Directory UMM :Data Elmu:jurnal:J-a:Journal Of Business Research:Vol48.Issue1.2000:
A Replication and Extension of Organizational
Growth Determinants
Laurence G. Weinzimmer
BRADLEY UNIVERSITY
Although the management literature contains an impressive volume of
studies attempting to identify factors that precipitate organizational
growth, fragmented theory has developed because of the absence of replicative studies that integrate multiple levels of determinants. Previous studies
have shown that exclusive use of either industry, strategy, or top management determinants can individually influence sales growth, but no existing
research has empirically demonstrated the simultaneous effects of all three
levels of determinants. Using a representative sample of 193 firms from
48 industries, this study replicated findings from several tangentially
related studies to provide empirical support for the simultaneous influence
of all three levels of determinants. Relative comparisons among the three
levels of determinants showed organization strategies to be most significant,
followed by top management characteristics and industry attributes. Interactions between industry/strategy determinants and strategy/top management determinants were also found to be significant. J BUSN RES 2000.
48.35–41. 2000 Elsevier Science Inc. All rights reserved.
O
rganizations can benefit from growth in many ways,
including greater efficiencies through economies of
scale, increased power, the ability to withstand environmental change, increased profits, and increased prestige
for organizational members. A review of the management
literature on organizational growth yields an extensive stream
of research on consequences of growth, but only a limited
bibliography on determinants that precipitate growth. Although earlier research has suggested a number of determinants of organizational growth, strategy and organization theory researchers have been unable to gain consensus regarding
factors leading to organizational growth; therefore, creating
fragmented theory (Davidsson, 1991; Kazanjian, 1990; Whetten, 1987).
This lack of consensus may in part, result from the absence
of replicative studies that attempt to integrate multiple levels
of determinants to examine simultaneous effects. For example,
Address correspondence to: Dr. L. G. Weinzimmer, Foster College of Business
Administration, Bradley University, Peoria, IL 61625, USA.
Journal of Business Research 48, 35–41 (2000)
2000 Elsevier Science Inc. All rights reserved.
655 Avenue of the Americas, New York, NY 10010
several researchers have exclusively examined the influence
of strategy factors on organizational growth (Donaldson, 1987;
Grinyer, McKiernan, and Yasai-Ardekani, 1988; Hamilton and
Shergill, 1992; Johnson and Thomas, 1987). Others have
examined relationships between characteristics of top management and organizational growth (Gupta, 1984; Hambrick and
Mason, 1984; Norburn and Birley, 1988).
Several studies have tried to examine the impact of two
levels of determinants on organizational growth. Researchers
have found concurrent effects of strategy and top management
characteristics on organizational growth (Feeser and Willard,
1990; Miller and Toulouse, 1986), strategy and industry characteristics on organizational growth (McDougall, Robinson,
and DeNisi, 1992; Romanelli, 1989), and industry and top
management characteristics on organizational growth (Eisenhardt and Schoonhoven, 1990). However, all of the studies
that examined growth determinants at two levels used restricted samples such as single industry samples or samples
consisting only of small businesses.
Although previous studies have shown that exclusive use
of either industry, strategy, or top management attributes can
individually influence organizational growth, researchers have
yet to develop a unified model of organizational growth that
provides concurrent empirical support for all three levels of
determinants. Moreover, limited attempts have been made to
investigate interactions among organizational growth determinants.
In an attempt to combine growth determinants from tangentially related studies into a single convergent model, findings from past research are replicated to develop a multilevel
model. We triangulate the simultaneous influences of industry
attributes, portfolio-level and competitive-level organizational
strategies, and characteristics of top management teams by
testing main effects, interaction effects, and relative comparisons among sets of determinants. In addition, to overcome
sampling limitations from previous research examining multiple levels of determinants, this study uses a broad-based sample of organizations from multiple industries to increase the
generalizability of findings.
ISSN 0148-2963/00/$–see front matter
PII S0148-2963(98)00073-3
36
J Busn Res
2000:48:35–41
Theoretical Framework
The variables chosen to be replicated in this study are not
intended to represent all possible growth determinants. Based
upon an exhaustive review of the literature from 1985 to
1994, the variables replicated in this study comprise the most
commonly studied determinants in the management literature
used to examine organizational growth. The journals reviewed
include Academy of Management Journal, Academy of Management Review, Administrative Science Quarterly, Entrepreneurship
Theory and Practice, Journal of Business Research, Journal of
Business Venturing, Journal of Management, Journal of Management Studies, Journal of Small Business Management, Management Science, Organization Science (1990 to 1994), and Strategic
Management Journal. Three sets of determinants were identified: industry attributes, organization strategies, and top-management characteristics.
Industry Determinants of Growth
Such market characteristics as product differentiation, monopolistic economies of scale, and capital requisites create barriers
to entry; thereby, protecting incumbents from the threat of
new entrants (Scherer, 1980). The model developed here replicates studies that considered three common entry barriers:
advertising intensity, research-and-development (R&D) intensity, and competitive concentration. Advertising expenditures
raise barriers to entry by creating economies of scale thresholds
below which advertising impact would diminish. Similarly,
R&D expenditures may deter new entrants by increasing initial capital expenditures and creating a potentially complex
knowledge base. Competitive concentration can also erect
entry barriers, because high concentration protects incumbent
firms from potential new entrants (Hamilton and Shergill,
1992).
When barriers to entry are high, firms within the industry
have increased protection against the possibility that new entrants may deplete the environment of critical resources necessary for existing firms to achieve growth. Therefore, firms
located in an industry with high entry barriers are expected
to achieve higher levels of growth relative to firms located in
industries with low entry barriers.
Strategy Determinants of Growth
Researchers have shown that organizational strategies are important determinants of organizational success (Feeser and
Willard, 1990; Grinyer, McKiernan, and Yasai-Ardekani,
1988; Palepu, 1985). This study replicates findings from two
central approaches to organizational strategy: portfolio-level
strategies and competitive-level strategies
PORTFOLIO-LEVEL STRATEGY. Portfolio-level or primary strategies address the selection of industries in which a firm diversi-
L. G. Weinzimmer
fies to define its domain (Bourgeois, 1980). Related diversification strategies build on synergies of existing businesses to
achieve growth. Because related diversification is a growth
strategy that builds upon existing synergies, a firm has existing
knowledge of the product and market in which it operates
(Palepu, 1985). Conversely, if a firm undertakes an unrelated
diversification strategy, typically through acquisitions, the
firms will have limited knowledge, and, thus, will face increasing uncertainty (Varadarajan and Ramanujam, 1987). Researchers have shown that related diversification strategies
outperform unrelated diversification strategies in terms of
over-all performance, including growth; whereas unrelated
diversification strategies decrease variability of sales growth
(Palepu, 1985).
