CORPORATE LINKAGES AND ORGANIZATIONAL ENVIRONMENT: A TEST OF THE RESOURC
BRIAN BOYD
Strategic Management Journal (1986-1998); Oct 1990; 11, 6; ABI/INFORM Global
pg. 419
Strategic Management Journal, Vol. 11, 419-430 (1990)
CORPORATE LINKAGES AND ORGANIZATIONAL
ENVIRONMENT: A TEST OF THE RESOURCE
DEPENDENCE MODEL
BRIAN BOYD
Blue Cross and Blue Shield of Michigan, Detroit, Michigan, U.S.A.
Two competing models of corporate boards are presented. Management control proposes
that the board is a rubber stamp for management, and plays a minor role in strategic
management, while resource dependence asserts that the board is a tool used to manage
environmental uncertainty. A structural model was developed to determine whether corporate
boards respond to different types of environmental uncertainly, using data on 147 companies
from nine industry groups. It was found that boards tended to be smaller in a more
uncertain environment, while having an increased number of interlocks. Thb relationship
was stronger in high-performing firms.
INTRODUCTION
The corporate board is a popular research agenda in
the social sciences. By one account (Richardson,
1987), there are over 100 major studies of directors
and interlocks. Given this level of interest it is not
surprising that several competing models have been
developed to explain the function of corporate boards
(for a review see Bazerman and Schoorman, 1983;
Koenig, Gogel, and Sonquist, 1979). Two of these
models are diametrically opposed regarding the
board's role in the strategic management process.
Mangement control (Drucker, 1981; Mace, 1971;
Sturdivant, 1981) compares the board to an
appendix—at best, an appendage with no useful
function; at worst, a major irritant. The board is
viewed as unwilling or unable to contribute to the
strategic direction of the firm. Reciprocity, or resource
dependence, asserts that the board is an integral
component of the effective firm. In this model the
board is used to gain access to scarce resources and
information (Pennings, 1980; Pfeffer and Salancik,
1978).
Resource dependence has two major implications
regarding boards of directors. First, composition of the
board should be affected by
0143-2095/90/060419-12S06.00
© 1990 by John Wiley & Sons, Ltd.
environmental pressures and demands. Second,
differences in board composition should affect a
firm's performance. Most studies focus on the latter
issue: board size, number of interlocks, number and
percentage of outside directors, financial ties, and
competitor ties have all been studied in attempts to
predict financial performance in organizations.
While attempting to test the relationship between
the board and performance, past research has placed
less emphasis on the relationship between the
environment and the board. Resource dependence
theory proposes that the need for environmental
linkage is a direct function of the level of dependence
facing an organization. While previous studies have
examined the effect of some environmental variables,
such as levels of competition, none have
systematically tested the effect of environmental
characteristics on board composition. The intent of the
present study is to rectify that omission by developing
a structural model of corporate boards and
environment. Dess and Beard's (1984) model of
environment is used to meausure resource scarcity,
volatility, and complexity, which are hypothesized to
affect board size and interlocking. A separate model is
evaluated for high-performing firms.
Received 13 August 1989
Revised 24 January 1990
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Commerce Department starting in 1982. For
previous years, Schmalensee (1977) suggests
several methods to approximate the H-index.
While the H-index may, in theory, range
between 0 and 1, observed levels of concentration
fall in a much narrower range. The 1982 Census
of Manufacturers reported Herfindahl indices for
440 four-digit SIC groups: The mean value was
0.067, and 95 percent of the industries had values
between 0.007 and 0.128.
Linkage
Two variables are used as measures of environmental linkage: number of interlocks and board
size. These data were collected from 1980 annual
reports and proxy statements.
Financial data was collected for the years
1979-'984, using Compustat tapes, annual
reports, and 10-K filings. Using data from 1980
through 1984, firms were classified as high
performance if they exceeded industry averages
for both sales growth and return on equity (N
= 41). Low-performance was characterized by
sales growth and ROE below industry average (N
= 51). The remainder of firms had experienced
high performance on variable and low
performance on another, or had missing data.
Analysis
The model shown in Figure 1 was tested in a
simultaneous-equation model, using LISREL VI
(Joreskog and Sorbom, 1986). Although not
designed to estimate nonlinear or interaction
terms, it is feasible to model these effects in
LISREL (Hayduk, 1987; Kenny and Judd, 1984).
