37? AlQU M). 3 S~2l
LINKAGE OF BUSINESS AND MANUFACTURING STRATEGIES AS A DETERMINANT OF ENTERPRISE PERFORMANCE: AN EMPIRICAL STUDY IN THE TEXTILE INDUSTRY
Dissertation
Presented to the Graduate Council of the University of North Texas in Partial Fulfillment of the Requirements
For the Degree of
DOCTOR OF PHILOSOPHY
By
Massoud Kassaee, B.B.A., M.B.A. Denton, Texas May, 1992 37? AlQU M). 3 S~2l
LINKAGE OF BUSINESS AND MANUFACTURING STRATEGIES AS A DETERMINANT OF ENTERPRISE PERFORMANCE: AN EMPIRICAL STUDY IN THE TEXTILE INDUSTRY
Dissertation
Presented to the Graduate Council of the University of North Texas in Partial Fulfillment of the Requirements
For the Degree of
DOCTOR OF PHILOSOPHY
By
Massoud Kassaee, B.B.A., M.B.A. Denton, Texas May, 1992 Kassaee, Massoud, Linkage of Business and Manufacturing
Strategies as a Determinant of Enterprise Performance: An
Empirical Study in the Textile Industry. Doctor of
Philosophy, May, 1992, 164 pp., 25 tables, 5 figures, references, 97 titles.
Lack of linkage between the business strategy of a firm and manufacturing strategy variables can be regarded as one of the basic reasons for declining productivity among manufacturing industries. The findings of this study show that businesses which linked their business strategy with manufacturing strategy variables, outperformed other businesses pursuing a similar business strategy.
The pressure on managers and strategists to improve the competitiveness of the manufacturing sector in the United
States is enormous. Researchers have attributed the lack of a fit between business and manufacturing strategies of a firm as one of the basic reasons for declining productivity among manufacturing industries. The U.S. textile industry, an important sector of the economy, has been adversely affected. The main question in this study was: do business units that exhibit a "linkage" or "fit" between their business strategy and manufacturing strategy variables, outperform competitors who lack such a fit? This exploratory research focused on two business strategies: cost leadership and differentiation. Based on existing literature, twenty-four hypotheses concerning the relationship between business strategy and selected manufacturing strategy variables were developed. The manufacturing executives of eighty-eight broadwoven cotton fabric mills (SIC 2211) were surveyed using a qualitative questionnaire. Two sets of comparisons were made between the manufacturing strategy variables of the sampled firms: first, high vs. low performers pursuing cost leadership strategy; and second, high vs. low performers focusing on differentiation strategy. Within each set of comparisons, high performers reported linkage between their business strategies and selected manufacturing strategy variables. This study re-affirms the importance of linking business strategy with manufacturing strategy variables as a forceful weapon for overcoming competition. Copyright by Massoud Kassaee
1992
111 AC KNOWLEDGEMENTS
This dissertation would not have been possible without the help of God and the many contributions of my honorable professors, friends, and family members. I gratefully acknowledge their help throughout my years at the University of North Texas. I especially want to express my gratitude to Dr. Martin E. Rosenfeldt, my dissertation committee chairman, for the countless hours of direction and support provided throughout this research. Without his encouragement and support, this dissertation could not have been completed. My sincere appreciation also to Dr. Derrick E. D'Souza for his valuable suggestions and support. I would also like to thank the many plant managers and manufacturing executives who participated in this study and willingly discussed problems facing their industry.
My family members have contributed enormously to my studies. I thank my mother and late father, Batoul Faraj and Mohamad Hadi Kassaee, for instilling in me the courage to continuously learn and work. My appreciation goes to my wife, Azam Sodagar, for her patience and support. Her understanding contributed greatly toward completion of this study. I have been honored in having a mentor as great as Dr. Mohamad Zaman Kassaee, my uncle. I would like to thank
iv my two lovely sons, Mohamad Hadi and Saiid, my brothers and sisters, especially Mohamad and Ahmad, for their support and cooperation.
v TABLE OF CONTENTS Page
ACKNOWLEDGEMENTS iv LIST OF TABLES X LIST OF FIGURES xii Chapter I. INTRODUCTION 1 Purpose of the Study 6 Statement of the Problem 7 Significance of the Study 8 Assumptions and Limitations of the Study 9 Organization of the Dissertation 11
II. LITERATURE REVIEW AND THEORETICAL FRAMEWORK 12 Levels of Organizational Strategy 12 Content Versus Process Research 15 Research on Manufacturing Strategy 15 Definition of Manufacturing Strategy 16 Importance of Manufacturing Strategy 18 Decision Categories in Manufacturing Strategy ... 20 Research on Business Strategy 29 Market-Share-Based Generic Business Strategies 31 Product-Life-Cycle-Based Generic Business Strategies 33
VI Competition-Based Generic Business Strategies 34 Linking Business and Manufacturing Strategies 37 U.S. textile industry 45 Problems Facing the U.S. Textile Industry 47 Reasons for selection of the Broadwoven Fabric Mill Cotton Sector 50 Theoretical Framework 53 Development of Hypotheses 55 List of Hypotheses 55 III. METHODOLOGY 61 Research Design 61 Sample 63 Data Collection Procedure 69 Survey Instrument 72 Measurement Scale 74 Instrument Validation 74 Instrument Reliability 77 Instrument Pretesting 81 Data Analysis 82
IV. ANALYSIS AND RESULTS 85 Characteristics of the Researched Sample 85 Researched Sample Respondents 86 Age and Employment Size of Surveyed Businesses 88 Classification of Surveyed Businesses 90
VI1 Classification by Strategy and Performance 93 Analysis of Questions: Business Strategy 95 Analysis of Questions: Manufacturing Strategy 96 Comparison of Manufacturing Strategy Variables: High vs. low performers Emphasizing Cost Leadership Strategy ... 97 Comparison of Manufacturing Strategy Variables: High vs. low Performers Emphasizing Differentiation Strategy ... 100 Results of Hypotheses Examined 104 Textile Firms Focusing on Cost Leadership Strategy 104 Textile Firms Focusing on Differentiation Strategy 107
V. SUMMARY, DISCUSSION AND CONCLUSIONS 110 Summary of the Findings and Discussion 110 Comparison of Textile Firms: High and low Performers Emphasizing Cost Leadership Strategy 112 Comparison of Textile Firms: High and Low Performers Pursuing Differentiation Strategy 115 Conclusions 118 Suggestions for Future Research 123
Appendices 129 A: QUESTIONNAIRE COVER LETTER 130 B: FIRST FOLLOW-UP LETTER 133 C: SECOND FOLLOW-UP LETTER 135 D: THIRD FOLLOW-UP LETTER 137
Vlll E: INSTRUMENT RELIABILITY COVER LETTER 139
F: BUSINESS AND MANUFACTURING STRATEGY SURVEY QUESTIONNAIRE 141
G: BROADWOVEN FABRIC MILL COTTON SECTOR: INDUSTRY DESCRIPTION 149
H: SUMMARY OF FIRMS CONTACTED AND PARTICIPATED IN THE STUDY 152
I: RESULTS OF SUBJECTIVE PERFORMANCE MEASURES 155
REFERENCES 157
IX LIST OF TABLES Table Page
1. Manufacturing Strategy Content Variables in Literature 22 2. Decision Variables in Manufacturing Strategy 30 3. Porter's Generic Business Strategies 38 4. Research on Linkage of Business and Manufacturing Strategies 40 5. U.S. Textile Shipments Per Production Employee, 1990 52 6. U.S. Textile Imports as a Percentage of Shipments, 1990 53 7. Manufacturing Strategy Characteristics of Generic Business Strategies 56 8. Broadwoven Cotton Textile Mills by State 65 9. Comparison of Respondents vs. Non-respondents: Size of Employment 68 10. Reliability of the Instrument: Cronbach's Coefficient Alpha 79 11. Responding Businesses Categorized by Age 88 12. Employment Size of the Surveyed Businesses 89 13. Performance Measures as Rated by Manufacturing Executives 92 14. Results of Discriminant Analysis: Businesses Pursuing Cost Leadership Strategy with High or Low Performance 99
x 15. Comparison of Manufacturing Strategy Variables: High and Low Performers Pursuing Cost Leadership Strategy 101 16. Results of Discriminant Analysis: Businesses Pursuing Differentiation Strategy with High or Low Performance 102 17. Comparison of Manufacturing Strategy Variables: High and Low Performers Pursuing Differentiation Strategy 103 18. Examination of Hypotheses: Logistic Regression Procedure 105 19. Results of Hypotheses Examined: Textile Firms Focusing on Cost Leadership Strategy 106 20. Examination of Hypotheses: Logistic Regression Procedure 107 21. Results of Hypotheses Examined: Textile Firms Focusing on Differentiation Strategy 109 22. Comparison of Manufacturing Strategy Variables: Businesses Pursuing Cost Leadership Strategy 113 23. Comparison of Manufacturing Strategy- Variables: Businesses Pursuing Differentiation Strategy 116 24. Summary of Firms Contacted and Participated in the Study 153 25. Performance of Business Units in Comparison to Business Objectives as Reported by Surveyed Manufacturing Executives 156
xi LIST OF FIGURES Figure Page
1. Strategy Types in a Corporate Organization 14 2. Total Employment in the U.S. Textile Industry - by Sector, 1990 51 3. Performance Measure Instrument 91 4. Respondent Classification Process 94 5. Linkage of Environment, Business Strategy, and Functional Strategies as Determinants of Business Performance 126
xxx CHAPTER I
INTRODUCTION
The pressure on managers and strategists to improve the competitiveness of the manufacturing sector in the United States is enormous. Academic researchers and practitioners have been focusing on the competitive position of manufacturing firms for a number of years. The Massachusetts Institute of Technology report (Dertouzos, Lester, and Solow 1989, 1) states that: "American industry is not producing as well as it ought to produce, or as well as it used to produce, or as well as the industries of some other nations have learned to produce." Growing trade deficits and slow economic growth, among other factors, attest to the lack of competitiveness among U.S. manufacturers. American manufacturers need to improve their competitiveness to reverse the existing trends facing their industries. The U.S. textile industry, an important sector of the economy, has been affected enormously.
During the past ten years, more than one thousand textile plants have been closed with a loss of over five hundred thousand jobs (American Textile Manufacturers Institute 1990). While the value of shipments for the textile industry has increased moderately over the past five years, textile imports into the U.S. have shown a strong increase. Since 1985, the value of shipments by the U.S. textile mill products industry has increased by twenty-four percent, whereas imports into the United States have jumped by eighty-five percent (U.S. Department of Commerce 1987; U.S. Department of Commerce 1991). In recent years, the industry has also been confronted with high cost of raw materials, and excess plant capacity. Skinner (1987) reported that a productivity approach to manufacturing is not enough since companies cannot cut costs deeply enough to restore competitive vitality. Since the early 1980s, some researchers have focused on the importance of the contribution of the manufacturing sector to overall business performance. A number of researchers have suggested that falling productivity in U.S. industry and loss of traditional markets to Japanese and European producers can be blamed on the indifference of top management toward manufacturing (Dertouzos, Lester, and Solow 1989; Buffa 1984; Council on Competitiveness 1987; Wheelwright 1981). Several studies have specifically identified manufacturing strategy as one way of improving competitive performance (Whybark 1987; Skinner 1985; Hays and Wheelwright 1984; Buffa 1984; Ferdows and Skinner 1983). More specifically, some have attributed the lack of a fit between business and manufacturing strategies of a firm as one of the basic reasons for declining productivity among manufacturing industries (Kotha and Orne 1989; Hays and Wheelwright 1984). A number of studies have recommended the necessity of a linkage between manufacturing strategy and business strategy (Kotha and Orne 1989; Fine and Hax 1985; Richardson, Taylor, and Gordon 1985). Wheelwright (1984) stated that an effective manufacturing function is "... not necessarily one that promises the maximum efficiency, or engineering production, but rather one that fits the business, that is, one that strives for consistency between its capabilities and policies and the business's competitive advantage"(p. 83).
Researchers have proposed that those firms that have established a fit between their manufacturing and business strategies outperform others. Based on a literature survey, only a few studies have empirically examined the above proposition (Deane, Gargeya, and McDougall 1990; Fine and Hax 1985; Richardson, Taylor, and Gordon 1985). Fine and Hax (1985) recommended a methodology for developing manufacturing strategies that have some level of congruence with business, corporate and other functional strategies. Richardson, Taylor and Gordon (1985) evaluated manufacturing performance based on the level of congruence between corporate mission and manufacturing task. Using sixty-four Canadian electronics companies, they found that those companies with a strong linkage between corporate mission and manufacturing task were more profitable than other companies. Kotha and Orne's (1989) study conceptually linked 'generic' business unit strategies and 'generic' functional structures in manufacturing and proposed that manufacturing structures implicitly represent generic manufacturing strategies. Kotha and Orne (1989) proposed four types of generic business strategies along with their manufacturing characteristics. Deane, Gargeya, and McDougall (1990) examined the relationship between business strategies and manufacturing strategies and their effect on performance. The study concluded that the successful firms are better able to devise manufacturing decisions which are linked to business strategies. This writer's survey of the literature left a number of important questions unanswered. For example, does the linkage between the two strategies provide a greater distinctive competence for the firm? How should congruent manufacturing strategies be formulated? How should the two strategies be linked together? Should operational capability be adjusted to achieve corporate objectives, or should corporate objectives be confined to what operations is capable of doing (Anderson, Cleveland and Schroeder 1989)? Hays and Schmenner (1978) summed up the prevailing attitude regarding the above linkage by stating that: "Manufacturing functions best when its facilities, technology, and policies are consistent with recognized priorities of corporate strategy. Only then can manufacturing gain efficiency without wasting resources..." (p. 110). Jenlink and Burstein (1982) stated that the failure to match production management systems with other administrative systems and corporate strategy is part of the problem with U.S. manufacturing systems. They further observed that: The basic problem is that most decisions, particularly those in manufacturing require trade-offs among various criteria. All too often the trade-offs that are made in such decisions reflect priorities that are internally inconsistent or that run counter to corporate strategy (Jenlink and Burstein 1982, 57). The traditional view holds that once plant location and capacity decisions have been established, usually with heavy involvement of top management, other manufacturing related decisions are ignored by top management (Jenlink and Burstein 1982). As Skinner (1978) stated: The most typical serious condition in most manufacturing plants is that of inconsistencies existing within the infrastructure. Different sectors of manufacturing policy are implicitly set up to accomplish conflicting objectives, (p. 110). The current study empirically evaluated the linkage between manufacturing and business strategies and how that linkage related to the performance of the firm. The manufacturing strategy variables of high and low performers pursuing the cost leadership strategy and the differentiation strategy were compared with each other. The study was performed by surveying the manufacturing executives of 334 broadwoven cotton fabric mills.
Purpose of the Study The primary objective of this study was to make a contribution to the research on the effect of the linkage between business strategy and manufacturing strategy variables as it related to performance of the firm. The main research question in this study was: Do business units that exhibit a "congruence", or "fit", between their business and manufacturing level strategies outperform business units who lack such a fit? A review of the literature indicated a void with respect to the above question. This study provided some evidence regarding the relative importance of a linkage between business level strategy and manufacturing level strategy as a determinant of performance of the firm.
Statement of the Problem Two inter-related issues motivated this research study. The first issue was the importance of congruence between business level and manufacturing level strategies in determining performance of a firm. Some studies have identified manufacturing strategy and its linkage with business strategy as an important issue (Cleveland 1986; McDougall 1986; Swamidass 1986; Fine and Hax 1985; Richardson, Taylor and Gordon 1985); however, the literature revealed limited empirical research regarding the above phenomenon. For example, the literature did not indicate whether a high level of congruence between manufacturing and business level strategies significantly improves performance. Most of the manufacturing strategy literature has r dealt predominantly with identifying strategy process variables and their importance, the conceptualization of manufacturing variables, or case study resolutions (Adam and Swamidass 1989). Few studies in the area of manufacturing strategy have been empirical (Amoako-Gaympah 1989). According to Adam and Swamidass (1989), the greatest weakness of operations strategy research was in the area of inter-relationships among variables, and the effect of strategy content and process variables on performance. The second issue was determining the level of emphasis to be placed on the different manufacturing strategy variables with respect to specific business strategies. Existing studies provided little information regarding this issue. Whybark (1987) stated that a firm can become 8 competitive by having manufacturing decisions reinforce the business strategy. Krajewski and Ritzman (1987) observed that the choice of technology, capacity utilization, materials management, inventory/ and scheduling policies differ under different business strategies. It is therefore important to determine the type of manufacturing strategy variables to be emphasized given a selected business strategy.
Significance of the Study This study lent support toward empirically validating the importance of developing a linkage between manufacturing and business level strategies. Empirical research regarding the above issues has been limited. The root cause of the competitiveness problems facing a number of U.S. industries has been the incompatibility and inconsistency between a firm's manufacturing policies and its competitive requirements (Wheelwright 1984). Thus, a fit between manufacturing strategy and business strategy can play a significant role in making a firm competitive. Few of the concepts and theories proposed in the manufacturing literature have been validated by empirical research. The current study contributes to the literature on the strategic aspects of manufacturing. The results of the study provide clear insights to managers and chief executives regarding the importance of developing concise and focused business strategy and manufacturing strategy variables.
Assumptions and Limitations of the Study Three assumptions were made throughout this research: First, all broadwoven cotton fabric mills that were listed in either Davidson's Textile Blue Book (1990) or The Textile Red Book (1991), were assumed to represent the total population of firms operating within SIC #2211. As reported by The Textile Red Book (1991), "...the many continual and rapid changes in the textile industry make it virtually impossible to achieve 100% accuracy". Second, based on frequency of citation, Porter's (1980) competition-based generic business strategies were assumed as the most acceptable business strategy model (see discussion in Chapter 2). Third, it was assumed that the nine decision categories that were discussed in literature fully represent manufacturing strategy (see discussion in Chapter 2).