Therefore, related diversification strategies should increase
the likelihood of organizational growth relative to unrelated
diversification strategies, when controlling for acquisitions.
This assertion does not imply that unrelated diversification will
prevent a firm from achieving short-term growth. However, a
related diversification strategy will enable a firm to achieve a
higher growth rate than would an unrelated diversification
strategy.
COMPETITIVE-LEVEL STRATEGY. Competitive-level or secondary strategies address how a firm competes within a particular
industry (Bourgeois, 1980). Several researchers have suggested or shown that strategic aggressiveness to pursue Porter’s
(1980) competitive strategies is positively associated with organizational performance (Feeser and Willard, 1990; Grinyer,
McKiernan, and Yasai-Ardekani, 1988; Romanelli, 1989). A
firm can aggressively pursue a low-cost strategy by decreasing
its cost base relative to competitors and pursue a differentiation
strategy by offering a product or service perceived as unique
by consumers. Aggressive firms may create such competitive
advantages as increased customer awareness and brand loyalty
(Feeser and Willard, 1990). Romanelli (1989) suggested that
aggressive organizations had a better chance of surviving,
because aggressive firms will acquire the resources necessary
to withstand competitive pressures and achieve growth. Thus,
aggressive strategies should lead to increased growth.
Top Management Determinants of Growth
In the organization theory and strategy literatures, beliefs and
characteristics of powerful individuals in organizations are
thought to have a pronounced impact on the initiation of
strategic change and organizational growth. Specifically, this
study replicates research initiated by Hambrick and Mason
(1984) and Gupta (1984). These authors contended that top
management team (TMT) characteristics that may influence
organizational growth include heterogeneity and age of the
top management team.
HETEROGENEITY OF TOP MANAGEMENT TEAMS. Eisenhardt
and Schoonhoven (1990) suggested that industry heterogene-
Organizational Growth Determinants
ity of top management team members produces constructive
conflict. They argued that this form of heterogeneity would
provide better decision making, because TMT members with
many years in a particular industry can offer insights based
upon rich experience; whereas, newcomers can provide fresh
perspectives. Additionally, heterogeneity of functional experience may affect the growth rate of an organization. Functional
heterogeneity can produce constructive conflict, wherein team
members with different functional backgrounds provide
checks and balances in decision making (Hambrick and Mason, 1984). Therefore, organizational growth may be positively related to the level of top managers’ industry and functional heterogeneity.
Hambrick and
Mason (1984) proposed that age of the upper echelon would
affect the growth rate of an organization, where older executives tend to be more conservative and have a bias for maintaining the status quo. Perhaps executives in the latter years
of their careers would not feel as mobile as younger executives
and, therefore, would not want to take the risks often necessary
when seeking to increase firm growth. Younger executives
typically take more risks, so youthfulness of executives leads
to organizational growth (Hambrick and Mason, 1984).
AGE OF TOP MANAGEMENT TEAM MEMBERS.
Control Variables
An organization’s growth rate is closely related to the growth
rate of its industry (Kotha and Nair, 1995). Therefore, this
study controls for industry growth rates, because the sample
uses firms from multiple industries. In addition, the number
of companies acquired and/or sold during the period of observation was considered as a control variable for the diversification measure, because organizations may grow or decline simply because of acquisitions or divestitures. Organizational
slack, defined as excess assets after debts have been satisfied,
was also considered as a control variable. Eisenhardt and
Schoonhoven (1990) showed that organizational slack was
positively related to growth.
Although previous studies have not been able to show
the simultaneous effects of all three levels of determinants,
replicating findings from tangentially related research provides
a strong theoretical foundation for examining these simultaneous influences. Therefore, to extend previous research, we
examine the proposition that organizational growth is influenced simultaneously by industry attributes, organizational
strategies, and TMT characteristics.
J Busn Res
2000:48:35–41
Manual, Moody’s Unlisted OTC Manual and Dun and Bradstreet’s Reference Book of Corporate Managements. All firms
on the COMPUSTAT database reporting quarterly figures for
sales, advertising expenditures, and research and development
expenditures for 20 consecutive quarters from the first quarter
of 1987 to the fourth quarter of 1991, and reporting top
management data were included in the sample. This yielded
a sample of 193 firms from 48 industries, based upon primary
Standard Industrial Classification (SIC) codes. Power analysis
(Cohen, 1977) indicated that a minimum of 168 firms would
be needed for this study.
Measurement of Variables
To replicate previous research, this study operationalized
growth variables using measures commonly found in the organization theory and strategy literatures.
ORGANIZATIONAL GROWTH. This study used a modified version of the ordinary least-squares beta coefficient approach
used by Grinyer, McKiernan, and Yasai-Ardekani (1988) and
Hamilton and Shergill (1992) over 20 quarterly observations
(adjusted for seasonality) to measure sales growth. Sales data
were chosen for reasons of replicability, because most recent
studies in our literature review used sales data to measure
growth. In addition, the beta coefficient approach was chosen
as the appropriate operationalization, because it captures the
fine-grained fluctuations of sales data better than other methods commonly used to measure growth, and it dampens the
effects of outliers (Weinzimmer, Nystrom, and Freeman,
1998). Growth rates were also standardized for the size of
each organization by dividing the beta coefficient by the mean
size of an organization over the period of observation (cf. Dess
and Beard, 1984; Grinyer, McKiernan, and Yasai-Ardekani,
1988). Therefore, the estimated slope of the regression line
(b9) of sales over time represents the rate of change (growth)
of an organization. Specifically, growth is operationalized as
(Equation 1).
n
n
n
i51
i51
i51
Data Collection Methods
Data were collected from multiple sources: Standard & Poors’
COMPUSTAT data tapes, Standard & Poors’ Corporate Records,
Predicast’s F&S Index, Moody’s Industrial Manual, Moody’s OTC
n
n
i51
i51
f(n o i Zijdi 2 o i o Zij di) / (n o i2 2 ( o i)2g
GRWj 5
mzjdi
(1)
where: GRWj 5 growth rate for jth firm; Zij 5 sales of the jth
firm the ith period; i 5 time period; di 5 discount rate for
inflation during the ith period; mzjdi 5 mean size of the jth
firm, discounted for inflation; and, n 5 20 consecutive quarters from 1987 to 1991.
A composite score was calculated for each
four-digit SIC code using advertising intensity, R&D intensity,
and a four-firm concentration ratio. Entry barrier measures
may not be comparable among different industries, given characteristics endogenous to different markets. Therefore, these
measures were standardized before the variables were summed
to calculate height of entry barriers (cf. McDougall, Robinson,
and DeNisi, 1992).
