Dimensions of Organizational Task Environments
Author(s): Gregory G. Dess and Donald W. Beard
Source: Administrative Science Quarterly , Mar., 1984, Vol. 29, No. 1 (Mar., 1984), pp.
52-73
Published by: Sage Publications, Inc. on behalf of the Johnson Graduate School of
Management, Cornell University
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Dimensions of Organizational Task Environments
Gregory G. Dess
and
Donald W. Beard
Industrial classifications were used as a basis for operational definitions of both industrial and organizational task
environments. A codification of six environmental dimensions was reduced to three: munificence (capacity),
complexity (homogeneity-heterogeneity, concentrationdispersion), and dynamism (stability-instability, turbulence). Interitem and factor analytic techniques were used to
explore the viability of these environmental dimensions.
Implications of the research for building both descriptive
and normative theory about organization-environment relationships are advanced.
INTRODUCTION
This paper describes one approach to measuring the task
environment of organizations, using the data and schema
developed to record resource transactions in the national social
accounts. The organizational task environment is
operationalized in ways that are consistent with the
population-ecology and resource-dependence conceptualization of the interaction of organization and environment.
A central assumption of the present study, and one that is
grounded in both the resource-dependence and population-
ecology paradigms, is that the resources required for organizational survival are the most relevant focus in defining organizational environments. Aldrich (1979) clearly articulated this view
when he stated that "environments affect organizations
through the process of making available or withholding re-
sources, and organizational forms can be ranked in terms of
their efficacy in obtaining resources" (1979: 61).
Population-ecology theory as developed by Campbell (1969)
and others (Aldrich and Pfeffer, 1976; Hannan and Freeman,
1977; Aldrich, 1979; McKelvey and Aldrich, 1983) focuses
primarily on the resources available to populations of organizations, the aggregate birth and death rates of these populations,
and their spatial distribution. This view of organizations generally takes a relatively long-term perspective and favors longitudinal designs in empirical research. Aldrich and Reiss (1976) and
Nielsen and Hannan (1977) showed that organizational populations change in number and characteristics as resources and
other elements of their environment change. Pennings (1982)
showed how variance in resources in metropolitan areas affected the birth frequencies of business organizations in three
industries.
? 1984 by Cornell University.
0001 -8392/84/2901 -00 52/$1 .00
We would like to thank Howard Aldrich,
Alan Bauerschmidt, Kirk Downey, Neil
Snyder, Charles Summer, Henry Tosi, and
the editors and anonymousASQ referees
for their many helpful suggestions. We
would also like to thank Jeannette Strauss
for typing the manuscript.
Resource dependence theory as developed by some investigators (Pfeffer, 1972a, 1972b; Jacobs, 1974; Mindlin and
Aldrich, 1 975; Aldrich and Pfeffer, 1976; Pfeffer and Salancik,
1978) takes a finer grained view of organizations by looking at
their dependence on other organizations for resources. Jacobs
(1974) and Pfeffer and Salancik (1978) defined environmental
dependence as the importance of a resource to the organization
and the number of sources from which the resource is available, as well as the number, variety, and relative power of
organizations competing for the resource. Pfeffer and others
have shown that the various strategies organizations can use to
reduce external resource dependence vary systematically with
the kinds and degrees of dependence, e.g., the size and
52/Administrative Science Quarterly, 29 (1984): 52-73
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Task Environments
composition of boards of directors (Pfeffer, 1 972a, 1973),
merger and joint-venture activity (Pfeffer, 1 972b; Pfeffer and
Nowak, 1974), and executive recruitment and succession (Pfeffer and Leblebici, 1973).
The principal focus of the present study is the variation in
resource transactions of organizations with their environments,
in particular, the operational specification and empirical description of the task environments of organizations. This focus is
obviously limited in excluding important environmental dimensions such as Thompson's (1967) domain consensus, legitimacy, and prestige. Only objective measures of organizational
environments are treated here, that is, measures in which
qualified observers can apply the same scientifically rigorous
measurement procedures in research on similar samples of
organizational environments. Thorough treatment of the topics
of objectivity and associated measurement procedures is given
in references such as Kerlinger (1973) and Nunnally (1978). The
methodological guidelines that McKelvey (1975) recommended
for objective measurement of organizational variables in developing taxonomies of organizations can also be applied to
environmental units of analysis.
ENVIRONMENTAL UNIT OF ANALYSIS
The environmental unit of analysis for the present research was
chosen in the context of Starbuck's (1976) two categories in his
review of the literature on organization-environment relationships: (1) the directness of interaction between an organization
and elements of its environment, and (2) whether or not the
information describing the environment was derived objectively
(from disinterested outside observers) or subjectively (from
members of the organization). Starbuck (1976: 1082) divided
the level of direct interaction into two parts: the first including
those elements of the environment with which the organization is in direct exchange and the second including elements
that compete with the organization for the resources being
directly exchanged. The first set is clearly made up of organizations such as customers and suppliers (Dill, 1958: 424) that can
gain by cooperating in an exchange of resources. The set can
include organizations that are enough unlike the focal organization that rivalry is minimal and cooperation is increased
(Yuchtman and Seashore, 1967: 889).
The second set includes other organizations in the environment
that most influence the organization's "goal attainment" as
defined by Dill (1958: 410), that is, competitors for the resources supplied by customers and suppliers in the first set.
Rivalry for such resources is believed to be influenced by the
number and relative size of competitors (Shepherd, 1979;
Scherer, 1980). Thus, the full range of competitive suppliers in
resource markets, the full range of competitors producing close
substitutes, and the full range of potential customers would be
included in the group of environmental elements that influence
the performance and social structure of an organization.
Industrial classification systems have been developed to study
competition and performance among the producing sectors of
the economy, and the most recent system, the Standard
Industrial Classification (SIC), accounts for all producing organizations within a nation or other geopolitical unit (U.S. Office of
53/ASQ, March 1984
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Management and the Budget, 1972). The appropriateness of
the industry as a useful aggregation of organizations has been
subjected to critical review (Nightingale, 1978; Scherer, 1980;
Porter, 1980), and it has generally been supported as a suitable
aggregate for studying competition among organizations. For
the present study, the typical industry was quite homogeneous
in product or resource output. The average specialization ratio
for all four-digit SIC industries is approximately 90 percent;
nevertheless, there was heterogeneity in employment size,
technology, formal organization structure, and branded product
differentiation. Furthermore, the organizational unit classified
was the single-site establishment including central administrative offices. In addition, the number of firms operating establishments in each industry was also counted. The average ratio
of establishments to firms was 115: 100 in the 1977 Census of
Manufactures. Thus, from a population perspective, the total
number of establishments is almost equal to the number of
separate firms. Finally, resource transactions at an interindustry
level seemed likely to be stable, whereas interorganizational
transactions were likely to vary much more over time. However,
this gain in stability may be offset in lost detail and dynamics
among the population of organizations.