Quadratic models may violate some assumptions
of maximum-likelihood estimation (McDonald,
1978), and generalized least-squares is the suggested alternative. GLS and MLE are both scaleinvariant procedures, and usually yield very
similar results (Saris and Stronkhorst, 1984).
The quadratic effect is measured by two
variables: the H-index and its squared value.
Interpretation of this effect requires the combination of both parameters (Stolzenberg, 1980).
Thus in Figures 2 and 3, the effect of complexity
is shown as an equation, not a single parameter.
The theoretical model was tested, and nonsignificant links were deleted. This produced the
final model shown in Figure 2. A separate model
for high-performing firms is shown in Figure 3.
RESULTS
Sample demographics
Average board size for this sample was 12
directors and 22 interlocks, and varied widely
across industries. The smallest board had three
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Figure 1. Structural model of hypotheses
Figure 2. Results of the final LISREL model. Note: Parameter estimates are based on GLS esttaation;
standard errors are listed in parentheses; unexplained vanance (zeta terms) are noted .n brackets
directors, and three firms had 29 directors.
Nineteen firms reported five or fewer interlocks,
and one company reported none at all. Summary
statistics are listed in Table 1.
Crude oil and oil refining had the most
munificent environments, with computers and
semiconductors close behind. All four of these
industries had growth rates at least double
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Figure 3. LISREL model for high-performance firms. Note: parameter estimates are based on GLS estimation;
standard errors are listed in parentheses; unexplained variance (zeta terms) are noted in brackets
those of paper products and hydraulic cement.
Dynamism scores reveal that banking had the
least volatile environment, and crude oil the
most. Sample firms face substantial variations
in complexity: H-scores ranged from 0.013
(commercial banking) to 0.083 (airlines). Concentration data were available for seven of the nine
industry groups.
Tests of hypotheses
Bentler and Bonett (1980) suggest that a theorized
structural model be compared against a baseline
null model. The null model would hypothesize
no causal links among any of the variables, and
produced a x2 statistic of 363.10 (11 d.f., p =
0.0001). Summary statistics for all models are
shown in Table 2, and the correlation matrices
used to test hypotheses are shown in Table 3.
The theoretical model is a significant improvement over the null. The effects of dynamism and
complexity on interlocking were not significant,
and were removed from the model. This adjusted
theoretical model is shown in Figure 2. x2 f°r
this model is 1.89, indicating a satisfactory fit to
the data. Reduction in x2 from the null is 361.21
(8 d.f.) and is highly significant. The goodness-
Table 2. Summary statistics for
d.f. LISREL models
GFI GFI. RMSR CED
Model
Full
sample 11 3
Null
model
Final model
0.287
0.009
363.10 0.654 0.119 1.89
0.9% 0.965
High performing firms
Null model 11 104.59 0.677 0.179 0.274 0.000
Final model 2 0.11 0.999 0.989 0.004 0.897
GFI = Goodness-of-fit index; GFI,, = GFI adjusted for
degrees of freedom; RMSR = root mean square residual;
CED = coefficient of determination for model.
of-fit (GFI) adusted for degrees of freedom is
0.965 for the final model, compared to 0.119 for
the null. The root mean square residual and
slope of the g-Plot a,so show tl>e final model to be an
effective fit to the data. Overall, this model
explains nearly three quarters of the variance of
both board size (A?2 = 0.69) and interlocking
(R2 = 0.74).
Test of hypotheses revealed some interesting
differences between board size and number of
interlocks. First, however, a clarification of the
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environmental variables is necessary. Munificence
measures the availability of resources in the
environment: The higher the score on this
variable, the more abundant the environment;
i.e. munificence is negatively correlated with
uncertainty. Dynamism measures the level of
volatility, and is positively associated with uncertainty. The relationship between complexity and
uncertainty is nonlinear. If boards are a response
to environmental uncertainty, one would expect
negative coefficients between munificence and
linkage, and positive coefficients for dynamism,
in relation to both board size and interlocks. The
nonlinear effect of complexity would be modeled
as a quadratic term. Factor models by Dess and
Beard (1984) and Rasheed and Prescott (1987)
found these three dimensions to be orthogonal.
Therefore, munificence, dynamism, and complexity are expected to produce effects independent of each other.
Hypothesis 1 state that munificence would have
a negative effect on environmental linkage. This
hypothesis was partially supported. A coefficient
of -0.191 revealed that, as hypothesized, the
number of interlocks is greater in a less munificent
environment. However, a coefficient of 0.183
indicated that board size actually increased as
resources became more abundant.