ENTRY BARRIERS.
Methods
37
38
J Busn Res
2000:48:35–41
This study uses Davis and Duhaime’s (1992) entropy measure of diversification to investigate the influences of related diversification from 1987
to 1991.
PORTFOLIO-LEVEL STRATEGY.
This study used Fombrun and
Ginsberg’s (1990) composite approach to measure strategic
aggressiveness. To calculate differentiation aggressiveness, ratios of advertising expenditures to sales and product R&D
expenditures to sales were used. To calculate low-cost aggressiveness, ratios of expenditures for new plant and equipment
to sales and process R&D expenditures to sales were used.
Industry effects were controlled by dividing firm ratios by
average industry ratios to assess firm-level aggressiveness relative to competition.
STRATEGIC AGGRESSIVENESS.
Top managers are defined as all officers above the level of vice president (Michel
and Hambrick, 1992). Industry heterogeneity was measured
by computing the coefficient of variation, defined as the standard deviation divided by the mean (cf. Wiersema and Bantel,
1992). Functional heterogeneity was measured by the number
of functional disciplines currently represented by top management members divided by the size of the TMT using a ninepoint scale offered by Michel and Hambrick (1992).
HETEROGENEITY OF TOP MANAGERS.
Age is measured by mean age in years of top managers
(Norburn and Birley, 1988).
AGE.
CONTROL VARIABLES. To measure industry growth, sales for
each 4-digit SIC code were regressed over time and the corresponding standardized coefficient (b9) was used as a measure
of industry growth (cf. Dess and Beard, 1984). Organization
slack was measured as assets/debt (Eisenhardt and Schoonhoven, 1990). Net acquisitions/divestitures for each organization
was a continuous variable, ranging from 2n to n acquisitions
during the period of 1987–1991.
Analytic Procedures
To replicate findings from previous research, this study used
multiple-lagged ordinary least-square (OLS) regression analysis to recognize the effects of time, because all previous growth
studies used correlation or regression analysis to test the significance of determinants. To ensure that the OLS multiple regression model fit the dataset, comprehensive diagnostic tests
were performed to check for both randomness and normality
of the residuals (cf. Weinzimmer, Mone, and Alwan, 1994).
Results
Descriptive Statistics and Correlations
Initial analysis revealed several significant correlations between independent variables, suggesting the need to check
for multicollinearity in the regression models. A two-stage
L. G. Weinzimmer
process was used to adjust for multicollinearity (cf. Berry and
Feldman, 1990). Means, standard deviations, and correlations
of variables are presented in Table 1.
OLS Multiple Regression Analysis
Table 2 presents OLS regression results. Model 1 tests for the
simultaneous effects of all three levels of determinants, and
Models 2 and 3 test for interaction effects. Multiple regression
results from Model 1 show a significant positive association
between organization growth and industry entry barriers (p ,
0.001), suggesting that an industry with high entry barriers
will lead to organizational growth. Results for strategy-level
determinants in Model 1 show a significant positive association
between organizational growth and related diversification (p ,
0.05) and strategic aggressiveness (p , 0.001), suggesting
that strategies pursued by a firm are significant in explaining
growth. Finally, the data suggest strong empirical support for
several top management team determinants, including significant positive associations between organizational growth and
the degree of TMT heterogeneity in terms of industry experience (p , 0.10), the level of TMT heterogeneity in terms of
functional backgrounds (p , 0.001) and a significant negative
association between organizational growth and average age of
the TMT (p , 0.05).
Model 2 shows a significant positive relationship between
growth and the multiplicative interaction between aggressiveness and TMT age. Similarly, Model 3 shows a significant
positive relationship between growth and the multiplicative
interaction between aggressiveness and industry growth.
Relative Effects Among Different Levels
of Determinants
Given the strong empirical evidence for the simultaneous influence of all three levels of determinants, highlighting the
relative importance for each level of determinants may provide
additional insights. To determine the relative importance of
one set of determinants over another, we used a x2-test procedure replicating methods used by other researchers comparing
the relative importance of variables to each other (cf. Kotha
and Nair, 1995).
Each of the three levels of determinants provided significant
improvements over the base model consisting of the control
variables. However, strategy-level determinants provided the
largest improvement (Dx2 5 87.43; p , 0.001; adj-R2 5
0.27), followed by top management team level determinants
(Dx2 5 51.49; p , 0.001; adj-R2 5 0.25) and industry-level
determinants (Dx2 5 11.05; p , 0.005; adj-R2 5 0.17). This
suggests that all three levels of determinants are individually
significant in explaining organizational growth, with strategy
being the most significant relative to TMT- and industry-level
determinants. However, the simultaneous presence of all three
levels of determinants provides the best explanation of organi-
Organizational Growth Determinants
39
J Busn Res
2000:48:35–41
Table 1. Descriptive Statistics and Correlationsa
Variable
Mean
SD
1
2
3
4
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
1.13
0.12
0.05
0.46
1.98
51.33
0.65
6.02
1.21
26.36
2.60
0.15
0.28
0.70
1.28
5.23
0.19
8.65
2.34
35.07
0.23**
0.11*
0.39**
0.13*
20.25**
0.32**
0.22**
0.26**
0.11*
0.10
0.11*
20.12*
20.03
0.03
20.08
0.11*
0.06
20.01
0.05
20.01
0.02
0.03
20.09
0.01
0.02
20.12*
0.09
0.03
0.12*
20.04
Firm growth
Entry barriers
Diversification
Aggressiveness
Industry heterogeneity
TMT age
Functional heterogeneity
Industry growth
Net acquisitions
Slack
5
6
7
20.02
0.14* 20.17*
0.11* 20.14
0.01
0.01
0.00 20.03
20.09 20.16*
0.04
8
9
0.01
20.07
20.03
a
n 5 193.
* p , 0.05.
** p , 0.001.
zational growth, in terms of explained variance, as compared
to any of the restricted models.
Discussion
Previous studies have shown that either industry, strategy, or
top management attributes individually influence organizational growth. Using measures and analytic methods from
previous research, this study replicated these findings and
confirmed many of these individual relationships using a gen-
eralizable sample. However, no research has demonstrated
the concurrent effects of all three levels of determinants. Therefore, findings from this study in some ways contradict previous
research that studied the effects of industry, strategy, and top
management variables in explaining organizational growth.