Although the principal information used to measure the vari-
ance in the environments of organizations was derived from the
SIC, the organizations that made up a particular SIC grouping or
industry were not the unit of analysis studied. The unit of
analysis was the organizational task environment, which included those elements that actively and directly cooperated and
competed with the focal organization, as discussed by Starbuck
(1976). This task environment mig ht have differed from that
specified by Dill (1958) in that it excluded regulatory groups of
an organization's environment. The organizational task environment included the industrial task environment, that is, that
set of all organizations with which members of a given industry
(including the focal organization) had transactions in the input
and output of resources, i.e., Ritz's (1979) producer-to-producer
transactions in input-output analysis. It did not, however, include organizations outside the industry of the focal organization that might otherwise have competed with it for input
resources.
DIMENSIONS OF ORGANIZATIONAL TASK
ENVIRONMENTS
The range of dimensions of organizational task environments,
as documented by Starbuck's (1976) monumental review of the
literature, is wide indeed. However, there is an emerging
consensus among researchers on a few important dimensions.
Aldrich (1979) discussed six dimensions (see below) derived
from an extensive review of the literature on populationecology theory and resource-dependence theory that "refer to
the nature and the distribution of resources in environments,
with different values on each dimension implying differences in
appropriate structures and activities" (1979: 63). The dimensions are clearly defined and can be readily applied to the task
environment as here defined. However, one dimension, domain consensus-dissensus, is omitted from this study because
of the difficulties in applying this dimension to profit-making
organizations such as those included in the present research.
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Task Environments
Aldrich's (1979) codification of environmental dimensions is
represented in a more parsimonious set as follows:
Munificence: capacity
Dynamism: stability-instability, turbulence
Complexity: homogeneity-heterogeneity, concentrationdispersion
The three dimensions are conceptually similar to those pro-
posed by others (Jurkovich, 1974; Pfeffer and Salancik, 1 978;
Mintzberg, 1979; Scott, 1981), and they are almost identical to
the important environmental conditions identified by Child
(1 972: 3): illiberality, variability, and complexity.
In the following discussion of the three dimensions, the place
of each dimension in Aldrich's scheme and its applicability in
further research will be considered, and operational definitions
of the variables used to measure each of Aldrich's dimensions
will be presented. Because of this early stage of theory building
measures were selected to ensure a high level of reliability.
Also, given the task-environment unit of analysis studied and
the available data sources, the domain of measures was
restricted.
Environmental Munificence
Starbuck's (1976) concept of environmental munificence as the
extent to which the environment can support sustained growth
is quite similar to Aldrich's concept of environmental capacity.
Both state that organizations seek out environments that
permit organizational growth and stability. Such growth and
stability may, for example, allow the organization to generate
slack resources (Cyert and March, 1963), which can in turn
provide a buffer for the organization during periods of relative
scarcity. Slack can also provide a means of maintaining organizational coalitions, provide resources for organizational innovation, and serve as a means of conflict resolution (Bourgeois,
1981; Chakravarthy, 1 982). Hirsch (1 975) found that organizations use complex, external social relationships to co-opt " in-
stitutional gate keepers" (e.g., physicians, for the pharmaceutical industry) in orderto ensure a flow of resources and to obtain
a more munificent environment. Staw and Szwajkowski (1975)
found that organizations competing in less munificent environments were more likely to commit illegal acts.
In the business-policy literature, Hofer (1975) stated that the
industry or product-evolution cycle is the "most fundamental
variable in determining an appropriate business strategy"
(1975: 789). The primary variable in this cycle is the rate of sales
growth, which is the primary factor determining an environment's munificence. Furthermore, several portfolio strategy
models consider market growth to be an important contingency
and a primary determinant in the long-term viability of a given
business strategy, for example, General Electric's Business
Screen and the Boston Consulting Group's Business Portfolio
Matrix (Hoferand Schendel, 1978). Ansoff (1965) proposed that
market growth permits member organizations to strengthen
their competitive position in a given market or to expand their
existing product-market scope.
In several empirical studies, the level of profitability of the
industry within which an organization competes has been
found to be a significant predictor of organizational perform55/ASQ, March 1984
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ance (Beard and Dess, 1979, 1981; Lieberson and O'Connor,
1972). For example, Beard and Dess (1981) found that industry
return on equity (a measure of interindustry variation) often
explained a greater percentage of variance in firm return on
equity than relative firm size, firm capital intensiveness, or firm
debt leverage (measures of intraindustry variation) combined.
Environmental Dynamism
Much of the literature in organization theory and businesspolicy theory has dealt with dynamism and suggests that
turnover, absence of pattern, and unpredictability are the best
measures of environmental stability-instability. Miles, Snow,
and Pfeffer (1974) and Jurkovich (1974) have contended that it
is important to distinguish between the rate of environmental
change and the unpredictability of environmental change.
Dynamism should be restricted to change that is hard to predict
and that heightens uncertainty for key organizational members.
Members of organizations competing in a dynamic industry will
be more likely to segment homogeneous elements of their
environments (March and Simon, 1958) to enable them to cope
with uncertainty. Thompson, for example, considered dealing
with uncertainty the "essence of the administrative process"
(1967: 159). The literature from organization theory, business
policy, and industrial organization has suggested organizational
strategies and tactics such as buffering, collusion, long-term
contracts, and vertical integration to create a more predictable
environment. Uncertainty will also affect organization structure,
because as task uncertainty increases, more information must
be processed among decision makers to achieve a given level of
performance (Galbraith, 1973: 4).
Aldrich (1979) stated that environmental turbulence "leads to
externally induced changes . .. that are obscure to administrators and difficult to plan for" (1979: 69). Aldrich's idea of
turbulence emphasized the degree of interconnection among
environmental elements, and it follows Emery and Trist's (1965)
definition without significant modification. Pfeffer and Salancik
contended that interconnectedness among organizations
creates uncertain and unstable environments for organizations
and that "changes can come from anywhere without notice
and produce consequences unanticipated by those initiating the
changes and those experiencing the consequences" (1978:
68). Terreberry (1968), building on Emery and Trist's (1965)
work, was among the first to stress the difficulty of planning for
changes in an organization's task environment when such
changes originate in its residual environment. She suggested
that the degree of interconnection in an organization's residual
environment was increasing as industrial economies evolved.
Environmental Complexity
Child's (1972) conceptualization of environmental complexity as
"the heterogeneity of and range of an organization's activities"
(1 972: 3) is typical of other theorists' views (Thompson, 1967;
Duncan, 1972). Duncan (1972), Pennings (1975), Tung (1979),
and others have contended that managers facing a more
complex (i.e., heterogeneous) environment will perceive
greater uncertainty and have greater information-processing
requirements than managers facing a simple environment.
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Task Environments
From the resource-dependence perspective, organizations
competing in industries that require many different inputs or
that produce many different outputs should find resource
acquisition or disposal of output more complex than organizations competing in industries with fewer different inputs and
outputs. This is due to the large number and variety of organizations with which they must interact in their "organization set"
(Evan, 1966) to deal with critical contingencies. Administratively, Chandler (1962) found that, in many industries, a diversification strategy that involved the organization in new product
and market activity led to a change in the organization's internal
organization structure, i.e., decentralization increased as the
diversity of an organization's activities increased. His findings
have been replicated for other organizations in the United
States (Rumelt, 1974), the United Kingdom (Channon, 1973),
Germany (Than heiser, 1972), and Italy (Pavan, 1972). Expansion
also increases the complexity and diversity both in the operations of an organization and its requirements for administrative
control. Galbraith (1973) suggested that as organizations expand their product or market scope, they use computers as a
means of maintaining organizational control.
