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Divergence between informant and archival measures of the environment: Real differences, artifact, or perceptual error?
Journal of Business Research 59 (2006) 268 – 277
Divergence between informant and archival measures of the environment: Real differences, artifact, or perceptual error? D. Harold Doty a,1, Mousumi Bhattacharya b,*, Kathleen K. Wheatley c,2, Kathleen M. Sutcliffe d,3 a
College of Business, University of Southern Mississippi, 118 College Drive # 5021, Hattiesburg, MS 39406, United States b Charles F. Dolan School of Business, Fairfield University, Fairfield, CT 06824, United States c Department of Management, College of Business Administration, 615 McCallie Avenue Dept. 6156, University of Tennessee at Chattanooga, Chattanooga, TN 37403, United States d The University of Michigan Business School, 701 Tappan, Ann Arbor, MI 48109-1234, United States Received 2 December 2002; accepted 25 April 2005
Abstract Although organizational environments are a central concern for researchers in organization theory and strategy, many researchers report that informant assessments and archival measures of the environment do not converge. This study investigates whether the observed divergence can be attributed to perceptual error, to real differences in constructive definitions, to differences between a firm’s environment and an aggregate industry environment, or simply to methodological artifacts. Results show that environmental uncertainty and environmental variation are distinct multidimensional constructs even when only informant measures from the same respondents are considered, and that organizational level mediating filters are more strongly related to informant measures of environmental variation than to environmental uncertainty. However, the effects of individual level filters on environmental variation do not decrease after controlling for organizational level filters. We conclude that, although there are some real differences between informant and archival measures of the environment, the possibility of perceptual bias cannot be ruled out. D 2005 Elsevier Inc. All rights reserved. Keywords: Environment; Perceived environment; Mediating filters; Measures
Constructs associated with organizations’ environments enjoy prominence in the organization theory and strategic management literatures, and there is a general consensus that the environment is multidimensional, is related to organizational form, functioning and perform-
* Corresponding author. Tel.: +1 203 254 4000x2893; fax: +1 203 254 4105. E-mail addresses: [email protected] (D.H. Doty), [email protected] (M. Bhattacharya), [email protected] (K.K. Wheatley), [email protected] (K.M. Sutcliffe) 1 Tel.: +1 601 266 4659. 2 Tel.: +1 423 425 2104; fax: +1 423 425 4158. 3 Tel.: +1 734 764 2312; fax: +1 734 936 8716. 0148-2963/$ - see front matter D 2005 Elsevier Inc. All rights reserved. doi:10.1016/j.jbusres.2005.04.005
ance, and that managers expend a good deal of energy trying to monitor and interpret environmental conditions. Beyond this, however, the environmental literature is in a surprising state of disarray that is characterized by conflicting definitions of constructs, confusion about appropriate measurement techniques, and contradictory results. Much of the confusion in the literature arises from the apparent lack of agreement between ‘‘informant’’ and ‘‘archival’’ measures of the same environmental constructs. For example, Sharfman and Dean (1991) found only moderate correlations between their ‘‘objective’’ environmental measures and informant measures of uncertainty; Sutcliffe (1994) reports small to moderate correlations between archival and informant measures of environmental
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instability and munificence. Sharfman and Dean (1991, p. 682) summarize what is probably the most widely held position when they state that There can be little doubt that managers’ perceptions play an important role in shaping their responses to the environment. But it also appears clear that there are differences between environments that are objective (i.e., observable through Fscientifically rigorous measurement procedures_, Dess and Beard, 1984, p. 53) as well as intersubjective (i.e., perceived similarly by most observers). At least two general approaches have emerged to deal with the problems caused by the divergence between informant and archival measures of the environment. The first is to ignore the potential problems associated with this divergence and include only one type of measure in the study. For example, Milliken (1990) and Doty et al. (1993) have relied exclusively on informant measures, while Wholey and Brittain (1989) and Keats and Hitt (1988) have used only archival measures of environment. The second approach is to address directly the causes and/or consequences of divergence between archival and informant measures of environmental constructs. For instance, Boyd et al. (1993) specified a set of mediating filters, including individual and organizational factors, to explain divergence between archival and informant measures; Sutcliffe (1994) demonstrated that divergence between informant and archival measures of environment can be predicted by characteristics of the top management team and organizational structure. We are concerned that these studies suffer from a common limitation—the assumption that the observed divergence between informant and archival measures of the environment is caused by perceptual error on the part of the informant. One alternative assumption is that this divergence reflects real differences between aggregate measures of an industry environment and the task environment that a firm actually faces. For example, Elenkov (1997) found that uncertainty scores differ significantly among various sectors of the environment. A second alternative is that this divergence exists because informant and archival assessments of the environment differ at both theoretical and measurement levels (Boyd et al., 1993). For example, Ebrahimi (2000) showed that executives scanned the task environment more intensely than the remote environment. We contend that divergence between archival and informant measures of the environment is based on factors other than perceptual error on the part of managers because informant measures of the environment are distinct both in terms of constructive definition and operational measurements. We also examine whether the observed divergence between informant and archival measures of the environment can be explained by organizational or individual level mediating filters so that this potential source of bias can be controlled in future research.
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1. Explanations for the observed divergence The main concern over the observed divergence is that this appears to be systematic variation rather than random error (Doty and Glick, 1998) and, generally, it is assumed that informant measures of the environment are biased and less accurate than archival measures of the environment. We suggest that the observed divergence may result from three factors that lead to real differences between archival and informant environmental measures: (a) divergence in the constructive definitions of environmental constructs, (b) divergence in the operational definitions of the alternative measures, and (c) mediating filters that differentially influence informant reports of environmental constructs. 1.1. Divergence in constructive definitions Different researchers have purportedly identified common constructs (or at least adopted the same names for their constructs) but invoked differing constructive definitions. As Milliken (1987) observes ‘‘. . .environmental uncertainty has been used both as a descriptor of the state of organizational environments and as a descriptor of the state of a person who perceives himself/herself to be lacking critical information about the environment’’ (p. 134). While there appears to be general consensus that the environment is multidimensional, there clearly is no strong agreement about the dimensions that should be used to characterize the environment or how these different constructs should be defined (Dess and Rasheed, 1991; Sharfman and Dean, 1991). Following Milliken, we argue that environmental uncertainty refers to an internal mental state of the manager (i.e., informant) and cannot be assessed using archival measures; it must be assessed using informant measures. Whereas environmental variation, which refers to changes external to the organization, occurs independent of the manager’s ability to notice, understand, or interpret, because it represents changes in environmental components such as sales, technology, customer preferences, and governmental regulations. Though often assessed using archival measures, these can also be assessed by asking managers questions on the changes in relevant components. However, even when assessed with informant measures, the constructive definition of environmental variation is different from that of environmental uncertainty. It is critical to recognize this difference because otherwise researchers are likely to specify measures of environment that do not match the construct and conclude that informant measures reflect perceptual error. Recognition of this difference would caution researchers to compare informant measures of environmental variation (and not environmental uncertainty, which can only be perceptual) with corresponding archival measures.
