Academic journal article Journal of Small Business Management

Competing Effectively: Environmental Scanning, Competitive Strategy, and Organizational Performance in Small Manufacturing Firms

Academic journal article Journal of Small Business Management

Competing Effectively: Environmental Scanning, Competitive Strategy, and Organizational Performance in Small Manufacturing Firms

Article excerpt

Environmental scanning is generally viewed by strategic management scholars as a prerequisite for formulating effective business strategies. Moreover, effective scanning of the environment is seen as necessary to the successful alignment of competitive strategies with environmental requirements and the achievement of outstanding performance. This study of small manufacturing firms competing in a wide variety of industries examines the effect of the frequency and scope of environmental scanning on environment-competitive strategy alignment. Results suggest that obtaining information on several aspects of specific environmental sectors (for example, customers, competitors, suppliers) facilitates alignment between some competitive strategies and environments (that is, industry life cycle stages) whereas the frequency of scanning has no effect on such alignments.

Superior firm performance is a major objective of all the stakeholders of a firm. Strategists and strategic management scholars generally agree that both large and small firms that align their competitive strategies with the requirements of their environment outperform firms that fail to achieve such alignment (Chaganti, Chaganti, and Mahajan 1989; Venkatraman and Prescott 1990). Environmental scanning is widely viewed as the first step in the process linking strategy and environment (Hambrick 1982; Daft, Sormunen, and Parks 1988). The underlying premise is that scanning the task and general environment allows a firm to learn about (1) opportunities that it may be positioned to take advantage of and (2) conditions or events that threaten its performance or survival (Bourgeois 1980; Lang, Calatone, and Gudmundson 1997), thus enabling the firm to formulate a competitive strategy congruent with critical environmental conditions.

Although research on environment-strategy alignment is extensive (Anderson and Zeithaml 1984; Miller and Toulouse 1986; Zahra 1993), tests of the hypothesis remain inconclusive due to several theoretical and methodological issues. Moreover, only a few empirical studies have examined relationships between Porter's (1980) generic competitive strategies and environmental scanning (Jennings and Lumpkin 1992; Tyler, Bettenhaus, and Daft 1989; Yasai-Ardekani and Nystrom 1993), and none of them focused exclusively on small businesses. Further, despite apparent linkages between the environment, competitive strategy, environmental scanning, and firm performance, no studies were found that examined probable interrelationships. This study attempts to address these gaps in the literature by delineating and empirically testing a theory-based model of factors that affect firm performance (specifically, environment/competitive strategy alignment and environmental scanning). The model is particularly applicable to small bus inesses, as it recognizes the central, often dominant, role that the CEO (or entrepreneur) plays in small firms.

Integrative Model and Supporting Research

This study is part of a larger study in which an integrative model was developed to test contingency theories that link environmental conditions (industry life cycle stages), competitive strategy (a modified version of Porter's generic strategy framework), CEO characteristics (functional experience), scanning (frequency and scope of scanning), and organizational performance. The integrated alignment model shown in Figure 1 incorporates: (1) external alignment (alignment between competitive strategy and an industry life cycle stage and its effect on performance); (2) internal alignment (alignment between competitive strategy and CEO functional experience and its effect on performance); and (3) the impact of frequency and scope of scanning on external alignment.

The major theme of the model is that both external and internal alignment influence a firm's performance. The second theme, the focus of this study, is that successful environment-competitive strategy alignment is significantly impacted by the scanning behavior of the CEO (or entrepreneur). Correspondingly, the research question of interest is: What relationship is there between the CEO's frequency and scope of scanning and the alignment of the firm's competitive strategy with the stage of the industry life cycle in which it competes? Thus, discussion of research supporting the model is limited to studies examining external alignment and environmental scanning.

Studies on environment-competitive strategy alignment have relied on several different conceptualizations of environments, including: environmental dynamism (Miller and Toulouse 1986); environmental uncertainty (Miller 1988); environmental munificence (Yasai-Ardekani 1989); and stages of industry life cycle (Anderson and Zeithaml 1984; Venkatraman and Prescott 1990; Miller and Dess 1993). These studies have also adopted different approaches to conceptualization and measurement of competitive strategies. Some of the studies have relied on multiple indices drawn from the PIMS database to operationalize competitive strategies (Anderson and Zeithaml 1984; Hambrick, MacMillan and Day 1982; Miller and Dess 1993). Others have adopted Porter's (1980) or Miles and Snow's (1978) typologies as operational measures of competitive strategies (Miller 1988).

Almost two decades ago, Hofer (1975) suggested that industry life cycle (ILC) was the most fundamental variable in determining an appropriate competitive strategy. Hofer also advanced several propositions on the effects of ILC on strategyperformance relationships. Hambrick and Lei (1985) further demonstrated empirically the importance of the ILC as a key environmental contingency.

Despite the importance of industry life cycle to the choice of competitive strategies (Hofer 1975; Porter 1980), little empirical research has centered on this critical contingency. The few studies that have focused on ILC have not examined the strategy-performance linkages across all stages of the ILC (Anderson and Zeithaml 1984; Hambrick, MacMillan, and Day 1982; Miller and Dess 1993). Moreover, these studies have primarily relied on Profit Impact of Market Strategies (PIMS)-based indices for operationalization of competitive strategies and multiple regression as the method of analysis. This approach does not permit a holistic view of competitive strategies (Venkatraman and Prescott 1990).

This study examines competitive strategy-performance linkages across the four stages of the ILC-introduction, growth, maturity, and decline. Second, it relies on a holistic approach to the operationalization of competitive strategy and the strategy typologies suggested by Porter (1980) and Mintzberg (1988). Third, the study moves beyond the examination of single generic strategies to assess the performance implications of hybrid competitive strategies (Miller and Dess 1993).

