Re-exploring the relationship between flexibility and the external environment

Journal of Operations Management 21 (2004) 629–649

Re-exploring the relationship between flexibility and the external environment夽 Mark Pagell a,∗ , Daniel R. Krause b,1 a

Department of Management, Marketing and International Business, College of Business, Oregon State University, Bexell Hall 200, Corvallis, OR 97331-2603, USA b Department of Supply Chain Management, W.P. Carey School of Business, Arizona State University, P.O. Box 874706, Tempe, AZ 85287-4706, USA Received 1 September 2002; received in revised form 1 October 2003; accepted 1 November 2003

Abstract The organizational structure literature has long posited that increases in uncertainty should lead to organic (adaptable) structures [T. Burnes, G.M. Stalker, The Management of Innovation, Tavistock Publications, London, 1961]. Similarly, the operations management literature has focused on the importance of flexibility as a competitive weapon [e.g., De Meyer et al., 1989 Strategic Manage. J. 10 (1989) 135], and as a response to environmental uncertainty [Manage. Sci. 33 (1987) 509; Manage. Sci. 39 (1993) 395]. However, our recent attempts to empirically validate a relationship between environmental uncertainty and operational flexibility in manufacturing plants have failed to show a significant relationship [J. Operations Manage. 3 (1999) 307]. The present research attempts to rectify these contradictory findings by replicating and extending the works of [Manage. Sci. 33 (1987) 509] as well as [J. Operations Manage. 3 (1999) 307]. The results find no support for the proposition that firms that respond to increased uncertainty with increased flexibility will experience increased performance. © 2003 Elsevier B.V. All rights reserved. Keywords: Environmental uncertainty; Flexibility; Performance; Strategic fit

1. Introduction One of the most widely accepted theoretical relationships in the field of operations management is the link between environmental uncertainty and flexibility. Drawing from the organizational structure and strategy fields (i.e., Burnes and Stalker, 1961; Lawrence and 夽 An earlier version of this paper won the Chan Hahn Best Paper Award for best operations management submission to the 2003 Academy of Management Conference in Seattle, WA. ∗ Corresponding author. Tel.: +1-541-737-4102; fax: +1-541-737-4890. E-mail addresses: [email protected] (M. Pagell), [email protected] (D.R. Krause). 1 Tel.: +1-480-965-9859; fax: +1-480-965-8629.

Lorsch, 1967), Swamidass and Newell (1987) created a model that examines the relationships among environmental uncertainty, manufacturing flexibility and performance. Tests of the model revealed statistical support for the linkages between uncertainty and flexibility, and between flexibility and performance. However, our recent research (Pagell and Krause, 1999) casts doubt on the traditional model of the relationships among these constructs. In research that used both a series of case studies and a survey, we found no link between increased uncertainty and increased flexibility. More importantly, we found no evidence that higher levels of flexibility in uncertain environments were linked to higher levels of performance. Our 1999 research used the same measure of

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environmental uncertainty as Swamidass and Newell. However, each study operationalized flexibility in a different manner. The methods used to test the theory in each study also differed. If the results across these studies had been consistent, the differences in methods and measures would have provided evidence that the model was robust. However, the divergence of results strongly indicates the need for additional testing to determine if the results of Swamidass and Newell generalize into the 21st century. Replication is a fundamental activity for building the knowledge base in any field (Kerlinger, 1986; Lamal, 1991; Rosenthal, 1991). The need for increased theory development and testing in Operations Management has long been noted (Flynn et al., 1990; Handfield and Melnyk, 1998). All studies have shortcomings and building theory from a single set of un-replicated findings is not good science. Replication becomes even more important when the research originally used to build the theory is later contradicted. The need for further testing is further reinforced by the recent published work of Badri et al. (2000). Their research was much more macro-level than the research of Swamidass and Newell or our previous work in that they looked at several environmental characteristics and several different manufacturing strategies. However, one of their key results was a strong positive relationship between increased dynamism, a form of uncertainty, and an increased emphasis on flexibility as a manufacturing strategy. Most importantly, high and low performers in their sample were both responding to increased uncertainty by increasing their level of flexibility, thus making it impossible to determine the true performance benefits, if any, of responding to uncertainty with flexibility. The present research revisits the relationships among environmental uncertainty, manufacturing flexibility and firm performance. We replicate both the Swamidass and Newell (1987), and Pagell and Krause (1999) studies. Such an effort is necessary because Swamidass and Newell’s findings of a significant relationship between flexibility and performance were validated only in the machinery and machine tools industry. More importantly their results are based on a very different business climate and it is possible that any or all of the myriad changes that have occurred since 1987 would influence the relationships of interest. Our previous findings, which did not

confirm the findings of the 1987 study, also strongly suggest that Swamidass and Newell’s findings might not generalize into today’s business climate. The importance of these issues to practicing managers is significant. The preponderance of the literature that is aimed at directing managerial behavior suggests that flexibility is a viable, perhaps even necessary, response to increasingly complex global markets. However, investments in flexibility are expensive and, in some instances, disruptive. Therefore, it behooves researchers to clearly determine the relationships between these constructs before directing practice. In the present study, we have attempted to overcome some of the limitations of each of the previous research efforts. We have also attempted to extend previous research to further understand the relationships between environmental uncertainty, flexibility and manufacturing performance in today’s business climate.

2. Literature review Many of the seminal works in organizational theory address how organizations should arrange their structures to respond to uncertainty in the external environment. For example, Burnes and Stalker (1961) posit that as a firm’s environment becomes more complex and/or unpredictable, there is a need for a more organic structure. Thus, firms in relatively certain and predictable environments should have a mechanistic structure with greater subdivision of tasks and simpler jobs. In contrast, firms in uncertain and unpredictable environments should have more organic structures, with less specialization and more complex jobs.The organizational theory literature continues to examine these issues, and recent work closely aligns with the seminal work of authors such as Burns and Stalker. For instance, Hitt et al. (1998) suggest that firms will require strategic flexibility to survive in a global environment characterized by rapid technological change. Schilling and Steensma (2001) specifically test flexibility as a response to the environment. Their findings suggest that firms in heterogeneous, or more difficult to predict, environments tend to have more modular or flexible structures than their counterparts facing more homogeneous environments. While the terminology differs, their results link very tightly to Burns and Stalker’s original formulation. Schilling