In addition to the above assumptions, several limitations were encountered in this study: First, this exploratory study was focused on firms operating in the broadwoven cotton fabric mill sector of the U.S. textile industry (SIC 2211). The results should be restricted to the surveyed population. Studies such as this one have limited external validity (Kidder and Judd 10
1986). Additional research involving different industries has to be conducted in order to generalize the findings of this study. Second, the number of variables under examination and the relatively small sample size precluded the use of other exhaustive statistical techniques such as multiple regression, and factor analysis. Third, the performance of the surveyed firms was evaluated based on the responses they provided with respect to the four subjective performance measures in Part Ill- Section One of the instrument (see figure 3). Surveyed businesses did not respond to the objective performance measures. Application of subjective performance measures may have contributed to the findings of this study. However, Dess and Robinson (1984) observed that subjective perceptions of performance have a strong correlation with objective measures of performance over the same time period. Therefore the findings using subjective performance measures lend credence to the results of this study. Fourth, recent studies have pointed out the application of business strategies other than the ones introduced by Porter (Green and Yasin 1991). This study focused on examination of the two business strategies introduced by Porter (1980) and their relationship with selected manufacturing strategy variables. Manufacturing executives were asked to classify their firms according to 11 cost leadership strategy or differentiation strategy. Further research is warranted in order to study the relationship of other business strategies with manufacturing strategy variables.
Organization of the Dissertation This study is composed of five chapters. Chapter 1 presents the introduction to the study including the objectives and the significance of the research. Chapter 2 covers the available literature on manufacturing strategy and business strategy as they relate to the objectives of this study. It also provides a description of the economic status of the U.S. textile industry with particular emphasis on the broadwoven fabric mill cotton sector. Chapter 3 contains the research design, the surveyed firms, and the survey instrument that was used in collecting the required data.
Chapter 4 covers the analysis of the collected data including some descriptive information on the participating firms. It also provides the test results for the hypotheses. Chapter 5 contains the conclusions of the study along with some suggestions for future research. The appendices include copies of the letters sent out to surveyed businesses plus a copy of the survey questionnaire. CHAPTER II
LITERATURE REVIEW AND THEORETICAL FRAMEWORK
The literature review section of this study is organized into five sections. First, strategy and strategy research are discussed in general. The second section contains a review of some pertinent literature regarding manufacturing strategy. The third section presents a discussion of the literature regarding developments in business strategy. The fourth section provides a review of some of the existing research on the linkage between manufacturing strategy and business strategy. The theoretical framework is explained in section five.
Levels of Organizational Strategy Three distinct levels of strategy have been identified in the strategy literature. First, corporate strategy revolves around: (1) the definition of businesses in which the corporation wishes to participate, and (2) the acquisition and allocation of resources to these business units (Christensen, Andrew, and Bower 1987). Corporate level strategy articulates the vision of the firm and its strategic posture. Second, business strategy focuses on how to compete in a particular industry or product-market
12 13
segment. It specifies the scope or boundaries of each business and its links with the corporation. Distinctive competencies and competitive advantages are usually the most important components of strategy at this level (Hofer and Schendel 1978; Wheelwright 1984). Third, functional level strategy specifies the ways a function will support the desired business strategy and how it will complement the other functional strategies (Wheelwright 1984). Individual business units employ a cluster of functional level strategies, including manufacturing strategy. Ideally, functional level strategies should support business level strategies, which in turn, should support corporate level strategies (St. John 1986). Figure 1 illustrates the relationship between the three levels of strategy within a single business corporation. A single business corporate organization needs to develop congruent functional strategies based on the firm's business and corporate strategies. According to Kotha and Orne (1989), decisions regarding facilities, level of automation, vertical integration, and capacity levels, which constitute parts of a firm's manufacturing strategy, differ depending on the type of business strategy pursued by the firm. 14
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>- >- z o £ >" o 9 0 O LU O G LU 0 O h •H K LU CL -U < (d < x o QC N •H QC Content Versus Process Research Existing research on strategy can be divided in terms of content and process variables. Content research links specific decisions and broader economic structures to performance outcomes. It deals with decisions regarding goals, scope, and/or competitive strategies of a firm or one of its strategic business units (Fahey and Christensen 1986) . Process research explores actions that lead to and support strategy. It also deals with the effectiveness of alternative means of generating and implementing strategy. The current study concentrated on the content portion of strategy research. It analyzed the relationship between manufacturing strategy variables and business strategy as it affects business performance. Research on Manufacturing Strategy During the 1960s and 1970s, high-level management decisions were mainly influenced by personnel in marketing and finance functions (Skinner 1985; Buffa 1984) . Although manufacturing typically has shouldered the responsibility for seventy-five percent of the firm's investment, eighty percent of the firm's personnel, and eighty-five percent of the firm's expenditure for materials and equipment, manufacturing issues have been treated by top management as operational rather than strategic (Skinner 1985). The strategic view of operations management dates back to 16 Skinner's research (1969, 1974) in which he introduced the following three ideas: 1. The manufacturing function can and should be employed as a competitive weapon. Manufacturing should be regarded as an important function that can significantly effect the performance of a firm. 2. Cost and efficiency are inadequate goals for manufacturing. 3. "A factory that focuses on a narrow product mix for a particular market niche will outperform a conventional plant" (Skinner 1974, 114). The above ideas have received significant attention as a result of some major developments in the environment. Since 1965, American manufacturers have been affected by growing foreign competition, an accelerating rate of technological change, and new modes of competition (Skinner, 1985). These changes resulted in a shift of emphasis from marketing and finance during the 1960s and 1970s, to manufacturing in the 1980s. The next section provides three different definitions of manufacturing strategy. Definition of Manufacturing Strategy Literature provides several definitions of manufacturing strategy. Three studies have defined manufacturing strategy using the business-manufacturing strategy linkage issue (Schroeder and Lahr 1990; Swamidass 17 1986; Hays and Wheelwright 1984). According to Schroeder and Lahr (1990), manufacturing strategy provided a vision for the manufacturing organization based on the business strategy of the firm. It consisted of objectives and programs which help the business gain or maintain a competitive advantage. Swamidass (1986) took a similar approach and observed that manufacturing strategy involved the development and deployment of manufacturing capabilities that were linked with the firm's goals, business and corporate strategies. Regardless of how manufacturing strategy is defined, it was essential that management viewed manufacturing strategy as a long-range plan which must be integrated with the business strategy of the firm. For the purposes of this study, manufacturing strategy was defined using the concepts developed by Swamidass (1986) and Hays and Wheelwright (1984). Manufacturing strategy consists of a sequence of decisions which are in alignment with the goals and strategies of the firm, and that, over time, enable a business unit to achieve a desired manufacturing structure, infrastructure, and a set of specific capabilities (Hays and Wheelwright 1984; Swamidass 1986). 18 Importance of Manufacturing Strategy Over the years, American manufacturers have used two approaches for improving their competitiveness (Skinner 1986). First, many American firms have attempted to improve productivity through cost reduction and waste elimination. Skinner (1986) stated that this approach to manufacturing was not enough because companies could not cut costs enough to restore competitiveness. Second, some firms, such as General Electric, Chrysler, Outboard Marine, and Allen-Bradley, have adopted the integrative approach to improving productivity (Skinner 1987). The integrative approach emphasizes some form of linkage between manufacturing decisions and the firm's business strategy. Lack of knowledge and theory regarding the elements of production in a firm have been cited as some of the key reasons why the United States manufacturing sector has become uncompetitive (Hill 1989; Skinner 1988). There is a need to develop a theory to guide implementation of manufacturing strategy to bring manufacturing to the level of other functions as a source of competitive advantage. Given the importance of the manufacturing function, Wheelwright and Hays (1985) argued that manufacturing strategy must reflect the goals and strategies of the business in a way that enables the manufacturing function to contribute to the long-term competitiveness and performance of the business. Unlike 19 traditional practices, manufacturing should no longer be regarded as an operational issue; rather, it should be considered a strategic variable. Manufacturing, along with other functions, should take an active role in defining the business strategy of the firm. Manufacturing should not be evaluated in terms of cost efficiency, but how well it supports the goals, objectives, and strategies set by the business unit. According to Schroeder and Lahr (1990), manufacturing strategies were necessary for several reasons: 1. To respond to business strategies or corporate guidelines, 2. To correct present weaknesses or to exploit strengths, 3. To cope with anticipated environmental changes, 4. To develop a distinctive competence within the market. Japanese firms have been approaching manufacturing strategy as a strategic variable for two decades. Wheelwright (1981) stated that one reason for the greater competitiveness of the Japanese industries has been their dedication towards establishing mutually supportive manufacturing and business strategies. Japanese firms consistently acknowledge a link between short- and long-term goals. They treat decisions regarding capacity, facilities, vertical integration, production technologies and processes, 20 work force, quality control, production planning and materials control, and organization as long term and strategic in importance. Most American companies are used to treating decisions regarding work force, quality, production planning and material control, and organization as operational short-term decisions. Experience has taught Japanese firms to view even short-term manufacturing decisions in the context of long-term strategy (Wheelwright 1981). Wheelwright observes that the Japanese minimize false choices between, say, costs and quality by acknowledging a link between short and long- term objectives. Manufacturing strategy involves a diverse set of decisions, which can be unique from one business to the next. One type of framework that has worked well in the past involved grouping the decisions into major categories. The next section will discuss such a framework. Decision Categories in Manufacturing Strategy A manufacturing strategy must be comprehensive, but at the same time, the complex web of decisions must be broken down into analyzable pieces (Fine and Hax 1985). Researchers have described the manufacturing strategy decision categories using somewhat different terms. There seemed to be some level of agreement across the literature regarding what encompasses manufacturing strategy (Hill 21 1989; Fine and Hax 1985; Buffa 1984; Hays and Wheelwright 1984; Skinner 1978). A list of nineteen manufacturing strategy decision variables identified in the literature are provided in Table 1. The nine decision categories cited most often included capacity planning, facilities layout, production processes, vertical integration, human resources, quality assurance, production planning, materials control, and organization. These nine decision categories were examined in the current study. The other ten decision categories were either highly correlated with the selected nine or were not of importance for the purposes of this study. For example, decisions such as flexibility or focus can be regarded as being highly correlated with production process. Like any other system, a manufacturing organization will do certain activities better than others. Therefore, trade-offs between different options and the manufacturing decision categories must be made. Such tradeoffs occur as a result of the internal and external environments surrounding a firm. The collective pattern of decisions in these nine categories determine the structure and capabilities of a manufacturing organization. The following section provides a discussion of the variables within each decision category. 22 w PQ g 23 <1) u>i u c a) pa> M-l & o in 1. Capacity Planning Capacity planning measures are intended to translate material requirements into production capacity requirements and to balance available capacities with required capacities. Decision variables in this area include capacity slack, timing and frequency of capacity changes, economies of size, and capacity balance. Under a differentiation strategy, a business has less capacity slack, requires more frequent capacity changes, greater scheduling complexity, and more uneven demands, because it deals with increased demand and supply uncertainties (Krajewski and Ritzman 1987). Since change in production capacity requires substantial investment, careful planning of an expansion project is vitally important. Poor capacity expansion decisions can severely affect the future viability of the firms (Buffa 1984). 2. Facilities Layout Decisions regarding facilities layout affect capacity and other areas of operation. Facility layout can be based on product, process, fixed-position, or a combination of these elements. A firm that competes using a differentiation strategy will use a process layout, in which employees and equipment are grouped by function or process (Krajewski and Ritzman 1987). Such a layout improves responses to changing product mixes, an important factor under a differentiation strategy. Under a cost leadership 25 strategy, product layout which uses fixed-path devices can be used (Krajewski and Ritzman 1987). In recent years, there has been a move towards flexible and cellular manufacturing. 3. Production Processes An efficient production facility requires appropriate application of different processes. Production process variables include positioning strategy, process types, span of process, level of automation, and process stability. A firm pursuing the differentiation strategy usually requires general purpose equipment, whereas a firm that pursues the cost leadership strategy utilizes automated machinery to produce the required high volume (Richardson, Taylor and Gordon 1985). The basic advantage of such automated machinery is lower unit cost with high quality. General purpose equipment yields the maximum level of product flexibility that can produce low volume, and custom parts. Typically, job shops and batch shops use such equipment. Such equipment is generally accompanied with high production costs, high in-process inventory cost and high quality levels. Developments within the past five years regarding Flexible Manufacturing Systems (FMS) have made it cost effective to automate the production lines even when dealing with small volumes. Technologies such as FMS have made it possible for efficient lot sizing, shorter setups, and 26 smaller volumes with greater variety of products. The above developments have become the basis for competition. The new technologies have changed manufacturing in a way that economies of scale have been replaced with economies of scope. Such technologies have reduced setup time, making it possible to manufacture smaller batch sizes. 4. Vertical Integration Vertical integration is the extent to which a company produces its own parts and raw materials or markets its own products (Vonderembse and White 1988). A business that uses a differentiation strategy, where low volumes have dominance, tends to rely on outside suppliers to manufacture the necessary parts. Firms that pursue a cost leadership strategy have the option of making some of the parts in house. 5. Human Resources Management needs to recognize that human resources are the most important aspect of an organization, and as such, management should create an environment in which human resources can be most productive. This area of operation involves job designs, frequency of worker training, supervision control, and wage and incentive system. A differentiation strategy may require a more flexible work force that has been cross trained in order to effectively manage the capacity imbalances. Ritzman, Krajewski and King (1984), using a simulation study showed that worker 27 flexibility can have considerable influence on manufacturing performance. They report that higher flexibility requires more training and a higher level of skill among workers. 6. Quality Assurance The degree to which a product fulfills the needs and expectations of a customer is its quality. Quality encompasses a wide range of attributes, including dependability, availability, reliability, and appearance. These attributes have to be consistently met by all firms in order to remain cost effective. Decision variables in this area include quality focus (prevention vs. control), acceptable defective rate, and quality procedures. A firm pursuing a differentiation strategy may select quality as its competitive priority. Firms pursuing a cost leadership strategy tend to have formalized procedures for monitoring the quality of parts. 7. Production Planning Production planning involves decisions regarding production strategy, short-term capacity alternatives, production run, and production system. Such decisions can either reinforce or undermine the firm's business strategy. During the past two decades, complex systems such as MRP have been developed that tie the organization together through the planning process. Those firms that compete on the basis of a differentiation strategy tend to emphasize the use of complex systems such as Materials Requirement 28 Planning (MRP) (Krajewski and Ritzman 1987). With a differentiation strategy, there is also a greater willingness to change the master production schedule to satisfy any last-minute requests. Within such strategy, products are more likely to be custom made. 8. Materials Control Materials control involves decisions regarding suppliers, inventory control, and demand. With a differentiation strategy, a product may need to pass through many work stations, which involves long lead times and process bottlenecks. Such problems require development of a high level of work-in-process inventory. 9. Organization Although recent developments have suggested eliminating multiple layers of management, a company should determine the importance of being in close contact with its customers and employees and determine the number of management levels on that basis (Krajewski and Ritzman 1987). Decision variables in this area include level of communication and formalization. A company should select the appropriate structure to best fit its operations, products, and the market to be served. The collective pattern of decisions within the above nine decision categories should be complementary and mutually supportive of business level strategy. Most of the studies reviewed within the manufacturing strategy 29 discipline addressed individual decision areas. The current study took a different approach and evaluated the linkage between business strategy and the nine manufacturing decision areas. The above nine decision categories are closely inter- related. For instance, work force policies can affect production process choices, and purchasing policies can affect vertical integration choices. Over time, management must make decisions in each one of the manufacturing categories. Any change in one of the nine categories may require change in other areas of manufacturing (Hays and Wheelwright 1984) . Table 2 presents a summary of the decision variables within each area of manufacturing strategy based on the current body of knowledge. The next section provides an overview of research on business strategy. Research on Business Strategy Business level strategy focuses on how to compete in a particular industry or product-market segment. Distinctive competencies and competitive advantages are usually the most important components of strategy at this level (Hofer and Schendel 1978). Business strategy research has been more extensive than manufacturing strategy research. Business strategy research often deals with identifying the environment, the strategy requirements, and the type of performance to be expected (Fahey and Christensen 1986). 