Starbuck's (1 976) argument that organizational density induces
organizational interdependence suggested that Aldrich's
concentration-dispersion dimension also underlies the environmental complexity construct. Williamson (1965) and Phillips (1960) theorized that oligopolistic behavior is fostered by
organizational density because interorganizational communication is facilitated. Organizations in a network probably deal with
a greater number of suppliers, customers, and competitors in
their task environments. On the other hand, if all organizations
in a given industry are evenly distributed throughout the range
of the environment, a given organization would have fewer
organizations of a similar kind in its organization set (Evan,
1966). Duncan (1972) viewed environmental organizations as
external factors that managers take into account in their decision making. Aldrich (1979: 72) contended that the increase in
structural complexity of the environment would increase the
need for strategic activities.
RESEARCH HYPOTHESES AND METHODOLOGY
Given the exploratory nature of this study, we have tried to
achieve parsimony in the description of environmental dim
sions. We do, however, recognize that there are trade-offs in
this approach. Weick (1979) and Blalock (1982), for example,
argued that generalizability, accuracy, and simplicity cannot be
achieved simultaneously. We have tried to achieve generalizability and simplicity in our study with an unavoidable reduction in accuracy.
It was hypothesized that environmental munificence, complexity, and dynamism could each be identified as a separate factor
in a factor analysis of variables selected to measure these
dimensions. The hypotheses were:
Hypothesis 1: Organizational task environments will vary in terms of
their munificence, and environmental capacity variables will load on
one common factor.
Hypothesis 2: Organizational task environments will vary in terms of
their complexity, and homogeneity-heterogeneity and concentrationdispersion variables will load on one common factor.
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Hypothesis 3: Organizational task environments will vary in terms of
dynamism, and stability-instability and turbulence variables will load on
one common factor.
To explore the three proposed environmental dimensions, a
sample of 52 manufacturing industries (Table 3, below) identified by their SIC codes was randomly selected from a population of approximately 460 such industries classified by the U.S.
Bureau of the Census (1 977).1 The use of four-digit SIC's as a
building block for the task environment allows straig htforward
generalization to the entire population of manufacturing organizations, presently numbering over 300,000, and operationalizing
manufacturing task environments allows for unambiguous replications. The sample may not reflect a representative sample of
establishments, since some SIC's may include many incumbents and others very few. Most of the data comes from large
samples (annual surveys) but not complete censuses of
establishments. Therefore, sampling variability associated with
the measures of industrial characteristics may vary inversely
with the number of establishments in an SIC industry.
1
The small ratio of subjects (N=52) to measures (N= 17) may result in instability in the
factor loadings due to sampling error (Kim
and Mueller, 1978; Nunnally, 1978). However, sampling error has been minimized
through the relatively large sample of industries (N=52) drawn from a relatively
small population (N=460) (Kerlinger, 1973),
and the industry-level data in this research
is much more reliable than data associated
with individual differences. For example,
most measures of the capacity and
stability-instability dimensions consisted of
a series of data points over time, rather
than just a single data point.
Aldrich's (1979) five dimensions were operationalized in the
present research by constructing four or five continuous scale
measures for each dimension, 23 in all. The data for these 23
variables were obtained from secondary sources, primarily the
U.S. Bureau of the Census (1977) and the U.S. Office of
Business Economics (1973), and covered some or all of the
years from 1967 through 1977. The Appendix provides a
complete listing of the variables, measurement scales, and data
sou rces.
Six variables (VI -V6) were used to operationalize capacity
(environmental munificence). The first five variables were
considered to be derivatives of overall market demand that
measured the relative rate of industry growth. The first four,
growth in sales (VI), growth in price-cost margin (V2), growth in
total employment (V3), and growth in value added (V4), were
specified as the rate of growth (regression coefficient) divided
by the mean value of the dependent variable (to adjust for
absolute industry size) over a ten-year period (1968-1977).
2
Growth in the number of establishments (V5) was measured as
The extent of dispersion about a trend line
the average annual percentage change over an eleven-year
-controlled forabsolute industry size- is
consistent with several researchers' conperiod (1967 to 1977) because annual data were not available for
ceptualization and measurement of volatilthis measure. Industry sales concentration (V6) was
ity, dynamism, instability, unpredictable
operationalized as the proportion of the total value of shipments
change, etc. We believe that our operational measures of the stability-instability accounted for by the eight largest companies, since sales
dimension are superior to many other meaconcentration is the most validated correlate of industry profsures that have been proposed. For example, Snyderand Glueck (1982) used a volatil- itability, as Weiss (1974) found in his review of about eighty
ity measure which incorporated the coeffi-
cient of variation: T(y ...y)2/XI(Y) where
X=number of years, Y=sales revenue.
studies.
Five variables (V 1I -VI 5) were used to operationalize Aldrich's
Since this measure does not distinguish
environmental stability-instability. Four of these variables (VI 1,
between the ordering of the data points
VI 2, VI 3, VI 5) used the same data sources as four (VI, V2, V3,
and measures only their dispersion orvariation from the mean, it is unable to detect
V4) of the six capacity (munificence) variables. Environmental
variation from a trend line. To illustrate,
stability-instability was measured by the dispersion about the
assume two different industries with sales
regression line obtained when each dependent variable was
volumes for each of five consecutive years
of 10, 20,30,40, 50 and 30, 50, 10, 20,40.
regressed on time over the period 1968 to 1977.2 This form of
These industries would have the same
measurement is consistent with the approach of other revolatility index. However, the former industry would have a perfectly linear relation- searchers (Tosi, Aldag, and Storey, 1973; Child, 1974;
ship between the dependent variable and
Bourgeois, 1978; Snyder and Glueck, 1982), all of whom
time; whereas, the latter industry's sales
consider instability to be unsystematic, unpredictable change.
pattern may be described as highly
Technological instability (VI 4) was operationalized as the perstochastic.
581ASQ, March 1984
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Task Environments
centage of scientists and engineers in the total number of
employees. Several researchers (Child, 1975; Bourgeois, 1978;
Snyder and Glueck, 1982) have used this measure of relative
expenditures on R&D to measure technological instability.
Turbulence was conceived and measured in two ways based on
data developed as part of the United States input-output
research (U.S. Bureau of Economic Analysis, 1974a, 1974b,
1974c). This use of data from an input-output model to measure
environmental turbulence is, we believe, original, although Evan
(1966) suggested long ago that input-output data would be
useful in operationalizing organizational environments.3 The
first measure, the degree to which a unit change in the output
of other industries affects an industry, was developed from
standard input-output coefficients. Variable 20 was the sum of
all requirements, both direct and indirect, placed on a given
industry if every industry increased its output by one unit;
variable 21 was the sum of indirect requirements. The second
measure was the proportion of industry output sold to sectors in
the input-output model for purposes other than consumption.
Variable 22 was the proportion of an industry's output sold to
other industries for further processing; variable 23 was the
proportion sold for investment. Investment is the least stable,
that is, the most radically cyclical, of all kinds of final demand.
