over to technical products to survive and grow ([12] Chang and Kilduff, 2002; [14] Chi, 2009).
Given the bright future and growing importance of technical textile sector within the US textile economy, there has been very little empirically based research devoted to understanding this critical sector. This is in part because much of the literature focuses on aggregate trends in textiles and apparel (e.g., [38] Hunter et al. , 2002; [42] Kilduff, 2005; [54] Rees and Hathcote, 2004). It is also because technical textile sector was a relatively small fraction of industry activity in the past and this has perhaps led to an unconscious neglect ([14] Chi, 2009).
In an effort to fill this gap in the literature, as an exploratory study, this research took a strategic approach to analyze how the US technical textile manufacturing companies managed their business operations and to determine whether there are differences on competitive priorities between high performing companies and low performing companies. By identifying the differences, the high performers will be able to maintain and further improve their competitiveness while the low performers will be able to find the problems and adjust or redesign their strategies. Competitive priority model consisting of four constructs low cost, quality, delivery performance, and flexibility, one of the most widely accepted operations strategy frameworks, was utilized to construct the analysis. Primary data was collected through a survey of senior executives in the US technical textile companies. Using 202 eligible survey returns, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) within structural equation modeling (SEM) were carried out to assess the model-to-data fit, unidimentionality, reliability, and validity of the model.
The remainder of this article is organized as follows. The next section reviews the relevant literature. Competitive priority model is then introduced with the corresponding measures and scales for each construct in the model. In the methodology section, the survey subjects, data sets, and statistical methods are described respectively. The results and discussion follow thereafter. Next, the conclusions are drawn based on the findings and the implications for both academic researchers and industrial practitioners are presented. Finally, some limitations of this study are addressed and some directions for future research are offered.
Literature review
Over the last four decades, the acceptance and use of strategic approaches to manage manufacturing organizations have experienced a continued growth. Since [56] Skinner's (1969) early work in the field, a common thread in operations strategy research has been the need of companies for choosing among and achieving one or multiple key capabilities ([67] Ward and Duray, 2000). Consistent with the mainstream of literature, the term competitive priorities has been broadly used to describe companies' choice of these competitive capabilities (e.g., [16] Chopra and Meindl, 2009; [37] Hayes and Wheelwright, 1984; [68] Ward et al. , 1995; [67] Ward and Duray, 2000). There are some other terms or classifications also proposed and/or used to describe and explore these concepts. For instance, manufacturing tasks was used by [56] Skinner (1969), [55] Richardson et al. (1985), and [5] Berry et al. (1991). developed a typology from a strategic perspective to categorize companies into one of the four groups namely prospector, analyzer, defender, and reactor.[46] Miles and Snow (1978) [1] Adam and Swamidass (1989) proposed to use content and content variables. [23] Ferdows and De Meyer (1990) labeled as organizational priorities and generic capabilities. [29] Fitzsimmons et al. (1991) named dimensions of competition. In spite of the differences in terminology, there is a general agreement in the literature that competitive priorities can be expressed in terms of low cost, quality, delivery performance (speed and reliability), and flexibility (e.g., [5] Berry et al. , 1991; [13] Chen and Paulraj, 2004; [37] Hayes and Wheelwright, 1984; [56] Skinner, 1969, [57] 1985; [67] Ward and Duray, 2000). These four constructs collectively measure the content of a company's competitive strategies ([68] Ward et al. , 1995).
Although all manufacturers are concerned to some degree with cost, most do not compete solely or even primarily on low cost . Companies that emphasize cost as a competitive priority usually focus on lowering production costs, improving productivity, maximizing capacity utilization, and reducing inventories ([37] Hayes and Wheelwright, 1984; [68] Ward et al. , 1995).
Engineering, marketing, manufacturing, and service functions have often been described as possessing different definitions of quality ([68] Ward et al. , 1995). Manufacturing's traditional observance of quality control reflects a focus on the conformance dimension of quality such as providing high performance design, offer consistent and reliable quality, and conformance to product design specification ([30] Flynn et al. , 1990; [67] Ward and Duray, 2000).
Delivery performance comprises reliability and speed. Delivery reliability is the ability to deliver according to a promised schedule. Here the business unit may not have the least costly nor the highest quality product but is able to compete on the basis of reliably delivering products as promised ([30] Flynn et al. , 1990). For some customers, only delivery reliability is not good enough, delivery speed is also necessary to win the order. Although the two dimensions are separable, long run success requires that promises of speedy delivery be kept with a high degree of reliability ([7] Boyer and Pagell, 2000; [30] Flynn et al. , 1990; [67] Ward and Duray, 2000).