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To formalize our argument, we hypothesize: Hypothesis 1. Environmental uncertainty and environmental variation are separate constructs that demonstrate discriminant validity even when both constructs are assessed using informant measures from same respondents. A second issue that complicates the constructive definitions of these environmental constructs arises from the multiple dimensions associated with the environment. At least three broad categories of environmental constructs have emerged in the literature: (a) constructs that deal with the content, segments and attributes of the environment, e.g., instability, munificence, and complexity (Bourgeois, 1985; Dess and Beard, 1984; Sutcliffe, 1994); (b) constructs that focus on the rate of change in the environment, both in terms of the level of change and the pattern of change, e.g., amplitude, frequency, and predictability—the first order dimensions of environmental variation (Wholey and Brittain, 1989); and (c) constructs highlighting the managers’ ability to understand the environment and how the environment and organization interact, e.g., state-, effect-, and responseuncertainty, first order constructs associated with environmental uncertainty validated by Milliken (1987, 1990) and Gerloff et al. (1991). If our arguments summarized in Hypothesis 1 are valid, then the first order dimensions of perceived environmental variation and environmental uncertainty would also demonstrate discriminant validity. In other words, informant measures of state uncertainty, effect uncertainty, response uncertainty, amplitude of change, frequency of change, and predictability of change would be distinguishable constructs because managers perceive environmental variation and environmental uncertainty differently. While state, effect, and response uncertainty are related to managers’ internal state of mind (i.e., uncertainty); amplitude, frequency, and predictability of changes are managers’ assessment of pattern of changes in the environment (i.e., variation). Thus, Hypothesis 2 states: Hypothesis 2. The environment can be assessed with six different constructs (state uncertainty, effect uncertainty, response uncertainty, amplitude of changes, frequency of changes, and predictability of changes) that demonstrate discriminant validity even when they are assessed by the same informants. 1.2. Divergence in operational definitions A second factor, which contributes to the observed divergence between informant and archival measures of the environment, is the differences in the operational definitions. Informant measures usually assess broader and more abstract conceptualizations of the environment than archival measures. The increased construct space and increased abstraction lead to less concrete operational definitions of the informant measures compared to archival measures,
which may increase the level of bias affecting a measure (Doty and Glick, 1998). Studies of generalizability theory suggest that differences in the construct space included in different measures of the same construct may reduce the convergence of the measures (Rentz, 1987; Cronbach et al., 1963). For example, Sutcliffe (1994) assessed environmental instability with both archival measures (growth in industry sales, capital expenditures, and net assets) and informant measures (questionnaire items that directly assessed each of these factors). However, as shown in Appendix C, her informant measures also included questionnaire items such as ‘‘customer demand and preferences are relatively stable in your industry’’, ‘‘your firm must frequently change the way it produces its goods and services in order to be competitive’’, that increased the construct space and decreased the concreteness of the operational definitions of the informant measure as compared to the archival measure. We suggest that the lack of convergence she observed may be a result of the differences in the operational definitions of her measures, rather than perceptual error on the part of the informant. If the informant operational definition is made more precise and comparable to archival measures, then the differences may decrease. Therefore, we hypothesize: Hypothesis 3. The divergence between informant and archival measures of the environment will decrease when the construct space and concreteness of the operational definitions associated with informant measures are constrained to match precisely the operational definitions of the archival measures. 1.3. The role of mediating filters The third and theoretically most interesting explanation for the observed divergence between informant and archival measures of the environment has focused on the role of individual and organizational mediating filters that are thought to distort managers’ perceptions of the environment. Characteristics of the respondent like individual cognitive processes, the variety of an individual’s experiences, and social expectations (Downey and Slocum, 1975; Downey et al., 1977) have been found to be sources of variation in the perception of uncertainty. Organizational level factors that have been hypothesized to explain the divergence between archival and informant measures of the environment include: (a) structural variables such as centralization, organizational position of the respondent, top management team size, the length of tenure, and functional diversity of the top management team (Sutcliffe, 1994); (b) the strategy of the firm such as cost leadership or defender (Boyd et al., 1993); and (c) firm and industry membership (Sutcliffe and Huber, 1998). We believe that the relationships of these mediating variables with environmental assessments are more complex than previously suggested. Consistent with the logic presented by Child (1972), Miles and Snow (1978),
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Sutcliffe and Huber (1998), and others who have discussed strategic choice and environmental enactment, we suggest that organizational level mediating filters relate more closely to the objective environment of the firm because firms determine their strategy and structure in response to environmental demands (Ebrahimi, 2000). Consequently, even when all measures are perceptual, organizational level mediating filters like strategy and structure would be more strongly related to managers’ reports about environmental variation than to their reports about environmental uncertainty because the latter refers more to an internal mental state of the manager. Thus, we posit: Hypothesis 4. Organizational level mediating filters will be more strongly related to informant measures of environmental variation than to informant measures of environmental uncertainty. A second prediction that is consistent with the preceding hypothesis is that the observed relationship between individual level mediating filters and environmental variation, which indicates perceptual bias, may be spurious. Managers may self-select into firms based on the match between their individual traits and characteristics (i.e., individual level mediating filters) with the organization’s strategy or structure (Miller and Dro¨ge, 1986), which in turn is closely related to the environment (Child, 1972; Miles and Snow, 1978; Sutcliffe and Huber, 1998). For example, a manager with high risk tolerance may choose to work in a company facing greater environmental changes and having fast product-market development strategy. Therefore, if one accounts for managerial self-selection by controlling for organizational level mediating filters, then the relationship between individual level mediating filters and informant perceptions of environmental variation would be reduced or eliminated. In other words, perceptual bias, based on personality traits and cognitive processes, may appear to bias assessments of environmental variation. However, the apparent effects of such perceptual bias are an artifact that can be eliminated by controlling for organizational factors that induce the managerial self-selection processes. Consistent with these arguments, our final hypothesis states: Hypothesis 5. Informant measures of environmental variation will initially be related to individual level mediating filters; however, when organizational level mediating filters are controlled, this relationship will not be significant.
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sets provide a unique opportunity to investigate the issues raised in this study. 2.1. Data set 1 The data used to test Hypotheses 1, 2, and 4 (data set 1) were collected from the banks, savings and loans, and credit unions in the state of Arkansas based on their willingness to allow the researchers access to customers’ names and addresses and their willingness to provide data and financially support (see Doty et al., 1994 for details). Individual respondents include upper level managers, lower level employees, and customers, but we draw only on the data that were collected from the upper level managers following Chen et al. (1993) recommendation that top managers provide greater accuracy and reliability for macro-strategy research. The final data set contains 137 complete questionnaires from 41 different financial institutions, between two and five usable questionnaires from each institution. 2.2. Data set 2 The second data set, used to test Hypothesis 3, is a random sample of 64 firms representing 34 different industries as described by Sutcliffe (1994) and Sutcliffe and Weber (2001). We used two sets of variables for this data set: (a) archival measures of instability and munificence computed from Compustat II and (b) informant measures of instability and munificence from top managers. Multiple members of the top management team within each firm completed and returned the research instrument resulting in a total of 364 individual responses. 2.3. Data set 3 The third data set is described in Doty et al. (1993) and Glick et al. (1990). The data used in the current study are limited to two different questionnaires, mailed and collected one after another, which were completed by 91 top managers. The first questionnaire asked respondents about the organization’s strategy, structure, and environment, and the second questionnaire asked for the individual characteristics of the top manager. Organizations were selected based on their membership in one of five selected industries and their agreement to provide multiple rounds of interview and questionnaire data over a 2-year period. 2.4. Environmental measures
2. Methods The data used to test the hypotheses were collected as parts of three different research projects as summarized in Appendix A. None of the individual studies were designed explicitly to address the questions raised in the current work; however, planned similarities across the three data
We have used three sets of environmental measures in this paper (see Appendix A): (a) archival and comparable informant measures of environmental instability and munificence; (b) informant measures of environmental variation components (frequency, amplitude, predictability); and (c) informant measures of environmental uncertainty components (effect, state, response). The two archival measures of
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instability and munificence used from data set 2 are compiled from Standard and Poor’s COMPUSTAT II industry-segment data file and annual company file following Dess and Beard (1984). Corresponding informant measures of instability and munificence in data set 2 were each assessed with nine Likert-type questionnaire items, rated on seven-point scales, based on Duncan (1972) and Bourgeois (1985). See Appendix C for an example of data set 2 environment measures. All three perceptual measures of environmental variation used in data set 1, frequency, amplitude, and predictability, were assessed with three questionnaire items using a sevenpoint Likert-type scale. These same three constructs were also in data set 3, with only minor differences in wording. Three constructs associated with environmental uncertainty in data set 1, i.e., effect, state, and response uncertainty (see Appendices A, B, and C), were each assessed with multiple Likert-type questionnaire items adapted from the measures reported in Milliken (1987, 1990); minor wording changes were necessary to adjust for industry differences. See Appendix B for data set 1 environment measures. 2.5. Organizational level mediating filters Four perceptual measures of organizational level mediating filters were used in data set 1: decentralization and formalization are structural variables, while product-market development and differentiation are strategic variables. Each of these was assessed with multi-item Likert-type scales (described in Doty et al., 1994). Five perceptual organizational variables were measured in data set 3: centralization and formalization, which are structural variables, and product-market efficiency, product-market development, and market scope, which are strategy variables. Each variable was assessed with multi-item Likert-type scales as described in Sutcliffe (1994, 2001). 2.6. Individual level mediating filters Five perceptual measures of individual characteristics were used in data set 3, each assessed with multiple questionnaire items on seven-point Likert-type scales. These measures are: need for achievement (Spence and Helmreich, 1978), locus of control (Rotter, 1966), tolerance for ambiguity (Budner, 1962), risk aversion (March and Shapira, 1987), and management discretion (Hambrick and Finkelstein, 1987). The specific items included in each measure were taken from Doty et al. (1993) and Glick et al. (1990). 2.7. Analysis Hypothesis 1 postulates that environmental uncertainty and environmental variation are different constructs that exhibit discriminant validity when both constructs are assessed with informant measures. We tested this hypothesis with exploratory and confirmatory factor analysis using 16
questionnaire items from data set 1. Similarly, Hypothesis 2, which postulated distinct dimensions for environmental uncertainty and environmental variation, was evaluated with both exploratory and confirmatory factor analysis of the informant data incorporated in data set 1. Hypothesis 3 posits that, when the construct space and the concreteness of the operational definitions of archival and informant measures are identical, the divergence between the alternative measures should decrease. We tested this hypothesis by examining the change in correlation between the archival measures of instability and munificence and two different operationalizations of the corresponding informant measures. The first informant measure was constructed using the full informant scale and the second using an informant scale reduced to include only the items that corresponded exactly to the content of the archival measure. Greater correlations among the matched scales would indicate convergence of the archival and informant measures. Hypothesis 4 posited that organizational level mediating filters will be more strongly related to variation measures than to uncertainty measures. We looked at the correlations among these variables to initially determine the relationships. Additionally, we regressed the set of structure and strategy measures onto each of the individual dimensional measures of environmental variation (frequency, amplitude, predictability) and environmental uncertainty (effect, state, response) in an omnibus analysis, after controlling for firm size. Tests of multicollinearity, as recommended by Neter et al. (1990), showed that all the VIFs (variance inflation factors) were within acceptable limits. Hypothesis 5 states that informant measures of environmental variation will be initially related to individual level mediating filters, but when organizational level mediating filters are used as control variables the strength of this initial relationship will decrease. We tested this hypothesis, using data set 3, with hierarchical regressions through four models (see Table 3). We entered industry (four dummy coded variables) and firm size in the control model, individual level mediating filters (need for achievement, locus of control, tolerance for ambiguity, risk aversion, management discretion) in model 1, organizational level mediating filters (centralization, formalization, product-market efficiency, product-market development, market scope) in model 2, and all mediating filters in model 3.