Most empirical research on environmental scanning has focused on relationships between scanning behaviors (frequency, scope, sources used, and interest) and environmental conditions such as environmental uncertainty, perceived threats and perceived opportunities (Daft, Sormunen, and Parks 1988; Lang, Calatone, and Gudmundson 1997; Sawyer 1983; Tyler, Bettenhausen, and Daft 1989). Only three studies were found that investigated relationships between competitive strategies and environmental scanning, the primary focus of this article (Jennings and Lumpkin 1992; Tyler, Bettenhausen, and Daft 1989; YasaiArdekani and Nystrom 1993).

Jennings and Lumpkin (1992) argued that the types of information that CEOs seek differ according to their firm's competitive strategies. This implies that strategy can determine scanning behavior as well as be affected by it. This perspective deviated from the traditional view posited by Design School proponents that environmental scanning and analysis are determinants of strategy rather than the products of it (Mintzberg 1990). Jennings and Lumpkin found support for their hypotheses that (1) firms following a differentiation strategy scanned their environments in search of opportunities; and (2) firms following a low cost strategy looked for threats to their survival. However, because the study included firms in only one industry (savings and loans), the generalizability of the results is limited.

In their investigation of the relationship between different environmental conditions and the usage of different types of information sources by executives in formulating competitive strategy, Tyler, Bettenhausen, and Daft (1989) found that: (1) high and low rich information sources were used less under highly changing, unpredictable environmental conditions than under stable, predictable conditions; and (2) low rich information sources (income statements, memos, or letters) were used more than high rich sources (face-to face discussions with coworkers, customers, or suppliers) under stable, predictable conditions. They also found that the executives in their 28-firm sample used more high rich information sources in formulating differentiation strategies than in formulating low cost strategies. These results suggest that environmental conditions affect the type of sources (low rich versus high rich) used by executives in selecting a competitive strategy (that is, low cost leadership or differentiation). Howe ver, the findings and implications of this study should be viewed with caution due to: (1) deficiencies in the operationalization of both Porter's (1980) generic strategies and the environmental conditions of stability and unpredictability--only two variables were used to measure the multidimensional constructs competitive strategy and environmental conditions; (2) the small sample size (28 firms); and (3) the lack of descriptions of the sampled firms or of their industries.

In a comprehensive study of the scanning systems of 179 small (50 employees) to large (more than 200,000 employees) manufacturing and service firms, among the relationships that Yasai-Ardekani and Nystrom (1993) examined was that between firms pursuing low cost leadership and the scope and frequency with which they scanned their environments. Results indicated that firms with effective scanning systems pursuing low cost leadership scanned their environments more frequently and more broadly than those firms with ineffective scanning systems pursuing the same competitive strategy. Furthermore, the findings suggest that firms employing effective scanning systems achieve alignment between strategy and environment. In addition, other findings indicated that organizational size was not a determinant of the effectiveness of scanning systems. That is, small as well as medium-sized and large organizations were able to develop effective scanning systems. Thus, some of Yasai-Ardekani and Nystrom's (1993) findings appea r relevant to this study of small manufacturing companies.


Frequency and scope are two important features of the scanning process (Fahr, Hoffman, and Hegarty 1984). Frequency refers to how often a firm scans its environment and is associated with the timeliness, relevancy, and amount of information that firms are able to obtain about various sectors (for example, customers, suppliers, and competitors) of their task environments (Daft et al 1988). Scope indicates the number of different environmental sectors monitored by a firm. Scanning environmental sectors informs a firm of events and trends affecting its survival and prosperity. For example, rivals' competitive actions (new product introductions, price changes), customer demands, desires and buying habits, technological advances, and economic developments all require adaptive responses by the firm.

The scope and frequency of environmental scanning will affect the firm's ability to align its competitive strategy with its environment (Yasai-Ardekani and Nystrom 1993). Frequent scanning of environmental sectors provides the firm with current information and allows it to verify the accuracy of the information and to adapt to changing environmental conditions more rapidly than does infrequent scanning. Frequent scanning also positions the firm to stay abreast of environmental events and trends that threaten its existence or offer opportunities to exploit. Small firms are particularly vulnerable to rapidly developing major threats because they often lack the financial resources to withstand them. Thus, our first hypothesis is:

[H.sub.1]: Frequent scanning of the environment will be positively related to environment/competitive strategy alignment.

Experience shows that opportunities or threats can arise from many different sources (Jackson and Dutton 1988). Thus, obtaining information about several different sectors furnishes the CEO with more relevant information in aligning the firm's competitive strategy with environmental conditions (Nutt 1984). For example, obtaining and analyzing information on competitors' lowering or raising its product prices may enable a firm to formulate and implement strategic actions to maintain current customers or secure additional ones. Moreover, in investigating rivals' manufacturing processes, a firm may learn of new and improved methods and processes that allow it to remain competitive. Thus, securing information across several environmental sectors may enable a firm to gain competitive advantage or maintain its market position. Thus:

H2: Broad scope of scanning will be positively related to environment/competitive strategy alignment.


Although there is no generally accepted definition of a small business, the Small Business Administration's (SBA) definition is the most widely used one. A firm that is independently owned and operated and not dominant in its market is classified as a small business by the SBA. Revenues and number of employees are also used by the SBA in identifying small businesses. In the case of manufacturing companies, those with fewer than 500 employees are considered small.