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and Steensma’s modular structures are very flexible and adaptable just like the organic structures originally envisioned. Further, Schilling and Steensma’s results provide empirical support for the theory that increases in uncertainty, in the guise of increased heterogeneity, lead to an increased emphasis on flexibility. Equally important, this research shows that the structure chosen is contingent upon the environment, which is a key element underpinning the work on flexibility and uncertainty in the operational field. The operations management literature has long addressed similar issues, typically from the perspective of linking manufacturing strategy decisions to the environment. Just as organic structures were deemed necessary for firms to adapt to uncertain external environments, flexibility is now seen as a way for manufacturing organizations to adapt to uncertain external environments. For example, Swamidass and Newell (1987) concluded that one way for firms to cope with increased environmental uncertainty is through increased manufacturing flexibility. In addition, Gerwin (1993), presented a model in which environmental uncertainty drives manufacturing strategy and hence the flexibility requirements of the system. In a comprehensive review of the flexibility literature, Beach et al. (2000) note that “the use of flexibility for the purpose of accommodating uncertainty is a notion that has received broad recognition” (p. 42). These authors note that there are various types of uncertainty that can be responded to with different types of flexibility. But they support the implicit theory that, in general, flexibility is a competitive response to uncertainty. The following from Garud and Kotha (1994) is a good example of the “broad recognition,” to which Beach et al. (2000) refer. Many firms compete in industries that require rapid responses to market and technological changes. Market changes reflect unpredictable customer needs for an increasing variety of products, whereas technological changes reflect continual advances that occur with the introduction of new products. In such industries, firms that possess the manufacturing flexibility to introduce modified or new products at minimal cost and lead time will gain a competitive advantage over others (p. 671). We would explicate the theory underlying all of these works as follows: Plants that respond to in-

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creased uncertainty in the environment by increasing their flexibility will experience higher performance than plants in the same or similar environments that do not increase flexibility. While most authors have been concerned with increased uncertainty and/or uncertain environments, we note that the works used to build this theory (e.g., Burnes and Stalker, 1961; Lawrence and Lorsch, 1967) were explicit in their treatment of the problem from a contingency perspective, that is, the organizational response is dependent upon specific environmental conditions (Steiner, 1979). Thus, in low uncertainty environments an increase in flexibility will lead to decreased performance. In our 1999 research, we empirically tested this contingency theory. Our methods included a crossindustry survey of manufacturers and a focused set of case studies on thirty companies using advanced manufacturing technologies (AMT). We found no significant relationship between the level of environmental uncertainty and the level of operational flexibility in either sample. More importantly we tried to establish a linkage to performance. Our underlying premise was that, all things being equal, plants that had a level of flexibility that “fit” the level of uncertainty would perform better than plants with no fit. To have “fit,” plants in low uncertainty environments would need low levels of flexibility, while plants in high uncertainty environments would require high levels of flexibility. Our tests showed no relationship between “fit” and performance. That is, no significant differences were found in the level of performance between the firms that had a good fit between operational flexibility and the level of environmental uncertainty, and those firms that had no fit. 2.1. Limitations of previous research Methodological shortcomings may explain the divergence in the findings between our previous research and that of Swamidass and Newell. For instance, the measure of the external environment (derived from Duncan, 1972) we used previously, was a potential source of error. We chose this measure because it was the same measure used by Swamidass and Newell. However, we then also noted that this measure of the environment was potentially flawed, which might invalidate the results.

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Table 1 Items used to assess environmental uncertainty—“Duncan’s measure” (1) Actual users of your products (2) Competitors for your supply of raw materials (3) Competitors for your customers (4) Government regulation controlling your industry (5) The public’s political views and attitudes towards your industry Source: Duncan (1972). Scored on a seven-point Likert scale: 1 (=always predictable) to 7 (never predictable). This measure is an index and hence reliability was not calculated.

Duncan’s (1972) measure (see Table 1) suffers from a number of limitations. First, it is perceptual and managerial perceptions may be flawed. Second, it is also possible that plant managers do not have the perspective to properly judge the environment in which their plant competes. In addition, some of the items in Duncan’s index could be interpreted in a variety of ways. For instance, the item on the predictability of the actual users of products could be interpreted to mean the number of users (demand uncertainty) or desires of users (mix uncertainty). The items may also be affected by respondents’ biases. The item on government regulation is a prime example of a question that could be affected both by respondents’ views of government and by what they think is the socially desirable answer. Next, is flexibility. Specifically, we looked only at operational flexibility; we did not examine flexibility at the strategic level studied by Swamidass and Newell. Therefore, the results from the two studies may differ purely for level-of-analysis reasons. Finally, both studies, like all research, suffer from limitations. The Swamidass and Newell data collection effort is commendable, especially their use of multiple respondents per plant. However, their analysis does not meet the generally accepted minimum criterion of five respondents per item to run a path analysis (e.g., Hatcher, 1994). The study is also limited to the machine tool industry where the importance of flexibility may be magnified due to the customized nature of the product. This last set of characteristics of Swamidass and Newell’s study are not limitations as much as simple facts: the theory has not been empirically validated beyond the machine tool industry, or in today’s business climate. To assume that it applies today across all industries is presumptuous.

The Swamidass and Newell model suggests that in highly uncertain environments, increased flexibility will lead to higher performance. However, the literature is also clear that in low uncertainty environments flexibility may not be needed. Burnes and Stalker’s (1961) original formulation proposed that firms create mechanistic, or inflexible, structures in certain environments. Theory suggests that when the environment is certain the increased costs of flexibility will be wasted; thus, economic performance will suffer. However, as tested, Swamidass and Newell’s model implies that a low level of flexibility under a condition of low environmental uncertainty is linked to low levels of performance; this is contrary to theory. Thus, we propose that the theory being tested is a contingency theory. However, the model they tested does not allow for the low uncertainty, high performance contingency. Our previous work (Pagell and Krause, 1999) does allow for the possibility of high performance in low uncertainty environments. However, this work suffers from a number of other limitations. The larger survey sample suffers from the use of a single respondent per organization (Boyer and Pagell, 2000) which may be vital because of the potential for bias in the Duncan measure. In addition, this sample was not very large (n = 91). The case study sample is large for research of this type (n = 30) but is limited to users of advanced manufacturing technologies. The preceding discussion suggests that previous findings need to be replicated, preferably in a manner that addresses both the measurement and methodological issues raised. Additional literature and references are incorporated into the following sections, which describe the research.