Eisenhardt and Schoonhoven (1990) concluded that, although
industry and managerial factors influence organizational
growth, strategy characteristics do not. A possible explanation
for inconsistencies between this study and Eisenhardt and
Schoonhoven’s (1990) study is these authors only used a
Table 2. Regression Resultsa
Independent
Variables
Industry attributes
Entry barriers
Organization strategy
Related diversification
Aggressiveness
Managerial attributes
Industry heterogeneity
Functional heterogeneity
TMT age
Control variables
Industry growth
Net acquisitions
Slack
Interactions
Aggressiveness 3
TMT age
Aggressiveness 3
industry growth
R2
Adjusted R2
F
a
n 5 193.
p , 0.10.
* p , 0.05.
** p , 0.01.
*** p , 0.001.
†
1
0.17***
0.11†
0.31***
Models
2
0.17***
0.10*
20.27
3
0.17***
0.10†
0.25***
0.08†
0.25***
20.12*
0.09†
0.25***
0.01
0.08†
0.27***
0.20***
0.22***
0.11*
0.17***
0.20***
0.11*
0.02
0.22***
0.13**
0.61†
0.22†
0.413
0.384
14.31***
0.474
0.430
10.73***
0.463
0.421
10.98***
40
J Busn Res
2000:48:35–41
single measure for strategy, technological innovation. This
study found both portfolio-level and competitive-level strategies to be significant in explaining growth. Similarly, Kotha
and Nair (1995) found environmental-level variables to be
significant in explaining growth, but concluded that strategy
variables had no impact on growth. A possible explanation
for this inconsistency is that these authors used single-dimensional measures for strategy, including individual measures
for efficiency, capital expenditures (low-cost strategies), and
advertising intensity (differentiation strategy). Our study used
a multidimensional measure of strategic aggressiveness from
a composite of both low-cost and differentiation strategies.
Hill (1988) showed that concurrent pursuit of both low-cost
and differentiation strategies can create competitive advantage.
Therefore, when examining determinants of organizational
growth, a composite aggressiveness measure may capture
over-all strategic orientation better than individual measures.
Interactions of Determinants
In addition to examining the direct effects of these factors on
growth, subsequent analysis found strong empirical evidence
for interrelationships among predictor variables. Analysis provided support for the relationship between organizational
growth and the correct alignment of strategic aggressiveness
and TMT age. A high-risk strategy needs support from top
managers to be successful. This association suggests that having the correct alignment between strategic aggressiveness and
average age of the TMT, rather than merely implementing an
aggressive strategy or having a young top management team,
will have a positive impact on organizational growth. Specifically, this implies that when a firm pursues an aggressive
strategy, support of top management will lead to organizational growth. Past studies have not empirically tested this
relationship, so the positive association between the interaction of strategic aggressiveness and TMT age with organizational growth provides empirical evidence for Gupta’s (1984)
theoretical propositions.
The analysis also provided support for the relationship
between organizational growth and the correct alignment of
industry growth and strategy. When a firm is located in a
growing industry, resources should be aggressively allocated
to that industry to exploit growth opportunities. Conversely,
as industry growth slows, firms should decrease aggressive
commitment to that particular industry to allow for the reallocation of resources to other growth-oriented industries. This
finding implies that when a firm is in a growing environment,
investment into this industry in the form of advertising, research-and-development and plant-and-equipment will lead
to organizational growth. Although no research exists on the
interaction between strategic aggressiveness and industry
growth and its influence on organizational growth, findings
of this study parallel previous empirical research. Previous
research has examined the extent to which a particular strategy
best matches a particular type of environment (Romanelli,
L. G. Weinzimmer
1989). This study, however, presents the first evidence for
a relationship between strategy and industry growth fit on
organizational growth. This finding suggests that certain strategies align a firm better with a particular industry, and,
thereby, enable it to achieve growth.
Conclusion and Directions for
Future Research
Although the replication approach used in this study provided
support for previously identified relationships between growth
and individual levels of determinants, contributions from this
study come from the theoretical and empirical convergence
of previously tangentially related studies to provide a unified
explanation of organizational growth. This study was able to
show simultaneous effects for all three levels of determinants
using a triangulation approach—including tests of main effects, interactions, and relative comparisons among sets of
determinants.
This study has attempted to provide an integrative framework to reduce fragmented theory in the organizational theory
and strategy literatures. Replicating studies from traditional
organizational theory and strategy paradigms provided a
method that may help to explain why a lack of consensus has
existed among single-level studies of organizational growth.
Future research may consider additional variables outside of
the traditional organization theory and strategy literatures,
such as concepts drawn from industrial and political economics (cf. Evans, 1987) and sociology research (cf. Baum and
Mezias, 1993; Hannan and Ranger-Moore, 1990) to provide
further insights into understanding why organizations grow.
In conclusion, by using variables identified in previous
studies and replicating analytic methods and measures, this
study provided clear evidence that all three levels of determinants from the strategy and organization theory literature are
significant concurrently. This approach provides a better explanation of organizational growth, as compared to exclusively
studying a single level of determinants. This implies that, as
researchers study organizational growth in the future, examination of one or even two sets of determinants will not suffice,
but that all three sets of determinants should be studied together. This study also yielded support for the idea that interactions among determinants add significantly to explaining
organizational growth. It also used a sample that included
diverse firms and a cross section of 48 industries to increase
generalizability over previous studies using restricted samples.
Finally, these findings may provide new insights for other
areas of research, such as organizational life-cycle (OLC) theory. Kazanjian (1990) contended that OLC research is limited
because of a lack of identifiable determinants of growth. He
argued that the growth literature lacks theoretical models of
factors that lead to a firm’s position in a particular stage of
growth. He suggested that, although life-stage models add to
our understanding of the rather complex phenomenon of
Organizational Growth Determinants
growth, these models suffer a deficiency, because the determinants precipitating growth are, at best, implied. Additionally,
because our study did not discriminate between growing and
declining firms, where growth could be either positive or
negative, findings may also contribute to the organizational
decline literature.
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Kazanjian, R. K.: Relation of Dominant Problems to Stages of Growth
in Technology-Based New Ventures. Academy of Management Journal 31 (1990): 257–279.
Kotha, S., and Nair, A.: Performance Determinants in the Japanese
Machine Tool Industry. Strategic Management Journal 16 (1995):
497–518.
McDougall, P., Robinson, R., Jr., and DeNisi, A. S.: Modeling New
Venture Performance: An Analysis of New Venture Strategy, Industry Structure, and Venture Origin. Journal of Business Venturing
7 (1992): 267–289.
Michel, J., and Hambrick, D.: Diversification Posture and Top Management Team Characteristics. Academy of Management Journal
35 (1992): 9–37.
Miller, D., and Toulouse, J.: Strategy, Structure, CEO Personality,
and Performance in Small Firms. American Journal of Small Business
(Winger 1986): 47–61.