A random sample of 500 manufacturing firms that satisfied the SBA definition of small business was obtained from the directory of manufacturers in a Midwestern state. These firms were independently owned and operated, not dominant in their markets, and employed fewer than 500 persons. Data were gathered by means of a mail survey that the CEOs of each firm were asked to complete. Completed questionnaires were returned by 101 CEOs, a response rate of 20 percent. In the vast majority of the sample firms, the CEO was either the sole owner (29 percent) or the principal owner of the firm (49.5 percent), and was the primary strategist.

The participating organizations ranged in size from 4 to 480 employees (median employment level of 55; mean of 94; and standard deviation of 11). Their revenue ranged from $250,000 to $219,000,000 (median=$5,000,000; mean=$15,814,200; and standard deviation=$34,664,900). The median age of the firms was 40 years (mean=45.86; standard deviation=29.5). The sample organizations belong to six different industrial categories: consumer durables, consumer non-durables, capital goods, industrial sub-components, industrial supplies, and raw or semi-finished materials.

The survey instrument used in the study was pilot-tested using five small Midwestern manufacturing companies. Modifications were made to the questions wherever necessary to increase the clarity of the survey instrument. None of the firms that participated in the pre-test were included in the sample.

The sample methods of this study do have the potential for common method bias and, more specifically, for single source bias, as the CEOs surveyed were the primary source of information on several constructs (competitive strategy, environmental conditions, scanning, and firm performance). However, as Phillips (1981) points out, several factors including "situational factors, individual differences, or implicit theories can contribute to variation or covariation in ratings" across several different but related constructs. Thus, without fully understanding how all these factors affect ratings, it cannot be concluded that ratings by a single source are "unequivocally biased" (Phillips 1981, p.412).

Measurement of Competitive Strategy

Twenty-three items were used to measure the five dimensions of competitive strategy. Twelve of these items were based on the operationalization by Dess and Davis (1984) and Miller (1988) of Porter's (1980) generic competitive strategies. These items were complemented with a set of eleven additional items to represent a multi-dimensional view of differentiation-based strategies as suggested by Miller (1988) and Mintzberg (1988). Respondents were asked to indicate the extent to which their firms emphasized each of the 23 competitive methods in the past three years. Data were recorded using five-point scales that ranged from 1=no emphasis to 5=major and constant emphasis.

Although the self-typing of business strategy by key informants (CEOs) has been challenged (Venkatraman and Grant 1986), findings of recent empirical studies examining the self-typing of business-level strategy by CEOs suggest that the methodology is valid (Shortell and Zajac 1990; James and Hatten 1995).

Principal component factor analysis was used to delineate the competitive strategy dimensions. This analysis resulted in a five-factor solution accounting for 64.5 percent of the variance that corresponded with our a priori expectation. Each of the five factors had eigenvalues greater than one. The result of the principal component factor analysis after varimax rotation is shown in Table 1.

Based on the sample size (101 firms) and a minimum significant correlation coefficient of p[less than].05, only variables with factor loadings of at least 0.4 can be used in interpreting a set of factors (Gorsuch 1983). Thus, only factor loadings of 0.4 and above are shown in Table 1.

As can be seen from Table 1, the pattern of loadings suggests that the five-factor solution represents a low-cost leadership dimension (Factor 3) and four distinct differentiation dimensions of: innovation differentiation (Factor 1); marketing differentiation (Factor 2); quality differentiation (Factor 4); and service differentiation (Factor 5). It should be noted that the highest loadings of variables within each cluster are nearly uniform. Thus composite measures representing each competitive strategy dimension are constructed as an average of the scores on the variables with highest loadings on each factor.

Innovation differentiation (alpba=0.86). Innovation differentiation (ID) involves the production and marketing of new products with unique features or performance characteristics. The six competitive methods that load on this factor are: research and development of new products; marketing of new products; selling high-priced products; obtaining patents/copyrights on new products; innovative marketing techniques; and improving sales force performance. This last measure was not included in the composite measure of innovation differentiation as it also had a high loading on the service differentiation factor and its inclusion did not improve the reliability coefficient alpha.

Marketing differentiation (alpha=0.80). Marketing differentiators (MD) create perceptions in the minds of targeted customers that the firm's products are distinctively different from those of their competitors. The five competitive methods that load on this factor are: building brand or company identification; advertising and promotional programs; securing reliable outlets for distributing products; improvement of existing products; and producing a broad range of products.

Low cost leadership (alpha=0.84). Firms pursuing low cost leadership (LC) seek to secure a low-cost position within their markets. The following five items load highly on this factor: efficiency and productivity improvements; development of new manufacturing processes; improvement of existing manufacturing processes; reducing costs throughout the firm; and reducing manufacturing costs primarily.

Quality differentiation (alpha=0.78). Emphasis on superiority in reliability and durability is the hallmark of quality differentiators (QD). The five competitive methods that have high loadings on this factor are: strict product quality control techniques; benchmarking best manufacturing processes in the industry; benchmarking best manufacturing processes in any industry; immediate resolution of customer problems; and product improvements based on detailed assessments of gaps in meeting customer expectations.

Service differentiation (alpha=0.71). Service differentiators (SD) distinguish the firm from its competitors by emphasizing customer services before, during, and after purchase. The three competitive methods loading on this factor are: development of new customer services; improvement of existing customer services; and improving sales force performance. The last item was not included in the composite measure of service differentiation as it also loaded highly on the innovation differentiation factor and its inclusion did not improve the reliability coefficient alpha.

The measures of combination or hybrid competitive strategy dimensions were constructed as an average of the two competitive strategies that make up the hybrid strategy of interest. The coefficient alpha for each hybrid competitive strategy was above 0.79, indicating high levels of reliability.