3. Replication Kerlinger (1986) makes the following statement: A good rule is: Replicate all studies. This does not mean literal duplication of studies. Indeed the word “replication” means doing additional studies based on the same problems and variables but with minor, sometimes major, variations. For example, the measurement instrument of the original study may have been found wanting. A replication of the study done with another sample and an improved

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instrument and similar results would be compelling evidence of the empirical validity of the original results (p. 593). Kerlinger’s call to replicate all studies is in line with the suggestions of many others (e.g., Reynolds, 1971; Lamal, 1991; Rungtusanatham et al., 1998). Without replication a scientific field cannot grow and the validity of its theories will be tenuous. Additionally, counter-intuitive results that conflict with the findings of previous studies’ must also be replicated. Replication in the present study closely follows Kerlinger’s description. In general, we have attempted to use more rigorous methods to retest the original models proposed by the two sets of authors. We also sought to extend both works. For example, we extend our previous tests to the strategic level-of-analysis. In addition, we address the potential limitations of Duncan’s (1972) perceptual measure of environmental uncertainty by testing both models using existing objective measures of the external environment. The primary purpose of this research is to provide a more definitive test of the theory surrounding uncertainty, flexibility and performance. In the process we wish to address the following questions raised by the previous research: 1. Does the choice of the measure of environmental uncertainty affect the results? 2. Does the choice of statistical method affect the results? 3. Are Swamidass and Newell’s results generalizable beyond the machinery and machine tools industry? 4. Are Swamidass and Newell’s results generalizable to today’s business climate or do they reflect conditions in 1987?

4. Measuring the external environment and flexibility 4.1. External environment measures Various researchers have conceptualized and measured the external environment differently. Kotha and Orne (1989) examined what they described as the organization’s scope, which included a firm’s mar-

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ket, customers and geographic scope. Wernerfelt and Karani (1987) addressed environmental uncertainty and defined it in terms of four dimensions: demand, supply, competitive and external. Both the Swamidass and Newell (1987) and our (1999) efforts used the perceptual measures proposed by Duncan (1972) to address a company’s ability to predict elements of their environment such as competitors, customers and suppliers. Duncan’s measure is one of a number of perceptual measures of the external environment available in the literature. Duncan (1972), Bourgeois (1980), and Swamidass and Newell (1987) agree that perceptions of the environment are more important than the actual environment. If managers perceive the environment as uncertain, they will make decisions that are designed for an uncertain environment. Thus, these researchers argued that the use of a perceptual measure of environmental uncertainty allowed for a stronger test of the relationship between flexibility and environmental uncertainty. We (Pagell and Krause, 1999) used a similar justification for our choice of a perceptual measure and used Duncan’s measure to build on previous research in the field. For the purposes of replication we have also used Duncan’s measure of the external environment. Table 1 details the specific items used. Following our 1999 research, the present research eliminated one item that dealt with the predictability of unions because the percentage of unionized workplaces has fallen dramatically in the last 25 years and thus would not apply to a majority of the prospective respondents (see Pagell and Handfield, 2000 for a complete discussion). The remaining five items were operationalized in the same manner as Duncan (1972), Swamidass and Newell (1987), and Pagell and Krause (1999). All items were scored on a seven-point Likert scale that ranged from 1 (=always predictable) to 7 (=never predictable). Duncan’s measure is an index rather than a scale (see Noble, 1995 for a discussion of indexes). This is an important distinction. In many measurement efforts, a researcher will measure a construct with a number of different items that are designed to tap the various dimensions of the construct, with some redundancy in the questions to address reliability and validity concerns. The researcher then checks the reliabilities of the various items used to measure the construct and

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drops items that do not co-vary well with the others. The goal is a valid and reliable measure of a single construct. However, Duncan’s index does not address a single construct; instead it attempts to measure the level of uncertainty in the external environment that is caused by a number of independent causes. For instance, the measure looks at the predictability of competitors, customers, and regulators. There is no supposition that these items need to co-vary. Instead there is an assumption, built on theory, that uncertainty comes from many sources and that the environment created from these sources can be addressed in an additive fashion. Hence, the items are summed in an index that indicates the overall level of uncertainty in the environment from a number of sources. This follows previous work in the field that used indexes (e.g., Noble, 1995; Flynn and Flynn, 2000). Because of the limitations of Duncan’s perceptual measure of uncertainty we included alternative operationalizations of the environment in the replication. Specifically, we followed Dean and Snell (1996) who used objective measures of the environment to examine firms’ investments in advanced manufacturing technologies. These objective measures are derived from previous research (e.g., Dess and Beard, 1984; Keats and Hitt, 1988), which indicates that the environment is composed of three factors: munificence, instability, and complexity. Munificence, or level of growth, measures changes in sales for a particular industry over time. High levels of munificence indicate that sales are growing for all firms. Therefore, increasing the performance of any one firm requires only that it keep up with the industry. Low levels of munificence indicate a shrinking market where firms compete fiercely just to maintain their present sales volumes. Instability, or demand uncertainty, measures the volatility or unpredictability of a firm’s environment. As instability rises, risk rises and firms may do more to protect themselves from the market. Traditional measures of uncertainty in operations have been based on demand uncertainty and most closely mirror this factor of the environment. Complexity is a measure of concentration. The more concentrated a market, the less competition a plant faces. These conditions may make it easier for a firm to survive without change.

All three factors are addressed with objective measures based on publicly available industry data. These measures were derived based on the plant’s 4 digit SIC code. The objective measures used to address these three factors are detailed in Appendix A. 4.2. Flexibility Flexibility is a multidimensional construct. Gerwin (1993) noted that there are at least seven types of flexibility. In addition, flexibility can be present at different levels of analysis (e.g. Beach et al., 2000). Swamidass and Newell (1987) examined managers’ perceptions of the importance of various indicators of manufacturing flexibility such as new product introductions. Much recent work (e.g., Miller and Roth, 1994; Ward et al., 1998) has followed a similar path. These researchers examined flexibility from the standpoint of the perceived strategic importance of various types of flexibility under the guise of developing an indicator for the firm’s manufacturing strategy. Recent literature has examined the competitive priorities of innovation and flexibility as separate constructs (see for instance Krause et al., 2001) which contrasts with Swamidass and Newell’s operationalization of flexibility which contained elements of innovation. Because one of our goals is to test the generalizability of theory in today’s environment we did not exactly follow Swamidass and Newell’s operationalization of flexibility. Rather, flexibility was assessed using the sub-set of measures (see Table 2) commonly found in the manufacturing strategy literature to address the competitive priority of flexibility (e.g., Ward et al., 1998). The primary analysis was done using this measure of flexibility (referred to as flexibility for the remainder of the paper). However, we are also trying to control measurement differences across the studies. Therefore, we also created a second flexibility scale (referred to as SNFLEX) that closely mirrors the operationalization of Swamidass and Newell. Appendix A describes this alternative. Note that the correlations between the flexibility items are very high and that much previous research (e.g., Ward et al., 1998) suggests that they are tapping the same construct, namely the strategic importance of flexibility. Our own results also suggest that this is a single factor with a Cronbach’s alpha of 0.90.