Flexibility in manufacturing companies has traditionally been achieved at a high cost by using generic purpose machinery instead of more efficient special purpose-built machinery and by deploying more highly skilled workers than would otherwise be needed ([68] Ward et al. , 1995; [69] Ward et al. , 1996). Advanced manufacturing technologies, when properly implemented, have reduced the cost of achieving flexibility ([7] Boyer and Pagell, 2000).
[57] Skinner (1985) stressed that each of these four competitive priorities must be given a weight by the company that reflects the degree of emphasis required to achieve the overall goals at a corporate level. The weights associated with each priority provide a broad measure of what a manufacturer deems important at a particular time.
The links between company competitive priorities and its business performance were affirmed by [65] Vickery et al. (1993). They found there is covariance relationship between competitive priorities and production competence with business performance. In an empirical study of Singaporean manufacturing companies, [68] Ward et al. (1995) found that a quality, delivery performance, and/or flexibility emphasis aimed at building capabilities for product or service differentiation while a cost emphasis is not. This is consistent with the viewpoint of [53] Porter (1980). proposed that a company can achieve profitability over its competitors in two fundamentally different approaches to strategy[53] Porter (1980) - differentiation or cost leadership. He views differentiation and cost leadership as mutually exclusive strategies. Differentiation strategy offers customers unique products or services that are differentiated in such a way that customers are willing to pay a price premium that exceeds the additional cost of the differentiation. In contrast, cost leadership strategy aims to provide an identical product or service at a lower cost. indicated that a company pursuing both strategies simultaneously is stuck in the middle, which almost guarantees low profitability.[53] Porter (1980) [68] Ward et al. (1995) stressed there is no one particular strategy that is applicable to all types of circumstances. [70] Wardet al. (1998) further developed a more comprehensive instrument for measuring competitive priorities. [70] Ward et al. (1998) addressed several issues related to the adequacy of measurement based on the data collected from 114 manufacturing plants in the USA They concluded that competitive priorities have long served as a foundation for strategy research, and that the choice of competitive priorities impacts company business performance.
Complete and accurate measurement of a company's business performance is still viewed as one of the challenges in operations management research ([44] Lancioni et al. , 2000). Typically, business performance is measured using financial metrics. [39] Jahera and Lloyd (1992) proposed that return on investment (ROI) is a valid performance measure for midsize firms. [68] Ward et al. (1995) used self-reported changes in profit before tax to measure firms' performance. [50] Morash et al. (1996) measured firm performance relative to competitors using return on asset (ROA), ROI, return on sales (ROS), ROI growth, ROS growth, and sales growth. [21] Duray et al. (2000) measured firm performance using the respondent's perception of performance in relation to competitors. The measures used were ROI, ROS, market share, growth in ROI, growth in ROS, growth in market share, and growth in sales. [60] Stock et al. (2000) indicated that financial perspective measures such as market share, ROI, and sales growth is more likely to reflect the performance assessment of a company.
Conceptual model and survey instrument development
Competitive priority model provides the theoretical foundation for this study, as shown in Figure 1 [Figure omitted. See Article Image.]. The model consists of four latent constructs - low cost, quality, delivery performance, and flexibility. Each of these four latent constructs is captured by multiple measures in a survey instrument. The use of such multi-item constructs increases the ability to draw finer distinctions among respondents over the use of single item ([30] Flynn et al. , 1990). The five-point Likert scales employed in this study provide a relative assessment on a continuum and are commonly used for collecting primary data for empirical research in operations management, and more generally in management research ([70] Ward et al. , 1998). Respondents answered all questions with respect to a particular product line in their companies. The product line contributed the most sales value in dollar terms for their companies. The measures for each latent construct in the survey instrument are also illustrated in Figure 1 [Figure omitted. See Article Image.].
The measures for four latent constructs were developed based on previous empirical literature ([6] Boyer, 1998; [47] Miller and Vollmann, 1984; [67] Ward and Duray, 2000). The five-point Likert scales for each measure are 1=No emphasis, 2=Little emphasis, 3=Moderate emphasis, 4=Strong emphasis, and 5=Extreme emphasis.