Measurement of Environment (Industry Life Cycle Stages)

Industry life cycle is a multidimensional concept. In searching the literature, we were unable to find a well developed multiple-item index for industry life cycle stages. Therefore, we constructed a multi-item index based on the perceptions of CEOs or top managers. The index combined eight different variables proffered by several scholars (Grant 1991; Onkvisit and Shaw 1989; Porter 1980) to characterize the different industry life cycle stages: (1) growth in the industry's sales during the past five years; (2) level of demand for the industry's products; (3) stage of development of the industry's products; (4) level of diffusion of information about the industry's products; (5) plant capacity of industry's firms over the past five years; (6) current price level of the industry's products; (7) growth in the different types of distribution channels for the industry's products over the past three years; and (8) level of the industry's advertising expenditures over the past three years. For each variable, respo ndents were asked to use a four-point scale to indicate which one of four possible conditions best described their industry's performance. The four conditions varied with each variable and corresponded to conditions that characterized the variable in each of the four life cycle stages. For example, the conditions for the variable assessing industry growth rate over the past five years were: (1) increased slowly; (2) increased rapidly; (3) stabilized; and (4) declined.

The author of the present study and a colleague, an expert in strategic management, determined the stage of the industry life cycle to which each sampled firm was to be assigned. An industry life cycle stage was assigned to each of the 101 firms based upon our independent analyses of the CEOs' responses; then through discussions we resolved differences in assignments. The rate of growth in industry sales was used as the key variable in making assignments. Responses on this variable suggested the following assignments: "increased slowly"--introduction or maturity stage; "increased rapidly"--growth stage; "stabilized"--maturity stage; and "declined"--decline stage. An "increased slowly" response suggested that a firm could be competing in either a new or mature industry. To determine the appropriate stage assignment for such a firm, the CEO's responses to the other seven items were examined. The firm was then assigned to the stage best characterized by the pattern of responses. Fifty-four firms were assigned t o the maturity stage, 20 firms to the introductory stage, 17 firms to the growth stage, seven firms to the decline stage; and four firms were unassigned because respondents failed to provide critical data.

Measurement of Scanning

The two most frequently used measures in constructing scanning indices are variables measuring how frequently managers monitor their environments (frequency of scanning) and how broadly managers scan their environments (scope of scanning). Initially, six scanning indices based on CEOs' scope of scanning and six indices based on the frequency of CEOs' scanning were developed. The indices related to five sectors of the task environment (competitors, customers, suppliers, technology, and the firm itself) and several factors of the general environment (economic, social, and political conditions).

Scope-of-Scanning Indices

The six a priori scope-of-scanning indices contained 28 items that asked respondents (the CEOs of the 101 small manufacturing companies) to answer "Yes" or "No" as to whether they used certain types of information in determining how to compete in major markets. The indices were constructed by totaling the number of information types used by the CEO. For example, the CEO of Firm #1 indicated that he used seven of the eight types of information listed for the competitor index. Thus, Firm #1's score on this index was seven, the sum of the number of items selected. This method is similar to that used by Yasai-Ardekani and Nystrom (1993) in their development of a composite measure of scanning scope.

The 28 items were factor analyzed to determine whether the data supported the six a priori scope-of-scanning indices. The Kaiser-Meyer-Olkin (KMO) measure of sample adequacy indicated that the 28-item sample was not adequate for factor analysis (KMO measure 0.51). A KMO of less than 0.60 is considered inadequate (Kaiser 1974). The data were then divided into two sets: (1) data associated with sectors of the task environment and firm resources/capabilities (20 items), and (2) data associated with the general environment (8 items). Factor analysis with varimax rotation of the first data set produced three factors accounting for 66.3 percent of the variance; analysis of the second data set produced two factors accounting for 64.4 percent of the variance of the items. Each of the five factors had eigenvalues greater than one. in addition, KMO values for the individual variables indicated that four of the 28 variables should not be included in interpreting the factors. The deleted items dealt with information abo ut (1) competitors' product improvements; (2) competitors' improvements in manufacturing processes; (3) availability of raw materials; and (4) the company's manufacturing capabilities and resources. Tables 2 and 3 contain the results of the factor analyses. Based on the sample size (n=101 firms) and p[less than].05 level of significance, only factor loadings of 0.40 are shown in Tables 2 and 3.

As shown in Table 2, the pattern of factor loadings suggests that the three factors may be interp reted as information related to three major aspects of the task environment: (1) competitors and customers (Factor 1); (2) internal factors such as the company's resources and capabilities (Factor 2); and (3) suppliers of labor and funds (Factor 3). Composite measures representing each scope-of-scanning factor were computed by summing the scores of the variables with loadings exceeding 0.40 on the factor.

Customer and competitor information (alpha=.78). Information about entities of the task environment that load on this factor are: competitors' prices; competitors' introduction of new products; competitors' advertising and promotional programs; competitors' entry into new markets; customers' buying habits; customers' product preferences; customers' demands and desires; and new product technology.

Supplier information (alpba=.66). Information about suppliers of capital and labor that load on this factor are: availability of external financing and availability of labor

Company (internal) capabilities and resources information (alpba=.81). The five variables loading on this factor are: company's manufacturing capabilities and resources; company's advertising and promotion capabilities/resources; company's sales capabilities/resources; company's financial capabilities/resources; and company's management capabilities/resources.

The pattern of results reflected in Table 3 suggests that the two-factor solution represents information about social and political conditions and information on economic conditions. As before, composite measures representing each scope of scanning factor were computed by summing the scores of the variables with loadings exceeding 0.40 on the factor with the highest loading.