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Table 2 Items used to measure flexibility in terms of strategic importance for the plant (alpha = 0.90) (1) Delivery flexibility: The ability to effectively respond to changes in planned delivery dates. (2) Volume flexibility: The ability to effectively increase or decrease aggregate production in response to customers. (3) Mix flexibility: The ability of a manufacturing system to effectively produce a wide variety of different products. (4) Changeover flexibility: The ability of a manufacturing system to effectively handle additions and subtractions to the product mix over time. (5) Modification flexibility: The ability of a manufacturing system to effectively implement minor changes in current products that result from corrective actions or changing customer requirements. Scored on a seven-point Likert scale: 1, extremely unimportant; 4, moderately important; 7, extremely important.

Therefore, these items were summed to create a scale for the strategic importance of flexibility. 4.3. Performance Performance was addressed by having respondents rate their plant’s performance relative to major industry competitors based on unit price of manufacturing, total cost, product quality, delivery speed, delivery dependability, flexibility and new product introduction. These self-reported measures were used for several reasons. First, we limited the data collection to outcomes over which the plant managers would have some control. In other words, financial measures such as growth in ROA are predicated on many decisions beyond the plant level. Second, because our level-of-analysis was at the plant level, financial measures would not be available for all plants, especially those that were cost centers within a multi-plant company (Youndt et al., 1996). Third, the data collection was cross-industry in nature and many authors have noted that the usefulness of individual financial measures vary by industry (e.g., Vickery et al., 1993; Youndt et al., 1996). Finally, the theory we are testing addresses relative performance with the supposition that plants that respond to increased uncertainty with increased flexibility should have higher levels of performance than plants in the same environment that respond differently. Therefore, a measure of performance that compares performance relative to competitors is preferred to a measure that looks at absolute performance. The seven items were operationalized as seven-point Likert scales where 1 is “far worse than competitors” and 7 is “far better than competitors.” The items were summed to form a manufacturing performance scale (Cronbach’s alpha = 0.85). The creation of a sin-

gle composite follows much previous research (e.g., Ahmad and Schroeder, 2003) and is appropriate in this setting. The theory we are testing does not posit improved performance along a single dimension of performance, but rather performance in general. The composite allows us to test this theory. Additionally, our statistical evidence (once more in line with previous research such as Ahmad and Schroeder, 2003) suggests that the dimensions of performance are strongly related to each other. In this sample, like many others, plants that perform well on one dimension of performance tend to perform well on most dimensions of performance. This measure of performance follows much recent research in operations strategy. However, it does not follow the measure used by Swamidass and Newell, who focused on financial measures such as ROI, which are generally not used in plant level studies. Once more we have attempted to control for these potential differences by creating a secondary measure of performance (referred to as SNPERF) that is described in Appendix A.

5. Methodology 5.1. Data collection and non-response bias All of the measurement scales and indexes used in this research were based on existing research. However, we were still interested in trying to ensure general ease of understanding for respondents and construct validity. Therefore, the survey instrument was pre-tested with executives who were asked to review the questionnaire for readability, ambiguity and completeness (Dillman, 1978). The questionnaire was also critiqued by several academics who were asked

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to review survey items for ambiguity and clarity, and to evaluate whether individual items appeared to be appropriate measures of their respective constructs (DeVellis, 1991). Several minor changes were made to the survey instrument based on the pretest. The data of interest in this paper are responses from managers at the plant level. To address concerns about the use of a single respondent we collected data from operations and purchasing executives in manufacturing industries. Both respondents were used to address the plant’s performance and level of environmental uncertainty. Having multiple respondents for these constructs should significantly reduce concerns about bias. However, since a purchasing respondent is not well suited in some organizations to address manufacturing flexibility, we gathered data on flexibility only from the manufacturing respondents. A target sample of 1283 high ranking purchasing executives who are members of the Institute of Supply Management (ISM) was selected. ISM provided a list of its members (classified by title) in manufacturing-based industries using Standard Industrial Classification (SIC) codes 20–39. To obtain responses from the purchasing executives and their high-level manufacturing counterparts, we sent a letter, survey and return envelope to the purchasing executive accompanied by a similar packet for the manufacturing executives. The purchasing executives were asked to pass the second packet on to the primary manufacturing executive at the plant. To achieve as high a response rate as possible, a variation of Dillman’s Total Design Method was used (Dillman, 1978). An initial mailing included a cover letter, the survey, and a postage-paid return envelope. Ten days after the initial mailing, reminder postcards were mailed to non-respondents. Approximately 30 days after the initial mailing, a second mailing of surveys, cover letters, and postage-paid return envelopes were mailed to non-respondents. Because our primary goal was to collect dyadic data, telephone calls were placed to purchasing respondents who had returned their survey, but whose manufacturing counterparts had not. The telephone calls yielded primary contact information for the manufacturing respondent and an additional copy of the survey was mailed directly to the remaining manufacturing respondents. This process yielded a total of 168 usable dyads of purchasing/manufacturing responses. From the 1283

purchasing executives in the target sample, 252 usable responses were received (24 additional purchasing surveys were set aside from the analysis because of incomplete information). Thus, the effective response rate was 19.6% for the purchasing executives. A 19.6% response rate for the primary respondent is comparable to much of the recent literature using a single respondent. In addition, Carter (2000) notes that, in general, studies using dyadic methods suffer from low response rates; thus, our response rate for the purchasing managers is actually higher than expected. The response rate for the operations executives should be considered much higher, since we could only receive operations responses from those firms where the purchasing executive had first responded. Thus, the effective response rate for operations executives is 66.7% (168/252). Although there is no generally accepted minimum percentage for response rates, non-response bias is always a concern. One method for testing non-response bias is to test for significant differences between the responses of early and late waves of returned surveys (Armstrong and Overton, 1977). This method is based on the assumption that the opinions of late responders are somewhat representative of the opinions of nonrespondents (Armstrong and Overton, 1977). For the present study, twenty of the survey items used for the analysis were randomly selected, two groups of fifty surveys were chosen from the first and last waves of surveys received, and t-tests were performed on the responses of the two groups. The t-tests yielded no statistically significant differences among the 20 survey items tested. We also tested non-response bias by comparing the plants where only the purchasing manager responded to plants where we had responses from both functional areas. There were no significant differences between the two groups on the items used to address the external environment or plant performance. In other words, there was no systematic difference between plants where manufacturing managers responded to the survey, and those where manufacturing managers did not on two of the key constructs of interest. Although these results do not rule out the possibility of non-response bias, they suggest that non-response bias may not be a problem to the extent that late responders represent the opinions of non-respondents. Additionally, our population of manufacturing

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managers was limited to the population of plants where we had a purchasing response. For the measures of the external environment and performance there were no significant differences between respondents and non-respondents within our sample population. Thus, the data analysis proceeded as described in subsequent sections.

ferent implicit hypotheses. Our previous research tests a single hypothesis which is best stated as:

5.2. The sample

This hypothesis tests the contingency theory first proposed by Burnes and Stalker (1961) and incorporated in recent work such as Schilling and Steensma (2001). Assumed in this theory is that low levels of uncertainty require only low levels of flexibility. Further, this hypothesis suggests that in low uncertainty environments, higher levels of flexibility will in essence be wasted. Flexibility has costs; thus, wasted flexibility will harm performance. Swamidass and Newell (1987) build off the same theoretical base. However, their path model makes it impossible to test a contingency model. We propose that their model incorporates two implicit hypotheses:

The respondents were comprised of executives with titles of Director (n = 79, 44%), Vice-President (n = 45, 25%), Operations or Production Manager (n = 36, 20%), and “Other” titles (n = 20, 11%). Respondents were employed by companies in a variety of industries. Industries most frequently represented were electrical and electronic equipment, miscellaneous manufacturing, food, transportation equipment, and chemicals. The respondents worked primarily for medium to large companies (more than 500 employees). Although many respondents elected not to report sales, companies with gross annual sales greater than US$ 100 million comprised at least 42% of the sample. This sample differs from both the Swamidass and Newell (1987) sample and the case study sample used in Pagell and Krause (1999). However, the present sample has many similarities to the survey sample we used in our previous research. Both of these survey efforts covered a wide range of industries and had a great deal of variability in terms of plant size, performance, and flexibility. However, the samples are not identical. Plants in the present work tend to be larger than those used in our previous study. One of the reasons to replicate is to ensure that biases in a sample did not cause the researchers to come to spurious conclusions. Therefore, these differences are both important to note and could explain differences in results between this study and previous work. However, if there are no differences in the results between the replications, these differences in the characteristics of the samples suggest that the results are robust across a wider variety of situations than tested in either individual sample. 6. Analysis Although both of the previous research efforts are addressing the same theory, their methods suggest dif-

H1. Plants that match their level of uncertainty to the environment will perform better than plants that have either more or less flexibility than the environment demands.

H1. The level of flexibility is related to the level of environmental uncertainty. H2. Performance is related to the level of flexibility. The nature of these relationships is not explicated, but their discussion implies that both of these relationships are hypothesized to be positive. The model hypothesizes that high performing companies will respond to the high levels of uncertainty with high levels of flexibility. This is what theory suggests for high uncertainty environments. Following the path model logically, low uncertainty is predicted to lead to low levels of flexibility. However, assuming that the path from flexibility to performance has a positive coefficient, then low levels of flexibility will lead to low levels of performance. This is not what theory suggests, nor do we believe that this was Swamidass and Newell’s intention. However, their analysis does not seem to make allowances for the possibility, suggested by theory, that low levels of flexibility can be linked to high levels of performance in specific environments. Therefore, when we replicated the Swamidass and Newell model we performed two sets of analyses. First, we tested the models in the manner they did. Noting the above, we recognize that this is in a sense

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flawed. However, it was important to see if we could replicate their results before we attempted to improve on their model or methods. Next, we attempted to find a way to use path analysis to remain true to the original work, while also trying to find a way to account for the contingency nature of the theory being tested. Specifically, theory would suggest that in a low uncertainty environment high performing plants would have low levels of flexibility. Conversely, high performing plants in high uncertainty environments would have high levels of flexibility. To find support for this theory, the path from flexibility to performance should be positive in high uncertainty environments, and negative in low uncertainty environments. To test this contingency we retested the models after splitting the sample into high and low uncertainty environments across each environmental measure. 6.1. Preliminary analysis In an effort to lend additional validity to our analysis, we compared the responses of the operations managers to those of the purchasing managers for the external environment and performance to assess inter-rater reliability. Boyer and Verma (2000) suggest that a minimum correlation of 0.20 between raters on an item is evidence of an acceptable level of inter-rater reliability. All of the correlations exceeded the suggested 0.20 by a large margin and all were significant at P < 0.01. Thus, the responses of the two groups of managers exhibited high levels of inter-rater reliability on both the environment and performance. This result indicates that the managers were in agreement

about the competitive space occupied by the plant and the plant’s performance. These tests significantly mitigate any concerns that bias introduced by the respondents influenced the results, at least for results related to the environment and performance. Table 3 shows the correlation matrix for the constructs of interest in the study. Three key areas of interest arise in the table. First, the primary and secondary measures of flexibility and performance are strongly correlated. The correlation between flexibility and SNFLEX is 0.863 and the correlation between performance and SNPERF is 0.558. Second, there is a significant positive correlation between performance and flexibility (based on the primary measures), suggesting that flexibility may be valuable in all environments, as opposed to only in uncertain environments. However, this relationship is not evidenced when examining SNFLEX and SNPERF. The matrix also indicates that the perceptual measure of the environment does not correlate with any of the objective measures of the environment. This divergence between perceptual and objective measures is not unique to our research (Boyd et al., 1993). However, it suggests that including objective measures of the environment will lead to a much stronger replication and help to determine the importance of the operationalization of the environment on the results. In sum, the correlations suggest that differences in levels of analysis and/or measurement choice could change the results, thus strengthening the case for a replication that considers multiple operationalizations of key constructs at various levels of analysis.

Table 3 Correlation matrix

Munificence Instability Complexity Duncan’s Flexibility Performance SNFLEXa SNPERFb ∗

Munificence

Instability

Complexity

Duncan

Flex

Perform

SNFLEX

SNPERF

1.0 0.876∗∗ 0.045 −0.045 −0.050 −0.08 −0.126 0.123

1.0 0.058 −0.042 −0.066 −0.049 −0.134 0.144

1.0 0.039 0.042 0.002 0.112 0.076

1.0 0.148 0.167∗ 0.098 0.255∗∗

1.0 0.220∗ 0.863∗∗ 0.152

1.0 0.188∗ 0.558∗∗

1.0 0.083

1.0

Significant at 0.05. Significant at 0.01. a Flexibility as measured by Swamidass and Newell (see Appendix A). b Performance as measured by Swamidass and Newell (see Appendix A). ∗∗

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Error Term 1 Manufacturing Flexibility

Error Term 3

Economic Performance

Perceived Environmental Uncertainty Role of Manufacturing Managers in Strategic Decision Making

Error Term 2

Fig. 1. Swamidass and Newell model.