30 TABLE 2 DECISION VARIABLES IN MANUFACTURING STRATEGY DECISION CATEGORY DECISION VARIABLES Capacity- Capacity Slack Capacity Changes - timing and frequency Economies of size Capacity Balance Facility Plant(s) - number, size and location Focus Focus - volume, product, process Positioning Strategy - product focus, process Production Process Type-job shop, batch shop, continuous Process Span of Process-number of processes, relatedness of processes Process Stability-innovation, frequency of change Process Technology-number of different technologies, reliability Vertical Forward integration Integration Backward integration Human Job Design - specialization, responsibility, Resources control Worker Training - frequency, number of different tasks Supervision Control Quality Quality Focus - prevention and control Assurance Acceptable Defective Rate in production and purchasing Quality Procedures - informal, formal (SPC) Production Production Strategy - chase or level Planning Production System - push or pull Materials Level of inventory - amount and type (raw- Control material, work-in-process, and finished goods) Organization Centralization Communication Formalization Sources: R. H. Hays and R. W. Schmenner. 1978. How Should You Organize Manufacturing? Harvard Business Review Jan-Feb: 106-118; W. Skinner. 1974. The Focused Factory. Harvard Business Review 52(3): 113-121; and S. C. Wheelwright. 1984, Manufacturing Strategy: Defining the Missing Link. Strategic Management Journal 5: 77-91. 31 Existing studies indicate that environmental conditions and the strategy of the firm affect performance (Prescott 1986; Hambrick and Lei 1985). The central question in business strategy research involves the relationship between performance and the strategy of a firm (Fahey and Christensen 1986). A number of researchers have developed different typologies with respect to business strategy. Depending on the set of objectives to be pursued, a firm has the option of selecting one or a combination of business strategies. Some of the most frequently cited business strategy typologies include the ones developed by Buzzell, Gale, and Sultan (1975), Hofer and Schendel (1978), and Porter (1980). The above three typologies will be analyzed next. Market-Share-Based Generic Business Strategies Given the importance of market share in determining profitability, Buzzell, Gale, and Sultan (1975) evaluated different business strategies in relation to market share. They concentrated on developing a link between market share and return on investment (ROI). Buzzell, Gale, and Sultan (1975) observed that appropriate market share strategies which would take into account the strength of competitors and the availability of resources, needed to be developed. They categorized market share strategies into three broad groups. 32 1. Building strategies These strategies were based on active efforts to increase market share by means of new product introduction, new marketing programs, or other techniques. Buzzell, Gale, and Sultan (1975) observed that in most markets, a minimum market share was required for viability. When a firm's market share was somewhere between the low and the high end, share-building strategies were the most appropriate (Buzzell, Gale, and Sultan 1975). 2. Holding Strategies These strategies aimed to maintain the existing level of market share (status quo). According to Buzzell, Gale, and Sultan (1975), holding strategies were most appropriate in mature industries. 3. Harvesting strategies Such strategies were designed to achieve high short- term earnings and cash flow by permitting market share to decline. Harvesting strategies were usually carried out as a result of limitations faced by the firm, rather than by choice. The underlying concept for the above three strategies is the level of market share. Buzzell, Gale, and Sultan (1975) stated that market share was positively related to the profitability of a business firm. The above 33 relationships had some consequences on a number of business decisions. Product-Life-Cycle-Based Generic Business Strategies Hofer and Schendel (1978) developed a somewhat different typology on the basis of the product life cycle. They observed that a firm can follow one or a combination of six generic types of strategies. These strategy types were closely related to the product/market evolution of the industry and the competitive position of the firm within that industry. These strategies included share-building strategies, growth strategies, profit strategies, market concentration and asset reduction strategies, turnaround strategies, and liquidation and divestiture strategies. Share-building strategies aimed for significantly and permanently increasing the market share of the businesses involved in order to change the competitive position of the business (Hofer and Schendel 1978). Growth strategies were designed to maintain the existing competitive position of a firm in a rapidly growing market. As competition began to stabilize, businesses usually shifted from a growth to profitability strategy. Concentration and asset reduction strategies were used to align both the scope and the level of assets possessed by a business in order to improve its short-run profits and long-run prospects. Such strategies were appropriate when a firm was facing a weak competitive 34 position or when it was on a decline stage of the product life cycle. According to Hofer and Schendel (1978), turnaround strategies were used to reverse the declining position of a firm, assuming that the firm was worthy of being saved. Liquidation and divestiture strategies were employed as a last resort to generate as much cash flow as possible before withdrawing from the business. Hofer and Schendel (1978) argued that the selection of a generic business strategy depended on several criteria. These criteria included evaluation of the product/market, present competitive position of the firm, the position it is seeking, and the financial conditions available to the firm. Competition-Based Generic Business Strategies Porter (1980) reported that a firm can pursue one of four types of competitive strategies, depending on its particular circumstances and the intensity of the competitive forces facing it. The competitive forces included rivalry among existing firms, threat of potential new entrants, bargaining power of suppliers, threat of substitute products or services, and the bargaining power of buyers (Porter 1980). Porter's four generic strategies are defined below: 1. Industry-Wide Cost Leadership Within this type of strategy, a firm attempts to generate a strategic advantage across the whole industry by 35 creating an overall cost leadership. Such strategy requires aggressive construction of efficient-scale facilities, vigorous pursuit of cost reductions from experience, plus tight cost and overhead control (Porter 1980). Management carries the low cost theme within the firm's entire strategy. This type of strategy is followed by a firm which can offer lower prices than the competition. 2. Industry-Wide Differentiation This type of strategy emphasizes achieving and maintaining a selected form of differentiation, such as style or quality, within the entire industry. The typical firm pursuing such a strategy attempts to create something that is considered unique by the market. Differentiation can be achieved in terms of design or brand image, technology, customer service, or other special features available within a product market (Porter 1980). Ideally, a firm differentiates itself along several dimensions. A firm pursuing differentiation does not ignore costs, but treats them as secondary issues with relatively less importance. 3. Segment Cost Leadership A firm pursuing this type of strategy attempts to generate a strategic advantage in terms of cost position within a narrow segment (niche) of the industry. This type of strategy is also referred to as focus strategy (Hofer and 36 Schendel 1978). The goal in a focus strategy is to serve a particular target market. Functional strategies are developed with this objective in mind (Porter 1980). 4. Segment Differentiation Similar to industry-wide differentiation, firms pursuing such strategy attempt to achieve a chosen form of differentiation within a narrow segment of the industry. The type of resources, skills and organizational requirements differ from one type of business strategy to another (Kotha and Orne 1989). The above strategies have one thing in common: profit maximization (Kotha and Orne 1989). Under cost-leadership strategies, profits would surge by having the lowest cost position, whereas under differentiation strategy, greater profits can be realized by developing some sort of uniqueness within the product, which would attract a premium price. Table 3 provides a summary of the implications of the four common business strategies introduced by Porter. Some researchers have questioned Porter's typology by introducing other possible business strategies beside the ones explained by Porter (Green and Yasin 1991). Introduction of technologies such as flexible manufacturing systems and the rapid flow of information permit businesses to pursue a strategy composed of characteristics from both cost leadership and differentiation. Given these 37 developments, Green and Yasin (1991) have introduced an additional strategy referred to as "mixed strategy". Despite the limitations pointed out by some studies, Porter's framework of competitive positioning within an industry has stood up well over time (Kotha and Orne 1989) . Kotha and Orne (1989) observed that, although other researchers had presented different typologies of business strategies, much of the research in recent years has been influenced by Porter's analysis of industry competitiveness. Porter's competition-based generic business strategies typology has been cited more than any other typology in literature. In this study, the linkage between Porter's business strategies and selected manufacturing strategy variables was examined in relation to firm performance. Linking Business and Manufacturing Strategies Manufacturing strategy researchers have discussed the importance of developing a strong linkage between manufacturing and business strategies (Wheelwright 1984; Kotha and Orne 1989). Research by McDougall (1986) and Swamidass (1986) provided some limited data on the importance of linking manufacturing and business strategies. 38 co CO xaJ >d) XGJ XuJ <1) o *HL* *xHJ £ a xJ »0 Researchers have identified a high level of congruence between manufacturing strategy and business strategy as an important factor in determining success (Kotha and Orne 1989; Hays, Wheelwright and Clark 1988). As illustrated in Table 4, existing literature provided limited empirical research in support of the above concept. Little research has explored the means to attain a high level of congruence between business and manufacturing strategies. Four studies have concentrated on the linkage between business and manufacturing strategies: Richardson, Taylor, and Gordon 1985; Fine and Hax 1985; Kotha and Orne 1989; and Deane, Gargeya, and McDougall 1990. Richardson, Taylor, and Gordon (1985) evaluated manufacturing performance based on the level of congruence between corporate mission and manufacturing task. In this study, the authors defined corporate mission as the way in which a firm competes in its markets. Manufacturing task was defined as a weighted set of criteria with which the chief executive evaluates the performance of manufacturing. Using sixty-four Canadian electronics companies, they found that those companies with a strong linkage between corporate mission and manufacturing task were more profitable than other companies. Fine and Hax (1985) discussed the linkage between business and manufacturing strategy by suggesting a methodology for developing manufacturing strategy. 40 01 0 to JJ 01 (0 U to d) O £ x: as Mc l u JJ o\ ^O — i 3 (1985 ) (1990 ) Richa r Fin e Gordo n Taylo r Deane , an d o w 41 They viewed the formulation of manufacturing strategy as hierarchical, and argued in favor of a level of congruence between the three levels of strategies. Fine and Hax (1985) identified a six-step procedure for formulating manufacturing strategies. The first step in formulating manufacturing strategies involves developing a framework for strategic decision making in manufacturing. The authors used nine manufacturing strategy decision categories plus the four competitive priorities (quality, cost, delivery, and flexibility) in order to assess the manufacturing strategies. The second step involves assuring that the business strategy and the manufacturing strategy are linked together. The third step involves conducting an audit of the manufacturing strategy decision categories to determine the strengths and weaknesses of each decision category. This step helps identify the status of the manufacturing function. Each major product line is also evaluated according to the four measures of manufacturing performance. The fourth step involves positioning the product lines in the product or process life cycle to assess the commonality of performance objectives. The objective of this step is to identify product lines with similar strategic performance characteristics and missions. Products with similar positions on the product-process life cycle are grouped together. 42 The fifth step involves evaluating the degree of focus at each plant. The degree of focus is determined by positioning every product of each plant on a separate product-process matrix. The degree of focus within each plant can be judged by the dispersion level of the products on the matrix. The greater the dispersion level, the less focus within a plant. The sixth step involves developing manufacturing strategies corresponding to the conditions surrounding each product line or plant. Manufacturing strategies can be formulated by stating strategic objectives for each decision category. Such strategies are usually accompanied by a specific set of action programs that can be monitored and measured over time. As is apparent from the above discussions, Fine and Hax's (1985) study was limited to recommending a methodology for developing manufacturing strategies. The study by Kotha and Orne (1989) came close to developing a conceptual framework that linked the manufacturing strategy to the business unit strategy. Kotha and Orne used Porter's model to categorize the different business strategies. They employed three dimensions to characterize the manufacturing structure framework: (1) process structure complexity, (2) product line complexity, and (3) organizational scope. Process structure complexity refers to the relationship between production technology, organizational 43 structure, and management characteristics (Kotha and Orne 1989). Specifically, it identifies the different production processes available to a firm. Product line complexity has been characterized by several variables including end- product complexity, variety of final products, individual product volumes, and end-product maturity. Product line complexity defines the types and variety of product lines of the business unit. Organizational scope refers to a composite of several variables: geographic manufacturing scope, geographic market focus, level of vertical integration, customer-market scope, and the scale of operations. Kotha and Orne (1989) used Porter's generic business unit strategies and the above three manufacturing structure characteristics to identify generic manufacturing strategies. Their study conceptualized the manufacturing characteristics for different business strategies, but it did not provide any justification regarding the linkage between business strategies and manufacturing structure. It moved from the relationship between business strategy types and manufacturing structure characteristics to generic manufacturing strategies, without discussing the full relationship between business strategy and manufacturing structure. Kotha and Orne (1989) proposed that the manufacturing structures implicitly represented "generic manufacturing strategies" without providing any evidence in 44 support of such a relationship. Despite its limitations, the study provided a rich theoretical base for further theoretical and empirical research on manufacturing strategy. Deane, Gargeya, and McDougall (1990) examined the relationship between business strategies and manufacturing strategies and their effect on performance. They studied 217 new venture firms in nine SIC codes in the computer and communication equipment manufacturing industries. In this study, the authors examined price leadership and quality differentiation as business strategies along with several manufacturing strategy decision categories. Deane, Gargeya and McDougall (1990) concluded that the successful firms were better able to devise manufacturing decisions which were linked to business strategies. Based on these results, the authors recommended additional research to examine the relationship between alternate business unit strategies and an expanded set of manufacturing strategy variables. Given the findings of the above four studies, the objective of the present study was to compare the manufacturing strategy variables of high and low performers pursuing the cost leadership or the differentiation strategy. It involved an empirical survey of the businesses operating within the broadwoven fabric mill cotton sector (SIC 2211) . The following section provides a description of 45 the U.S. textile industry with particular emphasis on the broadwoven fabric mill cotton sector. U.S. Textile Industry The textile mill product industry includes all operations involved in converting fiber to fabric. This industry manufactures sheets, towels, floor coverings, tire cordage and rope. The industry is composed of over 6,000 firms (Survey of Current Business 1990), the largest of which produces only five percent of the total industry output. This is a fragmented industry whereby the majority of the firms were small and privately owned (The Textile Red Book 1991). As of March 1990, the textile mill product sector employment stood at 711,000 (Survey of Current Business 1990). In terms of product categories, outputs of these firms are relatively homogeneous. There were no significant barriers to entry into the industry, and individual firms had minimal price discretion. The U.S. textile industry is highly concentrated in the Southeast. Over seventy percent of total industry employment is located in seven Southern states. North Carolina, South Carolina, and Georgia account for almost sixty percent of total employment (Rozelle 1990). In comparison to other industries, the textile industries are relatively labor intensive. During 1990, the textile mill industry employed four percent of the U.S. manufacturing work force (U.S. 46 Department of Commerce 1991). The textile industries employed a greater percentage of women and minority workers than any other U.S. industries. The importance of this industry is evident by the fact that these industries generated a Gross National Product in excess of forty-eight billion dollars compared with fifty-one billion dollars for the auto industry, and forty-three billion dollars for primary metals (National Academy of Engineering 1988). On the Basis of product life cycle, industries can be classified as operating in an introductory, growth, mature, or decline stage (Porter 1980). Mature industries such as the textile mill industry have three characteristics which make them significantly different from industries operating at other stages of the life cycle (Hambrick 1983) : 1. These industries grow at less than ten percent annually in real terms. 2. The vast majority of the users are familiar with the products. 3. The technology and competitive structure are reasonably stable. The above characteristics leave industries such as textiles with a limited number of options to improve productivity and overall performance. 47 Problems Pacing the U.S. Textile Industry The U.S. textile mill industry remains a critical element of the U.S. economy. However, since 1980 the textile industry has been faced with growing imports, mostly from the newly industrialized countries. While the U.S. has left its market open to foreign sales, other countries have closed markets controlled by high tariffs. During 1990, seventeen percent of the U.S. textile market was controlled by imports (U.S. Department of Commerce 1991). In terms of volume, textile imports have grown by an average of fifteen percent per year since 1980, whereas the U.S. textile market has grown by only one percent per year (U.S. Senate 1987). The U.S. Department of Commerce predicted that if penetration of U.S. textile markets continues at the pace of the last decade, domestic sales of U.S. textile mill products would approach zero by the year 2000. The sharp increase in imports and gains in productivity by foreign competitors have resulted in significant loss of employment in domestic firms. Between 1980 and 1990, employment in the textile mill products industry was reduced by twenty percent (U.S. Department of Commerce 1991) . During an exploratory set of interviews, selected manufacturing executives were asked about the problems facing the U.S. textile industry and what could be done to remedy such problems. Manufacturing executives identified six crucial issues: 48 1. According to some executives, during 1990, return on investment in the textile industry was three to four times lower than other industries. Several factors contributed to the lower return on investment: high level of imports, higher labor costs in comparison to foreign manufacturers, and the higher cost of cotton. According to Dertouzos, Lester and Solow (1988), the textile firms that have been successful competitors, generally compete in market niches where they experience little competition from low-cost producers. 2. Some manufacturing plants operated outdated machinery which was both slow and inefficient. Some plants operated equipment that was close to one hundred years old. Several manufacturing executives suggested that to remain competitive, equipment should be replaced every ten years. During the 1980s, more than 500 plants were closed. One reason for such closures was the lack of capital to replace outdated machinery. 3. A number of manufacturing executives stated that foreign manufacturers do not compete on a fair basis. U.S. textile firms are willing to and capable of competing within a fair market, free of subsidies and trade restrictions. Foreign government subsidies are by far the most critical reason for plant closures in the U.S. Developing countries such as Hong Kong, Taiwan, South Korea, and others exported 49 their fabric at prices significantly lower than what can be manufactured in the U.S. 4. The cost of raw materials such as cotton was higher than what was available in the world markets. Pressure to protect the cotton farmer brought about a ban that disallowed any import of cotton into the U.S. Some of the executives pointed out that the cotton yield produced in the U.S. was not as good as foreign cotton. During 1990 and 1991, the cost of cotton increased by thirty percent (Survey of Current Business 1991). 