Social and political information (alpba=.79). Variables that load on this factor include information on local social conditions, national social conditions, local political conditions, global political conditions, and global economic conditions.

Economic information (alpha=.79). The variables that load on this factor are: local economic conditions, national economic conditions, and national political conditions.

Frequency-of-Scanning Indices

The six a priori indices designed to measure CEOs' frequency of scanning contained 28 items that asked the CEOs of the 101 small manufacturing companies to indicate on a five-point Likert scale, ranging from "Seldom" to "Continuously," how frequently they sought information used to determine how their firms competed.

The same approach used in constructing the competitive strategy and scope-of-scanning indices was used in attempting to construct the frequency-of-scanning indices. However, a series of factor analyses failed to produce interpretable factors. On the other hand, item-analysis of the data supported the a priori indexes with minor modifications. The six a priori scanning-frequency indices represented information on (1) competitors, (2) customers, (3) suppliers of labor, raw materials, and financing, (4) firm's resources and capabilities, (5) technology, and (6) social, political, and economic conditions. One original variable was deleted from the two frequency-of-scanning indices--information on competitors and information about social, political, and economic conditions. Shown below are the six frequency-of-scanning indices, the variables constituting each index, and the reliability (Cronbach alpha) of each index.

Competitor Information (alpha=.82). The five variables comprising this scanning frequency index are: competitors' prices; competitors' introduction of new products; competitors' product improvements; competitors' entry into new markets; and competitors' improvements in manufacturing processes.

Customer Information (alpha=.82). Constituting this frequency-of-scanning index are three variables: customers' buying habits; customers' product preferences; and customers desires and demands.

Supplier Information(alpha=.74). The three variables included in this scanning frequency index are: availability of raw materials or components; availability of external financing; and availability of labor.

Company (Internal) Information (alpha=.85). This frequency-of-scanning index includes six variables: company's manufacturing capabilities/resources; company's R&D capabilities/resources; company's advertising/promotion capabilities/resources; company's sales capabilities/resources; company's financial capabilities/resources; and company's management capabilities/resources.

Technology Information (alpha=.84). The two variables comprising this frequency of scanning index are: new manufacturing technology and new product technologies.

Social, Political, and Economic Information (alpha=.87). This frequency-of-scanning index includes eight variables: local social conditions; national social conditions; local economic conditions; national economic conditions; global economic conditions; local political conditions; national political conditions; and global political conditions.

Measurement of Firm Performance

Although firm performance plays a key role in strategy research, there is considerable debate on the appropriateness of various approaches to the concept-ualization and measurement of organizational performance (Venkatraman and Ramanujam 1986). The complexity of performance is perhaps the major factor contributing to the debate. Despite such debate, there is general agreement among organization scholars that objective measures of performance are preferable to those based on manager's perceptions. However, objective data on the performance of small firms is usually not available because most small firms are privately held and the owners are neither required by law to publish financial results nor are they usually willing to reveal such information voluntarily to outsiders (Dess and Robinson 1984). Furthermore, when financial statements are available, they may be inaccurate because they are usually unaudited (Sapienza, Smith, and Gannon 1988). On the other hand, CEOs or owners of small firms are inclined to pr ovide subjective evaluations of their firms' performance (Sapienza, Smith, and Gannon 1988).

The study thus relies on perceptual measures of organizational performance. In particular, the approach to measuring financial performance by Naman and Slevin (1993) is adopted. The more popular subjective measure of small firm performance developed by Dess and Robinson (1984) was not used in this study because Sapienza, Smith, and Gannon (1988) were unable to replicate Dess and Robinson's results.

Respondents were asked to indicate on five-point scales, ranging from 1=very unimportant to 5=very important, the degree of importance they attached to each of six financial performance indicators. Included were measures of profitability (return on sales, return on investment, and return on assets) and growth (growth of sales and growth of profits), and total amount of profits. The latter measure was included because for many CEOs of small firms who derive the majority of their income from their businesses, the actual amount of profit is an important indicator of financial performance of their firms. The respondents were further asked to indicate the extent of their satisfaction with their firms' performance along each of the six performance indicators. The five-point scales used for this measurement range from (1) very dissatisfied to (5) very satisfied. The six satisfaction scores were then multiplied by their respective importance ratings. The resulting six scales were averaged to construct a composite me asure of firm performance. This composite measure reflects an aggregate view of performance based on the level of GEOs' satisfaction with their firms' performance along each of the six financial performance criteria weighted by their respective importance to their firms.

Hypothesis Testing

The purpose of this study is to examine the impact of environmental scanning on external alignment in the same sampling frame. Two hypotheses, [H.sub.1] and [H.sub.2], are presented: [H.sub.1]: Frequent scanning will be positively related to environment/competitive strategy alignment; [H.sub.2] Broad scope of scanning will be positively related to environment strategy alignment. In the larger study, eight environment/competitive strategy alignment hypotheses were presented. Of the eight hypotheses, five received strong support. In this study, the impact of scanning frequency and scanning scope on the five environment/competitive strategy alignments of the supported hypotheses is examined. Listed below are the external environment/competitive strategy alignments of focus.

ID.2: In the growth stage (2), increasing emphasis on the strategy of innovation differentiation (ID) will lead to higher levels of performance.

QD.2: In the growth stage (2), increasing emphasis on the strategy of quality differentiation (QD) will lead to higher levels of performance.

IDQD.2: In the growth stage (2), increasing emphasis on the combination strategy of innovation differentiation and quality differentiation (IDQD) will lead to higher levels of performance.