Norburn, D., and Birley, S.: The Top Management Team and Corporate Performance. Strategic Management Journal 9 (1988): 225–
237.
Palepu, K.: Diversification Strategy, Profit Performance, and the Entropy Measure. Strategic Management Journal 6 (1985): 239–255.
Porter, M.: Competitive Strategy. Free Press, New York. 1980.
Romanelli, E.: Environments and Strategies in Organization Startups: Effects on Early Survival. Administrative Science Quarterly 34
(1989): 369–387.
Scherer, F.: Industrial Market Structure and Economic Performance,
2nd ed. Houghton Mifflin, Boston. 1980.
Varadarajan, P., and Ramanujam, V.: Diversification and Performance: A Re-examination Using a New Two-Dimensional Conceptualization of Diversity in Firms. Academy of Management Journal 30 (1987): 380–393.
Weinzimmer, L. G., Mone, M. A., and Alwan, L. C.: An Examination
of Perceptions and Usage of Regression Diagnostics in Organization Studies. Journal of Management 20(1) (1994): 179–192.
Weinzimmer, L. G., Nystrom, P. C., and Freeman, S. J.: Measuring
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and Corporate Strategic Change. Academy of Management Journal
35 (1992): 91–121.
Growth Determinants
Laurence G. Weinzimmer
BRADLEY UNIVERSITY
Although the management literature contains an impressive volume of
studies attempting to identify factors that precipitate organizational
growth, fragmented theory has developed because of the absence of replicative studies that integrate multiple levels of determinants. Previous studies
have shown that exclusive use of either industry, strategy, or top management determinants can individually influence sales growth, but no existing
research has empirically demonstrated the simultaneous effects of all three
levels of determinants. Using a representative sample of 193 firms from
48 industries, this study replicated findings from several tangentially
related studies to provide empirical support for the simultaneous influence
of all three levels of determinants. Relative comparisons among the three
levels of determinants showed organization strategies to be most significant,
followed by top management characteristics and industry attributes. Interactions between industry/strategy determinants and strategy/top management determinants were also found to be significant. J BUSN RES 2000.
48.35–41. 2000 Elsevier Science Inc. All rights reserved.
O
rganizations can benefit from growth in many ways,
including greater efficiencies through economies of
scale, increased power, the ability to withstand environmental change, increased profits, and increased prestige
for organizational members. A review of the management
literature on organizational growth yields an extensive stream
of research on consequences of growth, but only a limited
bibliography on determinants that precipitate growth. Although earlier research has suggested a number of determinants of organizational growth, strategy and organization theory researchers have been unable to gain consensus regarding
factors leading to organizational growth; therefore, creating
fragmented theory (Davidsson, 1991; Kazanjian, 1990; Whetten, 1987).
This lack of consensus may in part, result from the absence
of replicative studies that attempt to integrate multiple levels
of determinants to examine simultaneous effects. For example,
Address correspondence to: Dr. L. G. Weinzimmer, Foster College of Business
Administration, Bradley University, Peoria, IL 61625, USA.
Journal of Business Research 48, 35–41 (2000)
2000 Elsevier Science Inc. All rights reserved.
655 Avenue of the Americas, New York, NY 10010
several researchers have exclusively examined the influence
of strategy factors on organizational growth (Donaldson, 1987;
Grinyer, McKiernan, and Yasai-Ardekani, 1988; Hamilton and
Shergill, 1992; Johnson and Thomas, 1987). Others have
examined relationships between characteristics of top management and organizational growth (Gupta, 1984; Hambrick and
Mason, 1984; Norburn and Birley, 1988).
Several studies have tried to examine the impact of two
levels of determinants on organizational growth. Researchers
have found concurrent effects of strategy and top management
characteristics on organizational growth (Feeser and Willard,
1990; Miller and Toulouse, 1986), strategy and industry characteristics on organizational growth (McDougall, Robinson,
and DeNisi, 1992; Romanelli, 1989), and industry and top
management characteristics on organizational growth (Eisenhardt and Schoonhoven, 1990). However, all of the studies
that examined growth determinants at two levels used restricted samples such as single industry samples or samples
consisting only of small businesses.
Although previous studies have shown that exclusive use
of either industry, strategy, or top management attributes can
individually influence organizational growth, researchers have
yet to develop a unified model of organizational growth that
provides concurrent empirical support for all three levels of
determinants. Moreover, limited attempts have been made to
investigate interactions among organizational growth determinants.
In an attempt to combine growth determinants from tangentially related studies into a single convergent model, findings from past research are replicated to develop a multilevel
model. We triangulate the simultaneous influences of industry
attributes, portfolio-level and competitive-level organizational
strategies, and characteristics of top management teams by
testing main effects, interaction effects, and relative comparisons among sets of determinants. In addition, to overcome
sampling limitations from previous research examining multiple levels of determinants, this study uses a broad-based sample of organizations from multiple industries to increase the
generalizability of findings.
ISSN 0148-2963/00/$–see front matter
PII S0148-2963(98)00073-3
36
J Busn Res
2000:48:35–41
Theoretical Framework
The variables chosen to be replicated in this study are not
intended to represent all possible growth determinants. Based
upon an exhaustive review of the literature from 1985 to
1994, the variables replicated in this study comprise the most
commonly studied determinants in the management literature
used to examine organizational growth. The journals reviewed
include Academy of Management Journal, Academy of Management Review, Administrative Science Quarterly, Entrepreneurship
Theory and Practice, Journal of Business Research, Journal of
Business Venturing, Journal of Management, Journal of Management Studies, Journal of Small Business Management, Management Science, Organization Science (1990 to 1994), and Strategic
Management Journal. Three sets of determinants were identified: industry attributes, organization strategies, and top-management characteristics.
Industry Determinants of Growth
Such market characteristics as product differentiation, monopolistic economies of scale, and capital requisites create barriers
to entry; thereby, protecting incumbents from the threat of
new entrants (Scherer, 1980). The model developed here replicates studies that considered three common entry barriers:
advertising intensity, research-and-development (R&D) intensity, and competitive concentration. Advertising expenditures
raise barriers to entry by creating economies of scale thresholds
below which advertising impact would diminish. Similarly,
R&D expenditures may deter new entrants by increasing initial capital expenditures and creating a potentially complex
knowledge base. Competitive concentration can also erect
entry barriers, because high concentration protects incumbent
firms from potential new entrants (Hamilton and Shergill,
1992).
When barriers to entry are high, firms within the industry
have increased protection against the possibility that new entrants may deplete the environment of critical resources necessary for existing firms to achieve growth. Therefore, firms
located in an industry with high entry barriers are expected
to achieve higher levels of growth relative to firms located in
industries with low entry barriers.