For three of the four variables (V7, V8, VI 0) used to
operationalize the homogeneity-heterogeneity dimension, the
3
Several authors have used input-output
data. These include Pfeffer's (1 972b) study
of merger activity, Beard's (1979) study of
task complexity, and Prescott's (1981)
study of product differentiation. Burt has
probably made the most extensive use of
this data to examine such issues as industry profit levels (Burt, 1980) and the relationship between market constraints and
the composition of boards of directors
(Burt, Christman, and Kilburn, 1980).
Gibbs-Martin Formula (Gibbs and Martin, 1962) was used. This
has the advantage of incorporating both structural differentiation (the number of categories) and distributive differentiation
(the dispersion among the categories). For example, concentration of inputs is an index that increases with the number of
different industries as well as with the evenness of the
distribution of inputs among these industries. Rumelt (1974),
for example, addressed the limitations of restricting measures
of diversification to product counts. Concentration of industry
inputs (V7) and outputs (V1 0) reflected the extent to which a
large portion of an industry's input was supplied by, or purchased by, relatively few industries. Diversity of industry products (V8) measures the degree to which the output of an
industry was concentrated within a few, seven-digit SIC products or was evenly spread among many. The specialization ratio
(V9) measured the proportion of the value of shipments by the
manufacturing sites in an industry accounted for by the one
primary product by which they were assigned to the classification (i.e., high specialization ratio indicating few products). All
four operational measures of environmental concentrationdispersion used data about the same variables that were used
for the measures of capacity and stability-instability. The
Gibbs-Martin formula (Gibbs and Martin, 1962) was used to
measure the geographical concentration of industry sales (V1 6),
value added by manufacture (V1 7), employment (V1 8), and
establishments (V19).
It is important to note here that although the sampling unit was
an industry, we were measuring variation in each industry's
input and output environments as well as variation in the
characteristics of member organizations, in keeping with our
definition of an organizational task environment. It is true that
our measures of both the input and the output subsets of the
organizational task environment were somewhat indirect. They
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were based on a given industry's transactions with other
industries supplying inputs or buying outputs. Nevertheless,
such measures say much about the input and output subsets of
any given organization's task environment. For example, the
measure, concentration of inputs (V7), was an index that
increased with the number of different input industries and the
evenness of the distribution of inputs among these industries.
This item provided one measure of the heterogeneity of a given
industry's input environment. Similarly, an index of output
concentration, (V1 0) provided a measure of the heterogeneity
of a given industry's output task environment.
For practical purposes, the characteristics of all member organizations making up an industry were assumed to be a close
enough approximation of all member organizations with any
one subject organization excluded. Thus, an industry's aggregate characteristics were assumed to be the same for any one
member organization. This component of any focal organization's task environment was measured in terms of characteristics internal to the organization's industry. For example, sales
concentration (V6) measured the degree to which a given
industry's output was concentrated in the hands of a relatively
few, large competitors internal to the industry. Geographic
concentration, e.g., number of establis hments (V1 9), measured
the degree to which member organizations of a given industry
were densely or widely distributed geographically.
The data were analyzed in two major steps: first by interitem
reliability analysis and, second, by factor analysis. The interitem
analysis assessed the internal consistency among the multiple
items used to measure each of the three dimensions of
organizational task environments specified in the hypotheses.
This "purification step" (Churchill, 1979: 69) made it possible to
delete internally inconsistent variables and thereby to minimize
the chance that theoretically meaningless factors would
emerge in subsequent factor analysis.
The factor analysis was done on the reduced data matrix
remaining after the interitem analysis. Because of the exploratory nature of the research and the need for simplifying
assumptions, the hypothesized factors were assumed to be
orthogonal. Although the effects (e.g., uncertainty) of some of
the environmental dimensions (e.g., complexity and dynamism)
might have been similar, there was no a priori theoretical
rationale for the assumption of independence of the three
environmental factors; therefore, a principal components
analysis followed by varimax rotation was used. These two
techniques provided the principal test of the hypotheses. Our
concern was to use the factor analysis to condense the data and
4
LISREL VI uses a maximum likelihood or a
least squares estimation procedure to
examine linearstructural equation systems.
The structural equation part of the LISREL
model specifies the number of factors and
the intercorrelations among them;
whereas, the measurement model
hypothesizes the pattern of relationships
(loadings) between the hypothesized factors and the environmental variables. The
relatively small sample size prevented the
application of the measurement model that
Bearden, Sharma and Teel (1982) and
Boosma (1984) suggested should be
applied to samples greater than 200.
to provide "statistical parsimony" (Nunnally, 1978: 348) rather
than to determine the number of factors, the factor patterns,
and the intercorrelations among factors as is normally done in
confirmatory procedures for factor analysis such as LISREL VI
(J6reskog and Sbrbom, 1983).4
Because the factor analysis performed using this limited sample size must be considered exploratory, heuristics or decision
rules are used to permit the analysis to be conducted in a spirit
of confirmation. The decision rules were used to examine a
priori beliefs about the underlying dimensions of the variables.
We argue that without such a priori beliefs, one must rely on
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Task Environments
post-factum interpretations, which have the disadvantages of
being excessively flexible, nonnullifiable, and dependent on
external confirmation (Rosenberg, 1968: 232-239).
The decision rules followed Kim and Mueller's (1978) suggestions. First, at least three variables were required to load at a
level greater than or equal to .30 on each a priori factor, since
loadings of smaller size represent less than 10 percent of the
variance of the factor. The minimum of three variables was also
recommended by Thurstone (1947). Second, the eigenvalue of
any common factor was required to be greater than or equal to
one. This criterion required that each significant factor had to
explain a proportion of the total variance that was greater than
or equal to the average percentage of the total variance of a
single variable. Finally, the three factors were required to
exhibit a simple structure; that is, each factor had to have
relatively high loadings, i.e., close to 1.0 with an appropriate set
of variables and fairly low loadings, i.e., close to 0.0 with the
remaining variables.
RESULTS
The interitem analysis of the 23 variables selected to measure
the three environmental dimensions specified in the hypotheses eliminated 6 variables, leaving 17 for further analysis. The
standardized alphas (As) for the scales corresponding to the
three hypothesized factors were munificence, .816; complexity, .657; and dynamism, .610. These alphas all equalled or
exceeded the value, e =.6, which Nunnally (1978) suggested
as appropriate for exploratory research.
The principal-factor solution with varimax rotation for the 17
variables is shown in abbreviated form in Table 1. Although five
significant factors (i.e., with eigenvalues greater than or equal
to 1) emerged from the factor analysis, only the three factors
explaining the greatest amount of total variance are included in
Table 1. The fourth and fifth factors (with eigenvalues of 1.21
and 1.11, respectively) were dropped in the interest of parsimony and because, in combination, they explained less total
variance than the third factor alone. Additionally, a scree test
(Cattell, 1965) dictated that they be excluded.
The factors in Table 1 are rank ordered from left to right
according to the proportion of the variance they explained. The
three factors are named in accordance with the hypotheses
and on the basis of those variables that loaded heavily on each
factor.