IDMD.2: In the growth stage (2), increasing emphasis on the combination strategy of innovation differentiation and marketing differentiation (IDMD) will lead to higher levels of performance.

LC.3: In the maturity stage (3), increasing emphasis on the strategy of low cost leadership (LC) will lead to higher levels of performance.

LCQD.3: In the maturity stage (3), increasing emphasis on the combination strategy of low cost leadership and quality differentiation (LCQD) will lead to higher levels of performance.

LCSD.3: In the maturity stage (3), increasing emphasis on the combination strategy of low cost leadership and service differentiation (LCSD) will lead to higher levels of performance.

To test the hypotheses, the sample of 101 small manufacturing firms was divided into two groups--scanners and non-scanners. These two groups can also be studied along two dimensions--frequency of scanning and scope of scanning. Thus, those small manufacturing firms whose CEOs frequently seek information regarding situations or events in an environmental sector (competitors, for example) are classified as scanners, whereas the non-scanners are those firms whose CEOs scan an environmental sector infrequently. For the second dimension, those firms whose CEOs seek and use information regarding numerous events or situations in an environmental sector (customers, for example) are scanners, whereas non-scanners seek little or no information. The mean value of the applicable scanning index was the statistic used to divide the sample into scanners and non-scanners. Firms scoring above the mean were classified as scanners, those below the mean as non-scanners.

The statistical methods used in testing the hypotheses were analysis of variance (ANOVA) and t-tests. ANOVA was used to partition the sample into scanners and non-scanners on each scanning index across the growth and maturity stages of the industry life cycle. (Growth and maturity were the two stages at which competitive strategies were found to align with environmental requirements, that is, alignments were found between these stages and competitive strategies.) In the ANOVA models, then, industry life cycle stages and scanning (scanners and non-scanners) were the independent variables, and competitive strategy was the dependent variable. Scanning was viewed as mediating the relationship between stage in the industry life cycle and competitive strategy. The implication is that scanning must be present for environment (industry life cycle stage) to affect competitive strategy.

T-tests were used to determine the significance of the difference in the means of scanners and non-scanners using each of the six scanning indices for each applicable life cycle stage/competitive strategy alignment. An hypothesis is supported if the difference was positive (that is, if the mean for scanners was greater than the mean for non-scanners) and statistically significant. Testing the hypotheses involved examining the effect of five frequency-of-scanning indices and five scope-of-scanning indices on seven external alignments, resulting in 70 ANOVA models and 70 t-tests. The following example illustrates the procedure for testing the hypotheses.


The external alignment hypothesis that firms in the growth stage that strongly emphasize innovation differentiation realize increasing levels of performance was supported. Taking this external alignment into consideration, one test of [H.sub.1] is to use ANOVA to partition the sample into scanners and non-scanners based on the growth stage and scanning frequency index of company information (company's resources/capabilities). Then, using the mean scores for scanners and non-scanners obtained from the ANOVA model, a t-test is conducted to determine whether the difference in the means is statistically significant. A statistically significant difference suggests that frequent monitoring of the company's resources and capabilities by the CEO will contribute to the attainment of the alignment between the firm's pursuit of innovation differentiation and the stage of the industry life cycle (growth) in which it competes. Thus, [H.sub.1] is supported. On the other hand, if there is no difference in means or the diff erence is not statistically significant, then [H.sub.1] is not supported.


[H.sub.1] (frequent scanning of the environment will be positively related to environment-competitive strategy alignment) is not supported--none of the 35 t-statistics were both positive and significant. Since all the results suggest rejection of the hypothesis, the results are not shown.

Support for [H.sub.2] (scanning of multiple situations or events occurring in an environmental sector will be positively related to environment-competitive strategy alignment) is mixed. In 31 percent (11 of 35) of the cases, [H.sub.2] is supported. Of the five scope-of-scanning indices, three are positively and significantly related to alignment between competitive strategy and the stage of the industry life cycle in which the firm competes. These are: (1) customer and competitor information; (2) supplier information; and (3) company capabilities/resources information. Information on social, political, or economic conditions does not appear to be useful to CEOs of small manufacturing firms in aligning their competitive strategies with environmental conditions.

Table 4 presents the results of the ANOVAs and t-tests that support hypothesis, [H.sub.2]. The table contains for each industry life cycle stage-competitive strategy alignment case that was supported the (1) means of scanners and non-scanners; (2) number of scanners and non-scanners; (3) mean square error of (MSE) of the ANOVA model; (4) standard error of the estimate (s.e.); and (5) value of the t-statistic and its statistical significance.

Discussion and Implications

Various schools of strategic management suggest that environmental scanning is of primary importance to strategy formulation and implementation (Daft, Sormunen, and Parks 1988; Lenz and Engledow 1986). In this study, environmental scanning was viewed as moderating the relationship between competitive strategy and the environment (stage of the industry life cycle). More specifically, frequent and broad scanning should be positively related to an alignment between an industry life cycle stage and a competitive strategy. While a positive, significant relationship does not connote a causal relationship between scanning behavior and environment-strategy alignment, it does indicate a strong association between them. Two hypotheses were offered in support of this thesis. However, only mixed support was found for the hypothesis involving scope of scanning, and no support was found for the hypothesis involving frequency of scanning.