3. Results A two-factor exploratory factor analysis supported Hypothesis 1. The 16 items generally loaded on the appropriate environmental uncertainty or variation factor, indicating that environmental uncertainty and environmental variation can be separated into different constructs even when both constructs are assessed using informant reports. The variance explained by the environmental uncertainty factor is 21.3 and the environmental variation factor is 15.8.
D.H. Doty et al. / Journal of Business Research 59 (2006) 268 – 277
In addition, the results of the confirmatory factory analysis conducted with a generalized least squares estimation technique supported the two-factor solution consistent with Hypothesis 1 (v 2 = 286, df = 103, p < 0.05). The six-factor exploratory factor analysis results provide support for Hypothesis 2. Thirteen of the 16 questionnaire items loaded cleanly on the appropriate environmental uncertainty or environmental variation dimensions. Further, the results of a generalized least squares confirmatory factor analysis demonstrate that the support for this hypothesized six-factor solution is statistically significant (v 2 = 213, df = 89, p < 0.05). Hypothesis 3 is not supported because the correlations between the archival scales and the informant scales do not increase after matching the operational definitions. The correlation between archival instability and complete informant instability is 0.24 ( p < 0.01), while that between archival instability and reduced informant instability is 0.22 ( p < 0.01). The correlation between archival munificence and complete informant munificence is 0.13 ( p < 0.10), while that between archival munificence and reduced informant munificence is 0.08. Therefore, we find that the correlations between archival and informant measures of environment decrease, rather than increase in magnitude, after constraining the construct space. Initial support for Hypothesis 4 is presented by the overall pattern of correlations among the organizational level mediating filters (decentralization, formalization, product-market development, differentiation) and the environmental measures incorporated in data set 1. Table 1 shows that, while most of the organizational variables analyzed are significantly correlated with the environmental variation measures (frequency, amplitude, and predictability), only three correlations are significant between the organizational level filters and the environmental uncertainty components. In addition, the omnibus regression results summarized in Table 2 indicate that all the environmental variation components (frequency,
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Table 2 Organizational level mediating filters regressed onto environmental uncertainty and variation Constructs
Dependent variables
Environmental uncertainty Environmental variation
Organizational level mediating filters
Effect State Response Frequency Amplitude Predictability
R2
Adj. R 2
0.13*** 0.05 0.04 0.17*** 0.22*** 0.23***
0.10*** 0.02 0.01 0.15*** 0.20*** 0.21***
N = 137. *** FF_s are significant at p < 0.01 or below.
amplitude, predictability) have significant model FF_s at p < 0.05 or below, while only effect uncertainty is significant for environmental uncertainty. These results indicate that the organizational level mediating filters are more strongly related to environmental variation than to environmental uncertainty and provide support for Hypothesis 4. The results of the hierarchical regression analysis used to test Hypothesis 5 are summarized in Table 3. Model 1 documents that the individual level mediating filters are initially related to the informant measures of variation as expected. The results from models 2 and 3, however, are opposite the prediction in Hypothesis 5. Model 2 results show that the organizational level mediating filters lead to only a small and non-significant increase in the level of explained variation when industry effects are controlled. Results from model 3 suggest that the individual level mediating filters are more strongly related to the informant measures of environmental variation than are the organizational level mediating filters. Thus, the effects of the individual level mediating filters do not vanish when we control for organizational level mediating filters. These results lead us to reject Hypothesis 5 and seem to confirm earlier assertions that individual mediating filters are related to informant measures of the environment
Table 1 Correlations among environmental and organizational variables in data set 1 Variables
Means S.D. 1
Effect uncertainty Sate uncertainty Response uncertainty Frequency Amplitude Predictability Decentralization Formalization Product-market development Differentiation
3.41 4.07 3.59 4.38 4.79 3.79 4.15 4.39 4.58 4.55
N = 137. * p < 0.10. ** p < 0.05. *** p < 0.01.
0.80 1.15 1.21 1.10 1.24 0.94 0.84 1.23 1.13 1.55
2 (0.59) 0.20 0.29*** 0.15* 0.18** 0.32*** 0.12 0.11 0.36*** 0.26***
3 (0.69) 0.32*** 0.15* 0.07 0.05 0.05 0.19** 0.04 0.08
4
(0.67) 0.03 0.12 0.17** 0.05 0.12 0.12 0.06
5
(0.63) 0.48*** 0.31*** 0.16* 0.12 0.41*** 0.29***
6
(0.68) 0.28*** 0.15* 0.36*** 0.38*** 0.25***
7
8
9
10
(0.62) 0.25*** (0.75) 0.15* 0.05 (0.72) 0.47*** 0.40*** 0.27*** (0.73) 0.37*** 0.39*** 0.22* 0.79*** (0.82)
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Table 3 Environmental variation: organizational vs. individual level mediating filters Model
Independent variables
Frequency Adj. R
Control 1 2 3
Industry, size Individual level mediating filters Organizational level mediating filters Organizational level mediating filters + individual level mediating filters
2
0.07 0.19 0.10 0.24
Amplitude DAdj. R 0.12 0.14
2
Adj. R 0.10 0.14 0.11 0.15
2
Predictability DAdj. R 0.04 0.04
2
Adj. R 2 0.06 0.13 0.07 0.12
DAdj. R 2 0.07 0.05
N = 91. All FF_s are significant at p < 0.05 or below.
(Boyd et al., 1993; Sutcliffe, 1994). Therefore, we cannot rule out perceptual bias for informant measures of environmental variation.