In addition, in order to reveal the differences in competitive priority between high performing companies and low performing companies, based on prior research, in this study, business performance is measured using the respondent's perception of performance in relation to competitors. The measures are comprised of market share, sales growth, profit margin, ROI, and ROA. The five-point Likert scales for each measure are 1=Significantly lower, 2=Lower, 3=Approximately equal, 4=Higher, and 5=Significantly higher. The developed survey instrument was first examined by academic and industrial experts. These provide the proof of content validity of the measures ([62] Swink et al. , 2005).
Methodology
Subjects
The US technical textile manufacturing companies were the research subjects. Although the US technical textile sector has proven less vulnerable than the apparel-related textile sector to global competition, it nevertheless has been confronting growing pressure from competitors in both developing and industrialized countries. In this sense, the sector is an epitome of the entire US manufacturing industry.
A sample of subjects was taken using the mailing list provided by the Industrial Fabrics Association International (IFAI). IFAI is a US based nonprofit trade association whose more than 2,000 members represent the majority of US technical textile companies. The Industrial Fabrics Foundation (IFF), a charitable organization associated with the IFAI, provided financial support and survey cooperation. The subjects targeted all occupied high-ranking management positions with an overview of the company's business operations to ensure they had knowledge of the issues the survey addressed.
Data collection
The developed survey instrument was pre-tested through five on-site interviews with senior executives of technical textile companies. The instrument was thus refined with regard to content, arrangement, wording accuracy, and relevance. This procedure helped make the final survey instrument more valid and clearer. A postal mail survey was selected as the principal method of data collection. The survey package was sent to a sample of 995 US technical textile companies. To improve the response rate, the targeted respondents each received a follow-up email written by the IFAI president four weeks after the initial postal mailing. This email was constructed to solicit those people who did not respond to the postal mail survey and invited them to return the questionnaire or, if they preferred, to complete the survey online. The web-based questionnaire was identical to the postal mail instrument.
Among the 995 mailed surveys, six were returned owing to incorrect contact information. The adjusted survey sample size was therefore 989. After eight weeks, 207 responses were received, of which 95 were from the postal mail survey and 112 were from the follow-up web survey. Some 202 out of 207 returns were eligible and complete responses. The adjusted response rate was 20.4 percent (202/989), which was very satisfactory compared to the response rates in previous empirical studies (e.g., [63] Tracey and Tan, 2001, 9 percent; [66] Vonderembse and Tracey, 1999, 13.4 percent), particularly in light of the difficult conditions prevailing in the US textile industry. For an industry survey, [20] Dillman (2000) indicated that there is no generally accepted minimum response rate and it really depends on the survey topics and industries chosen.
Table II [Figure omitted. See Article Image.] shows the profile of survey respondents. It covers a broad diversity of businesses in technical textile sector. Among the respondents, 52 percent were owner/president/CEO, 15.5 percent were vice presidents, and the remainders were general managers or other positions. This indicates that most respondents were high-ranking executives and had the knowledge to provide relatively accurate answers to the survey questions.
Statistical methods
Non-response bias testing
Non-response bias was evaluated using the t -test on demographic variables. As a convention, the responses of early and late groups of returned surveys were compared to provide support of non-response bias ([43] Lambert and Harrington, 1990).
Factor analysis
[22] Fabrigar et al. (1999) recommended using exploratory factor analysis (EFA) to identify measurement models and confirmatory factor analysis (CFA) to test the full model. In this study, the four measurement models are the latent constructs of low cost, quality, delivery performance, and flexibility. The full model is a second-order CFA model for competitive priority. The four first-order constructs are collectively represented by a second-order construct.
EFA with varimax rotation method was utilized to reduce attribute space from a larger number of measures to a smaller number of factors. SPSS software was employed in the EFA analysis. The extraction criterion was set as eigenvalue above one. The measures with low factor loadings (<0.50), high cross-loadings (>0.40), and item-to-total correlations (<0.30) ([17] Comrey, 1973; [40] Janda et al. , 2002) were excluded from the factor matrices. The deduction of certain measures required the recomputation of factor loadings, coefficient alpha, and item-to-total correlations and a reexamination of factor structure using the reduced number of measures. This iterative procedure was repeated until all requirements were met.
CFA represents a special case of structural equation modeling (SEM) ([10] Byrne, 2005). The primary goal of testing CFA models is to determine the goodness of fit between the proposed model and the sample data. The full model was tested by CFA using LISREL involving three levels: measures, first-order latent constructs (low cost, quality, delivery performance, and flexibility), and a second-order latent construct (competitive priority).