5. Many plants were faced with excess capacity and idle machinery. In an attempt to minimize their losses, some manufacturers continued to produce items with low marginal returns. Such productions essentially increased the supply of fabric, causing lower rates of return for textile firms. U.S. textile manufacturers have generally pursued a strategy of long-run production of standard goods for mass markets. Since 1970, imports from low-wage producers have taken away a significant portion of the market. Successful textile firms have made the transition from the mass production strategy to a strategy that aims towards a niche market (Dertouzos, Lester, and Solow 1988) . 6. Despite large investments in automated looms and other equipment, the textile industry remains very labor intensive. Manual labor was used to pass more than 2000 wefts of yarn through needles, to prepare looms for weaving. 50 Such a task took an average of three days per loom. Fabric inspection was also a slow process that was achieved by re- rolling the fabric and manually looking for damaged areas. Tincher and Daley (1990) reported that during the 1980s, approximately one percent of finished garments did not meet quality requirements due to fabric defects. A 1991 study noted that U.S. weaving mills are slow in making changes to their operations (Cahill 1991). Furthermore, textile employees lack knowledge and skill in operating advanced processes. Reasons for Selection of the Broadwoven Fabric Mill Cotton Sector The U.S. textile mill industry consists of seven sectors: yarn spinning mills, broadwoven fabric mill cotton, broadwoven fabric mill man made, broadwoven fabric mill wool, weft, lace and wrap knit fabric mills, carpets and rugs, and the man-made fibers (U.S. Department of Commerce 1990). The broadwoven fabric mill cotton sector was selected for several reasons: first, the majority of the fifteen textile plants in the State of Texas use cotton weaving as their primary activity. The State of Texas, which was once the largest producer of cotton, was of prime interest in this study. Availability of high technology, business infrastructure, and the presence of low-cost labor in bordering Mexico make this neighboring state a strong alternative for re-establishing textiles. 51 Second, as shown in Figure 2, in terms of total employment, the broadwoven cotton fabric sector ranks as the third largest among the seven textile sectors. The high level of employment indicates the importance of this sector to the textile industry and its overall contribution to the U.S. economy. Weft, Lace & Wrap Man-made Fibers 55.4 Carpets & Rugs 53.3 Broadwoven - Cotton 72.3 Broadwoven - Wool 13.8 Yarn Spinning 89.1 Broadwoven-man made 88.3 FIGURE 2. TOTAL EMPLOYMENT IN THE U.S. TEXTILE INDUSTRY BY SECTOR, 1990 (in thousands) Source: U.S. Department of Commerce 1991 52 Third, during 1990 the broadwoven fabric mill cotton sector had the lowest productivity rate among the textile sectors. Productivity was measured as the average value of shipments per employee. As stated in Table 5, the low productivity rate within this sector clearly identifies the broadwoven fabric mill cotton sector as an attractive segment for further evaluation. TABLE 5 U.S. TEXTILE SHIPMENTS PER PRODUCTION EMPLOYEE, 1990 INDUSTRY SECTOR SHIPMENT Yarn Spinning $ 92,389 Broadwoven - Cotton 84,869 Broadwoven - Man Made 104,532 Broadwoven - Wool 86,860 Weft, Lace & Wrap 121,109 Carpets & Rugs 241,256 Man-Made Fibers 270,236 Source: U.S. Department of Commerce 1991. Fourth, this sector is more vulnerable to imports than other sectors. During 1990, imports constituted 19.35 percent of overall sector shipments (U.S. Department of Commerce 1991) . As illustrated in Table 6, the high vulnerability of this sector to imports also makes it an excellent sector for further evaluation. 53 TABLE 6 U.S. TEXTILE IMPORTS AS A PERCENTAGE OF SHIPMENTS, 1990 INDUSTRY SECTOR IMPORTS Yarn Spinning 3.88% Broadwoven - Cotton 19.35% Broadwoven - Man Made 15.51% Broadwoven - Wool 22.74% Weft, Lace & Wrap 2.50% Carpets & Rugs 6.21% Man-Made Fibers 5.11% Source: U.S. Department of Commerce 1991. Theoretical Framework The basic topic of this research was concerned with the relationship between business strategy and manufacturing strategy variables. The selection of a specific type of business strategy requires establishment and recognition of different levels of emphasis on the manufacturing strategy decision categories (Kotha and Orne 1989) . For example, a firm that selects cost leadership as its competitive strategy, must take the required actions to achieve the lowest cost position in the industry. A firm that selects to compete on the basis of differentiation strategy, must seek unique positions on some attribute of the product or some marketing attribute. Manufacturing strategy involves decisions and trade-offs among a number of decisions. Since a business cannot excel in all decision areas 54 simultaneously, it is important to focus on the few objectives that correlate well with the strategy of the business unit. According to Porter (1980), business firms usually follow one of four business strategies: industry-wide cost leadership, industry wide differentiation, segment cost leadership, or segment differentiation. The objective of this study was to determine whether firms that establish manufacturing strategies that are congruent with their business strategies outperform others. Literature provides little evidence regarding the operationalization of the above variables. This study was limited to examining two of Porter's generic business strategies: cost leadership and differentiation. Twenty-four variables were used to represent the nine manufacturing strategy decision categories. These items were adapted from studies carried out by Krajewski and Ritzman (1987) and Deane, Gargeya, and McDougall (1990). Table 7 shows the nine decision categories and the items representing each variable. It also illustrates the hypothesized relationships between each of the two business strategies (cost leadership and differentiation) and the twenty-four manufacturing strategy variables. For example, it was hypothesized that a firm pursuing cost leadership strategy tends to have a lower level of capacity slack than a firm pursuing differentiation strategy. Similar 55 hypotheses were also identified for the other twenty-three manufacturing strategy variables. Table 7 forms the basis for developing the research hypotheses and the survey questionnaire. Development of Hypotheses The basic objective of this study was to analyze the linkage of business strategy and manufacturing strategy variables with respect to firm performance. The primary research premise in this study was that, those business units in which business strategy and manufacturing strategy variables are linked, perform significantly higher than those in which the two strategies are not linked. The above hypothesis was developed into two specific sets of hypotheses representing the two business strategies: cost leadership and differentiation. These hypotheses formed the basis for developing the survey questionnaire and determined the type of information required. List of Hypotheses Based on existing research, twenty-four hypotheses representing the nine manufacturing decision categories were developed for the two generic business strategies (Krajewski and Ritzman 1987; Deane, Gargeya, and McDougall 1990). 56 x: x: x: IXJs0 ) x: x: x:£ 3 x: 5 5 x: ^x: G JJ Oi Ol 0)o o Ol o o o> oi oi Ol g Ol X2 CO w cSd)J ^0 ) >x:x: .G xs •s £ S s £ goioi Ol Ol £ 13 <0 o o I o o o 5Q d> Sh J J V -H -H J J J 1-3 J JJ s ac CO O O 0) a) AJ 4J oCO oCO w o o a a n n Oi CQ ? 3 a) a Qi Q< •>H •H fS CO a a G a) CO .U riH r1H JJ 0 0 •H O) o> QQ U <0 (d G (d •H •H <0 G C § 0) D u •H 0) > JJ JJ H O-H U T5 0) o > o (d <0 ij-H a O O c CD o G n M I JJ-H * u U U Ui 0) a u CO GC O 01 01 CO (0 (0 u aao Ol CO Qi Q) -aH> 0) 0) CO Nu <0 >, u CO CO JJ JJ o JJ rH JJ c c o G JJ G G CO GC O c G U fH CQ -H o o O G O O 0) O •H u U 0) U ud) O •H<0 Q) >1 B# JC O rH T5 ccn u 1 G C <0r o C (d <0 a) O •d to • • > c >1 a>r H I SS; & (0 Oi BJ Hypotheses one through twelve are concerned with businesses pursuing the cost leadership strategy. Hypotheses thirteen through twenty-four are concerned with businesses pursuing the differentiation strategy. 1. There is no statistical difference between high and low performers pursuing cost leadership strategy based on level of emphasis on capacity slack. 2. There is no statistical difference between high and low performers pursuing cost leadership strategy based on level of emphasis on product layout. 3. There is no statistical difference between high and low performers pursuing cost leadership strategy based on level of emphasis on special-purpose (fixed) equipment. 4. There is no statistical difference between high and low performers pursuing cost leadership strategy based on level of emphasis on proven manufacturing processes. 5. There is no statistical difference between high and low performers pursuing cost leadership strategy based on level of emphasis on vertical integration. 6. There is no statistical difference between high and low performers pursuing cost leadership strategy based on level of emphasis on employee cross training. 7. There is no statistical difference between high and low performers pursuing cost leadership strategy based on level of emphasis on job specialization. 8. There is no statistical difference between high and low performers pursuing cost leadership strategy based on level of emphasis on product quality. 9. There is no statistical difference between high and low performers pursuing cost leadership strategy based on level of emphasis on flexibility in production scheduling. 10. There is no statistical difference between high and low performers pursuing cost leadership strategy based on level of emphasis on product customization. 59 11. There is no statistical difference between high and low performers pursuing cost leadership strategy based on level of emphasis on work-in-process inventory. 12 . There is no statistical difference between high and low performers pursuing cost leadership strategy based on level of emphasis on organizational variables. 13. There is no statistical difference between high and low performers pursuing differentiation strategy based on level of emphasis on capacity slack. 14. There is no statistical difference between high and low performers pursuing differentiation strategy based on level of emphasis on process layout. 15. There is no statistical difference between high and low performers pursuing differentiation strategy based on level of emphasis on general-purpose (flexible) equipment. 16. There is no statistical difference between high and low performers pursuing differentiation strategy based on level of emphasis on innovative manufacturing processes. 17. There is no statistical difference between high and low performers pursuing differentiation strategy based on level of emphasis on vertical integration. 18. There is no statistical difference between high and low performers pursuing differentiation strategy based on level of emphasis on employee cross training. 19. There is no statistical difference between high and low performers pursuing differentiation strategy based on level of emphasis on job specialization. 20. There is no statistical difference between high and low performers pursuing differentiation strategy based on level of emphasis on product quality. 21. There is no statistical difference between high and low performers pursuing differentiation strategy based on level of emphasis on flexibility in production scheduling. 22. There is no statistical difference between high and low performers pursuing differentiation strategy based l©vel of emphasis on product customization. 60 23. There is no statistical difference between high and low performers pursuing differentiation strategy based on level of emphasis on work-in-process inventory. 24. There is no statistical difference between high and low performers pursuing differentiation strategy based on level of emphasis on organizational variables. CHAPTER III METHODOLOGY The methodology section of this study consists of five parts. Part one presents a discussion of the research design that was used in the study. In part two, the sample selection process is discussed. Part three states the data collection procedures used in the study. Part four provides a review of the survey instrument used in the study, and part five presents a discussion of the statistical analysis techniques that were performed on the collected data. Research Design According to Churchill (1988), three types of designs can be used to conduct research: exploratory, descriptive, and causal. Exploratory research mainly deals with the discovery of ideas and insights. This type of research is usually conducted whenever there is limited knowledge about a problem (Kidder and Judd 1986) . According to Kerlinger, "exploratory studies have three purposes: to discern significant variables in the field situation, to discern relationships among variables, and to lay the groundwork for later, more systematic and vigorous testing of hypotheses" (Kerlinger 1973, 406). Further extensive research can later 61 62 be carried out using descriptive and causal research designs. Descriptive research is concerned with determining the frequency with which something occurs or the relationship between two variables. Causal research designs are concerned with determining cause and effect relationships. This study used the exploratory design in order to break the broad, vague problem statement into precise subproblem statements. Limited research has been conducted regarding the linkage of business and manufacturing strategies. There is a need to explore ideas and insights in this area. This study involved an exploratory cross-sectional survey of the broadwoven fabric mill cotton sector of the U.S. textile mill industry. In a cross-sectional study, a sample of elements selected from the population of interest are evaluated at a single point in time (Churchill 1988). The reason for this research was to explain the relationships among business and manufacturing strategy variables. Unlike causal research where the major issue of discussion is establishment of causal relationships, the aim of survey research is to determine whether the variables under study have any correlation with each other or under what conditions they correlate (Kidder and Judd 1986). 63 Sample In general, sample selection involves five steps (Churchill 1983). These steps include identification of the population, identification of the sampling frame, selection of a sampling procedure, determination of the sample size and finally collection of the data from the designated elements (Churchill 1983) . The first step in sample selection is identification of the population. Population refers to the totality of cases that conform to some specification (Churchill 1983) . In this study, population refers to all business firms belonging to Standard Industrial Classification (SIC) 2211, namely the Broadwoven Cotton Fabric Mills. All of the strategic business units that were surveyed in this study came from the same industry sector. The surveyed businesses manufacture fabric from cotton using the weaving process. These firms generally utilize processes and technology that are commonly known throughout the industry. The above steps have eliminated or significantly reduced potential sources of variation, resulting in a reasonably homogenous group of firms. The second step in sample selection involves identification of a sampling frame, that is, creating a list of population elements from which the sample will be drawn (Churchill 1983). The Broadwoven cotton fabric mill sector involves a variety of activities; some businesses are 64 primary producers of fabric while others are secondary users of fabric (see Appendix G). In this study, the intention was to survey the primary manufacturers of fabric. Two criteria were used for including a firm in the surveyed population: 1. The company had to be operating within the Standard Industrial Classification # 2211. 2. Each company had to be operating a fabric mill and manufacture fabric using cotton. For this study, the directory of manufacturers for different states, the 1988 Employment and Wages Annual Averages publication. The Textile Red Book (1991), and Davidson's Textile Blue Book (1990) served as the sampling frame. The Employment and Wages Annual Averages (1988) publication of the Bureau of Labor Statistics, reported that 504 business firms were operating in this industry sector. There was no consensus between the above three publications with respect to the exact number of broadwoven mills operating in the U.S. For the purposes of this study, all broadwoven cotton fabric mills that were listed either in Davidson's Textile Blue Book (1990) or The Textile Red Book (1991) were used as the survey population. As shown in Table 8, 334 broadwoven cotton textile mills in twenty-four states were identified as prospective participants in the study. No broadwoven cotton mills were operating in the other twenty-six states at the time the study was conducted. 65 TABLE 8 BROADWOVEN COTTON FABRIC MILLS BY STATE NUMBER OF STATE FABRIC MILLS Alabama 17 Arizona 1 California 13 Connecticut 6 Georgia 34 Illinois 3 Indiana 2 Kentucky 1 Maine 3 Maryland 3 Massachusetts 15 Mississippi 1 New Hampshire 2 New Jersey 12 New York 14 North Carolina 66 Ohio 4 Pennsylvania 36 Rhode Island 13 South Carolina 71 Tennessee 5 Texas 7 Virginia 4 Wisconsin 1 Total 334 Source: Davidson's Textile Blue Book, 1990. and The Textile Red Book, 1991. The third step in sample selection involves selection of a sampling plan. By definition, sampling plan includes utilization of either a probability or a nonprobability sample. In probability samples, each member of the 66 population has a non-zero chance of being included in the sample. Unlike a probability sample, a nonprobability sample involves personal judgment in the selection process. In this study, the total population of broadwoven cotton mills operating in the United States were asked to participate in the survey. This population included 334 primary manufacturers of fabric (see Table 8). The fourth step in sample selection involves determination of the sample size. Given the exploratory nature of the study, the characteristics of the respondents, and the fact that most of the firms were privately held, the decision was made to survey the total population. The last step in sample selection involves data collection. Within this step, one needs to be aware of the type of errors, namely sampling and nonsampling, that can occur during the data collection process. Sampling errors involve "the difference between the observed values of a variable and the long-run average of the observed values in repetitions of the measurement" (Churchill 1983, 400). Non- sampling errors involve biases in terms of non-coverage, non-response, data collection procedure and data processing. Non-coverage involves failure to include some units of the defined survey in the actual operational sampling frame. Non-coverage can become a problem during mail surveys when one is faced with inadequate mailing lists that do not include all population elements. In this study, the 67 directory of manufacturers for the fifty states in conjunction with The Textile Red Book (1991) and Davidson's Textile Blue Book (1990) were used in order to limit the effect of any non-coverage bias. Non-response errors raise the question of whether those that do respond are different in some important way from those that do not respond (Churchill 1983). Several criteria were examined to evaluate the existence of any differences between respondents and non-respondents, including: size of employment, age of businesses, location, and industry sector. The majority of the surveyed firms are privately owned; as such limited data was available on their characteristics. The size of employment for respondents was representative of the one for the surveyed population (see Table 9). In terms of years of operation (age of businesses), no public source was available to verify such data. The development of the textile industry dates back to the late 1700s and early 1800s, and it is perceived as a mature industry. Fifty-four of the eighty-eight businesses reported that they have been in operation for more than sixty years, which fairly resembles the age of the industry. 