Hypothesis 2 stated that dynamism would have
a positive effect on environmental linkage. This
hypothesis was partially supported by the 0.113
coefficient for board size. Dynamism was unrelated to interlocking.
Hypothesis 3 stated that complexity would
have a nonlinear effect on environmental linkage.
A quadratic effect was found for directors, but
not for interlocks. The equation for directors is:
Board size: ----- 3.993H + 3.471 H2
A graph of this relationship is shown in Figure
4, and is in the opposite direction of prediction:
Board size is larger under perfect competition or
monopoly, i.e., board size is negatively related
to this dimension of uncertainty.
Hypothesis 4 stated that firm size is positively
related to both board size and number of
interlocks. This hypothesis was supported for
both variables.
Hypothesis 5 stated that board size is positively
related to the number of interlocks. This hypothesis was supported with a significant coefficient
of 0.367.
Test of hypotheses: high-performance firms
Forty-one firms reported sales growth and return
on equity higher than their industry averages,
and were distributed across all nine industry
groups. A separate model for this subgroup is
shown in Figure 3. Results are similar to the
overall analysis, with some important differences.
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Figure 4. Quadratic models for complexity
The null model for this group produced a X2 =
104.59 (11 d.f.) indicating that this model is not
random variation. Adjusted goodness of fit for
the null is 0.179. x2 for the final model is 0.11 (2
d.f.), a significant improvement. Goodness-of-fit,
root mean square residual, and the Q-p\ol all
show this model to be an excellent fit to the data.
Overall coefficient of determination is 0.897. R2
values for board size and interlocking were 0.731
and 0.728, respectively.
As with the overall sample, munificence has
opposite effects on board size and interlocks. As
resources become more scarce, the number of
interlocks increase while board size declines. A
comparison of parameter estimates for highperforming firms versus the overall sample
suggests that high-performing firms are more
sensitive to changes in resource scarcity. Dynamism had a small effect on board size in the
overall sample, but was unrelated to board size
or interlocks in the high performance group.
High-performing firms reported nonlinear
effects for complexity with both board size and
number of interlocks. The complexity equations
Board size: -6.075 H + 5.213 H2
Interlocks: +4.488 H - 3.868 H2
Competitive uncertainty has a positive effect on
frequency of interlocks. As with munificence,
high-performing firms exhibit a stronger response
to this level of uncertainty: The parabola (see
Figure 4) is more pronounced for high performers
than the overall sample. Faced with the same
level of competitive uncertainty, high performers
will make greater reductions in board size.
Simultaneously, firms respond to the same source
of uncertainty by increasing their number of
interlocks.
Hypotheses 4 and 5 were also supported. Size
has a positive ettect on both board size and
linkage in the high performing group. Board size
also has a positive effect on interlocking.
DISCUSSION
The goal of the present study was to clarify the
board's role in strategic management. Resource
dependence theory proposes that the board is a
mechanism to manage strategic uncertainty, while
management control asserts that the board is
little more than a figurehead. Results reported
here indicate that corporate boards do respond
to environmental demands. One characteristic of
high-performing firms is that they are more
responsive to levels of uncertainty.
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of Management Journal, 31, 1988, pp. 570-598.
Allen, M. P. 'The structure of interorganizational elite
cooperation: Interlocking corporate directorates', Kelly, W. A. 'A generalized interpretation of the
American Sociological Review, 39, 1974, pp. 393Herfindahl index', Southern Economic Journal 48,
406.
1981, pp. 50-57.
Bavly, D. 'What is the board of directors good for?,' Kelly, D. A. and C. M. Judd, 'Estimating the nonlinLong Range Planning, 19, 1986, pp. 20-26.
ear and interactive effects of latent variables',
Bazerman, M. H. and F. D. Schoorman. 'A limited
Psychological Bulletin, 96, 1984, pp. 201-210.
rationality model of interlocking directorates', Kerr, S., and J. W. Slocum, Jr. 'Controlling the perAcademy of Management Journal, 8, 1983, pp.
formance of people in organizations', in P. C. Ny206-217.
strom and W. H. Starbuck (eds). Handbook of
Bentler, P. M., and D. G. Bonett. 'Significance tests
Organizational Design, Vol. 2, Oxford University
and goodness of fit in the analysis of covariance
Press, New York, 1981, pp. 116-134.
structures'. Psychological Bulletin, 88, 1980, pp. Koenig, T., R. Gogel and J. Sonquist. 'Models of the
588-606.
significance of interlocking corporate directorates,
Broder, J. M. and V. F. Zonana. 'Bank America
American Journal of Economics and Sociology,
board: A study in inertia', Los Angeles Times, IV
38, 1979, pp. 173-186.