Assessment criteria
Model-to-data fit
Goodness-of-fit indices are used to assess the model-to-data fit, which is the extent to which the data matches the proposed model. There are many goodness-of-fit indices and no single test best describes the model-to-data fit. In this study, the indices adopted for the model-to-data fit assessment included Normed Chi-square (χ2 ), the root mean squared error approximation (RMSEA), goodness-of-fit index (GFI), the Normed Fit Index (NFI), the Non-Normed Fit Index (NNFI), and the comparative Fit Index (CFI).
A Normed Chi-square (χ2 ) less than 2 indicates no significant difference between the observed and estimated covariance matrices. The RMSEA measures the discrepancy between the observed and estimated covariance matrices per degree of freedom. ([49] Maruyama, 1998) The lower the RMSEA value, the better the fit between the model (predicted data) and the actual data. Values less than 0.08 are deemed acceptable. The value of GFI should be larger than 0.9. ([9] Byrne, 1998) The NFI compares the fit between the proposed model and nested baseline or null model. An index score of 0.90 or higher are acceptable threshold for the NFI. The NNFI also compares the fit between the proposed model and the null model. It also measures parsimony by evaluating the degree of freedom from the proposed model to the degree of freedom of the null model ([48] Marsh et al. , 1988). The NNFI is highly recommended because of its resilience against variations in sample size. An index score of 0.90 or higher is acceptable for the NNFI. The CFI measures how the proposed model compares with other possible models with the same data ([49] Maruyama, 1998). An index score of 0.90 or higher is acceptable for the CFI ([34] Hair et al. , 1995).
Unidimensionality, reliability, and construct validity
The measurement properties of the constructs in the model were assessed by the following criteria: unidimensionality, reliability, and construct validity. These criteria have been widely utilized by previous empirical studies (e.g., [13] Chen and Paulraj, 2004; [68] Ward et al. , 1995). [13] Chen and Paulraj (2004) noted that these represent a three-stage continuous improvement cycle lying at the heart of the instrumentation.
Unidimensionality has been described succinctly by [36] Hattie (1985) as a set of variables forming a latent construct that all measure just one thing in common. This is a most critical and basic assumption for measurement theory. [45] Levine (2005) further indicated that unidimensionality is a prerequisite to meaningfully interpret the reliability of a measurement. In order to prove unidimensionality, [68] Ward et al.(1995) suggested that the [11] Carmines and Zeller (1979) criteria should be met:
the first indicator should explain a large proportion of the variance in the constructs (i.e. > 40 percent);
subsequent indicators should explain fairly equal proportions of the remaining variance, except for a gradual decrease;
all or most of the constructs should have sizeable loadings on the first indicator (i.e. > 0.3); and
all or most of the constructs should have higher loadings on the first indicator than on the subsequent indicators.
Also, the achievement of the model-to-data fit demonstrates sufficient internal consistency.
After all measures show unidimensionality, their reliability is then tested. Reliability is the consistency of a set of measurement variables in a latent construct. Cronbach's coefficient alpha and the construct reliability for each latent construct are calculated respectively to compare to criterion value. A Cronbach's coefficient alpha of 0.70 and above suggests adequate reliability ([51] Nunnally, 1978) while construct reliability values of greater than 0.50 indicate adequate reliability ([31] Fornell and Larcker, 1981).
Construct validity consists of convergent validity and discriminant validity. All of the measurement loadings are significantly high and all of the goodness of fit indices met recommended values to suggest convergent validity. An additional indication of convergent validity was the average variance extracted (AVE), which is the percentage of the total variance of a measure represented or extracted by the variance due to the construct, as opposed to being due to error ([31] Fornell and Larcker, 1981). The desired threshold AVE score is above 0.5. Discriminant validity is shown by the confidence interval of 2 standard errors around the correlation between each respective pair of constructs in the model. If the confidence interval does not include 1.0, discriminant validity is then demonstrated ([3] Anderson and Gerbing, 1988).
Results
As the measures for business performance showed unidimensionality, a single set of composite scores of these measures were used to represent the construct ([68] Ward et al. , 1995). The 202 responses were sorted in descending order in terms of their mean scores from the five business performance measures. The first half of the responses were designated as relatively high performers and the second half were designated as relatively low performers. [35] Hambrick (1984) indicated that dividing the sample into separate high and low performance sub-samples in this manner is a practical analytical technique for strategy research. This method has been successfully applied in various prior studies (e.g. [35] Hambrick, 1984; [68] Ward et al. , 1995; [67] Ward and Duray, 2000).