4. Discussion The primary assertion in the current study was that the observed divergence between archival and informant measures of the environment could be explained by real differences between the environmental constructs and measurement artifacts rather than by perceptual error on the part of the informants. The combination of results from this study, however, provides mixed support for our assertion. There is some evidence that the observed divergence between archival and informant measures may reflect real differences between constructs, but more and possibly stronger evidence that this divergence represents perceptual error or bias on the part of managers. The support generated for Hypotheses 1 and 2 indicates that our primary assertion has some merit. The constructs of environmental uncertainty and environmental variation are clearly different constructs. Thus, any reports of divergence between measures of variation and uncertainty should not be interpreted solely as perceptual error; it may represent real difference between changes external to the organization and a manager’s internal mental state that results from noticing and interpreting these changes. Further, the distinction between variation and uncertainty constructs translate directly to the six first-order environmental constructs we included in the study. These results not only serve as a caution to researchers to think carefully about which constructs are of interest in their research and how such constructs should be measured, but also provide additional validation of the studies by Milliken (1987, 1990), Gerloff et al. (1991), and Wholey and Brittain (1989). However, the results of testing Hypothesis 3 indicate that a portion of the observed divergence between archival and informant measures can be attributed to bias. Increasing the convergence of the operational definitions of the informant and archival measures did not reduce the observed divergence. At best, the informant measures can be used to predict 6% of the
variation in the archival measures. To further investigate this hypothesis, we disaggregated both the archival and informant indices into single items and computed the correlations between the corresponding single items. Only one of the item – item correlations, between the sales items in the instability scales, was significant (r = 0.11, p < 0.05). This finding is particularly troubling given the close correspondence between the content of the informant and archival measures. However, an important source of divergence may be matching the scope of the environment. [We thank an anonymous reviewer for raising this issue.] Most archival measures use aggregate measures of industry that may not be relevant to the specific segments faced by each firm which the informant considers. This may be the reason why the results do not support Hypothesis 3. Future research should attempt to match the scope of the environment in comparing archival measures of environment with informant measures. A final finding in this paper is that more attention needs to be focused on the role of mediating filters. Our findings for Hypotheses 4 and 5 clearly support the arguments of others (Sutcliffe and Huber, 1998; Sutcliffe, 1994; Boyd et al., 1993) that mediating filters play an important role in managers’ assessment of the environment. The results from Hypothesis 4 indicate that organizational level mediating filters are more strongly related to informant measures of environmental variation than environmental uncertainty. This could be interpreted to mean that firm structure and strategy are more closely aligned with managers’ assessments of the firm environment than their perception of uncertainty about the environment. The results of Hypothesis 5, however, challenge our claim that informant measures of environmental variation do not have perceptual bias. Individual level mediating filters appear to explain much more of the variation in informant assessments of environmental variation than do organizational level variables. Further, the organizational level variables added very little to the level of explained variation when industry was used as a control variable. These seem to suggest that divergence between perceptual and archival measures of the environment is related to perceptual error rather than to real differences or unique attributes of the firm.
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5. Limitations, applications, and future directions Divergence in levels of analysis in measuring the environment is a consideration that has been discussed by researchers (Dess and Rasheed, 1991; Sharfman and Dean, 1991) and may have imposed some limitations on the current study. Two issues are particularly relevant. The first involves potential differences between a researcher’s and a manager’s definition of the industry. Researchers typically define industry by the SIC code for the corporation’s dominant line of business. It is not clear, however, that managers always use this same industry classification system. Thus, careful attention is required to insure that there is exact correspondence between the manager’s and the researcher’s definition of the industry boundaries. A second issue arises because researchers typically assume that an industry environment is homogeneous. For example, archival environment measures may be calculated as some form of aggregate based on all firms in the industry. Managers are then typically asked to respond about the environment of their firm, which means that the observed divergence may reflect real differences between the industry and the organization’s environment rather than inaccuracy in either the informant or the archival measure. Therefore, researchers should not assume that managers’ reports about their firm’s industry environment will converge with archival measures of aggregate industry characteristics. In this study, due to data limitations, we could not tease out the aggregate industry environment from the environment of the firm to see if that explains some of the divergence between archival and perceptual measures. Despite these limitations, the findings of our study clearly indicate that researchers should continue studying the environment and clarify the causes and consequences of divergence between archival and informant measures of the environment. While the results of the current study are not conclusive, they do offer some insight for the design of future studies. First, researchers must recognize that environmental uncertainty and environmental variation are different constructs and that future studies will benefit from including both types of constructs. Second, the current study has examined the effects of blocks of mediating filters, but does not include detailed examination of specific organizational or individual level filters. Additional studies of the specific filters, especially the individual level mediating filters, may help researchers determine how to remove or control for the biasing effect that these filters appear to have on informant reports about the environment. Third, the data that was available for the current study forced us to focus more on environmental variation than on environmental uncertainty. Future studies that are designed to more directly assess the affect that mediating filters have on the level of environmental uncertainty experienced by managers should be conducted. The practical implications of our study are also meaningful. Managers should realize that when they assess the
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environment, they are actually indicating two aspects: (a) the actual environment facing the firm and (b) their perception about it. These findings provide useful guidance and practical applications of environmental scanning and forecasting. For example, through variation measures like rate of change in sales, technology, etc., they can better assess the firm environment, while through uncertainty measures like Fwe do not know what type of changes in the environment we will have to deal with_ they can assess the reaction of individuals. Moreover, managers need to be aware of other factors (i.e., mediating filters) that may influence their scanning and forecasting abilities. While strategy and structure of the firm affect informant reports of environmental variation, individual-level variables influence their uncertainty judgments. In conclusion, the current work has helped to provide theoretical arguments and empirical evidence examining the problems that are caused when informants’ reports of the environment appear to diverge from archival measures of the environment.
Acknowledgements The authors would like to thank Greg Dess, Ravi Dharwadakar, Gerry George, and Ken Smith for comments on previous versions of this manuscript.
Appendix A. Summary of data sets
Year collected N Respondents
Industries Archival data source Measures used
Data set 1
Data set 2
Data set 3
1993 137 (41 banks) Top management team Banking
1994 364 (65 firms) Top management team 34 industries Compustat II
1988 91 (5 industries) Top manager
Environment
Environment
Environment
(1) Informant frequency, amplitude, and predictability (2) Informant effect uncertainty, state uncertainty, and response uncertainty Organizational level mediating filters decentralization, formalization, product-market development, differentiation
Archival instability and munificence
(1) Informant frequency, amplitude, and predictability Organizational level mediating filters
Informant instability and munificence
5 industries
Centralization, formalization, product-market efficiency, product-market development, market scope
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Hypothesis tested
H1, H2, H4
H3
Individual level mediating filters need for achievement, locus of control, tolerance for ambiguity, risk aversion, management discretion H5
Appendix B. Data set 1: questionnaire items for environmental variation and uncertainty
1
1
1
1
1
1
State uncertainty
Questionnaire items
We never know what types of changes in the bank’ environment we will have to deal with. It is difficult to determine what our bank’s external environment will be like 1 year from now. Effect uncertainty We can usually tell if changes in the environment will be good for our bank. We can normally tell beforehand if our responses to environmental changes will have a positive effect on our bank’s performance. The effects that changes in the external environment will have on our bank are normally easy to determine. Response uncertainty When our bank’s environment changes, we are uncertain which of our possible responses will produce desirable outcomes. It is very difficult to determine what alternatives are available to our bank for responding to environmental changes. Amplitude Over the past year, how important to your bank were the changes in technology? Over the past year, how important to your bank were the changes in government regulations? Over the past year, how important to your bank were the changes in the types of customers using your bank? Frequency Over the past year, how many changes has your bank experienced in your depositors’ needs? Over the past year, how many changes has your bank experienced in your borrowers’ needs? Over the past year, how many changes has your bank experienced in technology?
Over the past year, how accurately has your bank been able to predict the changes in government regulations? Over the past year, how accurately has your bank been able to predict the changes in technology? Over the past year, how accurately has your bank been able to predict the changes in the types of customers using your bank?