Strategy Determinants of Growth
Researchers have shown that organizational strategies are important determinants of organizational success (Feeser and
Willard, 1990; Grinyer, McKiernan, and Yasai-Ardekani,
1988; Palepu, 1985). This study replicates findings from two
central approaches to organizational strategy: portfolio-level
strategies and competitive-level strategies
PORTFOLIO-LEVEL STRATEGY. Portfolio-level or primary strategies address the selection of industries in which a firm diversi-
L. G. Weinzimmer
fies to define its domain (Bourgeois, 1980). Related diversification strategies build on synergies of existing businesses to
achieve growth. Because related diversification is a growth
strategy that builds upon existing synergies, a firm has existing
knowledge of the product and market in which it operates
(Palepu, 1985). Conversely, if a firm undertakes an unrelated
diversification strategy, typically through acquisitions, the
firms will have limited knowledge, and, thus, will face increasing uncertainty (Varadarajan and Ramanujam, 1987). Researchers have shown that related diversification strategies
outperform unrelated diversification strategies in terms of
over-all performance, including growth; whereas unrelated
diversification strategies decrease variability of sales growth
(Palepu, 1985).
Therefore, related diversification strategies should increase
the likelihood of organizational growth relative to unrelated
diversification strategies, when controlling for acquisitions.
This assertion does not imply that unrelated diversification will
prevent a firm from achieving short-term growth. However, a
related diversification strategy will enable a firm to achieve a
higher growth rate than would an unrelated diversification
strategy.
COMPETITIVE-LEVEL STRATEGY. Competitive-level or secondary strategies address how a firm competes within a particular
industry (Bourgeois, 1980). Several researchers have suggested or shown that strategic aggressiveness to pursue Porter’s
(1980) competitive strategies is positively associated with organizational performance (Feeser and Willard, 1990; Grinyer,
McKiernan, and Yasai-Ardekani, 1988; Romanelli, 1989). A
firm can aggressively pursue a low-cost strategy by decreasing
its cost base relative to competitors and pursue a differentiation
strategy by offering a product or service perceived as unique
by consumers. Aggressive firms may create such competitive
advantages as increased customer awareness and brand loyalty
(Feeser and Willard, 1990). Romanelli (1989) suggested that
aggressive organizations had a better chance of surviving,
because aggressive firms will acquire the resources necessary
to withstand competitive pressures and achieve growth. Thus,
aggressive strategies should lead to increased growth.
Top Management Determinants of Growth
In the organization theory and strategy literatures, beliefs and
characteristics of powerful individuals in organizations are
thought to have a pronounced impact on the initiation of
strategic change and organizational growth. Specifically, this
study replicates research initiated by Hambrick and Mason
(1984) and Gupta (1984). These authors contended that top
management team (TMT) characteristics that may influence
organizational growth include heterogeneity and age of the
top management team.
HETEROGENEITY OF TOP MANAGEMENT TEAMS. Eisenhardt
and Schoonhoven (1990) suggested that industry heterogene-
Organizational Growth Determinants
ity of top management team members produces constructive
conflict. They argued that this form of heterogeneity would
provide better decision making, because TMT members with
many years in a particular industry can offer insights based
upon rich experience; whereas, newcomers can provide fresh
perspectives. Additionally, heterogeneity of functional experience may affect the growth rate of an organization. Functional
heterogeneity can produce constructive conflict, wherein team
members with different functional backgrounds provide
checks and balances in decision making (Hambrick and Mason, 1984). Therefore, organizational growth may be positively related to the level of top managers’ industry and functional heterogeneity.
Hambrick and
Mason (1984) proposed that age of the upper echelon would
affect the growth rate of an organization, where older executives tend to be more conservative and have a bias for maintaining the status quo. Perhaps executives in the latter years
of their careers would not feel as mobile as younger executives
and, therefore, would not want to take the risks often necessary
when seeking to increase firm growth. Younger executives
typically take more risks, so youthfulness of executives leads
to organizational growth (Hambrick and Mason, 1984).
AGE OF TOP MANAGEMENT TEAM MEMBERS.
Control Variables
An organization’s growth rate is closely related to the growth
rate of its industry (Kotha and Nair, 1995). Therefore, this
study controls for industry growth rates, because the sample
uses firms from multiple industries. In addition, the number
of companies acquired and/or sold during the period of observation was considered as a control variable for the diversification measure, because organizations may grow or decline simply because of acquisitions or divestitures. Organizational
slack, defined as excess assets after debts have been satisfied,
was also considered as a control variable. Eisenhardt and
Schoonhoven (1990) showed that organizational slack was
positively related to growth.
Although previous studies have not been able to show
the simultaneous effects of all three levels of determinants,
replicating findings from tangentially related research provides
a strong theoretical foundation for examining these simultaneous influences. Therefore, to extend previous research, we
examine the proposition that organizational growth is influenced simultaneously by industry attributes, organizational
strategies, and TMT characteristics.
J Busn Res
2000:48:35–41
Manual, Moody’s Unlisted OTC Manual and Dun and Bradstreet’s Reference Book of Corporate Managements. All firms
on the COMPUSTAT database reporting quarterly figures for
sales, advertising expenditures, and research and development
expenditures for 20 consecutive quarters from the first quarter
of 1987 to the fourth quarter of 1991, and reporting top
management data were included in the sample. This yielded
a sample of 193 firms from 48 industries, based upon primary
Standard Industrial Classification (SIC) codes. Power analysis
(Cohen, 1977) indicated that a minimum of 168 firms would
be needed for this study.
Measurement of Variables
To replicate previous research, this study operationalized
growth variables using measures commonly found in the organization theory and strategy literatures.
ORGANIZATIONAL GROWTH. This study used a modified version of the ordinary least-squares beta coefficient approach
used by Grinyer, McKiernan, and Yasai-Ardekani (1988) and
Hamilton and Shergill (1992) over 20 quarterly observations
(adjusted for seasonality) to measure sales growth. Sales data
were chosen for reasons of replicability, because most recent
studies in our literature review used sales data to measure
growth. In addition, the beta coefficient approach was chosen
as the appropriate operationalization, because it captures the
fine-grained fluctuations of sales data better than other methods commonly used to measure growth, and it dampens the
effects of outliers (Weinzimmer, Nystrom, and Freeman,
1998). Growth rates were also standardized for the size of
each organization by dividing the beta coefficient by the mean
size of an organization over the period of observation (cf. Dess
and Beard, 1984; Grinyer, McKiernan, and Yasai-Ardekani,
1988). Therefore, the estimated slope of the regression line
(b9) of sales over time represents the rate of change (growth)
of an organization. Specifically, growth is operationalized as
(Equation 1).
n
n
n
i51
i51
i51
Data Collection Methods
Data were collected from multiple sources: Standard & Poors’
COMPUSTAT data tapes, Standard & Poors’ Corporate Records,
Predicast’s F&S Index, Moody’s Industrial Manual, Moody’s OTC
n
n
i51
i51
f(n o i Zijdi 2 o i o Zij di) / (n o i2 2 ( o i)2g
GRWj 5
mzjdi
(1)
where: GRWj 5 growth rate for jth firm; Zij 5 sales of the jth
firm the ith period; i 5 time period; di 5 discount rate for
inflation during the ith period; mzjdi 5 mean size of the jth
firm, discounted for inflation; and, n 5 20 consecutive quarters from 1987 to 1991.