68 TABLE 9 COMPARISON OF RESPONDENTS VS. NON-RESPONDENTS: SIZE OF EMPLOYMENT Number of Respondents Non - Respondents Employees 1-50 15 (15%) 64 (26%) 51 - 99 3 ( 3%) 21 ( 8%) 100 - 249 18 (20%) 46 (19%) 250 - 499 19 (21%) 52 (21%) 500 - 999 18 (20%) 28 (11%) Over 1000 15 (15%) 9 ( 4%) Not Reporting 26 (11%) Total 88 (100%) 246 (100%) Location can be regarded as a decisive factor with respect to the cost of raw material (cotton), and labor. Traditionally, a major part of the textile industry has been concentrated in the Southern United States, where most of the cotton is grown and low cost labor is available. Seventy percent of the surveyed respondents were located in the states of Alabama, Georgia, North Carolina, and South Carolina. Similarly, fifty-seven percent of the industry is also located in these four states. Appendix H provides a comprehensive comparison of the respondents and the non- respondents by state. All surveyed businesses were operating within the broadwoven cotton fabric mill sector (SIC #2211), and 69 primarily manufactured fabric using cotton. Based on these four criteria, a reasonably homogenous group of business firms was obtained that could be used to evaluate the linkage between business strategy and manufacturing strategy variables. Field errors arise out of partial participation of some sample members (Churchill 1983). Field errors become an issue whenever an individual agrees to participate but only responds to some of the questions within the questionnaire. In this study, manufacturing executives that mailed incomplete responses were called and asked to provide complete answers to the questionnaire form. Data Collection Procedure According to Kidder and Judd (1986), survey analysis can be conducted using one or a combination of three techniques: written questionnaires, personal interviews, and telephone interviews. Each one of the above techniques carries some advantages and disadvantages. In this study, one set of written questions was mailed to the potential participants. Mail surveys cost less and minimize interviewer bias. Such surveys also place little pressure on respondents, and give respondents anonymity (Kidder and Judd 1986). Limitations of mail surveys include low response rate, relatively low accuracy, and lack of control over actual respondents. Mail questionnaire surveys have 70 the lowest response rate of all survey methods (Kidder and Judd 1986). In this study, the response rate was increased by sending three follow-up letters at different times. The researcher identified the manufacturing executives from each company and specifically requested their responses to the survey. Objective performance data on privately held firms is severely restricted. According to Dess and Robinson (1984), subjective performance data may be used in the absence of objective performance data. Dess and Robinson (1984) have suggested that subjective perceptions of performance have a strong correlation with objective measures of performance over the same time period. In this study, the manufacturing executives were asked to provide subjective and objective performance data regarding their business units. Within the subjective performance measures, the executives were asked to compare the performance of their business units with that of other businesses in the broadwoven cotton fabric mill industry. The population for this study was surveyed over a period of six months in 1991. Questionnaires were mailed to manufacturing executives, vice presidents of manufacturing, or plant managers of all the 334 broadwoven cotton fabric mills operating in the United States. The majority of the respondents had been with their firms at least five years at the time of the survey. The names and addresses for the 71 above population were initially collected from the Directories of Manufacturers for the fifty states in the United States. However, since Directories of Manufacturers list both primary manufacturers and secondary users of fabric, as a result, the initial list had to be modified. Two sources including Davidson's Textile Blue Book (1990) and The Textile Red Book (1991) were used to modify the list of cotton fabric manufacturers. The resulting population consists of 334 plants operating in twenty-four states (see Table 8). Before mailing any questionnaire forms, prospective participants were contacted by phone to verify their names and addresses. Each potential participant was mailed a cover letter (see Appendix A), a questionnaire form (see Appendix F), and a postage-paid return envelope. Two weeks after the first mail-out, a follow-up letter was sent to all potential participants that had not yet responded (see Appendix B). The purpose of this letter was to inform the participants that a questionnaire had been mailed to them and request their timely response to the survey. A second letter and a questionnaire form were sent to those businesses that had not responded by the fourth week after the first mail-out (see Appendix C). In some cases, manufacturing executives that had not responded were called and/or mailed additional reminder letters (see Appendix D). 72 Survey Instrument The required data for this study were collected using a structured qualitative questionnaire. The questions and the possible responses in the questionnaire were predetermined. The respondents were asked to provide their responses using a seven-point Osgood scale, with one representing very low and seven representing very high (Osgood, Suci, and Tennenbaum 1957). The questionnaire form was used to identify the type of business and manufacturing strategies currently being implemented by the strategic business units operating in the broadwoven cotton fabric mill sector. The instrument that was used in this study consisted of four parts. In Part I of the questionnaire form, manufacturing executives were asked to identify the type of business strategy emphasized by their respective enterprises. The participants were also asked about the level of differentiation being followed by their businesses. Part II of the questionnaire form included questions concerning the manufacturing strategy constructs that were identified in Chapter Two. Within this part, the respondents were asked to compare their firms to their competitors in terms of the nine manufacturing strategy decision categories. Based on a literature review, twenty- four variables were developed to represent the nine manufacturing strategy constructs. 73 Part III of the survey questionnaire concerned business performance of the participants. Manufacturing executives were asked to provide subjective and objective measures regarding the performance of their firms. The respondents were also asked to evaluate how their businesses performed in comparison to their set objectives. Part IV of the questionnaire consisted of four descriptive questions concerning the company and the interviewee. A copy of the business and manufacturing strategy questionnaire is presented in Appendix F. Manufacturing executives or other personnel with equivalent positions were asked to participate in this study. The following four-step procedure was used to administer the questionnaire to the targeted respondents: 1. A cover letter, a questionnaire form, and a postage-paid return envelope were sent to all potential respondents. 2. Approximately two weeks later, a follow-up letter was sent to all recipients of the first mailing. The purpose of this follow-up and the ones following it were to use a different strategy to get sample members' to respond to the questionnaire. 3. A second follow-up was sent four weeks after the first mailout. This follow-up involved sending a revised cover letter, a copy of the questionnaire 74 form, and a return envelope to those who had not yet responded. The revised cover letter informed the respondents that their responses had not yet been received, and restated some of the appeals from the original cover letter. 4. Those firms that did not respond by the second reminder letter were telephoned and were asked to respond to the questionnaire. Measurement Scale According to Kidder and Judd (1986), four types of multiple-item scales have been used by researchers. The Likert and Osgood scales are used more frequently in social research. Both Likert's five-point scale and Osgood's seven-point scale consist of a set of items that the subjects respond to with agreement or disagreement. Osgood's semantic differential scale is best applied when measuring the meaning of an object to an individual (Osgood, Suci and Tennenbaum 1957; Kidder and Judd 1986). This study used Osgood's seven-point scale to record respondents' attitudes regarding a number of variables. Instrument Validation Validity of a measuring instrument is defined as the extent to which differences in scores reflect the true differences among individuals (Churchill 1988). Three types 75 of validity tests can be carried out on an instrument: pragmatic validity, content validity, and construct validity (Churchill 1988) . Pragmatic validity involves the question of how well the measure accurately predicts the criterion. It can be determined by performing a correlational analysis between the measuring instrument and the characteristics being measured. A high correlation signifies a high level of pragmatic validity. Pragmatic validity is rarely considered an important kind of validity (Churchill 1988) . What is often more important is the content validity of an instrument. Content validity involves the issue of whether the measurement instrument adequately covers the most important aspects of the construct that is being measured. Content validity or face validity refers to the systematic examination of the questionnaire to determine whether it covers a representative sample of the behavior domain to be measured (Anastasi 1961). Face validity is performed by a group of judges or experts who evaluate a questionnaire and decide whether in their opinion it measures what it claims (Zikmund 1988) . Judgment is required in establishing the accuracy of items as well as their relevance to the universe specifications (Cronbach 1971) . Straub (1989) argues that it is usually difficult to create or verify content-valid instruments, 76 because the universe of possible contents is virtually- infinite. Therefore, the content validity of an instrument cannot be guaranteed. However, it can be maximized by verifying that the variables measure what they claim (Straub 1989). In this study, content validity of the instrument was established using a review process by a panel of experts. Six professors and Ph.D. students were asked to verify the content validity of the instrument. The questionnaire was repeatedly revised in order to approach a level of consensus. Construct validity involves the issue of whether an instrument measures what it was intended to measure (Churchill 1988) . To have construct validity, an instrument must meet two conditions: (1) it should assess the direction of a representative sample of the characteristics of the construct and (2) it should not be contaminated with elements from the domain of other constructs or error (Peter 1981). According to Churchill, construct validity can be assessed by "...whether the measure confirms or denies the hypotheses predicted from the theory based on the constructs" (Churchill 1983, 294). This assessment technique is problematic because rejection of the hypothesized relationship can be due either to a lack of construct validity or incorrect theory. Construct validity 77 can also be carried out by a plodding verification of every sentence written about the construct (Cronbach 1971). Instrument Reliability- Reliability refers to the consistency of results obtained from one set of measurements to another (Stanley 1971) . The reliability of an instrument can be established using one or a combination of four techniques: test-retest, equivalent-form, split-half, and inter-item consistency (Anastasi 1961). According to Zikmund (1988) the test- retest procedure involves repetition of the identical test on a second occasion. Equivalent-form reliability is established by administering two sets of questionnaires at the same time. Split-half is achieved by dividing the questionnaire into two comparable sets of questions and then performing a correlational analysis between the two sets. Inter-item consistency involves the consistency of the subjects' responses to all items in the test. A correlation analysis can be performed along with each one of the above four techniques to determine the level of instrument reliability. A correlation coefficient expresses the degree of correspondence, or relationship between two sets of data. In the current study, the reliability of the instrument was examined by surveying a number of respondents twice, using the test-retest procedure. After the first set of questionnaires was administered, a group of twenty 78 respondents were asked to complete a second questionnaire. The sequence of the questions was changed during the second administration. Such a change was necessary to minimize the chance of receiving responses based on memory alone. Cronbach's Coefficient Alpha was used to assess the reliability of the instrument. Cronbach's coefficient alpha is an estimate of the correlation between two samples (Cronbach 1951). High correlations between the responses from the two questionnaires argue for a high degree of reliability of the instrument. Nunnally (1978) suggested that in exploratory type research, a correlation level 0.50 to 0.60 is indicative of good measurement reliability. Eleven respondents participated during this phase of the study providing a response rate of fifty-five percent. Coefficient Alphas were calculated for each pair of the thirty-eight items within the questionnaire. The Coefficient Alphas range between 0.72 and 0.77. The coefficient alphas for each pair of items are presented in Table 10. The responses collected at this stage were for reliability purposes, and were excluded from any other analysis. Overall, the Coefficient Alpha for the instrument was 74.80 percent. 79 TABLE 10 RELIABILITY OF THE INSTRUMENT: CRONBACH'S COEFFICIENT ALPHA For For Variable Raw Standardized Variables Variables Business Strategy 0.7275 0.7364 Level of Differentiation 0.7317 0.7411 Capacity Slack 0.7518 0.7613 Capacity Changes 0.7434 0.7566 Process Layout 0.7594 0.7687 Product Layout 0.7614 0.7674 Facility Focus 0.7650 0.7688 General Purpose Equipment 0.7392 0.7447 Special Purpose Equipment 0.7537 0.7591 Proven Manufacturing Processes 0.7441 0.7501 Innovative Manufacturing Processes 0.7301 0.7426 Forward Integration 0.7075 0.7220 Backward Integration 0.7366 0.7452 Employee Cross-training 0.7356 0.7493 Job Specialization 0.7499 0.7524 Worker Training 0.7132 0.7173 Product Quality 0.7462 0.7577 80 TABLE 10 - Continued For For Variable Raw Standardized Variables Variables Quality Assurance Process 0.7354 0.7399 Variety of Final Products 0.7294 0 .7407 Product Customization 0.7219 0.7305 Production Planning Technique 0.7045 0 .7201 Frequency of Production Planning Modifications 0.7351 0.7456 Work-in-process Inventory 0.7400 0.7525 Centralization 0.7239 0.7362 Communication 0.7504 0.7608 Formalization 0.7141 0.7239 Return on Investment 0.7358 0.7359 Return on Sales 0.7450 0.7482 Total Sales Growth 0.7354 0.7367 Market Share 0.7244 0.7274 Sale - 1 0.7395 0.7467 Return on Sales - 1 0.7256 0.7237 Return on Assets - 1 0.7226 0.7229 Return on Investment - 1 0.7276 0.7275 Total Sales Growth - 1 0.7336 0.7377 Market Share - 1 0.7186 0.7210 Production Employees 0.7456 0.7436 Supervisory Employees 0.7389 0.7448 81 Instrument Pretesting Pretests are usually recommended before a questionnaire can be used at full scale. Pretesting involves the use of a questionnaire in a small pilot study to ascertain how well the questionnaire functions (Hunt, Sparkman, and Wilcox 1982). Several criteria need to be pretested within a questionnaire: length, layout, format, and the sequence of the questions. The size of the pretest sample is a function of the instrument and the target population (Hunt, Sparkman,and Wilcox 1982). The more complex instruments tend to require larger pretest samples. Zaltman and Burger (1975) suggested that the size of a pretest sample be "small". Churchill (1988) recommended the use of two sets of pretests. It is usually recommended that questionnaires be pre-tested using the type of respondents who will be interviewed in the planned study (Hunt, Sparkman, and Wilcox 1982) . Such interviews should be conducted under the same kind of conditions that will exist in the actual survey (Clover and Balsley 1974). Such pretesting assists in both re-wording and rearranging the questions. In this study, ten interviewees with characteristics similar to the survey sample were asked to pretest the survey questionnaire. After the above pre-tests, the questionnaire was revised. The above pre-tests served a number of purposes. They identified unforeseen problems in 82 wording of the questionnaire, sequence of the questions, comprehension of the respondents, and administration of the questionnaire. The above pre-tests also helped increase the clarity of the instructions and decreased ambiguity within the questionnaire. Data Analysis The primary objective of this study was to evaluate the linkage between the business strategies and the manufacturing strategy variables of selected broadwoven cotton fabric mills and their relationship with performance. The data collected from the structured questionnaire were analyzed using the following four procedures: 1. The data collected from the questionnaire were used to classify the respondent business units into two groups on the basis of their respective business strategies. 2. In order to apply discriminant analysis, two or more mutually exclusive groups are required (Hair, Anderson, Tathem, and Garblowsky 1979). According to the authors, one can create two or more distinct groups by artificially dividing the population based on a certain discriminating variable. Furthermore, when dealing with three or more distinct groups, one can select to examine only the extreme groups. Such a practice is referred to as the polar-extreme approach, whereby the comparison is limited to the two extreme groups and the 83 middle group is excluded from discriminant analysis (Hair, Anderson, Tathem, and Garblowsky 1979) . The performance data collected within the questionnaire were used to classify business units to one of three groups: high, medium, and low performers. Two financial measures including return on investment and return on sales were used to determine the business performance of the surveyed textile firms. Business units were classified on the basis of their overall ranks within the two performance measures. Firms in the upper forty percent in comparison to the sampled businesses were classified as high performers, those firms in the lower forty percent were classified as low performers. Firms within the middle twenty percent in comparison to the sampled firms were classified as middle performers and excluded from further analysis. The objective of this study was to distinguish between the manufacturing strategy variables of the surveyed business units. The manufacturing strategies of high performers and low performers within each business strategy category were compared in order to determine any significant differences between the two groups. The discriminant analysis procedure was used to determine whether there is any difference between the centroids of the two groups based on the twenty-four manufacturing strategy variables. 84 Student's jt tests were used to analyze the existence of any significant difference between the manufacturing strategy variables as emphasized by the two comparison groups. Student's t tests are usually applicable whenever the standard deviation and the mean of the population are not known (Siegel 1956) . The sample standard deviation and the sample mean were used to calculate the t. distribution. Four of the hypotheses involved examination of more than one variable. Logistic regression procedure was used to scrutinize these four hypotheses. CHAPTER IV ANALYSIS AND RESULTS This chapter is composed of four sections. The first section presents the characteristics of the researched sample, i.e. those business firms that responded to the survey. It identifies surveyed businesses by state and describes their years of operation (age), size of employment, business performance, and classifies them based on business strategy and performance. Business strategy questions are analyzed in the second section. The third section addresses the twenty-four manufacturing strategy variables that were used in the second part of the questionnaire. The section presents two sets of comparisons between surveyed businesses focusing on cost leadership and differentiation strategy with respect to performance. The fourth section presents the results of the statistical analysis of the hypotheses that were applied in the study. Characteristics of the Researched Sample This section consists of four parts. First, the surveyed businesses are identified by state. The second part presents the years of operation (age) and the 85 86 employment size of the firms. Business performance of the surveyed firms is explained in the third part. Part four describes the classification process of those that replied to the survey. Researched Sample Respondents All broadwoven cotton fabric mills operating in the United States were surveyed in this study. The majority of the population of 334 plants studied are located in the states of North Carolina, South Carolina, and Georgia (see Appendix H). Eighty-eight of the 334 surveyed businesses answered the questionnaire form. This represents a 26.3 percent response rate. Given the exploratory nature of the study, the economic status of the textile industry, and the issue of private ownership for the majority of the population, such a response rate was deemed acceptable. Business firms that did not respond to the survey were classified into four groups. Forty-six textile firms expressed their unwillingness to respond to the survey. Another thirty-one stated their willingness to participate, but did not respond by the deadline. These businesses were categorized under the lack of interest column. Ten questionnaire forms were returned by the post office because the businesses could not be located. Before mailing the questionnaire forms, the names and addresses of more than ninety percent of the potential participants were verified. 87 The last group of nonrespondents includes those that expressed neither their willingness nor their unwillingness to participate in the study. This group consisted of 159 plants comprising forty-seven percent of the sample. Lack of a higher response rate can be attributed to three factors: 1. The study was carried out during an economic recession, which had affected the major sectors of the economy, including textile industries. A number of manufacturing executives observed that the first two quarters of 1991 had been critical times and as such they did not participate in the study. 2. Despite repeated assurances regarding confidentiality of the data, some managers refused to respond to the questionnaire form. Some executives stated that they were not authorized to release any information. 3. Some manufacturing executives were found to be inaccessible because of the nature of their work activities. Other managers stated that they receive two or three surveys a week and don't have time to respond to them. 88 Age and Employment Size of the Surveyed Businesses The executives were asked about the age of their businesses, the number of employees, and the number of years they have been associated with their respective firms. The textile industry can be regarded as one of the oldest trades in the United States. More than half of the businesses reported that they had been in operation over sixty years (see Table 11). TABLE 11 RESPONDING BUSINESSES CATEGORIZED BY AGE AGE CATEGORY # OF FIRMS 1 to 20 years 11 21 to 40 years 12 41 to 60 years 11 61 to 80 years 12 81 to 100 years 17 Above 100 9 Not Reporting 17 Total 88 The manufacturing executives were also asked about their production and staff employees. In general, textile industries have been labor intensive and a major source of employment. During 1990, the U.S. textile industry employed approximately four percent of the manufacturing labor force (U.S. Department of Commerce 1991). On the average, sampled businesses pursuing differentiation strategy had fewer 89 employees than businesses pursuing cost leadership strategy (see Table 12). Fifty-six of the eighty-eight surveyed textile businesses indicated that they had fewer than fifty supervisory and staff employees. TABLE 12 EMPLOYMENT SIZE OF THE SURVEYED BUSINESSES Businesses pursuing Businesses Number of cost pursuing Total Employees leadership differentiation strategy strategy 1-50 9 6 15 51 - 99 2 1 3 100 - 249 9 9 18 250 - 499 11 8 19 500 - 999 9 9 18 over 1000 11 4 15 Total 51 37 88 The majority of the 6000 firms operating within the U.S. textile industry are privately owned. Approximately seventy-five percent of the surveyed firms were privately owned, twelve percent were publicly owned, and the other twelve percent did not report their form of ownership (The Textile Red Book 1991) . 