Appendix C. Data set 2: example of informant and archival measures from Sutcliffe (1994)
Construct Data set Construct
Predictability
Archival measure Compustat)
Informant measure (questionnaire)
Archival Construct Items (a – c)
Complete informant construct (items 1 – 9) Reduced informant construct (items 1 – 3)
Instability a. Industry sales
1. The volume of sales for firms in your industry fluctuates very little from year to year b. Industry capital 2. Capital expenditures within your expenditures firm’s principal industry are relatively constant from year to year c. Industry net assets 3. The total value of assets for the firms in your industry varies a lot from year to year 4. Customer demand and preferences are relatively stable in your industry 5. Your firm must frequently change the way it produces its goods or services in order to be competitive 6. It is difficult to foresee the actions of your firm’s competitors 7. The actions of your major suppliers (including materials, equipment, or labor suppliers) change very little from 1 year to the next 8. Public/political attitudes toward your industry and its products/ services are relatively stable 9. Your firm frequently changes its technology to keep up with competitors
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Divergence between informant and archival measures of the environment: Real differences, artifact, or perceptual error? D. Harold Doty a,1, Mousumi Bhattacharya b,*, Kathleen K. Wheatley c,2, Kathleen M. Sutcliffe d,3 a
College of Business, University of Southern Mississippi, 118 College Drive # 5021, Hattiesburg, MS 39406, United States b Charles F. Dolan School of Business, Fairfield University, Fairfield, CT 06824, United States c Department of Management, College of Business Administration, 615 McCallie Avenue Dept. 6156, University of Tennessee at Chattanooga, Chattanooga, TN 37403, United States d The University of Michigan Business School, 701 Tappan, Ann Arbor, MI 48109-1234, United States Received 2 December 2002; accepted 25 April 2005
Abstract Although organizational environments are a central concern for researchers in organization theory and strategy, many researchers report that informant assessments and archival measures of the environment do not converge. This study investigates whether the observed divergence can be attributed to perceptual error, to real differences in constructive definitions, to differences between a firm’s environment and an aggregate industry environment, or simply to methodological artifacts. Results show that environmental uncertainty and environmental variation are distinct multidimensional constructs even when only informant measures from the same respondents are considered, and that organizational level mediating filters are more strongly related to informant measures of environmental variation than to environmental uncertainty. However, the effects of individual level filters on environmental variation do not decrease after controlling for organizational level filters. We conclude that, although there are some real differences between informant and archival measures of the environment, the possibility of perceptual bias cannot be ruled out. D 2005 Elsevier Inc. All rights reserved. Keywords: Environment; Perceived environment; Mediating filters; Measures
Constructs associated with organizations’ environments enjoy prominence in the organization theory and strategic management literatures, and there is a general consensus that the environment is multidimensional, is related to organizational form, functioning and perform-
* Corresponding author. Tel.: +1 203 254 4000x2893; fax: +1 203 254 4105. E-mail addresses: [email protected] (D.H. Doty), [email protected] (M. Bhattacharya), [email protected] (K.K. Wheatley), [email protected] (K.M. Sutcliffe) 1 Tel.: +1 601 266 4659. 2 Tel.: +1 423 425 2104; fax: +1 423 425 4158. 3 Tel.: +1 734 764 2312; fax: +1 734 936 8716. 0148-2963/$ - see front matter D 2005 Elsevier Inc. All rights reserved. doi:10.1016/j.jbusres.2005.04.005
ance, and that managers expend a good deal of energy trying to monitor and interpret environmental conditions. Beyond this, however, the environmental literature is in a surprising state of disarray that is characterized by conflicting definitions of constructs, confusion about appropriate measurement techniques, and contradictory results. Much of the confusion in the literature arises from the apparent lack of agreement between ‘‘informant’’ and ‘‘archival’’ measures of the same environmental constructs. For example, Sharfman and Dean (1991) found only moderate correlations between their ‘‘objective’’ environmental measures and informant measures of uncertainty; Sutcliffe (1994) reports small to moderate correlations between archival and informant measures of environmental
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instability and munificence. Sharfman and Dean (1991, p. 682) summarize what is probably the most widely held position when they state that There can be little doubt that managers’ perceptions play an important role in shaping their responses to the environment. But it also appears clear that there are differences between environments that are objective (i.e., observable through Fscientifically rigorous measurement procedures_, Dess and Beard, 1984, p. 53) as well as intersubjective (i.e., perceived similarly by most observers). At least two general approaches have emerged to deal with the problems caused by the divergence between informant and archival measures of the environment. The first is to ignore the potential problems associated with this divergence and include only one type of measure in the study. For example, Milliken (1990) and Doty et al. (1993) have relied exclusively on informant measures, while Wholey and Brittain (1989) and Keats and Hitt (1988) have used only archival measures of environment. The second approach is to address directly the causes and/or consequences of divergence between archival and informant measures of environmental constructs. For instance, Boyd et al. (1993) specified a set of mediating filters, including individual and organizational factors, to explain divergence between archival and informant measures; Sutcliffe (1994) demonstrated that divergence between informant and archival measures of environment can be predicted by characteristics of the top management team and organizational structure. We are concerned that these studies suffer from a common limitation—the assumption that the observed divergence between informant and archival measures of the environment is caused by perceptual error on the part of the informant. One alternative assumption is that this divergence reflects real differences between aggregate measures of an industry environment and the task environment that a firm actually faces. For example, Elenkov (1997) found that uncertainty scores differ significantly among various sectors of the environment. A second alternative is that this divergence exists because informant and archival assessments of the environment differ at both theoretical and measurement levels (Boyd et al., 1993). For example, Ebrahimi (2000) showed that executives scanned the task environment more intensely than the remote environment. We contend that divergence between archival and informant measures of the environment is based on factors other than perceptual error on the part of managers because informant measures of the environment are distinct both in terms of constructive definition and operational measurements. We also examine whether the observed divergence between informant and archival measures of the environment can be explained by organizational or individual level mediating filters so that this potential source of bias can be controlled in future research.
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1. Explanations for the observed divergence The main concern over the observed divergence is that this appears to be systematic variation rather than random error (Doty and Glick, 1998) and, generally, it is assumed that informant measures of the environment are biased and less accurate than archival measures of the environment. We suggest that the observed divergence may result from three factors that lead to real differences between archival and informant environmental measures: (a) divergence in the constructive definitions of environmental constructs, (b) divergence in the operational definitions of the alternative measures, and (c) mediating filters that differentially influence informant reports of environmental constructs. 1.1. Divergence in constructive definitions Different researchers have purportedly identified common constructs (or at least adopted the same names for their constructs) but invoked differing constructive definitions. As Milliken (1987) observes ‘‘. . .environmental uncertainty has been used both as a descriptor of the state of organizational environments and as a descriptor of the state of a person who perceives himself/herself to be lacking critical information about the environment’’ (p. 134). While there appears to be general consensus that the environment is multidimensional, there clearly is no strong agreement about the dimensions that should be used to characterize the environment or how these different constructs should be defined (Dess and Rasheed, 1991; Sharfman and Dean, 1991). Following Milliken, we argue that environmental uncertainty refers to an internal mental state of the manager (i.e., informant) and cannot be assessed using archival measures; it must be assessed using informant measures. Whereas environmental variation, which refers to changes external to the organization, occurs independent of the manager’s ability to notice, understand, or interpret, because it represents changes in environmental components such as sales, technology, customer preferences, and governmental regulations. Though often assessed using archival measures, these can also be assessed by asking managers questions on the changes in relevant components. However, even when assessed with informant measures, the constructive definition of environmental variation is different from that of environmental uncertainty. It is critical to recognize this difference because otherwise researchers are likely to specify measures of environment that do not match the construct and conclude that informant measures reflect perceptual error. Recognition of this difference would caution researchers to compare informant measures of environmental variation (and not environmental uncertainty, which can only be perceptual) with corresponding archival measures.