6.2. Replicating Swamidass and Newell The present sample differs from the Swamidass and Newell (1987) sample in that it is larger and much more diverse. In addition, some of the correlations reported in Table 3 are different for this sample than those reported in Swamidass and Newell. Specifically, they found no relationship between uncertainty and performance. In this sample, the relationship between uncertainty, when measured using Duncan’s measure (the same measure used by Swamidass and Newell), and performance is significant. However, the relationships between the objective measures of the environment and performance are not significant. Additionally, Swamidass and Newell found a significant positive correlation between flexibility and

performance, which we do find, but only when using measures that differ from theirs. To test their initial model we replicated the path analysis as closely as possible, with two minor changes. First, their model had two paths from uncertainty to performance as shown in Fig. 1. Because our interest was only with the relationships between uncertainty, flexibility and performance we did not include their “Role of Manufacturing Managers in Strategic Decision Making” construct in the data collection or the analysis. Fig. 2 displays the simplified path model we tested. The second difference is that we tested the model with the objective measures of the environment using all three measures simultaneously, as shown in Fig. 3. This decision was based on the theory underpinning the original work, that it

Error Term 2

Error Term 1

Perceived Environmental Uncertainty

Manufacturing Flexibility

Fig. 2. Simplified Swamidass and Newell model used for replication.

Manufacturing Performance

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Error Term 1

Error Term 2

Munificence

Manufacturing Flexibility

Instability

Performance

Complexity

Fig. 3. Swamidass and Newell model modified for objective measures of the environment.

is the uncertainty of the environment as a whole that drives flexibility. We initially tested two path models. The first model was based on the original formulation from Swamidass and Newell, using Duncan’s perceptual measure of the environment as a driver of strategic flexibility. This model, shown in Fig. 4, was not significant. The overall model did not fit the data well, and the amount of variance explained for the endogenous variable was miniscule. The path from flexibility to performance was positive, but the overall model did not fit and the path from uncertainty to flexibility was not posi-

tive suggesting that something other than uncertainty is driving managers to flexibility. Thus, this portion of the replication generally does not support a conclusion that the findings of Swamidass and Newell generalize to today’s business climate. The second model assessed all three objective measures of the environment simultaneously. The model, shown in Fig. 5, did show sufficient fit. However, the paths between the constructs are generally not significant (the only exception is the path from flexibility to performance which is once more positive and significant), and the amount of variance explained for the

Error Term 2

Error Term 1 R2 = .007 Perceived Environmental Uncertainty

.08

R2 is the amount of variance in the endogenous variable that is accounted for by its direct antecedents

R2 = .046 .21 P<.05

Manufacturing Flexibility

Measure

Model fit Recommended * >.05

P for ChiSquared NNFI >.9 * From Hatcher 1994

Fig. 4. Perceived uncertainty and flexibility.

Performance

Actual .04 .68

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Error Term 1

641

Error Term 2

Munificence -.03

-.05 Instability

R2 = .006

Manufacturing Flexibility

R2 = .045 .21 p<.05

Performance

.044 Complexity

Measure

Model fit Recommended * >.05

Actual

P for ChiSquared NNFI >.9 * From Hatcher 1994

.85 .98

Fig. 5. Objective measures of the external environment and strategic flexibility.

endogenous variable is small. In other words, using the criteria described by Hatcher (1994) the model does not show support for the theory being tested. To address the contingency nature of the theory we then examined each environmental variable separately. Specifically, we split the sample into roughly equal high and low uncertainty categories (for each of the four measures) and tested each model. In this manner we ended up with eight models. This approach addresses our concerns about high performance in low uncertainty environments. In addition, this method is similar to the analysis performed in our original research, which may make comparisons between the studies easier. The results of these analyses are displayed in Appendix B. The tests do not provide support for the theory. There is some evidence that increased flexibility will lead to increased performance in general, but there is no evidence to suggest that plants that respond to uncertain environments with an increased emphasis on flexibility will increase performance. Finally, we ran the analysis using the SNFLEX and SNPERF scales to more closely mirror the original analysis of Swamidass and Newell. None of these tests (see Appendix B) find support. In sum, the present analysis does not support generalizing the conclusions of Swamidass and Newell (1987) in today’s business

climate, that is, we find no evidence of the theorized relationships between uncertainty and flexibility, or flexibility and performance, when using path analytic methods. 6.3. Replicating Pagell and Krause The correlation between uncertainty and flexibility provides some preliminary support for our previous results. The correlation between Duncan’s measure and flexibility in the present study is 0.148 (not significant), which is a larger effect than in the previous study (correlation of 0.049) but still insignificant. In addition, none of the other relationships between the environment (perceptual or objective measures) and flexibility (at either level-of-analysis) are significant. However, the issue of paramount interest is performance. Specifically, do plants whose managers have aligned flexibility with the external environment benefit in terms of better performance? If alignment leads to better performance, then the finding of a lack of a relationship between flexibility and the external environment could yield a prescription for firms to strive for alignment. We replicated the previous work as closely as possible in terms of our test of the relationship between uncertainty, flexibility and performance. Specifically,

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because there were no obvious gaps in any of the distributions, categories were developed by dividing each measure (one measure of flexibility and four measures of the external environment) into three nearly equal categories. Once categorized, alignment was assessed as follows: If the category of flexibility was equivalent to the environmental uncertainty category, the plant was judged to be in alignment with its external environment. This category of firms was dubbed “Fit”. If the categories were not equivalent, the plant was judged to be out of alignment, and labeled either “Exceed” or “Lag”, respectively, depending on whether the level of flexibility exceeded the level of the environment or lagged behind it. Following the previous research, the relationships between performance and fit were assessed using regression with dummy variables entered in each model simultaneously. This approach allowed us to determine if there was a significant relationship between level of fit and performance. In addition, it allowed us to determine if there were differences in performance among the three groups of plants (see Hardy, 1993 for a complete discussion of regression using dummy variables). We used Fit as our reference group because it is both the largest group, and the group whose performance we were most interested in. The results (for both the primary and secondary analyses), shown in Table 4, support the original findings of Pagell and Krause (1999). The very low R2 -values indicate that there is no relationship between the group a plant is in (Lag, Fit, or Exceed) and plant performance. Performance is driven by many factors; however, much of the literature on flexibility suggests that flexibility is a key response to uncer-

tainty. Thus, we would expect the Fit group to account for more variance than it does. In addition, there is no evidence of performance differences between the categories because none of the betas for the independent variables (P-lags and P-exceeds) were significant. Regardless of the manner in which the environment was operationalized, there was no link between “Fit” and performance when flexibility was addressed at the strategic level-of-analysis. Thus, the replication supports our previous findings using multiple measures of the external environment, as well as alternative operationalizations of flexibility. 6.4. Additional analysis In a perfect laboratory replication it is possible to control almost all variables, or at least to ensure that conditions in the replication match conditions in the original study. However, a social sciences replication does not have such a luxury. One of our concerns is that Swamidass and Newell’s data was collected from firms that were using different technologies to address a different economic environment. One area of high importance is the change in outsourcing behavior by firms over the last 20 years. It is possible that some or all of the differences between the original tests and our subsequent research, are due to an increase in outsourcing. We tested this possibility as follows. First, we examined the relationship between outsourcing (measured as ratio of direct materials costs as a percentage of total cost) and flexibility. If increased outsourcing could indeed explain the results we would expect to see a negative relationship between outsourcing and