A composite score was calculated for each
four-digit SIC code using advertising intensity, R&D intensity,
and a four-firm concentration ratio. Entry barrier measures
may not be comparable among different industries, given characteristics endogenous to different markets. Therefore, these
measures were standardized before the variables were summed
to calculate height of entry barriers (cf. McDougall, Robinson,
and DeNisi, 1992).
ENTRY BARRIERS.
Methods
37
38
J Busn Res
2000:48:35–41
This study uses Davis and Duhaime’s (1992) entropy measure of diversification to investigate the influences of related diversification from 1987
to 1991.
PORTFOLIO-LEVEL STRATEGY.
This study used Fombrun and
Ginsberg’s (1990) composite approach to measure strategic
aggressiveness. To calculate differentiation aggressiveness, ratios of advertising expenditures to sales and product R&D
expenditures to sales were used. To calculate low-cost aggressiveness, ratios of expenditures for new plant and equipment
to sales and process R&D expenditures to sales were used.
Industry effects were controlled by dividing firm ratios by
average industry ratios to assess firm-level aggressiveness relative to competition.
STRATEGIC AGGRESSIVENESS.
Top managers are defined as all officers above the level of vice president (Michel
and Hambrick, 1992). Industry heterogeneity was measured
by computing the coefficient of variation, defined as the standard deviation divided by the mean (cf. Wiersema and Bantel,
1992). Functional heterogeneity was measured by the number
of functional disciplines currently represented by top management members divided by the size of the TMT using a ninepoint scale offered by Michel and Hambrick (1992).
HETEROGENEITY OF TOP MANAGERS.
Age is measured by mean age in years of top managers
(Norburn and Birley, 1988).
AGE.
CONTROL VARIABLES. To measure industry growth, sales for
each 4-digit SIC code were regressed over time and the corresponding standardized coefficient (b9) was used as a measure
of industry growth (cf. Dess and Beard, 1984). Organization
slack was measured as assets/debt (Eisenhardt and Schoonhoven, 1990). Net acquisitions/divestitures for each organization
was a continuous variable, ranging from 2n to n acquisitions
during the period of 1987–1991.
Analytic Procedures
To replicate findings from previous research, this study used
multiple-lagged ordinary least-square (OLS) regression analysis to recognize the effects of time, because all previous growth
studies used correlation or regression analysis to test the significance of determinants. To ensure that the OLS multiple regression model fit the dataset, comprehensive diagnostic tests
were performed to check for both randomness and normality
of the residuals (cf. Weinzimmer, Mone, and Alwan, 1994).
Results
Descriptive Statistics and Correlations
Initial analysis revealed several significant correlations between independent variables, suggesting the need to check
for multicollinearity in the regression models. A two-stage
L. G. Weinzimmer
process was used to adjust for multicollinearity (cf. Berry and
Feldman, 1990). Means, standard deviations, and correlations
of variables are presented in Table 1.
OLS Multiple Regression Analysis
Table 2 presents OLS regression results. Model 1 tests for the
simultaneous effects of all three levels of determinants, and
Models 2 and 3 test for interaction effects. Multiple regression
results from Model 1 show a significant positive association
between organization growth and industry entry barriers (p ,
0.001), suggesting that an industry with high entry barriers
will lead to organizational growth. Results for strategy-level
determinants in Model 1 show a significant positive association
between organizational growth and related diversification (p ,
0.05) and strategic aggressiveness (p , 0.001), suggesting
that strategies pursued by a firm are significant in explaining
growth. Finally, the data suggest strong empirical support for
several top management team determinants, including significant positive associations between organizational growth and
the degree of TMT heterogeneity in terms of industry experience (p , 0.10), the level of TMT heterogeneity in terms of
functional backgrounds (p , 0.001) and a significant negative
association between organizational growth and average age of
the TMT (p , 0.05).
Model 2 shows a significant positive relationship between
growth and the multiplicative interaction between aggressiveness and TMT age. Similarly, Model 3 shows a significant
positive relationship between growth and the multiplicative
interaction between aggressiveness and industry growth.
Relative Effects Among Different Levels
of Determinants
Given the strong empirical evidence for the simultaneous influence of all three levels of determinants, highlighting the
relative importance for each level of determinants may provide
additional insights. To determine the relative importance of
one set of determinants over another, we used a x2-test procedure replicating methods used by other researchers comparing
the relative importance of variables to each other (cf. Kotha
and Nair, 1995).
Each of the three levels of determinants provided significant
improvements over the base model consisting of the control
variables. However, strategy-level determinants provided the
largest improvement (Dx2 5 87.43; p , 0.001; adj-R2 5
0.27), followed by top management team level determinants
(Dx2 5 51.49; p , 0.001; adj-R2 5 0.25) and industry-level
determinants (Dx2 5 11.05; p , 0.005; adj-R2 5 0.17). This
suggests that all three levels of determinants are individually
significant in explaining organizational growth, with strategy
being the most significant relative to TMT- and industry-level
determinants. However, the simultaneous presence of all three
levels of determinants provides the best explanation of organi-
Organizational Growth Determinants
39
J Busn Res
2000:48:35–41
Table 1. Descriptive Statistics and Correlationsa
Variable
Mean
SD
1
2
3
4
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
1.13
0.12
0.05
0.46
1.98
51.33
0.65
6.02
1.21
26.36
2.60
0.15
0.28
0.70
1.28
5.23
0.19
8.65
2.34
35.07
0.23**
0.11*
0.39**
0.13*
20.25**
0.32**
0.22**
0.26**
0.11*
0.10
0.11*
20.12*
20.03
0.03
20.08
0.11*
0.06
20.01
0.05
20.01
0.02
0.03
20.09
0.01
0.02
20.12*
0.09
0.03
0.12*
20.04
Firm growth
Entry barriers
Diversification
Aggressiveness
Industry heterogeneity
TMT age
Functional heterogeneity
Industry growth
Net acquisitions
Slack
5
6
7
20.02
0.14* 20.17*
0.11* 20.14
0.01
0.01
0.00 20.03
20.09 20.16*
0.04
8
9
0.01
20.07
20.03
a
n 5 193.