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To formalize our argument, we hypothesize: Hypothesis 1. Environmental uncertainty and environmental variation are separate constructs that demonstrate discriminant validity even when both constructs are assessed using informant measures from same respondents. A second issue that complicates the constructive definitions of these environmental constructs arises from the multiple dimensions associated with the environment. At least three broad categories of environmental constructs have emerged in the literature: (a) constructs that deal with the content, segments and attributes of the environment, e.g., instability, munificence, and complexity (Bourgeois, 1985; Dess and Beard, 1984; Sutcliffe, 1994); (b) constructs that focus on the rate of change in the environment, both in terms of the level of change and the pattern of change, e.g., amplitude, frequency, and predictability—the first order dimensions of environmental variation (Wholey and Brittain, 1989); and (c) constructs highlighting the managers’ ability to understand the environment and how the environment and organization interact, e.g., state-, effect-, and responseuncertainty, first order constructs associated with environmental uncertainty validated by Milliken (1987, 1990) and Gerloff et al. (1991). If our arguments summarized in Hypothesis 1 are valid, then the first order dimensions of perceived environmental variation and environmental uncertainty would also demonstrate discriminant validity. In other words, informant measures of state uncertainty, effect uncertainty, response uncertainty, amplitude of change, frequency of change, and predictability of change would be distinguishable constructs because managers perceive environmental variation and environmental uncertainty differently. While state, effect, and response uncertainty are related to managers’ internal state of mind (i.e., uncertainty); amplitude, frequency, and predictability of changes are managers’ assessment of pattern of changes in the environment (i.e., variation). Thus, Hypothesis 2 states: Hypothesis 2. The environment can be assessed with six different constructs (state uncertainty, effect uncertainty, response uncertainty, amplitude of changes, frequency of changes, and predictability of changes) that demonstrate discriminant validity even when they are assessed by the same informants. 1.2. Divergence in operational definitions A second factor, which contributes to the observed divergence between informant and archival measures of the environment, is the differences in the operational definitions. Informant measures usually assess broader and more abstract conceptualizations of the environment than archival measures. The increased construct space and increased abstraction lead to less concrete operational definitions of the informant measures compared to archival measures,
which may increase the level of bias affecting a measure (Doty and Glick, 1998). Studies of generalizability theory suggest that differences in the construct space included in different measures of the same construct may reduce the convergence of the measures (Rentz, 1987; Cronbach et al., 1963). For example, Sutcliffe (1994) assessed environmental instability with both archival measures (growth in industry sales, capital expenditures, and net assets) and informant measures (questionnaire items that directly assessed each of these factors). However, as shown in Appendix C, her informant measures also included questionnaire items such as ‘‘customer demand and preferences are relatively stable in your industry’’, ‘‘your firm must frequently change the way it produces its goods and services in order to be competitive’’, that increased the construct space and decreased the concreteness of the operational definitions of the informant measure as compared to the archival measure. We suggest that the lack of convergence she observed may be a result of the differences in the operational definitions of her measures, rather than perceptual error on the part of the informant. If the informant operational definition is made more precise and comparable to archival measures, then the differences may decrease. Therefore, we hypothesize: Hypothesis 3. The divergence between informant and archival measures of the environment will decrease when the construct space and concreteness of the operational definitions associated with informant measures are constrained to match precisely the operational definitions of the archival measures. 1.3. The role of mediating filters The third and theoretically most interesting explanation for the observed divergence between informant and archival measures of the environment has focused on the role of individual and organizational mediating filters that are thought to distort managers’ perceptions of the environment. Characteristics of the respondent like individual cognitive processes, the variety of an individual’s experiences, and social expectations (Downey and Slocum, 1975; Downey et al., 1977) have been found to be sources of variation in the perception of uncertainty. Organizational level factors that have been hypothesized to explain the divergence between archival and informant measures of the environment include: (a) structural variables such as centralization, organizational position of the respondent, top management team size, the length of tenure, and functional diversity of the top management team (Sutcliffe, 1994); (b) the strategy of the firm such as cost leadership or defender (Boyd et al., 1993); and (c) firm and industry membership (Sutcliffe and Huber, 1998). We believe that the relationships of these mediating variables with environmental assessments are more complex than previously suggested. Consistent with the logic presented by Child (1972), Miles and Snow (1978),
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Sutcliffe and Huber (1998), and others who have discussed strategic choice and environmental enactment, we suggest that organizational level mediating filters relate more closely to the objective environment of the firm because firms determine their strategy and structure in response to environmental demands (Ebrahimi, 2000). Consequently, even when all measures are perceptual, organizational level mediating filters like strategy and structure would be more strongly related to managers’ reports about environmental variation than to their reports about environmental uncertainty because the latter refers more to an internal mental state of the manager. Thus, we posit: Hypothesis 4. Organizational level mediating filters will be more strongly related to informant measures of environmental variation than to informant measures of environmental uncertainty. A second prediction that is consistent with the preceding hypothesis is that the observed relationship between individual level mediating filters and environmental variation, which indicates perceptual bias, may be spurious. Managers may self-select into firms based on the match between their individual traits and characteristics (i.e., individual level mediating filters) with the organization’s strategy or structure (Miller and Dro¨ge, 1986), which in turn is closely related to the environment (Child, 1972; Miles and Snow, 1978; Sutcliffe and Huber, 1998). For example, a manager with high risk tolerance may choose to work in a company facing greater environmental changes and having fast product-market development strategy. Therefore, if one accounts for managerial self-selection by controlling for organizational level mediating filters, then the relationship between individual level mediating filters and informant perceptions of environmental variation would be reduced or eliminated. In other words, perceptual bias, based on personality traits and cognitive processes, may appear to bias assessments of environmental variation. However, the apparent effects of such perceptual bias are an artifact that can be eliminated by controlling for organizational factors that induce the managerial self-selection processes. Consistent with these arguments, our final hypothesis states: Hypothesis 5. Informant measures of environmental variation will initially be related to individual level mediating filters; however, when organizational level mediating filters are controlled, this relationship will not be significant.
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sets provide a unique opportunity to investigate the issues raised in this study. 2.1. Data set 1 The data used to test Hypotheses 1, 2, and 4 (data set 1) were collected from the banks, savings and loans, and credit unions in the state of Arkansas based on their willingness to allow the researchers access to customers’ names and addresses and their willingness to provide data and financially support (see Doty et al., 1994 for details). Individual respondents include upper level managers, lower level employees, and customers, but we draw only on the data that were collected from the upper level managers following Chen et al. (1993) recommendation that top managers provide greater accuracy and reliability for macro-strategy research. The final data set contains 137 complete questionnaires from 41 different financial institutions, between two and five usable questionnaires from each institution. 2.2. Data set 2 The second data set, used to test Hypothesis 3, is a random sample of 64 firms representing 34 different industries as described by Sutcliffe (1994) and Sutcliffe and Weber (2001). We used two sets of variables for this data set: (a) archival measures of instability and munificence computed from Compustat II and (b) informant measures of instability and munificence from top managers. Multiple members of the top management team within each firm completed and returned the research instrument resulting in a total of 364 individual responses. 2.3. Data set 3 The third data set is described in Doty et al. (1993) and Glick et al. (1990). The data used in the current study are limited to two different questionnaires, mailed and collected one after another, which were completed by 91 top managers. The first questionnaire asked respondents about the organization’s strategy, structure, and environment, and the second questionnaire asked for the individual characteristics of the top manager. Organizations were selected based on their membership in one of five selected industries and their agreement to provide multiple rounds of interview and questionnaire data over a 2-year period. 2.4. Environmental measures
2. Methods The data used to test the hypotheses were collected as parts of three different research projects as summarized in Appendix A. None of the individual studies were designed explicitly to address the questions raised in the current work; however, planned similarities across the three data
We have used three sets of environmental measures in this paper (see Appendix A): (a) archival and comparable informant measures of environmental instability and munificence; (b) informant measures of environmental variation components (frequency, amplitude, predictability); and (c) informant measures of environmental uncertainty components (effect, state, response). The two archival measures of
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instability and munificence used from data set 2 are compiled from Standard and Poor’s COMPUSTAT II industry-segment data file and annual company file following Dess and Beard (1984). Corresponding informant measures of instability and munificence in data set 2 were each assessed with nine Likert-type questionnaire items, rated on seven-point scales, based on Duncan (1972) and Bourgeois (1985). See Appendix C for an example of data set 2 environment measures. All three perceptual measures of environmental variation used in data set 1, frequency, amplitude, and predictability, were assessed with three questionnaire items using a sevenpoint Likert-type scale. These same three constructs were also in data set 3, with only minor differences in wording. Three constructs associated with environmental uncertainty in data set 1, i.e., effect, state, and response uncertainty (see Appendices A, B, and C), were each assessed with multiple Likert-type questionnaire items adapted from the measures reported in Milliken (1987, 1990); minor wording changes were necessary to adjust for industry differences. See Appendix B for data set 1 environment measures. 2.5. Organizational level mediating filters Four perceptual measures of organizational level mediating filters were used in data set 1: decentralization and formalization are structural variables, while product-market development and differentiation are strategic variables. Each of these was assessed with multi-item Likert-type scales (described in Doty et al., 1994). Five perceptual organizational variables were measured in data set 3: centralization and formalization, which are structural variables, and product-market efficiency, product-market development, and market scope, which are strategy variables. Each variable was assessed with multi-item Likert-type scales as described in Sutcliffe (1994, 2001). 2.6. Individual level mediating filters Five perceptual measures of individual characteristics were used in data set 3, each assessed with multiple questionnaire items on seven-point Likert-type scales. These measures are: need for achievement (Spence and Helmreich, 1978), locus of control (Rotter, 1966), tolerance for ambiguity (Budner, 1962), risk aversion (March and Shapira, 1987), and management discretion (Hambrick and Finkelstein, 1987). The specific items included in each measure were taken from Doty et al. (1993) and Glick et al. (1990). 2.7. Analysis Hypothesis 1 postulates that environmental uncertainty and environmental variation are different constructs that exhibit discriminant validity when both constructs are assessed with informant measures. We tested this hypothesis with exploratory and confirmatory factor analysis using 16
questionnaire items from data set 1. Similarly, Hypothesis 2, which postulated distinct dimensions for environmental uncertainty and environmental variation, was evaluated with both exploratory and confirmatory factor analysis of the informant data incorporated in data set 1. Hypothesis 3 posits that, when the construct space and the concreteness of the operational definitions of archival and informant measures are identical, the divergence between the alternative measures should decrease. We tested this hypothesis by examining the change in correlation between the archival measures of instability and munificence and two different operationalizations of the corresponding informant measures. The first informant measure was constructed using the full informant scale and the second using an informant scale reduced to include only the items that corresponded exactly to the content of the archival measure. Greater correlations among the matched scales would indicate convergence of the archival and informant measures. Hypothesis 4 posited that organizational level mediating filters will be more strongly related to variation measures than to uncertainty measures. We looked at the correlations among these variables to initially determine the relationships. Additionally, we regressed the set of structure and strategy measures onto each of the individual dimensional measures of environmental variation (frequency, amplitude, predictability) and environmental uncertainty (effect, state, response) in an omnibus analysis, after controlling for firm size. Tests of multicollinearity, as recommended by Neter et al. (1990), showed that all the VIFs (variance inflation factors) were within acceptable limits. Hypothesis 5 states that informant measures of environmental variation will be initially related to individual level mediating filters, but when organizational level mediating filters are used as control variables the strength of this initial relationship will decrease. We tested this hypothesis, using data set 3, with hierarchical regressions through four models (see Table 3). We entered industry (four dummy coded variables) and firm size in the control model, individual level mediating filters (need for achievement, locus of control, tolerance for ambiguity, risk aversion, management discretion) in model 1, organizational level mediating filters (centralization, formalization, product-market efficiency, product-market development, market scope) in model 2, and all mediating filters in model 3.