7 September 1986, pp. 4-5.
Business Roundtable. The Role and Composition of Mace, M. L. Directors: Myth and Reality, Harvard
Business School Press, Boston, MA, 1971.
the Board of Directors of the Large Publicly Owned
McDonald, R. P. 'A simple comprehensive model for
Corporation. Author, New York, 1978,
the analysis of covariance structures', British Journal
Daughen, J. R. and P. Binzel. The Wreck of the Penn
of Mathematical and Statistical Pyschology, 31,
Central. Little, Brown, Boston, MA, 1971.
1978, pp. 59-72.
Dess, G. and D. Beard. 'Dimensions of organizational
task environments,' Administrative Science Quar- Moody's Investor Service. Moody's Bank and Finance
Manual, Vol. 1, Moody's Investor Service, New
terly, 29, 1984, pp. 52-73.
York, 1981.
Dooley, P. C. "The interlocking directorate', American
Pennings, J. Interlocking Directorates, Jossey Bass, San
Economic Review, 59, 1969, pp. 314-323.
Francisco, CA, 1980.
Dobrzynski, J. H., M. Schroeder, G. L. Miles and
J. Weber. 'Taking charge: Corporate directors start Pfeffer, J. 'Size and composition of corporate boards
of directors: The organization and its environto flex their muscle', Business Week, 3 July, 1989,
ment', Administrative Science Quarterly, 17, 1972a,
pp. 66-71.
pp. 218-228.
Drucker, P. F. Toward the Next Economics, and Other
Essays, Harper & Row, New York, 1981.
Pfeffer, J. 'Merger as a response to organizational
Emery, F. and E. Trist. "The causal texture of organiinterdependence', Administrative Science Quarterly,
zational environments', Human Relations, 18, 1965,
17, 1972b, pp. 382-394.
pp. 21-31.
Pfeffer, J. 'Size, composition and function of hospital
Grossack, I. M. 'Toward an integration of static
boards of directors: A study of the organizationand dynamic measures of industry concentration',
environment linkage'. Administrative Science QuarReview of Economics and Statistics, 47, 1965, pp.
terly, 18, 1973, pp. 349-364.
301-308.
Pfeffer, J. 'A resource dependence perspective on
Hackman, J. R. and C. G. Morris. 'Group tasks,
intercorporate relations', in M. S. Mizruchi and
group interaction process, and group performance
M. Schwartz (eds), Intercorporate Relations, Cameffectiveness: A review and proposed integration',
bridge Jniversity Press, Cambridge, MA, 1987,
in L. Berkowitz (ed.). Advances in Experimental
pp. 25-55.
Social Psychology, Academic Press, New York, Pfeffer, J. and P. Nowak. 'Joint ventures and interorvol. 8, 1975, pp. 45-99.
ganizational interdependence', Administrative SciHayduk, L. A. Structural Equation Modeling With
ence Quarterly, 21, 1976, pp. 398-418.
LISREL: Essentials and Advances Johns Hopkins
Pfeffer, J. and G. Salancik. The External Control of
University Press, Baltimore, MD, 1987.
Organizations: A Resource Dependence Perspective,
Herfindahl, O. C. 'Concentration in the steel industry'.
Harper & Row, New York, 1978.
Unpublished Ph.D. dissertation, Columbia UniverPorter, M. E. Competitive Strategy: Techniques for
sity, New York, 1950.
Analyzing Industries and Competitors Free Press,
Merold, D. E. 'The effectiveness of work groups',
New York, 1980.
in S. Kerr (ed.). Organizational Behavior Grid
Rasheed, A. and J. E. Prescott. 'Dimensions of organiPublishing, Columbus, OH, 1979, pp. 95-118.
zational task environments: Revisited'. Paper preJoreskog, K. G.andD. Sorbom. LISREL VI: Analysis
sented at the Academy of Management annual
of Linear Structural Relations by Maximum Likelimeeting. New Orleans, 1987.
hood, Instrumental Variables, and Least Squares
Richardson, R. J. 'Directorship interlocks and corpoMethods, Scientific Software, Mooresville, IN, 1986.
rate profitability", Administrative Science Quarterly,
Keats, B. and M. Hitt. *A causal model of linkages
32, 1987, pp. 367-386.
among environmental dimensions, macro-organiSaris, W. and H. Stronkhorst. Causal Modelling in
zational characteristics, and performance'. Academy
Nonexperimental Research Sociometric Research
Foundation. Amsterdam. 1984.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Scherer, F. Industrial Market Structure and Economic
Performance, Houghton Mifflin, Boston, MA, 1980.