The iterative procedure of data analysis was repeated for both the relatively high performers sub-sample and the relatively low performers sub-sample and resulted in 14 final measures in both sub-samples. Achieve/maintain lowest inventory in low cost construct and make rapid design changes in flexibility construct were dropped due to low factor loading.
Non-response bias testing results
The non-response bias testing shows there are no significant differences between early and late groups of returned surveys.
Results of model-to-data fit, unidimensionality, reliability, and construct validity
Table III [Figure omitted. See Article Image.] summarizes the final results from factor analysis. The results suggest that all four measurement constructs for both high performers sub-sample and low performers sub-sample met the unidimensionality criterion of [11] Carmines and Zeller (1979). The measures capture four distinct dimensions and the individual measures contribute to the expected construct. The eigenvalues for each factor are relatively large, from 1.453 to 4.327 for high performers sub-sample and from 1.573 to 3.293 for low performers sub-sample. The four constructs cumulatively account for 69.8 percent of the variance in competitive priority for high performers sub-sample and 67.4 percent for low performers sub-sample. They are very satisfactory. Cronbach's coefficient alphas and construct reliability scores all are above 0.70 for both sub-samples, the evidence of reliability is then established for both sub-samples.
Table IV [Figure omitted. See Article Image.] exhibits the AVE scores of all four constructs for both sub-samples. All of the AVE scores are above the desired threshold of 0.5 ([3] Anderson and Gerbing, 1988), which indicates the criterion of convergent validity is met.
Table V [Figure omitted. See Article Image.] shows none of the confidence intervals (of 2 standard errors around the correlation between each respective pair of factors in the model) capture 1.0. Therefore, the criteria of discriminant validity are met for both sub-samples.
Table VI [Figure omitted. See Article Image.] summarizes the goodness of fit indices of all four constructs for both sub-samples. The results show all constructs meet the model-to-data fit requirements.
Results of the second-order CFA model
Figure 2 [Figure omitted. See Article Image.] illustrates the second-order CFA models for high performers and low performers respectively, including the standardized factor loadings and corresponding t -values. The final CFA model showed an excellent fit to the collected data. The four constructs designed to measure competitive priority, low cost, quality, deliver performance, and flexibility, all exhibited high and significant factor loadings.
Discussion
Competitive priority model was rigorously tested using collected survey data from the US technical textile industry. The self-perception answers from senior executives were relied on in this study. The competitive priorities embraced by senior executives are crucial and affect many other decision-making processes such as supply chain arrangement ([52] Pagell and Krause, 2004). For example, if manager perceives the company mainly competes on low cost, its supply chain arrangement might be lean oriented rather than be agile focused in order to maximize profit through minimizing cost in each operations stage ([28] Fisher, 1997). Thus, many researchers have argued that the use of perceptual measures of competitive priority permits a stronger test of the relationships between strategy orientation and other key corporate decisions ([68] Ward et al. , 1995). The establishment of unidimensionality, reliability, and validity of constructs and the model-to-data fit make perceptual measures viable and dependable in large-sample empirical studies. It is consistent with the prior research (e.g., [13] Chen and Paulraj, 2004; [41] Ketokivi and Schroeder, 2004)
The results of factor analysis show that there are distinctions on emphasis of competitive capabilities between high performers and low performers. For higher performers, quality contributes the most in the variance of competitive priority at 24.5 percent, followed by delivery performance at 17.1 percent, low cost at 14.8 percent, and flexibility at 13.4 percent. This indicates that higher performers consider quality and delivery performance as the most important competitive capabilities although low cost and flexibility are also given certain emphasis. According to [68] Ward et al. (1995), such strategic approach aims at building capabilities for product or service differentiation. In contrast, for low performers, low cost contributes the most in the variance of competitive priority at 19.2 percent, followed by quality at 18.3 percent, delivery performance at 15.5 percent, and flexibility at 14.4 percent. This reveals that low performers grant very close weights to all four types of competitive capabilities although the emphasis on low cost and quality is a little greater than delivery performance and flexibility. According to [57] Skinner (1985), each of these four competitive priorities must be given different weights by the company in order to achieve the overall corporate goals. Equal emphasis means no emphasis.