3. Results A two-factor exploratory factor analysis supported Hypothesis 1. The 16 items generally loaded on the appropriate environmental uncertainty or variation factor, indicating that environmental uncertainty and environmental variation can be separated into different constructs even when both constructs are assessed using informant reports. The variance explained by the environmental uncertainty factor is 21.3 and the environmental variation factor is 15.8.
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In addition, the results of the confirmatory factory analysis conducted with a generalized least squares estimation technique supported the two-factor solution consistent with Hypothesis 1 (v 2 = 286, df = 103, p < 0.05). The six-factor exploratory factor analysis results provide support for Hypothesis 2. Thirteen of the 16 questionnaire items loaded cleanly on the appropriate environmental uncertainty or environmental variation dimensions. Further, the results of a generalized least squares confirmatory factor analysis demonstrate that the support for this hypothesized six-factor solution is statistically significant (v 2 = 213, df = 89, p < 0.05). Hypothesis 3 is not supported because the correlations between the archival scales and the informant scales do not increase after matching the operational definitions. The correlation between archival instability and complete informant instability is 0.24 ( p < 0.01), while that between archival instability and reduced informant instability is 0.22 ( p < 0.01). The correlation between archival munificence and complete informant munificence is 0.13 ( p < 0.10), while that between archival munificence and reduced informant munificence is 0.08. Therefore, we find that the correlations between archival and informant measures of environment decrease, rather than increase in magnitude, after constraining the construct space. Initial support for Hypothesis 4 is presented by the overall pattern of correlations among the organizational level mediating filters (decentralization, formalization, product-market development, differentiation) and the environmental measures incorporated in data set 1. Table 1 shows that, while most of the organizational variables analyzed are significantly correlated with the environmental variation measures (frequency, amplitude, and predictability), only three correlations are significant between the organizational level filters and the environmental uncertainty components. In addition, the omnibus regression results summarized in Table 2 indicate that all the environmental variation components (frequency,
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Table 2 Organizational level mediating filters regressed onto environmental uncertainty and variation Constructs
Dependent variables
Environmental uncertainty Environmental variation
Organizational level mediating filters
Effect State Response Frequency Amplitude Predictability
R2
Adj. R 2
0.13*** 0.05 0.04 0.17*** 0.22*** 0.23***
0.10*** 0.02 0.01 0.15*** 0.20*** 0.21***
N = 137. *** FF_s are significant at p < 0.01 or below.
amplitude, predictability) have significant model FF_s at p < 0.05 or below, while only effect uncertainty is significant for environmental uncertainty. These results indicate that the organizational level mediating filters are more strongly related to environmental variation than to environmental uncertainty and provide support for Hypothesis 4. The results of the hierarchical regression analysis used to test Hypothesis 5 are summarized in Table 3. Model 1 documents that the individual level mediating filters are initially related to the informant measures of variation as expected. The results from models 2 and 3, however, are opposite the prediction in Hypothesis 5. Model 2 results show that the organizational level mediating filters lead to only a small and non-significant increase in the level of explained variation when industry effects are controlled. Results from model 3 suggest that the individual level mediating filters are more strongly related to the informant measures of environmental variation than are the organizational level mediating filters. Thus, the effects of the individual level mediating filters do not vanish when we control for organizational level mediating filters. These results lead us to reject Hypothesis 5 and seem to confirm earlier assertions that individual mediating filters are related to informant measures of the environment
Table 1 Correlations among environmental and organizational variables in data set 1 Variables
Means S.D. 1
Effect uncertainty Sate uncertainty Response uncertainty Frequency Amplitude Predictability Decentralization Formalization Product-market development Differentiation
3.41 4.07 3.59 4.38 4.79 3.79 4.15 4.39 4.58 4.55
N = 137. * p < 0.10. ** p < 0.05. *** p < 0.01.
0.80 1.15 1.21 1.10 1.24 0.94 0.84 1.23 1.13 1.55
2 (0.59) 0.20 0.29*** 0.15* 0.18** 0.32*** 0.12 0.11 0.36*** 0.26***
3 (0.69) 0.32*** 0.15* 0.07 0.05 0.05 0.19** 0.04 0.08
4
(0.67) 0.03 0.12 0.17** 0.05 0.12 0.12 0.06
5
(0.63) 0.48*** 0.31*** 0.16* 0.12 0.41*** 0.29***
6
(0.68) 0.28*** 0.15* 0.36*** 0.38*** 0.25***
7
8
9
10
(0.62) 0.25*** (0.75) 0.15* 0.05 (0.72) 0.47*** 0.40*** 0.27*** (0.73) 0.37*** 0.39*** 0.22* 0.79*** (0.82)
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Table 3 Environmental variation: organizational vs. individual level mediating filters Model
Independent variables
Frequency Adj. R
Control 1 2 3
Industry, size Individual level mediating filters Organizational level mediating filters Organizational level mediating filters + individual level mediating filters
2
0.07 0.19 0.10 0.24
Amplitude DAdj. R 0.12 0.14
2
Adj. R 0.10 0.14 0.11 0.15
2
Predictability DAdj. R 0.04 0.04
2
Adj. R 2 0.06 0.13 0.07 0.12
DAdj. R 2 0.07 0.05
N = 91. All FF_s are significant at p < 0.05 or below.
(Boyd et al., 1993; Sutcliffe, 1994). Therefore, we cannot rule out perceptual bias for informant measures of environmental variation.