90 Classification of Surveyed Businesses The majority of the firms operating in the U.S. textile industry are relatively small and privately owned (The Textile Red Book 1991). Private ownership presented obstacles in the obtaining of performance-related data. Manufacturing executives of textile firms were asked to respond to three sets of objective and subjective measures of performance. Part III of the survey instrument measured the performance of the sampled business units (see Figure 3). The first section of part III evaluated performance based on four measures: return on investment, return on sales, total sales growth, and market share. Using a scale of one to seven, the respondents observed neutral performance in relation to all four measures (see Table 13). Within the second section, surveyed firms were asked to provide objective figures regarding sales, return on sales, and return on assets. Only ten of the 334 surveyed manufacturing executives answered this section. Therefore, it was not used in classification of the respondents. Within the third section of Part III of the survey instrument, the manufacturing executives were asked to evaluate how their businesses performed with respect to their business objectives. Performance was measured in terms of sales, return on sales, return on assets, return on investment, total sales growth, and market share. 91 Part III: Business Performance 1. Please check the box which you feel best estimates how the performance of vour business unit compares to similar businesses in your industry over the last 3 years. Top Next Middle Lower Lowest 20% 20% 20% 20% 20% Return on Investment • • • • • Return on Sales • • • • • Total Sales Growth • • • • • Market Share D • • • • 2. Please provide the following descriptive information for the stated years. Sales 1985 $ 1987 $ 1990 $ Return on Sales 1985 % 1987 1990 % Return on Assets 1985 % 1987 1990 % 3. On the basis of the following criteria, how well did your business unit perform in comparison to vour set obiectives l Very Low 2 Quite Low 3 Somewhat Low 4 Neither Nor 5 Somewhat High 6 Quite High 7 Very High Sales 2 3 5 6 Return on Sales 2 3 5 6 Return on Assets 2 3 5 6 Return on Investment 2 3 5 6 Total Sales Growth 2 3 5 6 Market Share 2 3 5 6 Figure 3. Performance Measurement Instrument 92 TABLE 13 PERFORMANCE MEASURES AS RATED BY MANUFACTURING EXECUTIVES A. Businesses Pursuing Cost Leadership Strategy STATISTICS 95% Performance Mean Minimum Maximum Standard Confidence Measure Deviation Interval Return on Investment 3.90 2.0 5.0 0.9728 3.13 - 4.67 Return on Sales 3.90 2.0 5.0 0.9280 3.13 - 4.67 Total Sales Growth 3.38 1.0 5.0 1.0442 2.61 - 4.15 Market Share 3.35 1.0 5.0 1.2963 2.58 - 4.12 B. Businesses Pursuing Differentiation Strategy STATISTICS 95% Performance Mean Minimum Maximum Standard Confidence Measure Deviation Interval Return on Investment 3.56 1.0 5.0 1.1855 2.69 - 4.43 Return on Sales 3.56 1.0 5.0 1.1855 2.69 - 4.43 Total Sales Growth 3.62 1.0 5.0 1.1551 2.75 - 4.49 Market Share 3.24 1.0 5.0 1.2806 2.37 - 4.11 Similar responses were provided by both businesses pursuing cost leadership strategy and those pursuing differentiation strategy (see Appendix I). Businesses were classified as high, medium, or low performers based on the cumulative score reported for the two financial measures: return on investment and return on sales (see Figure 4). 93 Classification by Strategy and Performance Respondent business units were classified into two groups: (1) businesses pursuing cost leadership strategy, and (2) businesses pursuing differentiation strategy (see Figure 4). Fifty-one firms stated that they emphasize cost leadership strategy, and the other thirty-seven firms stated that they emphasize differentiation strategy. Surveyed businesses were then classified on the basis of their reported business performance. Businesses were classified as high or low performers based on their performance in comparison to other firms. The cumulative score reported for the two financial measures including return on investment, and return on sales, was used to rank the respondent firms (see Figure 3).. The upper forty percent and the lower forty percent of the respondents on the performance scale were assigned as high and low performers, respectively. Following the polar extreme approach, the middle twenty percent, classified as middle performers, was eliminated from further analysis (Hair, Anderson, Tatham, and Garblowsky 1979). Two business strategy groups were identified as a result of the classification process. The two business strategy groups were subdivided based on business performance. Manufacturing strategy variables of high performers were compared with low performers (see Figure 4). 94 LU 0 CO >- «0 0 Ul Ul 5 & STRATEG Y co ec BUSINES S RFORMAN C UFACTURI N 3 H Ul z a> co < a. 2 CO O cr z Ul 5 > S9«j z Err- 1"—* ImD M 2 < < o 22 5 « E 3 h- H cc z 0 ^ < ui < CL 2 |- Z LU DC LU ll LL. to 0 tr z Q in z >. CO § s w 1 2 H W m 0 QC il (- 01 2 « < CO £ F 5 X £ CO 3 » Is c o -H •U d a •H CO a iw £3C z •H UJ S >- J® co 3 0 w CO t- UJ m (d 0 !< < rH LO W 2 Q_ 2 5 a IFOR M D H ~ CL z CO $ LU X < flL 2 a h CO a) CO 0C a O LU 0 01 O0 w CO O (1) Businesses pursuing cost leadership strategy: a. Businesses focusing on cost leadership strategy with high performance, b. Businesses emphasizing cost leadership strategy with low performance. (2) Businesses pursuing differentiation strategy: a. Business emphasizing differentiation strategy with high performance, b. Businesses focusing on differentiation strategy with low performance. Analysis of Questions: Business Strategy In the first part of the questionnaire form, manufacturing executives were asked about their business strategy and level of differentiation of their businesses in comparison with other businesses competing in the broadwoven cotton fabric sector (see Appendix F). Fifty-one executives reported that their firms emphasize cost leadership strategy in their business activities. Such firms usually place more emphasis on operation of efficient-scale facilities, tight cost and overhead controls, and cost minimization in areas such as research and development, sales force and advertising (Kotha and Orne 1989; Porter 1980). Thirty-seven of the eighty-eight surveyed businesses reported that their businesses place more emphasis on 96 differentiation strategy. Businesses that pursue the differentiation strategy usually place more emphasis on creating a unique product and relatively less emphasis on cost control (Kotha and Orne 1989; Porter 1980) . Manufacturing executives were also asked to identify the degree of differentiation of their respective business units in comparison to that of their competitors. Executives emphasizing cost leadership strategy viewed their businesses as neutral (neither high / nor low) at 3.79, based on a scale of one to seven. Whereas, businesses emphasizing the differentiation strategy rated their businesses as somewhat highly differentiated at 5.35, based on a scale of one to seven. Analysis of Questions: Manufacturing Strategy Part Two of the survey instrument consisted of twenty- four questions concerning the manufacturing strategy variables (see Appendix F). The following manufacturing strategy constructs were addressed: capacity planning, facilities layout, production processes, vertical integration, human resources, quality assurances, production planning, material control, and organization. These nine constructs were operationalized using twenty-four variables. Using a seven point Osgood scale, 334 manufacturing executives were asked to compare their businesses to others 97 competing within the industry (Osgood, Suci and Tennenbaum 1957) . As discussed in Section one, surveyed textile businesses were classified on the basis of their business strategy and level of performance. Two sets of comparisons were made between the resultant groups: (1) Manufacturing strategy variables of firms with high performance emphasizing cost leadership strategy were compared with low performers. (2) Manufacturing strategy variables of firms with high performance emphasizing differentiation strategy were compared with low performers. Comparison of Manufacturing Strategy Variables: High vs. Low Performers Emphasizing Cost Leadership Strategy The responses provided by businesses pursuing cost leadership strategy were first analyzed using the discriminant analysis procedure. Discriminant analysis was used to determine whether there is any significant difference between high and low performers with respect to the twenty-four manufacturing strategy variables. Discriminant analysis investigates the differences between two groups based on several variables simultaneously (Klecka 1980) . Examining all variables simultaneously gives a more accurate account of the nature of group differences than single variable comparisons. Also, as the number of 98 variables increases, the difficulty of interpreting differences between two groups based on each variable taken singly becomes a difficult task (Tatsuoka 1970). Researchers employ discriminant analysis to achieve two objectives: first, to interpret group differences based on some set of characteristics, and second, to classify cases based on mathematical equations (Klecka 1980). According to Klecka (1980), the minimum sample size required for this type of analysis is two cases more than the number of discriminating variables. A discriminant function can be evaluated by how accurately the cases under examination are classified. In this study, all businesses were accurately classified based on the twenty-four manufacturing strategy variables (see Table 14). To examine the significance of the difference between the two groups, Wilks' Lambda statistic was calculated. Wilks' Lambda takes into consideration both the differences between two groups and how well the cases cluster near their group centroid (Klecka 1980). The value of Wilks' Lambda in this analysis is 0.1963, which is significant at the five percent significance level. 99 TABLE 14 RESULTS OF DISCRIMINANT ANALYSIS: BUSINESSES PURSUING COST LEADERSHIP STRATEGY WITH HIGH OR LOW PERFORMANCE value F Pr > F Wilks' Lambda 0.1963 2.3878 0.0469 Posterior Probability of Membership in each Performance Group Based on the Twenty-four Manufacturing Strategy Variables (number of respondents and percent classified) From business strategy group Total la 20 0 20 100.00% 0.00% 100.00% 2b 0 19 19 30.00% 100.00% 100.00% Total 20 19 39 Percent 51.28% 48.72% 100.00% a Low performers b High performers The discriminant analysis results stated how significantly the two groups differ. The next task involved determining which of the twenty-four manufacturing strategy variables are better differentiators between the two groups. The responses of high and low performers that focused on cost leadership strategy were compared using Student's t. statistics. As illustrated in Table 15, comparison of the 100 responses provided by the two performance groups indicates that three of the twenty-four manufacturing strategy variables are significant differentiators. High performers placed more emphasis on special- purpose equipment, worker training and centralization in decision making than their competitors pursuing the cost leadership strategy (see Table 15). The two groups placed similar levels of emphasis with respect to the remaining twenty manufacturing strategy variables. Comparison of Manufacturing Strategy Variables: High vs. Low Performers Emphasizing Differentiation Strategy Thirty-seven of the eighty-eight surveyed businesses reported that they focus on the differentiation strategy. Based on level of performance, fifteen firms were classified as high performers, and another fifteen firms were classified as low performers. The overall difference between high and low performers pursuing differentiation strategy was examined using discriminant analysis. The discriminant analysis results indicated a significant difference between the two groups with a ten percent level of significance (see Table 16). In addition, all business firms were accurately classified. Student's t statistics were then used to compare the level of emphasis placed by high and low performers on the twenty- four manufacturing strategy variables. 101 TABLE 15 COMPARISON OF MANUFACTURING STRATEGY VARIABLES: HIGH AND LOW PERFORMERS PURSUING COST LEADERSHIP STRATEGY a b Student Manufacturing Strategy Variable Mean l Mean 2 t-Score o Capacity Slack 2.85 t o -0.0945 Capacity Changes 3.40 4.00 -1.1013 Process Layout 5.50 6.10 -1.3176 Product Layout 5.65 6.10 -1.2124 Facility Focus 4.70 4.53 0.3497 General Purpose Equipment 4.40 3.55 1.5963 Special Purpose Equipment 4.15 5.80 -3.9077 * Proven Manufacturing Processes 5.35 6.00 -1.9898 Innovative Manufacturing Processes 4.05 3.90 0.2942 Forward Integration 5.00 5.30 -0.5472 Backward Integration 2.90 3.60 -1.2747 Employee Cross-training 4.40 4.70 -0.7254 Job Specialization 4.40 4.25 0.3584 Worker Training 4.00 5.05 -2.1951 * Product Quality 6.15 6.50 -1.4697 Quality Assurance Process 5.05 5.35 -0.7015 Variety of Final Products 4.55 4.40 0.2957 Product Customization 4.95 4.05 2.5349 Production Planning Technique 3.95 4.80 -1.7648 Frequency of Production Planning Modifications 4.45 4.60 -0.2817 Work-in-process Inventory 3.75 3.65 0.1927 Centralization 4.20 5.50 -2.2382 * Communication 5.35 5.65 -0.8521 Formalization 4.55 4.95 -0.9070 Significant at alpha = 0.05 Low performers High performers 102 TABLE 16 RESULTS OF DISCRIMINANT ANALYSIS: BUSINESSES PURSUING DIFFERENTIATION STRATEGY WITH HIGH OR LOW PERFORMANCE value F, Pr > F Wilks' Lambda 0.0540 3.6504 0.0771 Posterior Probability of Membership in each Performance Group Based on the Twenty-four Manufacturing Strategy Variables (number of respondents and percent classified) From business strategy group 1 2 Total la 15 0 15 100.00% 0.00% 100.00% 2b 0 15 15 0.00% 100.00% 100.00% Total 15 15 30 Percent 50.00% 50.00% 100.00% a Low performers b High performers High and low performers placed significantly different levels of emphasis with respect to three manufacturing strategy variables (see Table 17). High performers had less capacity slack, and placed more emphasis on proven manufacturing processes than other businesses pursuing a 103 TABLE 17 COMPARISON OF MANUFACTURING STRATEGY VARIABLES: HIGH AND LOW PERFORMERS PURSUING DIFFERENTIATION STRATEGY a b Student Manufacturing Strategy Variable Mean l Mean 2 jt-Score Capacity Slack 3.60 2.53 2.0780 * Capacity Changes 4.27 4.47 -0.3268 Process Layout 5.27 5.67 -0.6552 Product Layout 5.13 5.93 -2.0284 Facility Focus 3.47 4.87 -1.7970 General Purpose Equipment 5.53 4.67 1.4496 Special Purpose Equipment 4.87 4.60 0.4044 Proven Manufacturing Processes 4.60 6.07 -3.5164 * Innovative Manufacturing Processes 5.87 4.67 2.0628 Forward Integration 5.00 5.40 -0.5808 Backward Integration 3.13 3.60 -0.6528 Employee Cross-training 5.33 4.67 1.8708 Job Specialization 3.67 4.27 -1.2264 Worker Training 5.00 4.47 0.9837 Product Quality 6.60 6.53 0.2646 Quality Assurance Process 5.33 6.07 -1.6747 Variety of Final Products 6.40 5.20 2.5640 * Product Customization 5.93 5.13 1.4199 Production Planning Technique 4.87 4.67 0.3593 Frequency of Production Planning Modifications 5.27 5.53 -0.5782 Work-in-process Inventory 4.07 3.73 0.6463 Centralization 4.60 4.33 0.4145 Communication 5.67 5.67 0.0000 Formalization 4.27 4.93 -1.1558 Significant at alpha = 0.05 Low performers High performers 104 similar business strategy. In addition, high performers placed less emphasis on variety of final products. The remaining twenty-one manufacturing strategy variables were rated similarly by both high and low performers. Results of Hypotheses Examined Twenty-four hypotheses were examined in this study. Hypotheses one through twelve were tested with respect to businesses competing on cost leadership strategy and hypotheses thirteen through twenty-four were examined with respect to businesses competing on differentiation strategy. The following two sections present the results of the twenty-four hypotheses. Textile Firms Focusing on Cost Leadership Strategy Hypotheses five and twelve involved more than one variable. These hypotheses were examined using the Logistic Regression Procedure. Logistic procedure fits linear regression models for ordinal response data by the method of maximum likelihood (Freeman 1987). Examination of hypotheses five and twelve indicates that there is no difference between high and low performers pursuing the cost leadership strategy with respect to vertical integration and organizational variables (see Table 18). Therefore, the two null hypotheses of no difference 105 between the comparison groups are not rejected at the five percent significance level. TABLE 18 EXAMINATION OF HYPOTHESES: LOGISTIC REGRESSION PROCEDURE Hypothesis Variable Chi - Square JP-Value 5 Vertical 1.4567 0.2275 Integration 12 Organization 1.8741 0.1710 For hypotheses one through five, and seven through eleven, Student's t. statistics were applied to determine whether there was any significant difference between above- average and below-average performers pursuing cost leadership strategy (see Table 19). The following section presents an analysis of hypotheses three and ten that resulted in significant findings. 3 . There is no statistical difference between high and low performers pursuing cost leadership strategy based on level of emphasis on special- purpose (fixed) equipment. High performers placed more emphasis on the application of special purpose equipment than low performers. The null hypothesis concerning level of emphasis on special-purpose equipment was rejected, concluding that there was a significant difference between the two groups. 106 a) * * 3 rH cm 00 o\ r- O a\ r- in co (d in CM o CO CM "5 J r- in co «s <2 M 00 in r- 00 rH a* vo rjt w 73 is r* C •H D) C 0) C C 0 LD £ •H *H •H O •H 10. There is no statistical difference between high and low performers pursuing cost leadership strategy based on level of emphasis on product customization. High performers placed less emphasis on product customization than low performers, resulting in the rejection of null hypothesis number ten. Textile Firms Focusing on Differentiation Strategy- Hypotheses thirteen through twenty-four concerned businesses pursuing the differentiation strategy. Hypotheses seventeen and twenty-four, which involved more than one variable, were examined using the Logistic Regression Procedure (see Table 20). TABLE 20 EXAMINATION OF HYPOTHESES: LOGISTIC REGRESSION PROCEDURE Hypothesis Variable Chi - Square P-Value 17 Vertical 0.9736 0.3238 Integration 24 Organization 0.0197 0.8885 At the five percent significance level, hypothesis seventeen concerning level of vertical integration was not rejected. In addition, both groups placed similar levels of emphasis on organization variables. Therefore, the null 108 hypothesis regarding organizational variables was not rejected. Hypotheses thirteen through sixteen, and eighteen through twenty-three were examined using Student's t. statistics. At the five percent level of significance, three hypotheses provided significant findings when comparing the two performance groups (see Table 21}. The following section examines hypotheses thirteen and sixteen in greater detail. 13. There is no statistical difference between high and low performers pursuing differentiation strategy based on level of emphasis on capacity slack. High and low performers placed significantly different levels of emphasis on level of capacity slack. As a result, at the five percent significance level, null hypothesis number thirteen was rejected. 16. There is no statistical difference between high and low performers pursuing differentiation strategy based on level of emphasis on innovative manufacturing processes. With respect to innovative manufacturing processes, the two performance groups differed significantly. High performers placed more emphasis on this variable than low performers. Null hypothesis sixteen was therefore, not rejected at the five percent level of significance. 109 * • Q) a o r- on in o\ co CO r- i—i r- r- 00 00 rH 0 co 0 vo CO <0 rH in r- co •E1 <2 5 LO rH rH 00 00 co rH c\ 00 v tJ l> rH o^ vo in Kfi oa co s| rH rH rH rH 0 rH 0 rH rH rH "B J vo co rH in rH vo CO CO o> SUMMARY, DISCUSSION AND CONCLUSIONS This chapter is composed of three sections. The first section presents a summary of the findings based on the results discussed in Chapter Four. The conclusions for this study are covered in the second section. The final section of the chapter presents directions for future research. Summary of the Findings and Discussion The purpose of this section is to summarize the results of the study and to draw conclusions based on the findings of this research. As previously discussed, 334 broadwoven cotton fabric mill businesses were surveyed in this study using a qualitative questionnaire. The surveyed textile firms were classified into two business strategy groups, subdivided into high and low performers (see Table 7 and Figure 4). Chapter Four presented the results of the following two sets of comparisons: 1. High and low performers that emphasized cost leadership strategy were compared based on the twenty-four manufacturing strategy variables identified in literature (see Table 7). 110 Ill 2. Equally, high and low performers that focused on differentiation strategy were compared in a similar manner. Two statistical techniques were used to carry out the above two comparisons: (a) Discriminant Analysis Procedure, and (b) Student's t tests. The discriminant analysis procedure was applied in order to determine the existence of statistically significant differences between each of the two business strategy groups. The results of the analysis show that all textile firms were accurately classified (see discussion in Chapter 4, and Tables 14, 16, and 18). Based I on Wilks' Lambda Statistic, the two groups were significantly different. Textile businesses with high performance differed significantly from low performers that focused on cost leadership strategy. Statistically significant results were also found with respect to high and low performers pursuing differentiation strategy. The results of discriminant analysis showed that the surveyed textile firms placed significantly different levels of emphasis on the manufacturing strategy variables. These findings pointed out that the surveyed textile firms do differentiate between the two business strategies, i.e., cost leadership and differentiation. In addition, the findings indicated that there was some evidence of linkage between business strategy and manufacturing strategy variables that influenced business performance. 