Table 4 Fit and performance—replicating Pagell and Krause R2 Main analysis Flexibility and Flexibility and Flexibility and Flexibility and

Duncan munificence instability complexity

Secondary analysis (SNFLEX and SNPERF) Flexibility and Duncan Flexibility and munificence Flexibility and instability Flexibility and complexity

F-significance

P-lags

P-exceeds

0.005 0.004 0.046 0.018

0.735 0.286 0.054 0.333

0.537 0.151 0.220 0.756

0.483 0.868 0.192 0.270

0.008 −0.013 0.010 0.001

0.230 0.837 0.538 0.946

0.445 0.553 0.267 0.967

0.296 0.803 0.605 0.764

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Flexibility

Outsourcing and Flexibility 40 35 30 25 20 15 10 5 0 0

20

SNFLEX

(A)

80

100

80

100

Outsourcing and SNFLEX

40 35 30 25 20 15 10 5 0 0

(B)

40 60 percent outsourced

20

40 60 percent outsourced

Fig. 6. (A) Outsourcing and flexibility main analysis. (B) Outsourcing and SNFLEX secondary analysis.

flexibility. The correlation between outsourcing and flexibility for the main analysis is −0.064. The correlation between outsourcing and SNFLEX is −0.07. Neither of these correlations suggests that outsourcing is being used as a substitute for flexibility. However, the relationship may not be linear, thus, we also graphed the relationship. Fig. 6A and B are visual displays of these relationships. Like the correlations, these figures suggest that outsourcing is not being used as a substitute for flexibility. Finally, none of the correlations between outsourcing and the measures of the external environment are positive, once more reinforcing the conclusion that within this data set there is no evidence that firms are now responding to increased uncertainty with increased outsourcing. These results do not rule out the possibility that other changes such as improved planning and control tools or the use of IT might explain the divergence in results. However, they do indicate that in the present data set there is no evidence to suggest that firms that respond to uncertain environments through either flexibility or outsourcing will have increased performance.

7. Discussion The goal of this research was to reach a more definitive conclusion as to the relationships among the constructs of uncertainty, flexibility and performance. Specifically, we set out to replicate two previous studies that came to different conclusions. Our design attempted to alleviate some of the limitations of the previous research. Our results substantiate the findings of Pagell and Krause (1999), which run counter to the results reported by Swamidass and Newell (1987). This result suggests that Swamidass and Newell’s model may no longer be valid due to changes in the business climate since 1987. In the replication process, we were concerned about the measure of the environment used in both works. Therefore, we performed tests with both the Duncan measure used in the previous works and the three objective measures of the environment taken from the literature. The choice of measure for the environment did not affect the results. By using two different

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operationalizations of the environment we have reduced the possibility that the results are due to the choice of environmental measure. We also wanted to know if the methodology used to test the theory was affecting the results. Therefore, we tested all of the models using the hierarchical regression of our previous research and the path analysis used by Swamidass and Newell. Once more the choice of method did not alter the results. In sum, regardless of measurement choice, or statistical method we find no relationships among uncertainty, strategic flexibility and performance. Therefore, we must suggest, that while the results of Swamidass and Newell fit well with existing theory and are intuitive, they do not stand up to replication in today’ business climate, at least for our cross-industry sample. In contrast, our previous, counter-intuitive results do stand up to replication. We need to acknowledge the time that has passed since Swamidass and Newell published their results. In the intervening >15 years much has changed in the way business is practiced. Technology has led to new ways to make products, and new ways to manage data. Globalization has increased dramatically. Moreover, the increased strategic use of supply chain management practices, such as outsourcing, has dramatically changed the way firms create their products. In sum, our results suggest that the results of Swamidass and Newell may indeed have been valid for directing practice in 1987, but the business climate has changed and our prescriptions must change with it.

8. Conclusion Our primary contribution is that we find no evidence to support a theory that has gained a large amount of acceptance in the field. Because our results replicate previous results, they should provide an impetus to start building new theories on the relationship between the environment, flexibility and performance. Because the present work was done for theory confirmation purposes, what follows is presented to direct future research and not as definitive conclusions. First, we would note that the generalizability of Swamidass and Newell’s results was always limited due to the fact that their research focused exclusively on the machine tool industry. The present study did

not attempt to replicate these results solely in the machine tool industry because the objective measures of the external environment would not allow such a test (there would be no variance among firms within any one industry on these measures). It is possible that in the machine tool industry, the proper response to uncertainty is increased flexibility, which, in turn, affects performance. However, our results suggest that Swamidass and Newell’s results are not generalizable to other industries. Therefore, we suggest that future research on the relationship between these constructs could focus on single industries, or groups of related industries to determine if there are industry-specific factors (or perhaps product or process factors) that determine the importance of flexibility as a response to increased uncertainty. Similarly, the results of Badri et al. (2000) were based on a sample of small firms in a developing country. Perhaps the nature of the economy in which a plant is operated is an important contingency. Related to the above is the possibility that the models proposed by Swamidass and Newell and by our 1999 research, with direct linkages between uncertainty and flexibility, are incomplete. For instance, Ward and Duray (2000) conceptualize a model where the environment drives the choice of business strategy, which then drives the manufacturing strategy. In other words, business strategy may mediate the relationship between the environment and manufacturing strategy. Ward and Duray find limited support for this model, although they only examine the environment along one dimension (dynamism, which is a measure of the rate of product and process change). In addition, they did not find any relationship between the choice of manufacturing strategy and performance. More complete testing of such a model, along all dimensions of the environment would also shed more light on the relationship between uncertainty, flexibility and performance. Finally, Pagell et al. (2000) propose a model where firms can respond to uncertainty in a number of ways. In addition, they (and others, such as Flynn and Flynn, 2000) note that uncertainty can be created and reduced within an organization or supply chain. Therefore, some efforts at internal uncertainty reduction, such as JIT, may mitigate the effects of external uncertainty, while other activities such as the building of buffers may actually magnify the effects of uncertainty. In

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any case, this literature suggests that the relationships between uncertainty, flexibility and performance are much more complex than the traditional formulation found in much of the literature. In sum, there is no support for these theoretical relationships as is commonly assumed in the literature. Future research will have to be directed at exploring other variables that link the external environment to the choice of manufacturing strategy, be it to pursue flexibility or any other priority, and performance. Replication is fundamental to the development of a field of study. In this research we have replicated two studies that have come to contradictory conclusions. We replicated both studies. More importantly, we extended both studies in a manner that made them more comparable. Additionally, we used a larger and more diverse sample than either of the previous studies. Moreover, the present sample had multiple respondents to reduce concerns of bias. We conclude that while the model of Swamidass and Newell (1987) is intuitive and fits with much of the existing literature in Organizational Studies, we find no support for it in our cross-industry sample of manufacturing firms. Our results suggest that the field of operations management needs to re-explore the theoretical relationship between uncertainty and flexibility, perhaps by expanding models to include variables such as industry and business strategy, and also by refining our measures of the constructs of interest.