When the decision rules presented above were applied to the
factor-structure matrix in Table 1, all hypotheses received
support. At least three a priori variables loaded hig hly (i.e., factor
loadings > .30) on each factor; the eigenvalues for each of the
three factors were greater than 1.0; and they exhibited a simple
structure. However, since the principal components analysis
with varimax rotation extracts factors that are by definition
independent (orthogonal), the viability of such independence
was investigated. A principal-factor solution with an oblique
rotation was also developed. The variables that loaded on the
three significant factors were strikingly similar, further supporting the orthogonality of the factors. (These results are available
from the first author.)
61/ASQ, March 1984
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Table 1
Seventeen Environmental (Industry) Variables: Factor Structure and Communalities
Factor
1
Factor
2
Factor
3
Munificence Complexity Dynamism Communalities
Squared Squared Squared
Factor factor Factor factor Factor factor
loadings loadings loadings loadings loadings loadings
Industry variable (aj1) (aj12) (a!2) (a122) (aj3) (aj32) (hj2)
Growt h
Sales (V1) .92095* .84815 -.07482 .00560 .04196 .00176 .85551
Price-cost margin (V2) .89898* .80817 -.10973 .01204 .06985 .00488 .82509
Total employment (V3) .89310* .79763 .09359 .00876 -.05010 .00251 .80890
Value added (V4) .96117* .92385 -.08532 .00728 .03215 .00675 .93788
No. of establishments (V5) .46774* .21878 .11835 .01401 -.00685 .00004 .23283
Concentration of inputs (V7) -.18339 .03363 -.15073 .02272 -.04804 .00231 .05866
Specialization ratio (V9) -.02895 .00084 -.30621* .09376 -.53626* .31726 .41186
Geographical concentration
Sales (V16) -.04131 .00171 .97554* .95168 .06298 .00397 .95736
Value added (V17) -.01437 .00021 .96786* .93675 .07796 .00608 .94304
Total employment (V18) .00989 .00010 .96892* .93881 .08547 .00731 .94622
No. of establishments (V19) -.08689 .00755 .83587* .69868 -.06210 .00375 .70998
Instability
Sales (V11) .08894 .00791 -.09246 .00855 .83417* .69583 .71229
Price-cost margin (V12) .19111 .03652 .07399 .00547 .65123* .42410 .46609
Employment (V13) -.19607 .03844 .04213 .00178 .64185* .41197 .45219
Value added (V15) .02228 .00050 -.02898 .00084 .92508* .85577 .85711
Indirect effect on industry
output (V21) -.11866 .01408 -.10979 .01205 .00600 .00004 .02617
Intermediate market
orientation(V22) -.15797 .02495 -.14126 .01995 .31519* .09934 .14424
Eigenvalue 3.92140 3.73708 2.83367 10.49215
Common variance (% 37.37 35.62 27.01 100.00
Total variance (%) 23.07 21.98 16.67 61.72
*Factor loadings> .30.
Table 1 shows that five munificence variables that were
postulated a priori (V1 -V5) loaded highly on the munificence
factor. Four of the five factor loadings approached or exceeded
.90, and the loading of the other variable exceeded .40. Thus,
measures relating to the growth in the total value of industry
shipments appeared to underlie a single construct.
Five of the eight complexity variables (V9, 16-19) loaded hig hly
on the complexity factor. Four of these factor loadings exceeded .80, and the other loading was slightly above the
threshold level of .30. This factor was clearly dominated by
measures of geographical concentration. Five of the nine
dynamism variables loaded highly on the dynamism factor.
Industry sales, price-cost margin, total employment, and value
added each had a factor loading greaterthan .60. However, only
one variable, intermediate market orientation (V22), used to
operationalize and measure Aldrich's (1 979) turbulence dimension, loaded on this factor at a level slightly greater than the
threshold level of .30. Thus, the dynamism factorappears to be
largely a unidimensional construct like the complexity factor. In
general, all three factors showed a simple structure. Each
factor had relatively high loadings with an appropriate subset of
variables and fairly low loadings with the remaining variables,
except for the specialization ratio (V7), which loaded negatively
and significantly on both the complexity and dynamism factors.
Although the empirical results support each of the three
hypotheses, each factor in Table 1 was dominated by a set of
variables that uses common scaling procedures and similar
components. For example, the variables that loaded highly on
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Task Environments
the munificence factor were all derivatives of overall market
demand, and all but one of these, growth-number of establishments (V5), were operationalized as the slope of a regression line fora ten-yeartime period divided bythe mean value of
the relevant dependent variable. Thus, one must consider the
possibility that important variables that may represent additional environmental dimensions may have been eliminated in
the interitem analysis. Also, some of the proposed dimensions
based on the theory construction and measurement may
actually be multidimensional. Although the interitem analysis
helped to provide a more parsimonious solution, an exploratory
analysis of all twenty-three variables was conducted for added
precision.
The factor structure that emerged from an explanatory factor
analysis (with varimax rotation) of all twenty-three variables is
shown in Table 2. Eight significant factors (eigenvalues > 1)
emerged from this analysis.
Table 2
Factor Structure and Communalities: Twenty-Three Environment (Industry) Variables
Industry variable Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7 Factor 8
Growth
Sales (V1) -0.05602 0.93133* 0.01309 0.08788 0.04708 0.01732 0.03502 -0.05469
Price-cost margin (V2) -0.08586 0.91160* 0.04918 -0.03774 0.07203 -0.03884 -0.00427 0.13519
Employment (V3) 0.08724 0.85738* -0.05429 -0.01201 0.23946 -0.06375 -0.05532 -0.17837
Valueadded(V4) -0.07376 0.96535* 0.05812 0.04161 0.07471 -0.06433 0.00563 0.03927
No.ofestablishments(V5) 0.09117 0.30060* 0.02915 0.01358 0.83243* 0.00176 0.05067 -0.10751
Industry sales concentration (V6) 0.02251 -0.03859 -0.05913 -0.09593 0.13891 0.08562 0.92030* 0.10209
Concentration of inputs (V7) -0.06031 -0.11628 -0.03090 -0.08796 0.11321 0.82029* -0.03130 -0.07981
Diversity of industry products (V8) -0.10627 0.04625 -0.15494 0.14898 0.14403 0.19759 -0.08733 -0.77984
Specialization ratio (V9) -0.30268* -0.04296 -0.54533* -0.03784 0.10223 0.06180 -0.00525 0.49664
Concentrationofindustryoutputs(V10) -0.30171* 0.03450 0.07737 0.35291* -0.13861 0.64739* 0.23906 -0.09556
Instability
Sales(V11) -0.03981 0.15786 0.82913* -0.07075 -0.09494 0.15550 -0.07510 0.15344
Price-costmargin(V12) 0.04014 0.17674 0.61132* 0.14485 -0.07708 -0.28742 0.41605* -0.20751
Employment (V13) 0.04781 -0.20887 0.66697* 0.12796 0.05093 -0.05931 -0.22041 0.05188
Technological (V14) 0.08967 -0.06110 -0.07475 0.17435 0.57396* 0.42947* 0.44070* -0.00711
Valueadded(V15) -0.00949 0.02721 0.92270* 0.13448 0.05673 0.01403 0.09954 0.06676
Geographical Concentration
Sales(V16) 0.97868* -0.04093 0.03915 0.00672 -0.02138 -0.02012 0.02937 0.01312
Valueadded(V17) 0.97305* -0.01272 0.05306 0.01302 -0.01492 0.01346 0.04581 -0.04047
Employment (V18) 0.97321* 0.00594 0.05755 0.00829 -0.01053 -0.02193 0.12857 -0.00134
No. of establishments (V1 9) 0.81750* -0.10783 -0.06303 -0.01611 0.00619 -0.28639 -0.20260 0.06075
Impact of all other industries
onoutputofgivenindustry(V20) -0.00535 0.15534 0.10776 0.70562* 0.01017 0.11257 -0.02479 0.44882*
Indirect effects on industry
output(V21) -0.10906 -0.08478 0.03492 -0.86859* 0.01904 0.11895 0.03139 0.19299
Intermediate market orientation
(V22) -0.10295 -0.15536 0.30793* 0.68489* -0.07676 0.15098 -0.02295 -0.20523
Proportion of industry shipments
sold for Investment (V23) -0.15389 0.12034 -0.04099 -0.14184 0.85288* -0.00467 0.02753 0.00261
Eigenvalue 4.14219 3.80917 3.80917 2.24203 1.79687 1.31498 1.14088 1.01051
Common of variance(%) 18.0 16.6 13.4 9.7 7.8 5.7 5.0 4.4
Total variance (%) 18.0 34.6 48.0 57.7 65.5 71.3 76.2 80.6
*Factor loadings > .30.