112 In this study, Jt statistics were used to examine the differences between two business strategy groups based on twenty-four manufacturing strategy variables, one variable at a time. The results of Student's Jt test pointed out specific differences between the comparison groups. The following sections present a summary of the specific findings regarding the two sets of comparisons. Comparison of Textile Firms: High and Low Performers Emphasizing Cost Leadership Strategy In this section, high and low performers that focused on cost leadership strategy have been compared. This is the first time such comparisons are made between high and low performers. Existing studies have been limited to comparing manufacturing strategy variables of businesses pursuing a similar strategy. This study moves one step further by comparing the manufacturing strategy variables of selected businesses in conjunction with their business performance. High and low performers significantly differed with respect to three manufacturing strategy variables: level of emphasis on special-purpose equipment, worker training, and centralization (see Table 22). A. As expected, high performers placed more emphasis on application of special-purpose equipment than their competitors that focused on cost leadership strategy. Null hypothesis number three, which addressed level of emphasis 113 on special-purpose equipment, was rejected, concluding that there was a significant difference between the two groups. Special-purpose equipment is generally applicable whenever a firm is dealing with high volume production of a few standard products (Krajewski and Ritzman 1987). In general, businesses pursuing cost leadership strategy aim to minimize costs, and thereby reduce uncertainty within all aspects of manufacturing. As Porter (1980) explained, low cost relative to competitors becomes the underlying goal throughout the entire strategy of such firms. Special- purpose equipment helped such businesses achieve lower overall costs. The above results were indicative of linkage between manufacturing strategy and the business strategy of the firm. TABLE 22 COMPARISON OF MANUFACTURING STRATEGY VARIABLES: BUSINESSES PURSUING COST LEADERSHIP STRATEGY Manufacturing Mean la Mean 2b Student P-Value Strategy Variable t-Score Special-purpose equipment 4 .15 5 .80 -3 .9077 0 .0005 Worker training 4 .00 5 .05 -2 .1951 0 .0349 Centralization 4 .20 5 .50 -2 .2382 0 .0313 a Low performers b High performers 114 B. The results of this study indicated that high performers pursuing cost leadership strategy tended to place more emphasis on worker training than low performers. This finding does not support what was presented by Krajewski and Ritzman (1987) . Worker training becomes a vital factor whenever the business unit incurs changes in its products, processes, and/or equipment. Businesses pursuing cost leadership strategy tend to manufacture a limited number of products using proven manufacturing processes. Therefore, worker training was not as essential to these businesses when compared to businesses pursuing differentiation strategy. Worker training is an important factor when dealing with changing manufacturing equipment and processes. The results of this study showed that high performers placed more emphasis on this variable than low performers. Higher emphasis on worker training may be explained by the introduction of new manufacturing processes and equipment within the textile industry. Further studies need to be carried out to clarify the importance of worker training for businesses pursuing cost leadership strategy. C. Centralization in decision making was identified as a significant source of difference between the two performance groups. As explained in existing literature, high performers placed more emphasis on centralization than their competitors with low performance. 115 The above findings indicated that high performers pursuing the cost leadership strategy tended to link their business level strategy with three of the twenty-four manufacturing strategy variables. High performers linked the cost leadership strategy with special-purpose equipment, worker training, and centralization. The presence of linkage between these three manufacturing strategy variables and the cost leadership strategy may be regarded as a strong determinant of high performance for some of the participants in the study. Lack of linkage with the other twenty-one variables may be attributed to either management understanding of such linkages and/or the importance of such linkages to the industry. Linkage between these twenty-one variables and business strategy may prove to be important for other industries. Comparison of Textile Firms: High and Low Performers Pursuing Differentiation Strategy This section presents a comparison of the manufacturing strategy variables of high and low performers that focused on the differentiation strategy (see Table 23). High and low performers placed significantly different levels of emphasis in relation to three manufacturing strategy variables: capacity slack, proven manufacturing processes, and variety of final products. 116 TABLE 23 COMPARISON OF MANUFACTURING STRATEGY VARIABLES BUSINESSES PURSUING DIFFERENTIATION STRATEGY Manufacturing Mean la Mean 2b Student P-Value Strategy Variable t-Score Capacity slack 3 .60 2 .53 2 .0780 0 .0470 Proven manufacturing processes 4 .60 6 .07 -3 .5164 0 .0018 Variety of final products 6 .40 5 .20 2 .5640 0 .0206 a Low performers b High performers A. Capacity slack indicates the degree to which equipment, space, or labor is not under utilization (Krajewski and Ritzman 1987). High performers indicated less emphasis on capacity slack than low performers. This finding was not consistent with what has been presented in the literature. Capacity slack may be caused by a number of factors including changes in production plan. Businesses pursuing the differentiation strategy usually face frequent changes in production plan, which influence the level of capacity slack. The above finding can be attributed to better production scheduling by high performers to minimize capacity slack. 117 B. Depending on the type of business strategy being pursued by a firm, proven or innovative manufacturing processes may be applied. Innovative manufacturing processes are usually used by firms that are attempting to differentiate their businesses from other competitors. The findings of this study showed that high performers pursuing differentiation strategy placed less emphasis on such processes than low performers. Businesses with high performance placed more emphasis on proven manufacturing processes than innovative manufacturing processes. This finding may have been affected by the product life cycle stage of the surveyed businesses. In a mature industry such as textiles, manufacturing processes are generally well known by all competitors. Therefore, this variable plays an important role as a differentiator between business competitors. C. Unlike findings presented in the literature, high performers placed less emphasis on variety of final products than low performers. This finding may also be explained by the product life cycle stage of the surveyed businesses. High performers pursuing differentiation strategy linked their business strategy with three manufacturing strategy variables. The number of manufacturing strategy variables linked to the differentiation strategy was not significantly high, which suggested a greater need for 118 cooperation between business executives and manufacturing executives in establishing the overall strategy of the firm. Conclusions The objective of this study was to examine the linkage between business strategy and manufacturing strategy variables and its effect on firm performance. Using a mail- survey questionnaire, 334 textile firms were asked to participate in the study. The surveyed businesses were classified based on their reported business strategy and level of performance. Two sets of comparisons were made between the manufacturing strategy variables of businesses focusing on the two business strategies, with high and low performance. The results of the comparisons indicated that high performers established linkage between their business strategy and selected manufacturing strategy variables. Several observations may be made about these findings. All surveyed businesses were operating in a mature industry, namely the broadwoven cotton fabric mill sector of the U.S. textiles (SIC 2211). Firms operating in a mature industry usually face less rapid product change, increasingly stable manufacturing processes, and long production runs with stable manufacturing techniques (Porter 1980) . As an industry moves from the introductory to the maturity stage, uncertainties within various factors are reduced, resulting in selection of successful strategies and abandonment of 119 poor ones. Therefore, the life cycle stage of the industry is perceived as having a significant influence on the level of linkage between business strategy and manufacturing strategy variables. Also, the type of industry and the level of automation, among other factors, may play an important role in determining the level of linkage between business strategy and manufacturing strategy variables. In this empirical study, the manufacturing strategy variables that significantly differentiated between high and low performers, were identified. These findings supported the integration of manufacturing strategy with business strategy. As Hays and Wheelwright observed, "to be effective, each functional strategy must support, through a consistent pattern of decisions, the competitive advantage being sought by the business strategy" (Hays and Wheelwright 1984, 29) . The findings of the study supported the theories developed by Skinner (1969, 1974, 1985), Fine and Hax (1985), Kotha and Orne (1989), and Deane, Gargeya, and McDougall (1990) (see discussion in Chapter 2). Furthermore, these findings provided empirical support as to the importance of linking business and manufacturing strategies. Skinner's (1969, 1974) hypotheses regarding manufacturing as a competitive weapon, inadequacy of cost and efficiency as manufacturing goals, and the focused factory principle were empirically validated by the findings 120 of the study. Surveyed businesses with high performance have illustrated that the manufacturing function can contribute to higher performance, and firms in which business and manufacturing strategy are linked (focused), outperform other businesses. Thus, the study contributes to existing literature by empirically examining the linkage of business strategy and manufacturing strategy variables in a mature industry. The empirical findings supported the existing literature and further expanded the theory of manufacturing strategy. The results of the study have implications for both researchers and top management executives. Based on the findings of this study, additional issues and questions have been raised requiring further research. Top management executives need to realize the significant impact of linking business and functional level strategies, i.e. manufacturing, marketing, and finance. The research findings show that linkage between business strategy and manufacturing strategy of the surveyed businesses exist and are related to high business performance. This study re-affirmed the importance of linking business strategy with manufacturing strategy variables as a forceful weapon for overcoming competition. U.S. textile businesses, and mature industries in general, need to develop a strong linkage between business strategy and 121 manufacturing strategy variables in order to overcome competitive pressures. The findings of this study present executives with alternative measures for improving the overall productivity of their firms. Based on these findings, it can be concluded that consistency between business and manufacturing strategy contributes to productivity and further improves the performance of the firm. Within literature there is an increasing call for strategic management of manufacturing. This study provided empirical evidence as to the importance of such theories. For instance, Skinner (1985) stated that manufacturing decisions regarding facilities, equipment, personnel and basic controls should be viewed as sensitive affecting the overall posture of a business. This study empirically validated the above ideas. Businesses that have remained competitive have done so by treating manufacturing as a competitive weapon, they have further linked business strategy with functional level strategies. Although linkage was identified between business strategy and specific manufacturing strategy variables, there is a need to improve the level of such linkage. In each case, surveyed businesses reported linkage with only four of the twenty-four manufacturing strategy variables. Therefore, there is a need for greater cooperation between executives in all functional areas in setting the long term 122 strategies of the business. Lack of greater linkage between business strategy and manufacturing strategy variables can be attributed to unawareness on the part of executives regarding manufacturing strategy and its importance. Similarly, a number of problems including low productivity, inefficient plants, under utilized capacity, low labor productivity, and others can be traced to a larger problem, namely lack of linkage between business strategy and manufacturing strategy variables. The methodological approach that was used to assess linkage between business strategy and manufacturing strategy variables is an important contribution to the study of manufacturing strategy. Such methodology can be replicated to study other industries in various stages of the product life cycle. Most of the existing research on manufacturing strategy involved case studies. This study opened the way for other empirical research and laid the groundwork for a better understanding of manufacturing strategy. Additional research is called for to clarify the importance of manufacturing strategy and its contribution to the overall performance of business firms. As illustrated in literature, within many operations manufacturing strategy is non-existent or misunderstood by many executives. The following section describes the suggestions for future research. 123 Suggestions for Future Research The need for additional research in the area of linkage between business strategy and manufacturing strategy variables is obvious based on the findings of this study. Several issues and questions need to be examined in greater detail. An important topic for future research is to determine whether linkage of certain manufacturing strategy variables is more important than that of other manufacturing strategy variables in relation to business performance. Results of this study show that the surveyed businesses which linked their business strategy with selected manufacturing strategy variables, outperformed other businesses. Research results further revealed that linkages were found to be existent between specific manufacturing strategy variables and business strategy, among the successful (high performer) firms. Another topic for future research involves development of a systematic method of linking business and functional level strategies. Linkages were found between the business strategies and the specific manufacturing strategy variables of the surveyed businesses. However, the study did not address how the two strategy levels should be linked together. A word of caution is due at this point. Additional research involving non-textile industries needs to be 124 performed in order to draw conclusions regarding the linkage of business and manufacturing strategies with respect to other manufacturing industries. As discussed earlier, textile businesses operating in the broadwoven cotton fabric mill sector (SIC 2211) were surveyed in this study. The results of this study concentrate on this particular industrial sector. Therefore, these results should not be generalized to other textile industry sectors without further study. Future studies need to apply objective measures in order to validate the findings of the study. The surveyed firms were classified as high and low performers based on two subjective performance measures: return on investment and return on sales, as reported by the individual participants. The findings of this study were influenced by the application of the above subjective performance measures (see discussion in Chapter 3). Additional research is called for on other types of business strategy linkages not included in this study. Depending on the type of organization, a business unit consists of a number of functions. Hays and Wheelwright (1984) identified marketing, manufacturing (operations), and finance as the major functions within a business unit. A host of other supporting functions may be present which interface with the main three functions (Hays and Wheelwright 1984) . Each functional strategy is organized in 125 terms of several decision categories. As illustrated in Figure 5, decisions at each level (business strategy, functional strategy, and decision categories) have to be linked in order to maximize business performance. In addition to examining linkage between business strategy and manufacturing strategy, researchers need to examine the following linkages in depth (see Figure 5): a. Degree of linkage between manufacturing strategy and other functional strategies. b. Degree of linkage among decision categories that make up manufacturing strategy (see discussion in Chapter 2). c. Degree of linkage between manufacturing strategy and business environments (Hays and Wheelwright 1984). Another avenue for future research involves examination of business strategies other than those pursued in this study. With the development of new manufacturing technologies such as flexible manufacturing system, group technology and similar, business strategy no longer is limited to a choice between cost leadership and differentiation. Rather, there rather exists a continuum of business related strategies. Assumptions such as the dichotomy between cost and quality which supported the existence of the above two extreme business strategies, can no longer be made. 126 4-1 0} O Generic manufacturing strategies that would be applicable to a wide range of industries need to be developed. Additional studies would involve selection of larger samples from different industries to be scrutinized with different business and manufacturing strategies, before cross-industry-wide generic manufacturing strategies might be developed. Final recommendations with respect to additional research regarding textile industry can be made at this point. The textile industry represents one of the oldest trades in the U.S. The industry has provided a significant base for employment and has contributed to the Gross National Product. On a global basis, textiles have become a major source of revenue and employment for developing countries. Rise of textile imports into the U.S. has been noted as one of the reoccurring reasons for plant closures during 1980s and 1990s. Availability of low-cost labor within developing countries along with application of modern textile machinery, have placed U.S. textile manufacturers at a disadvantage. Thus the U.S. textile industry which once dominated the world markets using mass production facilities, has become the most protected of all industries in the United States. As expected in every industry, some businesses are more successful than others. According to Dertouzos, Lester and Solow (1988), textile business firms that have been 128 successful competitors, generally operate in market niches. Such businesses experience little competition from low-cost producers. Japanese and German textile business firms have pursued this strategy with significant achievements. Five contributing factors tend to differentiate successful U.S. textile businesses (such as Milliken, Russell, Crepe Weavers) from others: long term planning (emphasizing long term performance), product mix (niche), leading the industry in technology innovation, frequency of textile machinery replacement (every 4-5 years), and manufacture of top quality textiles (Harding 1990; Kalogeridis 1990; Kovaly 1990) . Despite the continuing success of the small number of textile businesses, the future does not look bright for the U.S. textile industry. Increasing percentages of debt to capital (58% during 1991), unavailability of funds to invest in new technologies and equipment, along with rising imports from developing countries, make the U.S. textile industry vulnerable (Konrad 1991). Furthermore, the GATT negotiations along with the Free Trade Agreement prospect with Mexico and Canada tend to move towards gradual elimination of existing tariffs and quotas which thus far are protecting the U.S. textile industry. Therefore, additional research is recommended to explore possibilities of increasing the productivity level of this industry and thereby insuring its global competitiveness. APPENDICES 129 APPENDIX A: QUESTIONNAIRE COVER LETTER 130 Institute for Regional Industrialization and Manufacturing Technology University of North Texas College of Business Administration June 16, 1991 Dear Mr. : For the past two years, we have been studying productivity issues concerning the U.S. textile industry because of its importance to the national economy. This study is done under the auspices of the Institute for Regional Industrialization and Manufacturing Technology as a pilot project entitled "Linkage of Business and Manufacturing Strategies as a Determinant of Enterprise Performance: An Empirical Study in the Textile Industry". In this study, several significant facts have surfaced. During the past ten years, more than one thousand textile plants were closed, with a loss of over five hundred thousand jobs. Also, since 1986, three legislative bills regarding the textile industry, initiated by the U.S. Congress, were vetoed by the president of the United States. In view of the above, we have concluded that legislative measures alone are not enough to assist the textile industry to regain its former favorable position in the national economy. Based on subsequent studies, our preliminary conclusions indicate that variables such as integration of factor resources, product quality, system flexibility, and operations efficiencies, among others, are critical elements, which may determine the success of a business firm in the national and global market place. The principal objective of this research, then, is to study and determine specific business strategies and the impact of selected manufacturing strategies on overall performance indicators of a sample of textile firms. The reason for writing this letter is to ask for your participation in this research project as the Manufacturing Executive of your organization. Enclosed with this