Nowadays, dynamism is the most prominent environmental characteristic facing the US technical textile sector ([14] Chi, 2009). The companies are confronting increasing uncertainty in domestic and international markets. There are rapid and discontinuous changes in supply, demand, competitors, technology, and regulations/rules ([12] Chang and Kilduff, 2002). In this environment, the large scale, mass production model that brought the industry great prosperity in the past has been no longer ensured future competitiveness ([8] Bruce et al. , 2004). Market needs have become more changeable and fragmented. These explain why differentiation strategies, including quality and delivery service were emphasized more by the high performing companies in the US technical textile sector over low cost strategy. In contrast, the lack of clear emphasis on strategies could be one of the reasons resulting in a relatively low business performance.
Conclusions and implications
The US textile industry is undergoing a radical transition from traditional labor-intensive sectors such as apparel-related textiles and home textiles to more technology- and capital-intensive sectors such as technical textiles. This study represents the first empirical investigation into corporate strategy issues in the US technical textile sector. The adequacy of the measurements and validity of the model are rigorously addressed. The confirmation process followed the typical standards of measure and scale development in management research ([9] Byrne, 1998; [13] Chen and Paulraj, 2004). The results of this study are offered as an effort in a process of continued advancement in the understanding of corporate competitive strategies.
Overall, this study contributes to the literature in four ways. First, based on previous theoretical and empirical research, it develops a survey instrument for effectively measuring corporate competitive strategies in four distinct constructs - low cost, quality, delivery performance, and flexibility. Second, using the primary data from an industry survey, it statistically assesses the unidimensionality, reliability, validity, and model-to-data fit of competitive priority model and proves the model is valid and the survey instrument can generate reliable data. Moreover, these four constructs can capture most of the variance in competitive priority. The influences of each construct on the variance of competitive priority are also determined. Third, the differences on emphasis of competitive capabilities between high performing companies and low performing companies are quantitatively identified. Finally, the statistical analysis reveals the possible cause in terms of strategic approach for low performing companies. As previous studies indicated (e.g. [53] Porter, 1980; [61] Swamidass and Newell, 1987; [68] Ward et al. , 1995), differentiation that is embraced by high performing companies is an appropriate strategy in an increasingly complex, dynamic, and hostile environment.
This study also imparts several implications. For academia, this study provides a springboard for future studies of corporate competitive strategies and its relationships with other key decisions (such as supply chain arrangement) and outcomes (such as business performance). Although the measures and scales were tested in the US technical textile sector, the methodology may, therefore, be transferred to other industries and to other market sectors. In addition, this study substantiates that an effective survey strategy can lead to higher response rates. First of all, cooperation with the industry trade association, IFAI was vital in providing privileged access to member companies through access to the association's database and, more importantly, a personal communication from the association president. A second factor was perhaps that senior executives perceived the content of the study as an important issue. A third factor was perhaps the use of a mixed-mode survey method, which included a postal mailing with a follow-up email that provided an online version of the questionnaire. As [20] Dillman (2000) indicated that a mixed-mode survey may be the only alternative for immediately gaining access to all members in the survey sample.
For industrial practitioners, as they continue to experience intensifying international competition, shifting market needs, and constant technological innovations, the business environment is likely to become even more dynamic, complex, diverse and hostile. Under such turbulent conditions, the configuration and deployment of effective strategies and other organizational arrangements is imperative to achieve superior business performance, and perhaps, even just to survive. To be effective, it is essential for senior executives to understand the characteristics of their environment so they can choose appropriate competitive capabilities accordingly. Companies also need to constantly monitor their environment for shifts so they can make timely adjustment.
Limitations and future studies
This study overcame some limitations of previous research by using a well-developed survey instrument, an effective industrial survey strategy, and the rigorous application of EFA and CFA techniques for data analysis. However, there are still several limitations that need to be addressed and also can be considered as possible directions for future studies.
First of all, one of most obvious limitations is about time constraint. This research provides a measure of what a manufacturer deems important at a particular time. With the changes of business environmental characteristics, it is worth conducting follow-up studies in the future. Second, as an exploratory study, this research is dedicated to understanding strategic emphasis in a transitional industrial sector. In future studies, the relationships between corporate competitive strategies and other key arrangements such as supply chain management can be examined. A decision making model can be developed accordingly. Finally, although four constructs - low cost, quality, delivery performance and flexibility can capture most of the variance in corporate competitive strategies, some underlying factors that contribute to the unexplained variance in the model can be identified in the future.