The results of the two analyses were quite similar (compare
Tables 1 and 2) for the first two factors, although the order of
the percentage of variance explained was reversed. The complexity factor in both factor structures was dominated by
measures of geographical concentration, and the munificence
factor was dominated by measures of industry growth. Con-
centration of industry outputs (V1 0), a variable dropped in the
interitem analysis and which was used to operationalize Aldrich's "homogeneity-heterogeneity" dimension, loaded on
the complexity dimension, but its factor loading was just above
the threshold level (.30), and the sign was not in the expected
direction. Clearly, the first two factors were largely unidimen63/ASQ, March 1984
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sional in both factor structures. However, the factor structure
results indicate that the dynamism construct is multidimensional. The third factorshowed four of the five variables used to
operationalize the stability-instability dimension displaying high
loadings. Since these variables are all derivatives of overall
market demand, this factor may be labeled "market
dynamism." Three of the variables used to operationalize
environmental turbulence loaded on a fourth factor that may be
labeled "turbulence." However, the sign for indirect effects of
industry outputs (V21) was not in the expected direction. Three
variables had high loadings on the fifth factor: growth in the
number of establishments (V5), instability-technological (V14),
and proportion of shipments sold for investment (V23). This
factor documented a positive association among technological
innovation, capital equipment manufacturing, and growth in the
number of manufacturing establishments; therefore, the fifth
factor may be labeled "technological dynamism." The sixth
factor may be considered to be another dimension of the
complexity construct because two variables used to
operationalize homogeneity-heterogeneity -concentration of
inputs (V7) and concentration of outputs (V1 0)- showed high
loadings. The seventh and eighth factors had less interpretive
appeal and explained less combined variance than any of the
first four factors.
DISCUSSION
This research suggests that an industrial classification system
provides useful building blocks to improve the conceptualization and measurement of organizational task environments.
Interitem analysis and factor analysis provided tentative support
for the operational viability of at least three environmental
dimensions - munificence, dynamism, and complexity.
This research used statistical procedures and tapped data
sources that improve construct validity; whereas, previous
research has often conceptualized and measured dimensions
of the organizational environment with little confirmation beyond face validity. For example, Duncan (1972) and Thompson
(1967) used two dimensions (simple vs. complex, and stable vs.
shifting) to describe organizational environments. Similarly,
Bourgeois (1978) and Tosi, Aldag, and Storey (1973) used
measures of volatility to represent what they considered a priori
to be the key dimenion of the external environment. Child
(1975) viewed measures of technology and marketing as the
more significant environmental variables in moderating the
relationship between organization design and performance.
Child attributed the higher significance of these variables to
Duncan's (1972) findings and his ability to measure such
variables more precisely with the information available (Child,
1975: 12). In many empirical efforts, measurement often
precedes rather than follows the development of substantive
theory. We agree with Blalock (1982) that the processes of
theory construction and measurement should not be considered distinctly different. Otherwise, as Blalock contended, the
substantive and auxiliary measurement theories will be confounded together in empirical tests of a theory. 'This means
that if we obtain a poor fit, we will not know whether it is the
substantive theory, the auxiliary measurement theory, or both,
that is at fault" (Blalock, 1982: 25).
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Task Environments
The present research methodology has emphasized internal
consistency and reliability in measurement. The selection of
operationally related measures in conceptually restricted
categories has led to a relatively high degree of redundancy in
our measurement results. The reliability of the factor analytic
results obtained here could be assessed further by comparing
them with the factor structure derived from a different random
sample of task environments.
An important transition in the process of developing objective
environmental measures occurs when the assessment of
construct validity begins to replace measurement with content
or face validity only. As Nunnally (1978) noted, consistency is a
necessary but not sufficient condition for construct validity. "To
enhance construct validity, the analyst must determine (1) the
extent to which the measure correlates with other measures
designed to measure the same thing, and (2) whether the
measure behaves as expected" (Churchill, 1979: 70). The
convergent and discriminant validity of the measures developed here could be assessed more thoroughly with the multitrait, multimethod matrix (Campbell and Fiske, 1959) commonly
used in psychology. For example, perceptions of executives
and industry experts could be compared with the environmental
measures developed with the present methodology.5 Also, the
predictive validity of the present findings could be assessed by
correlating the factor scores of selected industries on the
munificence and dynamism dimensions with aggregate mea-
sures of industry profitability and industry risk, respectively.6
Generalizability of Research Results
5
It would be most difficult to ensure that the
perceptions of executives or outside experts about the domain of the industry
would be congruent with the SIC classification. Also, in the present research context,
the unit of analysis, operational measures,
and available data sources would make
further assessments of convergent and
discriminant validity very difficult.
6
Studies that have used measures of industry profitability (Conrad and Plotkin, 1968;
Bass, 1974) and industry risk (Bass, 1974),
however, have generally derived their
industry-level measures by aggregating
secondary financial data about organizations that are typically unrepresentative of a
given industry (i.e., large, publicly held
firms). Substantial measurement error
would be introduced if these two sets of
data were compared, because (1) the units
of analysis are different (i.e., establishment
versus firm), (2) large organizations tend to
be classified ata level of industrial aggregation (i.e., two-digit or three-digit SIC level)
higher than that used in the present
analysis, and (3) such organizations often
are highly diversified, making assignment
of their output to a four-digit SIC industry a
source of significant measurement error.
7
A copy of the factor score coefficient matrix and descriptive statistics for the
twenty-three variables is available from the
first author.
The present study is useful as an operational guide for classifying organizational task environments defined in terms of currently accepted industrial classification. Accordingly, the factor
analytic results can be used to generate standardized factor
scores forany four-digit SIC manufacturing industry on all three
environmental dimensions. Since the original sample (N=52)
was randomly selected from the total population (N=460), any
industrial or organizational environment can be ordered along
continua representing environmental dimensions through the
conversion of a given industry's raw data values. Thus, any
industry can be located precisely on each of the three factors
according to the equation:
f1=a1lzj + a2iZ2 + * * . +. a7iZ17.
where a11 is the factor score coefficient for variablej on factori
and zj is the industry's standardized value for variable].7 The
factor scores for the sample of the 52 industries on each of the
three environmental dimensions are shown in Table 3.