Acknowledgements The authors wish to acknowledge the guidance provided by the Associate Editor and the anonymous reviewers. The efforts of these individuals made for a much better paper and forced us to consider issues such as outsourcing that we would never have considered if left to our own devices.

Appendix A. Objective measures of the external environment Existing objective measures were used to address the three dimensions of the external environ-

645

ment. The data to calculate the measures came from Manufacturing USA, who get their data from The United States Census of American Manufacturers as well as industry-specific sources. Industry was defined via four digit SIC codes. Therefore all plants in the same four digit SIC code should be facing the same environment and hence use the same data for these measures. Complexity was measured in the manner suggested by authors such as Boyd (1990) and Dean and Snell (1996), using the MINL formulation proposed by Schmalensee (1977). MINL is an approximation of the H index for industry concentration. Following Dean and Snell, we used four firms in each rank and assumed that the market share of all firms in the industry was equal. MINL (complexity) is measured as follows: MINL =

MIN + (a1 + a2)2 [Ni2 − 1] 3Ni

where MIN is the minimum value of H which is 1/N, a1 the average market dare of the four largest firms in the industry, a2 the average market share of the next four largest firms and Ni is the number of firms in each rank (in this case four). Munificence (growth) and instability (demand uncertainty) are both calculated by regressing the previous 5 years’ sales (for the industry) against time. Because sales data for all firms/plants in an industry is not available, we used shipments as a proxy. Specifically, the natural log of the previous 5 years’ shipment data for the industry was the dependent variable while time was the independent variable. Keats and Hitt (1988) note that when using these measures the log transformation leads to smoother results. Boyd (1990) used the actual sales/shipment data as opposed to the natural log and reported that there was no effect on his results. However, the log transformation is the most common way to operationalize these measures so that is the method we used. Munificence addresses the relative growth or decline in the industry over time. The specific measure to address munificence is the anti-log of the slope of the regression described above. In periods of growth, the slope will be positive, while in periods of decline the slope will be negative.

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Table A.1 Swamidass and Newell’s original item

Item used to create SNFLEX

New product introductions (most frequent to least frequent)

Product innovation (extremely unimportant to extremely important)

Introducing new production processes (most frequent to least frequent)

Process innovation (extremely unimportant to extremely important)

Product varieties (widest range in industry to narrowest range in industry)

Mix flexibility—the ability of a manufacturing system to effectively produce a wide variety of products (extremely unimportant to extremely important)

Product features (widest range in industry to narrowest range in industry)

Changeover flexibility—the ability of a manufacturing system to effectively handle additions and subtractions to the product mix over time (extremely unimportant to extremely important)

R&D effort (most aggressive in industry to none)

New product development time—the ability to minimize the time to make product improvements, variations to existing products, or to introduce completely new products (extremely unimportant to extremely important)

Instability is generated from the same regression. Rather than addressing the trend in sales growth or decline, the instability measure addresses the volatility in sales. The more volatile or uncertain demand is, the more difficult it is to plan production. Instability is measured as the anti-log of the standard error for the regression slope coefficient (Keats and Hitt, 1988; Boyd, 1990). When the market is not volatile, growth (or decline) will follow a predictable pattern and the standard error will be small. However, when changes in the market do not follow a predictable pattern the standard error will be large and instability will be high. A.1. Alternative measure of flexibility: SNFLEX Table A.1 details the items used by Swamidass and Newell to address flexibility as well as the proxies we used to closely recreate this measure which we

refer to as SNFLEX. While the items used are not identical, the general areas covered by the items are very similar. The SNFLEX measure has an alpha of 0.87. A.2. Alternative measure of performance: SNPERF Swamidass and Newell assessed performance by looking at managers’ perceptions of specific financials over a 5-year period. Given the pace of change in today’s environment (in both performance and managers) we felt a 3-year window was more appropriate. While the measures are not identical they do share two out of three items as well as a focus on changes in financial performance as compared to competitors over time. Table A.2 details the items used by Swamidass and Newell as well as the proxies we used to recreate this measure which we refer to as SNPERF. The alpha for SNPERF is 0.92.

Table A.2 Swamidass and Newell’s original Item (all items scored from below average to above average compared to industry average)

Item used to create SNPERF (all items scored from far worse than competitors to far better than competitors)

Average annual rate of growth in return on total assets during 1977–1981

Average annual rate of growth in return on total assets over the past 3 years

Annual rate of growth in sales 1977–1981

Average annual sales growth over the past 3 years

Annual rate of growth in return on sales during 1977–1981

Average annual market share growth over the past 3 years

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Appendix B. Testing the contingency theory using path analysis

Model Main analysis High uncertainty

Measure of environment

Path 1

R2 flexibility

Duncan Instability Munificence Complexity

0.10 −0.09 −0.1 0.03

0.01 0.008 0.01 0.000

R2 performance

Fit of model

0.20 0.13 0.22∗ 0.26∗

0.04 0.0179 0.05 0.07

No fit No fit Marginal fit Marginal fit

0.11 0.19 0.145∗ 0.03

0.012 0.04 0.02 0.017

Fit No fit No fit No fit

Path 2

Duncan Instability Munificence Complexity

0.42∗ 0.005 0.03 0.19

0.17 0.000 0.000 0.04

SNFLEX and SNPERF High uncertainty Duncan Instability Munificence Complexity

0.14 −0.22∗ −0.21∗ 0.15

0.019 0.05 0.04 0.02

0.17 −0.017 0.078 0.004

0.028 0.000 0.006 0.000

No fit No fit Marginal fit Marginal fit

0.093 0.000 0.037 0.022

0.07 0.22 0.096 0.18

0.005 0.047 0.009 0.034

Marginal fit No fit No fit No fit

Low uncertainty

Low uncertainty

∗

Duncan Instability Munificence Complexity

0.305∗ 0.006 0.19 0.146

P < 0.05.

Fig. B.1. Perceived uncertainty and flexibility—using SNFLEX and SNPERF.

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Fig. B.2. Objective measures of the external environment and flexibility—using SNFLEX and SNPERF.

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