letter, you find a copy of the questionnaire form designed to collect the required data for the above study. The responses generally consist of circled items, checks, or short answers. Your response to the enclosed questionnaire will be valuable and essential to the success of this study. We anticipate the questionnaire will take about 20 minutes for you to complete. P.O.Box 13677 • Denton,Texas76203-3677 Tel: 817/565-4333 • 214/387-3911 Fax: 817/565-6540 IRIMT 6-16-91 2 of 2 This study is expected to reveal significant insights into patterns of business and manufacturing strategies of successful versus less successful industries. Collected data and information pertaining to your organization will remain strictly confidential. Research results made available to the public will be presented at the aggregate level with individual firm responses coded. Final results will be provided to the participants of this research project. Please mail your reply by Aug 25. For additional information, please call Massoud Kassaee, Research Associate collect at (817) 566-9536 or contact Dr. Martin E. Rosenfeldt, Executive Director at (817) 565-4333. Thank you for your cooperation. Sincerely, Massoud Kassaee Martin E. Rosenfeldt, Ph.D. Research Associate Executive Director Attachments/ MKMR.INT APPENDIX B: FIRST FOLLOW-UP LETTER 133 Institute for Regional Industrialization and Manufacturing Technology University of North Texas College of Business Administration June 28, 1991 Dear Mr. : Recently we sent you a letter with an attached questionnaire dealing with strategic issues in the U.S. textile industry. The pilot study is entitled "Linkage of Business and Manufacturing Strategies as a Determinant of Enterprise Performance: An Empirical Study in the Textile Industry". We consider your response to the questions raised very significant in the completion of this study. If you have already completed and returned the questionnaire, thank you very much. If not, kindly do so by September 4, so that your contribution can be included in the survey. As a participant, you will receive a brochure covering the survey results and recommendations resulting from this study. If by some chance you have not received the questionnaire, or it got misplaced, please call me collect at (817) 566-9536 and I will get another one in the mail to you today. We thank you, again, for your time and cooperation. Sincerely, Massoud Kassaee Research Associate cc: Dr. Martin E. Rosenfeldt, Executive Director P.O. Box 13677 • Denton, Texas 76203-3677 Tel: 817/565-4333 • 214/387-3911 Fax: 817/565-6540 APPENDIX C: SECOND FOLLOW-UP LETTER 135 Institute for Regional Industrialization and Manufacturing Technology University of North Texas College of Business Administration August 16, 1991 Dear Mr. : In June, we mailed you a questionnaire regarding our study on the relationship between business and manufacturing strategies. Unfortunately so far, we have not received the completed form. To our knowledge, this is the first time that such study has been undertaken in the textile industry. We would like to include your contributions in this research. The results of this study will present findings that are expected to be of significant value to your company. As a participant in this study, you will receive a brochure summary of the findings and recommendations. Kindly return your completed questionnaire to us by September 4. In the event that your questionnaire has been misplaced, a replacement is enclosed. If you have any questions, please call me collect at (817) 566-9536 or contact Dr. Martin E. Rosenfeldt, Executive Director at (817) 565-4333 or (214) 387-3911. We appreciate your cooperation. Sincerely, Massoud Kassaee Research Associate Attachments/ MK.SEC cc: Dr. Martin E. Rosenfeldt Executive Director P.O. Box 13677 • Denton, Texas 76203-3677 Tel: 817/565-4333 • 214/387-3911 Fax: 817/565-6540 APPENDIX D: THIRD FOLLOW-UP LETTER 137 Institute for Regional Industrialization and Manufacturing Technology University of North Texas College of Business Administration September 13, 1991 Dear Mr. : Per our conversation dated September 11, enclosed with this letter, we are mailing you a questionnaire regarding our study on the relationship between business and manufacturing strategies. To our knowledge, this is the first time that such study has been undertaken in the textile industry. We would like to include your contributions in this research. The results of this study will present findings that are expected to be of significant value to your company. As a participant in this study, you will receive a brochure summary of the findings and recommendations. Would you kindly return your completed questionnaire to us by September 26. If you have any questions, please call me collect at (817) 566-9536 or contact Dr. Martin E. Rosenfeldt, Executive Director at (817) 565-4333 or (214) 387-3911. We appreciate your cooperation. Sincerely, Massoud Kassaee Research Associate Attachments/ MK.SEC cc: Dr. Martin E. Rosenfeldt Executive Director P.O.Box 13677 • Denton,Texas76203-3677 Tel: 817/565-4333 • 214/387-3911 Fax: 817/565-6540 APPENDIX E: INSTRUMENT RELIABILITY COVER LETTER 139 Institute for Regional Industrialization and Manufacturing Technology University of North Texas College of Business Administration September 10, 1991 Dear Mr. : We would like to hereby thank you for participating in our study of the relationship between business strategy and manufacturing strategy. More than 50 companies have responded to the questionnaire. We would greatly appreciate your assistance in one additional way. Manufacturing executives in 20 companies are being asked to provide assistance in this phase of the study. Mr. , would you kindly fill out the enclosed questionnaire. The results of these questionnaires will provide a basis for validating the instrument We appreciate your assistance in this final phase of the study. Thank you. Sincerely, Massoud Kassaee Research Associate Attachments/ MK.VAL CC: Dr. Martin E. Rosenfeldt Executive Director P.O. Box 13677 • Denton, Texas 76203-3677 Tel: 817/565-4333 • 214/387-3911 Fax: 817/565-6540 APPENDIX F: BUSINESS AND MANUFACTURING STRATEGY SURVEY QUESTIONNAIRE 141 Institute for Regional Industrialization and Manufacturing Technology University of North Texas College of Business Administration BUSINESS AND MANUFACTURING STRATEGY SURVEY-QUESTIONNAIRE Part I: Business Strategy 1. Please circle the business strategy that best reflects your business firm: A. Industry-Wide Cost Leadership Strategy: Firms pursuing such strategy are characterized as having efficient-scale facilities, vigorous pursuit of cost reductions from experience, tight cost and overhead control, avoidance of marginal customer accounts, and cost minimization in areas like R&D, service, sales force, advertising, and so on. Low cost relative to competitors becomes the theme running through the entire strategy, though quality, service and other areas can not be ignored. B. Industry-Wide Differentiation Strategy: Firms pursuing such strategy are characterized as attempting to differentiate the product offering of their firms, creating something that is perceived as being unique. Such firms view costs as secondary in importance and place a great deal of emphasis on creating products that are unique along one or more dimensions. They select one or more attributes that many buyers in an industry perceive as important, and uniquely position themselves to meet those needs. C. Segment Cost Leadership Strategy: Firms pursuing such strategy are characterized as having efficient-scale facilities, vigorous pursuit of cost reductions from experience, tight cost and overhead control, avoidance of marginal customer accounts, and cost minimization in areas like R&D, service, sales force, and advertising limited to one segment of the industry. Low cost relative to competitors becomes the theme running through die entire strategy, though quality, service and other areas can not be ignored. D. Segment Differentiation Strategy: Firms pursuing such strategy are characterized as attempting to differentiate the product offering of their firms, creating something that is perceived as being unique in one segment of the industry. Such firms view costs as secondary in importance and place a great deal of emphasis on creating products that are unique along one or more dimensions. They select one or more attributes that many buyers in an industry perceive as important, and uniquely position themselves to meet those needs. 2. How differentiated is your business unit in comparison to your competitors? 1 m Very Low 5 = Somewhat High 2 s Quite Low 6 = Quite High 3 s Somewhat Low 7 = Very High 4 > Neither Nor Level of differentiation 1 2 3 4 5 6 7 P.O.Box 13677 • Denton,Texas76203-3677 Tel: 817/565-4333 • 214/387-3911 Fax: 817/565-6540 Page 2 of 7 Part II: Manufacturing Strategy ****************************************************************************** Instructions: Each of the following items consists of a phrase which represents the methods by which businesses may compete. Please respond to each question using the following instructions: 1. Please consider each phrase as it describes your business unit compared to other business units within your industry. 2. Circle the position on the scale that best describes the emphasis your business unit has placed on each item. 3. Please evaluate each phrase using the seven point scale. 4. Example: The following illustrates a situation in which an organization makes office desks on a repetitive basis. Product Customization Range of product features or options offered by your business. Level of product customization 1 2 3 4 5 6 7 The circle indicates that the business unit maintains product customization levels that are quite lower than other firms in the industry sector in which the business unit competes. *****************************************************************************1¥ 1 = Very Low 2 s Quite Low 3 = Somewhat Low 4 s Neither Nor 5 s Somewhat High 6 a Quite High 7 « Very High Capacity slack The extent of capacity slack. Level of capacity slack 1 2 3 4 5 6 7 Capacity changes The frequency of changes in capacity. Frequency of capacity changes 1 2 3 4 5 6 7 Process layout A layout that groups similar machines together. Level of emphasis on process layout 1 2 3 4 5 6 7 Page 3 of 7 1 Very Low 2 Quite Low 3 Somewhat Low 4 Neither Nor 5 Somewhat High 6 Quite High 7 Very High Product layout A layout that arranges machines with the flow of the product Level of emphasis on product layout 1 2 Facility Focus Level of focus on a narrow product mix for a particular market niche. Level of facility focus General-purpose equipment Equipment that are capable of handling a variety of tasks. Level of emphasis on general-purpose equipment 1 Special-purpose equipment Equipment designed for handling a specific operation. Level of emphasis on special- purpose equipment Proven manufacturing processes Processes that have been repeatedly tested and generally recognized by majority of the firms in the industry. Level of emphasis on proven manufacturing processes Innovative manufacturing processes Processes that have been either innovated by your firm or only a relatively small number of firms use them. Level of emphasis on innovative manufacturing processes 1 2 3 4 5 6 7 Forward integration The extent to which a company markets its own products. Level of forward (vertical) integration 1 Page 4 of 7 1 = Very Low 2 = Quite Low 3 • Somewhat Low 4 = Neither Nor 5 = Somewhat High 6 = Quite High 7 • Very High Backward integration The extent to which a company produces semifinished components. Level of backward (vertical) integration 12 3 4 Employee cross-training Number of jobs an employee is trained for. Level of employee cross-training 12 3 4 Job Specialization Degree to which individual workers are assigned a narrow range of activities. WorkeLevel orf jotraininb specializatiog n 12 3 4 Frequency and extent of training received by workers. Frequency of worker training Product quality Degree of product conformance to relevant features and characteristics requested by customers. Level of emphasis on superior product quality 1 2 3 4 5 6 7 Quality assurance process Level of sophistication of the techniques used to assure process quality. Level of quality assurance process sophistication Variety of final products Number of final products offered by your business unit Variety of final products 12 3 4 Page 5 of 7 1 = Very Low 2 = Quite Low 3 = Somewhat Low 4 • Neither Nor 5 = Somewhat High 6 = Quite High 7 = Very High Product customization Range of product features or options offered by your business. Level of product customization 1 2 Production planning Technique Complexity of the production planning techniques. i.e. MRP (Manufacturing Resource Planning) vs. conventional methods. Complexity of the production planning technique 1 2 3 4 5 6 7 Frequency of production planning modifications Relative number of changes made to the production plan. Frequency of production planning modifications Work-in-process inventory Level of average annual work-in-process (WIP) inventory held by your firm. Level of work-in-process inventory Centralization Level of decision making authority held by upper management Level of centralization in decision making 1 2 3 4 5 6 7 Communication Frequency of communication (verbal and written) between the supervisors and the subordinates. Level of communication between supervisors and subordinates Formalization Degree of emphasis on rules, procedures, and precedents to direct behavior of employees. Level of formalized rules, procedures and precedents governing managerial decision-making Page 6 of 7 Part 1H: Business Performance 1. Please check the box which you feel best estimates how the performance of vour business unit compares to similar businesses in your industry over the last 3 years. Top Next Middle Lower Lowest 20% 20% 20% 20% 20% Return on Investment • • • • • Return on Sales • • • • • Total Sales Growth • • • • • Market Share • • • • • 2. Please provide the following descriptive information for the stated years. Sales 198S $ 1987 $ 1990 $ Return on Sales 1985 % 1987 % 1990 % Return on Assets 1985 % 1987 % 1990 % 3. On the basis of the following criteria, how well did your business unit perform in comparison to vour set objectives: 1 Very Low 2 Quite Low 3 Somewhat Low 4 Neither Nor 5 Somewhat High 6 Quite High 7 Very High Sales 2 3 4 5 6 Return on Sales 2 3 4 5 6 Return on Assets 2 3 4 5 6 Return on Investment 2 3 4 5 6 Total Sales Growth 2 3 4 5 6 Market Share 2 3 4 5 6 Page 7 of 7 Part IV: Descriptive information concerning the business and the interviewee 1. Year this business unit made its first sale: 2. Top three products at this business unit (Ranked by Sales) Product Contribution to revenue of business unit % 3. Number of employees at this business unit: (a) Production (b) Supervisory and staff • 1. 0- 50 • 1. 0- 50 • 2. 51 - 99 • 2. 51- 99 • 3. 100 - 249 • 3. 100 - 249 • 4. 250 - 499 • 4. 250 - 499 • 5. 500 - 999 • 5. 500 - 999 • 6. over 1000 • 6. over 1000 4. How many years have you been associated with this company: years 5. If you would like a summary of the survey results, please provide us with your mailing address: Thank you for talcing the time to fill out this questionnaire. Please return it to the address on the front page using the self-addressed envelope. APPENDIX G: BROADWOVEN FABRIC MILL COTTON SECTOR: INDUSTRY DESCRIPTION 149 Major Group 22.—TEXTILE MILL PRODUCTS The Major Group as a Whole This major group includes establishments engaged in performing any of the following op- erations: (1) preparation of fiber and subsequent manufacturing of yarn, thread, braids, twine, and cordage; (2) manufacturing broadwoven fabrics, narrow woven fabrics, knit fab- rics, and carpets and rugs from yarn; (3) dyeing and finishing fiber, yarn, fabrics, and knit apparel; (4) coating, waterproofing, or otherwise treating fabrics; (5) the integrated manufac- ture of knit apparel and other finished articles from yarn; and (6) the manufacture of felt goods, lace goods, nonwoven fabrics, and miscellaneous textiles. This classification makes no distinction between the two types of organizations which op- erate in the textile industry: (1) the integrated mill which purchases materials, produces tex- tiles and related articles within the establishment, and sells the finished products; and (2) the contract or commission mill which processes materials owned by others. Converters or other nonmanufacturing establishments which assign materials to contract mills for processing, other than knitting, are classified in nonmanufacturing industries; establishments which assign yarns to outside contractors or commission knitters for the production of knit products are classified in Industry Group 225. Industry Group Industry No. No. 221 BROADWOVEN FABRIC MILLS, COTTON 2211 Broadwoven Fabric Mills, Cotton Establishments primarily engaged in weaving fabrics more than 12 inches (30.48 centimeters) in width, wholly or chiefly by weight of cotton. Establish- ments primarily engaged in weaving or tufting carpet and rugs are classified in Industry 2273; those making tire cord and fabrics are classified in Industry 2296; and those engaged in finishing cotton broadwoven fabrics are classified in Industry 2261. Airplane cloth, cotton Casement cloth, cotton Alpacas, cotton Chambrays Automotive fabrics, cotton Cheesecloth Awning stripes, cotton—mitse Chenilles, tufted textile—mitse Balloon cloth, cotton Cheviots, cotton Bandage cloths, cotton Chintz, cotton Bark cloth, cotton Corduroys, cotton Basket weave fabrics, cotton Cotton broadwoven goods Bathmats, cotton: made in weaving Cottonades milla Coutil, cotton Batiste, cotton Coverts, cotton Bedspreads, cotton: made in weaving Crash toweling, cotton mills Crepes, cotton Bird's-eye diaper cloth, cotton Cretonne, cotton Blankets and blanketings, cotton- Crinoline mitse Damasks, cotton Bombazine, cotton Denims Book cloth—mitse Diaper fabrics Broadcloth, cotton Dimities Brocade, cotton Dishcloths, woven: made in weaving Brocateile, cotton m»H« Buckram—mitse Draperies and drapery fabrics, cotton— Bunting—mitae mitse Butter cloths Dress fabrics, cotton Cambric, cotton Drills, cotton Camouflage nets—mitse Duck, cotton Canton flannels, cotton Duvetyn, cotton Canvas—mitse Elastic fabrics, cotton: more than 12 Industry Group Industry No. No. 221 BROADWOVEN FABRIC MILLS, COTTON-•Con. 2211 Broadwoveit Fabric Mills, Cotton—Con. inches in width Pocketing twill, cotton Express stripes, cotton Pongee, cotton Filter cloth, cotton Poplin, cotton Flannelette Press cloth Flannels, cotton Print cloths, cotton Furniture denim Ratine, cotton Gabardine, cotton Rep, cotton Galatea, cotton Sailcloth—mitse Gauze—mitse Sateens, cotton Ginghams Scrim, cotton Glass toweling, cotton Scrub cloths—mitse Glove fabrics, cotton—mitse Seat cover cloth, automobile: cotton Grosgrain, cotton Seersuckers, cotton Handkerchief fabrics, cotton Sheets and sheetings, cotton—mitse Hickory stripes, cotton Shirting fabrics, cotton Huck toweling Shoe fabrics—mitse Interlining material, cotton Silesia, cotton Jacquard woven fabrics, cotton Slipcover fabrics, cotton Jean fabrics, cotton Stretch fabrics, cotton Laundry fabrics, cotton Suiting fabrics, cotton Laundry nets—mitse Surgical fabrics, cotton Lawns, cotton Table cover fabrics, cotton Leno fabrics, cotton Table damask, cotton Long cloth, cotton Tapestry fabrics, cotton Luggage fabrics, cotton Tarlatan, cotton Marquisettes, cotton # Tentage—mitse Matelasse, cotton Terry woven fabrics, cotton Mitten flannel, cotton Tickings—mitse Moleskins—mitse Tobacco cloths—mitse Momie crepe, cotton Towels and toweling, cotton: made in Mosquito netting—-mitse weaving mills Muslin, cotton Tracing cloth, cotton Nainsook, cotton Trouserings, cotton Nets and nettings—mitse Tubing, seamless: cotton Opaline, cotton Twills, cotton Organdy, cotton Typewriter ribbon cloth, cotton Osnaburgs Umbrella cloth, cotton Outing flannel, cotton Underwear fabrics, woven: cotton Oxfords (cotton fabrics) Upholstery fabrics, cotton Pajama checks, textile Velours Percale Velveteens Percaline, cotton Voiles, cotton Pile fabrics, cotton Waffle cloth, cotton Pillow tubing—mitse Washcloths, woven: made in weaving Pillowcases—mitse mill* Pin checks, cotton Weaving mills, cotton broadwoven fab- Pin stripes, cotton rics Piques, cotton Wignan, cotton Plaids, cotton Window shade cloth, cotton Plisse crepe, cotton Yarn-dyed fabrics, cotton Plushes, cotton 222 BROADWOVEN FABRIC MILLS, MANMADE FIBER AND SILK 2221 Broadwoven Fabric Mills, Manmade Fiber and Silk Establishments primarily engaged in weaving fabrics more than 12 inches (30.48 centimeters) in width, wholly or chiefly by weight of silk and manmade fibers including glass. Establishments primarily engaged in weaving or tufting carpets and rugs from these fibers are classified in Industry 2273; those manu- facturing tire cord and fabrics are classified in Industry 2296; and those en- gaged in finishing manmade fiber and silk broadwoven goods are classified in Industry 2262. Acetate broadwoven fabrics Blanketings, manmade fiber Acrylic broadwoven fabrics Canton crepes Automotive fabrics, manmade fiber Comforters, manmade fiber—mitse Bedspreads, silk and manmade fiber— Crepe satins mitse Draperies and drapery fabrics, no Source: Office of Management and Budget. 1987. Standard Industrial Classification Manual Springfield, VA: National Technical Information Service. APPENDIX H: SUMMARY OF FIRMS CONTACTED AND PARTICIPATED IN THE STUDY 152 w §in 00 CM CN] Ch o\ P W W EH En 0} O W &H « w 00 U EH < a ^ H P W 53 H J a w p CM w PQ w £ a*rfi H LO CN] u H § g P W EH ro in g V£> ro ro csj ro ro a o a U1 CQ 4J u 4-> •H Pn (d 3 a) 1 •H u CQ S •H CQ <3 Q (d P W K3 oV> H O CTi CM rH CM &W EH PN CO O W o\° PCJ CO « W CM ro CM U FN a\ 3S P W oY> A O H L> J CJ ro W \—I P W A. in H ro U (M LT> CM CM H KO EH CM $ P-I P W EH oY> CM o g CM LP) o T5 § CD u 3 A •H 4J a w 05 o 0 A a *H •H H *H rH CN X3 >1 O CQ MH CD u CD A O ft CQ 05 CD 05 -H & A CQ •H CQ 4J CM RD CQ 0) A A a W 33: & RESULTS OF SUBJECTIVE PERFORMANCE MEASURES 155 A) CD U U A 00 o L> CTK ro CH CM ro M ro A a) CN] A) ro rH• rH rH CM ro LT) M *3 •H in LO in in •H in in in in in in I 4H A I I I I O o U ro o V0 O ro ro o T> ro o o CNI CM in A KO a .si fd VD fd in ro CM ro ro A) CD 2 S LO CM & ID T* A; W CD 4J CD CO 4J td -P £ CQ $H fd CD O 4J N £ G PI CO 4J •H CD CO cq PS FT J3 •H A PQ CQ •A o fd CQ rH •H I> O O I> O MH in LO in in in M -P ro ro ro ro ro ro O CQ A; fd CD T5 •H CD > fd CQ 4J u -H Ja ) A a 4J cu rg u SH S a) 4J CD CQ O RQ o M-J O O •H p A) CQ di A< CQ a tn CD •H A a 2 4J -H 4J •H CQ a 3 a CQ JCS CQ JC| 3 4J 3 CQ i u I 4J PQ a* 4J § 3 CQ u § CQ 4J CQ CQ •U CQ a) CD PH CQ >C U 0) CD 0) CD rH CQ O rH CQ > CQ CQ O CQ fd < A CD 0) fd Adam, E. 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