An example will be used to provide support for the construct
validity of the three factors developed here and to illustrate how
these results can be used in further research on organizationenvironment relationships. The two industries, Paints and Allied
Products (SIC 2851) and Sawmills and Planing Mills (SIC 2421),
are quite similar in their munificence and complexity factor
scores and quite different in their dynamism factor scores. It is
reasonable to assume that only 5.4 percent of all manufacturing
industries would be located between these two industries on
the munificence factor (the percentage figure is calculated
using areas under a normal curve), only 1 2 percent on the
complexity factor, but 86.7 percent on the dynamism factor.
65/ASQ, March 1984
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Table 3
Factor Scores and Ranks on Munificence, Dynamism, and Complexity Factors for 52 Sample Industries
SIC Munificence Dynamism Complexity
code factor factor factor
Sample Industries (1972) Score Rank Score Rank Score Rank
Meat Packing Plants 2011 .4646 (16) -1.0811 (47) -.2930 (28)
Canned Fruits and Vegetables 2033 .0392 (29) -1.0133 (45) .0195 (19)
Cereal Breakfast Foods 2043 1.9365 ( 2) 1.5530 ( 5) .7206 ( 9)
Cookies and Crackers 2052 .5272 (15) -.9198 (43) -.3166 (29)
Bottled and Canned Soft Drinks 2086 .5763 (12) -.6198 (37) -1.1112 (52)
Roasted Coffee 2095 -.4452 (41) .4481 (13) -.8307 (43)
Chewing and Smoking Tobacco 2131 .4311 (17) .0322 (19) -.1924 (24)
Knit Outerwear Mills 2253 .0556 (28) -.5859 (36) 1.4424 ( 6)
Tufted Carpets and Rugs 2272 .8209 ( 8) -.2086 (23) 2.7951 ( 2)
Padding and Upholstery Filling 2293 -1.7234 (49) 2.1977 ( 2) -.8292 (42)
Mens/BoysShirtsand Nightwear 2321 .3772 (18) -1.0193 (46) .2857 (12)
Women's and Misses' Suits/Coats 2363 -.1829 (37) -.1266 (22) -.1501 (23)
Children's Coats and Suits 2363 -1.6409 (47) -.8554 (42) 1.7392 ( 5)
Curtains and Draperies 2391 -.1075 (35) -.2096 (24) -.4641 (34)
Sawmills and Planing Mills, Gen. 2421 .1142 (27) 1.8448 ( 3) -.2431 (26)
Nailed Wooden Boxes and Shook 2441 2.3803 (52) 1.2862 ( 7) -1.0141
Mattresses and Bedsprings 2515 -.3959 (39) -1.1022 (48) -.9926
Paper Mills, exc. Building Paper 2621 .6435 (11) -.4645 (33) -.8433
Pressed and Molded Pulp Goods 2646 -.7317 (43) -1.1579 (50) -.3545
(49)
(48)
(44)
(32)
Building Paper and Building
Board/Mills 2661 -1.7818 (50) -.2162 (26) -1.0368 (50)
Manifold Business Forms 2761 1.0171 ( 6) -.2156 (25) -.9134 (47)
Industrial Gasses 2813 -.4116 (40) -.4723 (34) -1.1024 (51)
Organic Fibers, Noncellulosic 2824 .1453 (25) .0855 (18) 3.6362 ( 1)
Paints and Allied Products 2851 .2524 (20) -1.2455 (52) -.5812 (38)
Printing Ink 2893 .5412 (14) -.2915 (29) -.4015 (33)
Rubber Footwear 3021 -1.1823 (44) .0151 (20) .1003 (16)
House Slippers 3142 -1.4952 (46) -.9675 (44) .5634 (10)
Products of Purchased Glass 3231 .2683 (19) -.8029 (41) .2899 (11)
Fine Earthenware Food Utensils 3263 -.0720 (33) 1.0804 ( 9) -.5586 (37)
Asbestos Products 3292 -.7160 (42) -.3066 (30) -.7837 (41)
Steel Pipe and Tubes 3317 .9841 ( 7) 1.3425 ( 6) -.0377 (22)
Primary Lead 3332 .5675 (13) 1.5802 ( 4) -.5179 (36)
Nonferrous Wiredrawing and
Insulating 3357 .0344 (30) .4127 (14) -.7088 (40)
Cutlery 3421 .7661 ( 9) -1.1657 (51) 1.2103 ( 7)
Metal Doors, Sash, and Trim 3442 -.0765 (34) -.3096 (31) -.8939 (45)
Plating and Polishing 3471 -.0078 (31) -.7015 (40) -.2366 (25)
Metal Foil and Leaf 3497 .7210 (10) 3.5268 ( 1) .0191 (20)
Elevatorsand Moving Stairways 3534 -1.9415 (51) 1.0517 (10) .1832 (15)
Machine Tool Accessories 3545 .1298 (26) .2657 (16) .8951 ( 8)
Ball and Roller Bearings 3562 .2322 (22) -.6398 (38) .0835 (18)
Electronic Computing Machinery 3573 2.4783 ( 1) .2495 (17) -.3383 (31)
Transformers 3612 -.3306 (38) -.6983 (39) -.4766 (35)
Household Cooking Equipment 3631 1.0202 ( 5) .3565 (15) .0166 (21)
Electric Lamps 3641 .1880 (24) -1.1053 (49) -.2596 (27)
Electron Tubes, All Types 3672 -1.6624 (48) -.2571 (28) .2674 (13)
Storage Batteries 3691 1.4306 ( 4) -.3330 (32) -.9053 (46)
Ship Building and Repair 3731 1.7358 ( 3) -.5727 (35) -.7034 (39)
Guided Missile and Space Vehicles 3761 -1.4904 (45) -.0123 (21) 1.9338 ( 3)
Automatic Temperature Controls 3822 .2441 (21) .5472 (12) .2342 (14)
Watches, Clocks, Watchcases 3873 -.1293 (36) -.2261 (27) -.3166 (30)
Dolls 3942 .2243 (23) 1.1383 ( 8) 1.9191 ( 4)
Artificial Flowers 3962 -.0619 (32) .8893 (11) .0908 (17)
These factor scores are consistent with anecdotal data collected from chief executive officers from approximately thirty
firms during field research by the first author. The factor
analytic results for these two industries suggest one way to
advance understanding of the relationships between organiza66/ASQ, March 1984
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Task Environments
tional task environments and internal administrative structure.
Mintzberg (1979) contended that the dynamism and complexity
dimensions have very different effects on organization structure and that researchers have tended to mix effects of these
two dimensions together. Specifically, Mintzberg hypothesized
that: (1) the more dynamic the environment, the more organic
the structure, and (2) the more complex the environment, the
more decentralized the structure. The orthogonality of the
dynamism and complexity factors developed in this study
would permit testing for the separate effects on structure of
each environmental dimension. A research procedure to test
Mintzberg's first hypothesis would entail studying a sample of
organizations competing within each of the two industries
discussed above, since their environmental munificence and
their environmental dynamism on an organic structure dimension could be readily assessed. That is, the effects of munificence or complexity on the organic structure dimension would
be adequately controlled if these two industries were compared
on the dynamism and the organic structure dimensions.