4. Discussion The primary assertion in the current study was that the observed divergence between archival and informant measures of the environment could be explained by real differences between the environmental constructs and measurement artifacts rather than by perceptual error on the part of the informants. The combination of results from this study, however, provides mixed support for our assertion. There is some evidence that the observed divergence between archival and informant measures may reflect real differences between constructs, but more and possibly stronger evidence that this divergence represents perceptual error or bias on the part of managers. The support generated for Hypotheses 1 and 2 indicates that our primary assertion has some merit. The constructs of environmental uncertainty and environmental variation are clearly different constructs. Thus, any reports of divergence between measures of variation and uncertainty should not be interpreted solely as perceptual error; it may represent real difference between changes external to the organization and a manager’s internal mental state that results from noticing and interpreting these changes. Further, the distinction between variation and uncertainty constructs translate directly to the six first-order environmental constructs we included in the study. These results not only serve as a caution to researchers to think carefully about which constructs are of interest in their research and how such constructs should be measured, but also provide additional validation of the studies by Milliken (1987, 1990), Gerloff et al. (1991), and Wholey and Brittain (1989). However, the results of testing Hypothesis 3 indicate that a portion of the observed divergence between archival and informant measures can be attributed to bias. Increasing the convergence of the operational definitions of the informant and archival measures did not reduce the observed divergence. At best, the informant measures can be used to predict 6% of the
variation in the archival measures. To further investigate this hypothesis, we disaggregated both the archival and informant indices into single items and computed the correlations between the corresponding single items. Only one of the item – item correlations, between the sales items in the instability scales, was significant (r = 0.11, p < 0.05). This finding is particularly troubling given the close correspondence between the content of the informant and archival measures. However, an important source of divergence may be matching the scope of the environment. [We thank an anonymous reviewer for raising this issue.] Most archival measures use aggregate measures of industry that may not be relevant to the specific segments faced by each firm which the informant considers. This may be the reason why the results do not support Hypothesis 3. Future research should attempt to match the scope of the environment in comparing archival measures of environment with informant measures. A final finding in this paper is that more attention needs to be focused on the role of mediating filters. Our findings for Hypotheses 4 and 5 clearly support the arguments of others (Sutcliffe and Huber, 1998; Sutcliffe, 1994; Boyd et al., 1993) that mediating filters play an important role in managers’ assessment of the environment. The results from Hypothesis 4 indicate that organizational level mediating filters are more strongly related to informant measures of environmental variation than environmental uncertainty. This could be interpreted to mean that firm structure and strategy are more closely aligned with managers’ assessments of the firm environment than their perception of uncertainty about the environment. The results of Hypothesis 5, however, challenge our claim that informant measures of environmental variation do not have perceptual bias. Individual level mediating filters appear to explain much more of the variation in informant assessments of environmental variation than do organizational level variables. Further, the organizational level variables added very little to the level of explained variation when industry was used as a control variable. These seem to suggest that divergence between perceptual and archival measures of the environment is related to perceptual error rather than to real differences or unique attributes of the firm.
D.H. Doty et al. / Journal of Business Research 59 (2006) 268 – 277
5. Limitations, applications, and future directions Divergence in levels of analysis in measuring the environment is a consideration that has been discussed by researchers (Dess and Rasheed, 1991; Sharfman and Dean, 1991) and may have imposed some limitations on the current study. Two issues are particularly relevant. The first involves potential differences between a researcher’s and a manager’s definition of the industry. Researchers typically define industry by the SIC code for the corporation’s dominant line of business. It is not clear, however, that managers always use this same industry classification system. Thus, careful attention is required to insure that there is exact correspondence between the manager’s and the researcher’s definition of the industry boundaries. A second issue arises because researchers typically assume that an industry environment is homogeneous. For example, archival environment measures may be calculated as some form of aggregate based on all firms in the industry. Managers are then typically asked to respond about the environment of their firm, which means that the observed divergence may reflect real differences between the industry and the organization’s environment rather than inaccuracy in either the informant or the archival measure. Therefore, researchers should not assume that managers’ reports about their firm’s industry environment will converge with archival measures of aggregate industry characteristics. In this study, due to data limitations, we could not tease out the aggregate industry environment from the environment of the firm to see if that explains some of the divergence between archival and perceptual measures. Despite these limitations, the findings of our study clearly indicate that researchers should continue studying the environment and clarify the causes and consequences of divergence between archival and informant measures of the environment. While the results of the current study are not conclusive, they do offer some insight for the design of future studies. First, researchers must recognize that environmental uncertainty and environmental variation are different constructs and that future studies will benefit from including both types of constructs. Second, the current study has examined the effects of blocks of mediating filters, but does not include detailed examination of specific organizational or individual level filters. Additional studies of the specific filters, especially the individual level mediating filters, may help researchers determine how to remove or control for the biasing effect that these filters appear to have on informant reports about the environment. Third, the data that was available for the current study forced us to focus more on environmental variation than on environmental uncertainty. Future studies that are designed to more directly assess the affect that mediating filters have on the level of environmental uncertainty experienced by managers should be conducted. The practical implications of our study are also meaningful. Managers should realize that when they assess the
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environment, they are actually indicating two aspects: (a) the actual environment facing the firm and (b) their perception about it. These findings provide useful guidance and practical applications of environmental scanning and forecasting. For example, through variation measures like rate of change in sales, technology, etc., they can better assess the firm environment, while through uncertainty measures like Fwe do not know what type of changes in the environment we will have to deal with_ they can assess the reaction of individuals. Moreover, managers need to be aware of other factors (i.e., mediating filters) that may influence their scanning and forecasting abilities. While strategy and structure of the firm affect informant reports of environmental variation, individual-level variables influence their uncertainty judgments. In conclusion, the current work has helped to provide theoretical arguments and empirical evidence examining the problems that are caused when informants’ reports of the environment appear to diverge from archival measures of the environment.
Acknowledgements The authors would like to thank Greg Dess, Ravi Dharwadakar, Gerry George, and Ken Smith for comments on previous versions of this manuscript.
Appendix A. Summary of data sets
Year collected N Respondents
Industries Archival data source Measures used
Data set 1
Data set 2
Data set 3
1993 137 (41 banks) Top management team Banking
1994 364 (65 firms) Top management team 34 industries Compustat II
1988 91 (5 industries) Top manager
Environment
Environment
Environment
(1) Informant frequency, amplitude, and predictability (2) Informant effect uncertainty, state uncertainty, and response uncertainty Organizational level mediating filters decentralization, formalization, product-market development, differentiation
Archival instability and munificence
(1) Informant frequency, amplitude, and predictability Organizational level mediating filters
Informant instability and munificence
5 industries
Centralization, formalization, product-market efficiency, product-market development, market scope
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D.H. Doty et al. / Journal of Business Research 59 (2006) 268 – 277
Hypothesis tested
H1, H2, H4
H3
Individual level mediating filters need for achievement, locus of control, tolerance for ambiguity, risk aversion, management discretion H5
Appendix B. Data set 1: questionnaire items for environmental variation and uncertainty
1
1
1
1
1
1
State uncertainty
Questionnaire items
We never know what types of changes in the bank’ environment we will have to deal with. It is difficult to determine what our bank’s external environment will be like 1 year from now. Effect uncertainty We can usually tell if changes in the environment will be good for our bank. We can normally tell beforehand if our responses to environmental changes will have a positive effect on our bank’s performance. The effects that changes in the external environment will have on our bank are normally easy to determine. Response uncertainty When our bank’s environment changes, we are uncertain which of our possible responses will produce desirable outcomes. It is very difficult to determine what alternatives are available to our bank for responding to environmental changes. Amplitude Over the past year, how important to your bank were the changes in technology? Over the past year, how important to your bank were the changes in government regulations? Over the past year, how important to your bank were the changes in the types of customers using your bank? Frequency Over the past year, how many changes has your bank experienced in your depositors’ needs? Over the past year, how many changes has your bank experienced in your borrowers’ needs? Over the past year, how many changes has your bank experienced in technology?
Over the past year, how accurately has your bank been able to predict the changes in government regulations? Over the past year, how accurately has your bank been able to predict the changes in technology? Over the past year, how accurately has your bank been able to predict the changes in the types of customers using your bank?
Appendix C. Data set 2: example of informant and archival measures from Sutcliffe (1994)
Construct Data set Construct
Predictability
Archival measure Compustat)
Informant measure (questionnaire)
Archival Construct Items (a – c)
Complete informant construct (items 1 – 9) Reduced informant construct (items 1 – 3)
Instability a. Industry sales
1. The volume of sales for firms in your industry fluctuates very little from year to year b. Industry capital 2. Capital expenditures within your expenditures firm’s principal industry are relatively constant from year to year c. Industry net assets 3. The total value of assets for the firms in your industry varies a lot from year to year 4. Customer demand and preferences are relatively stable in your industry 5. Your firm must frequently change the way it produces its goods or services in order to be competitive 6. It is difficult to foresee the actions of your firm’s competitors 7. The actions of your major suppliers (including materials, equipment, or labor suppliers) change very little from 1 year to the next 8. Public/political attitudes toward your industry and its products/ services are relatively stable 9. Your firm frequently changes its technology to keep up with competitors
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