Obtaining information across several different environmental sectors appears to be strongly associated with pursuing (1) several effective competitive strategies in the growth stage of industry development, and (2) several effective strategies involving low cost leadership in the maturity stage. However, it appears that broad scanning of information dealing with customers and competitors appears to be more associated with aligning competitive strategy with industry life cycle stage than does the scanning of other sectors. In all seven cases of industry life cycle-competitive strategy alignment found in this study, obtaining sundry information on competitors (for example, prices or new products introduced) and customers (for example, buying habits and product preferences) was associated with the alignments. These findings regarding small firms' use of information about competitors conflicts with Fann and Smeltzer's findings (1989) but are consistent with Lang, Calatone, and Gumundson's (1997) results.

In a study of 48 small construction, manufacturing, wholesale, retail, and service companies, Fann and Smeltzer (1989) found that, on the whole, small firms did not obtain nor use significant amounts of information about competitors in their long-range or operational planning. The conflicting findings may be attributable to several factors. First, Fann and Smeltzer (1989) did not investigate the performance of the firms studied as was done in this research. Second, the sample for our study is significantly broader than Fann and Smeltzer's in terms of sample size (101 firms vs. 48), the number of industries surveyed (six vs. one), and firm size (mean revenues of $15.8 million vs. $2.5 million).

Lang, Calatone, and Gudmundson (1997) examined the scanning behavior of 671 small businesses. Their research showed positive relationships between information gathering behavior and both perceived threats and perceived opportunities. That is, small business managers' information seeking increased as their perceptions of opportunities and threats increased. These findings appear consistent with this study's findings of positive relationships between environment-strategy alignment and the acquisition of information about customers (information indicative of opportunities) and information about competitors (indicative of threats). Thus, the results of these two studies suggest that managers of some small businesses do recognize the relevancy of gathering information on external environmental sectors--customers and competitors.

Sundry information regarding the company's resources and capabilities was directly related with external alignment in only two cases: (1) alignment of the maturity stage in ILC and a low cost leadership/quality differentiation combination strategy and (2) the fit between the combination of low cost leadership and service differentiation and the maturity stage. Obtaining and analyzing a variety of information on suppliers of labor and capital was strongly associated with only one external alignment--that between the maturity stage and the combination strategy of low cost leadership and quality differentiation. Information on economic, political, and social conditions does not appear to be associated with competing successfully in the mature or growth stages of industry development.

These findings appear to imply that general managers of small manufacturing firms seeking to compete effectively in growth and mature industries should obtain and analyze various types of information about competitors and customers independent of the strategy employed. On the other hand, firms pursuing either a combination of low cost leadership and quality differentiation or a combination of low cost leadership and service differentiation in mature industries should monitor and analyze information regarding their own resources and capabilities as well as diverse information on customers and competitors.

The absence of any significant, positive relationships between the various frequency of scanning indices and external alignments was totally unexpected. There are at least three plausible explanations for these results. First, the set of questions used to measure scanning frequency may lack content validity. Although constructing the frequency-of-scanning indices similar to those adopted by Hambrick (1981) and Fahr, Hoffman, and Hegarty (1984) resulted in reliable indices (Chronbach alphas ranged from 0.74 to 0.87), the indices may not have been content valid. While determining content validity is judgmental, we believe that the set of questions designed to capture CEOs' frequency of scanning constitute adequate coverage of the various environmental sectors scanned-- competitors, customers, suppliers, manufacturing and product development technology, economies (local, state, and national)--and the frequency (daily, weekly, monthly, quarterly, and annually) with which the sectors are scanned.

Second, CEOs of small manufacturing firms, constrained by their involvement in their firms' daily operations, may not have time for frequent scanning of their external environments. Consequently, environmental scanning may be relatively infrequent. Relatively infrequent scanning should be reflected in low mean values in the frequency-of-scanning indices. The means of five frequency-of-scanning indices involving elements of the task environment (competitors, customers, technology, suppliers, and the firm, itself) show quarterly-annual gathering of information about these elements. Thus, scanning of the environmental sectors that arguably have the most impact on firm performance and the formulation/implementation of competitive strategy occurs relatively infrequently. This finding provides the most plausible explanation for the non-significant relationships found between frequency of scanning and external alignment.

Third, the frequency at which CEOs of small manufacturing companies scan their environments may not be critical to aligning their firms' competitive strategies with the stage of the industry life cycle in which the firms compete. Other factors such as scope of scanning (indicated in this study), accurate assessment of opportunities and threats, and effective use of competitive information may be key.

This study contributes to the literature by deepening the understanding of the linkages between environmental scanning, environmental conditions, competitive strategy, and firm performance. A few previous studies explored the relationship between scanning activities, competitive strategy, and environmental conditions (Jennings and Lumpkin 1992; Tyler, Bettenhausen, and Daft 1989; Yasai-Ardekani and Nystrom 1993). However, none of them investigated the effect of scanning on environment-strategy alignment and organizational performance. This study is an initial attempt at filling the aforementioned gaps in the literature. In addition, the study focused on environmental scanning in small firms across a variety of environmental sectors--a focus of only one other study, Lang, Calatone, and Gudmundson (1997).

Two factors of the study--its cross-sectional approach and the type of companies sampled--are limitations. Although the cross-sectional approach provides snapshots of firms' strategies and environments, it does not capture the incremental process of strategy formulation and implementation or the achievement of external and internal alignments over time. These limitations were mitigated by requesting respondents to recall (1) strategic actions taken by their firms over the most recent three year period and (2) environmental characteristics prevailing during that period. The generalizability of the results is limited to small manufacturing firms. However, as the sample firms compete in a wide variety of industries, the results and their implications are not industry-specific.

Dr. Beal is an assistant professor at Florida A&M University in Tallahassee, FL. He has founded and managed two businesses, served as general manager of three firms, and was district manager of a major convenience store chain. He has also served as a consultant to several business organizations. His research interests include competitive strategy, CEO characteristics, entrepreneurship, small business management, and supply chain management.