We believe that more sophisticated studies could extend the
present research and methodologyto combine both perceptual
and objective measures of the environment, technology, and
internal administrative structure. Miles et al. (1 978), for example, clearly articulated how managers must make strategic
choices that simultaneously involve technology, structure, and
process variables that partially determine an organization's
performance. The relevance of both objective and perceptual
measures for complex, dynamic models of organizational adaptation such as Miles et al. (1978) is clear. Their model derives
support from several strategic choice theorists, including
Chandler (1962), Child (1972), Cyert and March (1963),
Thompson (1967), and Weick (1979), who argue that both the
environment and top managers' choices determine the behaviorand performance of organizations. Empirical research in this
vein would provide a better understanding of the relationship
between objective and subjective measures of the environment and their respective abilities to predict variation in organi-
zational technology, structure, and performance.
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Task Environments
Appendix: Operationalization of the Aldrich (1979) Dimensions
Industry Variable Measurement Scale Data Source
Vi. Growth in total Value of shipments; regression U.S. Bureau of the
sales slope coefficient (B), dividedby Census, 1977 Census
mean value (Y); 1968-1977. of Manufactures,
Preliminary Reports
MC 771 (20A to 39A).
V2. Growth in price- Value added by manufacture minus Same as VI.
cost margin total wages; same measurement
procedure as Vi.
V3. Growth in total Total employment; same measure- Same as Vi.
employment ment procedure as Vi.
V4. Growth in value Value added by manufacture; same Same as Vi.
added by manu- measurement procedure as Vi,.
facture
V5. Growth in the Number of manufacturing establish- Same as Vi.
number of ments, average annual percentage
manufacturing change, 1967-1977.
establishments
V6. Sales concen- Percentage of total value of U.S. Bureau of the
tration shipments by the largest 8 Census, Census of
companies, 1972. Manufactures, 1972,
vol. 1, ch. 9.
V7. Concentration n n U.S. Bureau of
of inputs = i " j) Economic Analysis,
Input-Output Structure
of the U.S. Economy,
1967, vol. 1.
C = concentration of inputs
I = dollar volume of inputs
i= 1,2,...,n
j= 1,2_. .,m
n = number of industries supplying
inputs = 352
m = number of industries in
sample = 52.
V8. Diversity of m m Same as Vi.
products Di = PF21Il( P11)2
D = product diversity index
P = dollar volume of 7-digit
SIC products
i= 2,2,...,n
j= 1,2,...,m
m = number of 7-digit SIC
product codes
n = number of industries in
sample = 52.
V9. Specialization Ratio of primary product ship- Same as Vi.
ratio ments to total (primary and
secondary, excluding miscellaneous)
product shipments for the establishments classified in the
industry.
V1O.
Concentration
Same
as
V7.
of outputs Cj
C = concentration of outputs index
0 = dollar value of outputs
Same measurement procedure as V7.
Vi1i. Instability in Value of shipments; standard Same as Vi.
total sales error of the regression slope
coeffkicent (Sb1) divided by mean
value (Y); 1968-1977.
V12. Instability in Value added by manufacture minus Same as Vi.
price-cost total wages; same measurement
margin procedure as V1 .
Vi13. Instability in to- Total employment; same measure- Same as Vi.
tal employment ment procedure as Vi1i.
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V14. Technological Percentage of scientists and U.S. Bureau of the
instability engineers in total number of Census, 1970 Census of
employees, 1970. Population, Subject
Reprints, Occupation
Industry, PC(2)-7C.
V15. Instability in Value added by manufacture; Same as V1.
value added by same measurement procedure as
manufacture V1 1.
U.S. Bureau of the
V16. Geographical m m Census, Census of
concentration S 2 E S 2 Manufactures, 1972,
of
total
sales
"Table
2.
General
Statistics by Geographical Area;
C = concentration of industry 1967 and 1972."
sales index
S = dollar volume of industry
sales
i= 1,2_. n
j= 1,2,. .m
m = no. of census divisions = 9
n = no. of industries in
sample = 52.
V17.
Geographical
Same
concentration
of
value
C,
as
V16.
=VA2jI(
E
VA11
added by
manufacture
C = concentration of industry
value added by manufacture
index
VA = dollar volume of industry
value added by manufacture
Same measurement procedure as V1 6.
V18. Geographical m m Same as V16.
concentration C = E TE,21( 2
of total
employment
C = concentration of industry
total employment index
TE = total industry employment
Same measurement procedure as V1 6.
V19. Geographical m m Same as V16.
concentration C= IIEE2I( IEj)2
of industry
establishments
C= = concentration of industry
establishments index
IE = no. of industry establishments
Same measurement procedure as V16.
V20. Impact of all m U.S.. Bureau of Economic
other industries T1= YTRj, where: Analysis, Input-Output
on output of i Structure of the U.S.
given industry T = total impact of other indus- Economy, 1967, Table 3.
tries on industry output
TRij = direct and indirect
(intermediate transactions)
(m TR-I:1 .00) (Table 3)
i= 1,2,...,n
= 1,2,..., m
m = no. of industries directly
or indirectly placing requirements on industryi =
352
n = no. of industries in the
sample = 52.
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Task Environments
V21. Relative power U.S. Bureau of Economic
of indirectly m q q Analysis, Input-Output
linked factors Pi = E TR~j - _DRjj1 X
on
output
Economy,
1967, Tables
2 and 3.
P = power of indirectly linked
factors on industry output
TRBj = direct and indirect
(intermediate) transactions
m
(I TRj1 .00) (Table 3)
DRBj = direct requirements
(intermediate) transactions
matrix
m
(DRj =0) (Table 2)
i= 1,2,. .n
j= 1,2_. m
k= 1,2_. q
m = no. of industries directly
or indirectly placing requirements on industryi = 352
n = no. of industries in the
sample = 52.
q = no. of industries directly
placing requirements on
industry/ = 352.
V22. Proportion of U.S. Bureau of Economic
industry ship- PlI = _A jj/ _Dkj +
ments sold to i k i Structure of the U.S.
intermediate Economy, 1967, Table 1.
markets
PI = industry intermediate market orientation
A1j = dollar transactions of
producing industries
Dki = final demand dollar transactions of industry by
final demand sector
1= 1,2,.. n
= 1,2_.qm
k= 1,2,..q
n = no. of sample industries = 52
m no. of industries directly or
indirectly placing require-
ments on industryi = 352
q = no. of final demand sectors.
V23.
Proportion
of
Same
industry _ Ad PDjk
shipments sold PNDI
for investment
PN = Proportion of shipments sold
to most indirectly linked
markets
Di2= Gross private fixed capital
formation
Dik = Final demand dollar transactions of industry by final
demand sector
Aj = dollar transactions of
producing industries
i= 1,2_. n
j= 1,2,.. m
k =1,2,.. q
n = no. of sample industries = 52
m no. of industries directly or
indirectly placing requirements on industryi = 352
q= no. of final demand sectors.
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as
V22.