Schmalansee, R. 'Using the H-index of concentration
with published data'. Review of Economics and
Statistics, 59, 1977, pp. 186-213. Senate Committee on
Government Affairs. Interlocking Directorates
Among the Major U.S. Corporations, U.S.
Government Printing Office, Washington, DC, 1978.
Slater, P. E. 'Contrasting correlates of group size',
Sociometry, 21, 1958, pp. 129-139. Stern, L. W.
and W. M. Morgenroth. 'Concentration, mutually
recognized interdependence, and the allocation
of marketing resources'. Journal of Business, 41,
1968, pp. 56-67. Stolzenberg, R. M. 'The
measurement and decomposition of causal effects in
nonlinear and nonadditive models', in K. F.
Schuessler (ed.). Sociological Methodology,
1980, pp. 459-488. Sturdivant, F. D. Business and
Society: A Managerial
Approach, R. D. Irwin, Homewood, IL, 1981.
Tolbert, P. S. 'Institutional environments and resource
dependence: Sources of administrative structure in
institutions of higher learning'. Administrative
Science Quarterly, 30, 1985, pp. 1-13. U.S. Bureau of
the Census. Census of Manufactures. Vol. 1, U.S.
Government Printing Office, Washington, DC, 1977.
U.S. Bureau of the Census. Census of Manufactures.
Vol. 1, U.S. Government Printing Office, Washington,
DC, 1981. U.S. Civil Aeronautics Board. Air Carrier
Financial Statistics, U.S. Government Printing Office,
Washington, DC, December 1980. Warner, W. L.
and D. Unwalla. 'The system of interlocking
directorates', in W. L. Warner, D. Unwalla and J. H.
Trimm (eds). The Emergent American Society:
Large-Scale Organizations, Yale University Press,
New Haven, CT, 1967. Weidenbaum, M. L.
'Updating the corporate board', Journal of Business
Strategy, 7, Summer 1986, pp. 77-83.
APPENDIX: DATA SOURCES USED FOR
ENVIRONMENTAL MEASURES
Munificence: Abundance of resources in an
industry.
Measurement: Regression slope coefficient, divided by mean value. Coefficients are based on
regression of time against value of shipments.
Estimate for any given year is based on the 5
preceding years, i.e. munificence estimate for
1980 is based on data from 1976-1980. Industries
are defined using four-digit SIC codes. Data
Source: U.S. Industrial Outlook. Note: This is the
measurement method suggested by Dess and
Beard (1984). Keats and Hitt (1988) suggest
regressing time against the natural log of
industry sales. Munificence would then be
measured by the antilog of the regression
coefficient. Both procedures yield very similar
results. Using data from the present study,
Pearson correlation for these two measures of
munificence is 0.99 (p < 0.0001). Alternate
measures of dynamism are also strongly correlated
(r = 0.88, p < 0.001).
Dynamism: Instability or volatility in an industry.
Measurement: Standard error of regression slope
coefficient divided by the mean value; using same
regression model as for munificence. Data
source: U.S. Industrial Outlook
Complexity: Heterogeneity in the environment,
and concentration of resources. Measurement:
Herfindahl-Hirschman index. Data sources: (a)
Airline: Index was estimated from Table 1:
'Income statement, data of certified route
carriers', Air Carrier Financial Statistics,
December 1980, Civil Aeronautics Board, Washington, DC; U.S. GPO. (b) Commercial banks:
Index estimated from table 'The three hundred
largest banks in the United States in order of
deposits December 31, 1980', Moody's Bank and
Finance Manual, 1981: a4-a5. (c) All other
groups: U.S. Bureau of the Census, Census of
Manufacturers, 1982. Index for 1980 based on
interpolation of 1977 and 1982 measures. Index
was estimated using 4, 8, 20, and 50 firm
concentration ratios, and the MINL approximation suggested by Schmalensee (1977: 187).
Using data from the present study, MINL
estimates for 1982 were found to correlate highly
(r = 0.95, p < 0.001) with published Commerce
Department H-values for that year.
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