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          Factor Analysis of Strategy Variables--Varimax Rotation
Variable                                  Factor Factor Factor Factor Factor
                                            One    Two  Three   Four   Five
                                           (ID)   (MD)   (LC)   (QD)   (SD)
Innovation Differentiation (ID)
  R&D of new products                      .827
  Marketing of new products                .804
  Selling high-priced proudcts             .810
  Obtaining patents or copyrights          .527
  Innovative marketing techniques          .700
Marketing Differentiation (MD)
  Building brand/company identification           .829
  Adverstising/promotional programs               .705
  Securing reliable distribution channels         .649
  Improving existing products                     .426
  Producing broad range of products               .724
Low Cost Leadership (LC)
  Improving efficiency and productivity                  .560   .469
  Developing new manufacturing
    processes                                            .700
  Improving existing manufacturing
    processes                                            .800
  Reducing overall costs                                 .776
  Reducing manufacturing costs                           .829
Quality Differentiation (QD)
  Strict product quality control                                .637
  Benchmarking best manufacturing
    processes in the industry                                   .863
  Benchmarking best manufacturing
    processes anywhere                                          .753
  Immediate resolution of customer
    problems                                                    .508
  Prouduct improvements based on gaps
    in meeting customer expectations       .467                 .587
Service Differentiation (SD)
  New Customer services                                                .608
  Improvement of existing
    customer services                                                  .833
  Improvement of sales force
    performance                            .566                        .488
Only loadings [greather than] .400 are shown.
                 Varimax Rotated Factor Analysis of Scope-
                  of-Scanning Data--Operating Environment
                                           Factor Factor Factor
Variable                                     One    Two  Three
Competitors' prices                         .628
Competitors' introduction
  of new products                           .617  -.457
Competitors' advertising/
  promotional programs                      .493
Competitors' entry
  into new markets                          .579   .431
New product technologies                    .534
Customers' buying habits                    .736
Customers' product preferences              .789
Customers' demands and desires              .570
Company's R&D capabilities
  and resources                             .445   .455
Company's advertising and
  promotions resources                      .511   .559
Company's sales capabilities/resources             .756
Company's financial capabilities/resources         .811
Company's management
  capabilities/resources                           .824
Availability of external financing                        .704
Availability of labor                                     .726
New manufacturing technologies                            .594
Only loadings [greater than] .400 are shown.
               Varimax Rotated Factor Analysis of Scope-of-
                Scanning Data--General (Remote) Environment
Variable                      Factor One Factor Two
Local social conditions          .751
National social conditions       .766
Local political conditions       .685
Global political conditions      .605       .468
Global economic conditions       .525       .499
Local economic conditions        .584
National economic conditions     .675
National political conditions    .489       .501
Only loadings [greater than] .400 are shown.
            Effect of Scope of Scanning on External Alignments
                                                 Mean     Standard
                                       Means  n Square df Error of     t
Alignment: Innovation Differentiation
and Growth Stage                                Error     Estimate
Information on customers, competitors,
and product technology
Scanners                               3.60   5
Non-Scanners                           1.96   8
                                                 .502  87   .29    5.73 [**]
Alignment: Quality Differentiation
and Growth Stage
Information on customers, competitors,
and product technology
Scanners                               3.94   5
Non-Scanners                           2.21   8
                                                 .323  87   .18    9.40 [**]
Alignment: Innovation Differentiation
and Quality Differentiation
with Growth Stage
Information on company's
resources and capabilities
Scanners                               4.12   5
Non-Scanners                           2.91   8
                                                 .333  87   .19    6.37 [**]
Alignment: Innovation Differentiation
and Marketing Differentiation
with Growth Stage
Information on customers, competitors,
and product technology
Scanners                               3.94   5
Non-Scanners                           2.21   8
                                                 .323  87   .18    9.40 [**]
Alignment: Low Cost Leadership
and Maturity Stage
Information on customers, competitors,
and product technology
Scanners                               3.37  27
Non-Scanners                           3.34  29
                                                 .568  87   .14    1.64 [*]
Alignment: Low Cost Leadership
and Quality Differentiation
with Maturity Stage
Information on customers, competitors,
and product technology
Scanners                               3.71  27
Non-Scanners                           3.32  29
                                                 .364  87   .10    4.02 [**]
Information on company's
Scanners                                           3.63 38
Non-scanners                                       3.27 20
                                                           .391 87 .11
Information on availability of
financing and labor
Scanners                                           3.63 33
Non-scanners                                       3.34 25
                                                           .390 89 .10
Alignment: Low Cost Leadership and
Service Differentiation with
Maturity Stage
Information on customers, competitors,
and product technology
Scanners                                           3.56 27
Non-scanners                                       3.04 29
                                                           .395 87 .11
Information on company's resources/capabilities
Scanners                                           3.50 38
Non-scanners                                       3.80 20
                                                           .429 89 .12
Information on availability of financing and labor
Scanners                                           3.51 33
Non-scanners                                       2.93 25
                                                           .448 89 .12
Information on company's
                                                   3.33 [**]
Information on availability of
financing and labor
                                                   2.82 [**]
Alignment: Low Cost Leadership and
Service Differentiation with
Maturity Stage
Information on customers, competitors,
and product technology
                                                   4.91 [**]
Information on company's resources/capabilities
                                                   5.93 [**]
Information on availability of financing and labor
                                                   4.92 [**]
(*.)p [less than].10
(**.)p [less than].001
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