* p , 0.05.
** p , 0.001.
zational growth, in terms of explained variance, as compared
to any of the restricted models.
Discussion
Previous studies have shown that either industry, strategy, or
top management attributes individually influence organizational growth. Using measures and analytic methods from
previous research, this study replicated these findings and
confirmed many of these individual relationships using a gen-
eralizable sample. However, no research has demonstrated
the concurrent effects of all three levels of determinants. Therefore, findings from this study in some ways contradict previous
research that studied the effects of industry, strategy, and top
management variables in explaining organizational growth.
Eisenhardt and Schoonhoven (1990) concluded that, although
industry and managerial factors influence organizational
growth, strategy characteristics do not. A possible explanation
for inconsistencies between this study and Eisenhardt and
Schoonhoven’s (1990) study is these authors only used a
Table 2. Regression Resultsa
Independent
Variables
Industry attributes
Entry barriers
Organization strategy
Related diversification
Aggressiveness
Managerial attributes
Industry heterogeneity
Functional heterogeneity
TMT age
Control variables
Industry growth
Net acquisitions
Slack
Interactions
Aggressiveness 3
TMT age
Aggressiveness 3
industry growth
R2
Adjusted R2
F
a
n 5 193.
p , 0.10.
* p , 0.05.
** p , 0.01.
*** p , 0.001.
†
1
0.17***
0.11†
0.31***
Models
2
0.17***
0.10*
20.27
3
0.17***
0.10†
0.25***
0.08†
0.25***
20.12*
0.09†
0.25***
0.01
0.08†
0.27***
0.20***
0.22***
0.11*
0.17***
0.20***
0.11*
0.02
0.22***
0.13**
0.61†
0.22†
0.413
0.384
14.31***
0.474
0.430
10.73***
0.463
0.421
10.98***
40
J Busn Res
2000:48:35–41
single measure for strategy, technological innovation. This
study found both portfolio-level and competitive-level strategies to be significant in explaining growth. Similarly, Kotha
and Nair (1995) found environmental-level variables to be
significant in explaining growth, but concluded that strategy
variables had no impact on growth. A possible explanation
for this inconsistency is that these authors used single-dimensional measures for strategy, including individual measures
for efficiency, capital expenditures (low-cost strategies), and
advertising intensity (differentiation strategy). Our study used
a multidimensional measure of strategic aggressiveness from
a composite of both low-cost and differentiation strategies.
Hill (1988) showed that concurrent pursuit of both low-cost
and differentiation strategies can create competitive advantage.
Therefore, when examining determinants of organizational
growth, a composite aggressiveness measure may capture
over-all strategic orientation better than individual measures.
Interactions of Determinants
In addition to examining the direct effects of these factors on
growth, subsequent analysis found strong empirical evidence
for interrelationships among predictor variables. Analysis provided support for the relationship between organizational
growth and the correct alignment of strategic aggressiveness
and TMT age. A high-risk strategy needs support from top
managers to be successful. This association suggests that having the correct alignment between strategic aggressiveness and
average age of the TMT, rather than merely implementing an
aggressive strategy or having a young top management team,
will have a positive impact on organizational growth. Specifically, this implies that when a firm pursues an aggressive
strategy, support of top management will lead to organizational growth. Past studies have not empirically tested this
relationship, so the positive association between the interaction of strategic aggressiveness and TMT age with organizational growth provides empirical evidence for Gupta’s (1984)
theoretical propositions.
The analysis also provided support for the relationship
between organizational growth and the correct alignment of
industry growth and strategy. When a firm is located in a
growing industry, resources should be aggressively allocated
to that industry to exploit growth opportunities. Conversely,
as industry growth slows, firms should decrease aggressive
commitment to that particular industry to allow for the reallocation of resources to other growth-oriented industries. This
finding implies that when a firm is in a growing environment,
investment into this industry in the form of advertising, research-and-development and plant-and-equipment will lead
to organizational growth. Although no research exists on the
interaction between strategic aggressiveness and industry
growth and its influence on organizational growth, findings
of this study parallel previous empirical research. Previous
research has examined the extent to which a particular strategy
best matches a particular type of environment (Romanelli,
L. G. Weinzimmer
1989). This study, however, presents the first evidence for
a relationship between strategy and industry growth fit on
organizational growth. This finding suggests that certain strategies align a firm better with a particular industry, and,
thereby, enable it to achieve growth.
Conclusion and Directions for
Future Research
Although the replication approach used in this study provided
support for previously identified relationships between growth
and individual levels of determinants, contributions from this
study come from the theoretical and empirical convergence
of previously tangentially related studies to provide a unified
explanation of organizational growth. This study was able to
show simultaneous effects for all three levels of determinants
using a triangulation approach—including tests of main effects, interactions, and relative comparisons among sets of
determinants.
This study has attempted to provide an integrative framework to reduce fragmented theory in the organizational theory
and strategy literatures. Replicating studies from traditional
organizational theory and strategy paradigms provided a
method that may help to explain why a lack of consensus has
existed among single-level studies of organizational growth.
Future research may consider additional variables outside of
the traditional organization theory and strategy literatures,
such as concepts drawn from industrial and political economics (cf. Evans, 1987) and sociology research (cf. Baum and
Mezias, 1993; Hannan and Ranger-Moore, 1990) to provide
further insights into understanding why organizations grow.
In conclusion, by using variables identified in previous
studies and replicating analytic methods and measures, this
study provided clear evidence that all three levels of determinants from the strategy and organization theory literature are
significant concurrently. This approach provides a better explanation of organizational growth, as compared to exclusively
studying a single level of determinants. This implies that, as
researchers study organizational growth in the future, examination of one or even two sets of determinants will not suffice,
but that all three sets of determinants should be studied together. This study also yielded support for the idea that interactions among determinants add significantly to explaining
organizational growth. It also used a sample that included
diverse firms and a cross section of 48 industries to increase
generalizability over previous studies using restricted samples.
Finally, these findings may provide new insights for other
areas of research, such as organizational life-cycle (OLC) theory. Kazanjian (1990) contended that OLC research is limited
because of a lack of identifiable determinants of growth. He
argued that the growth literature lacks theoretical models of
factors that lead to a firm’s position in a particular stage of
growth. He suggested that, although life-stage models add to
our understanding of the rather complex phenomenon of
Organizational Growth Determinants
growth, these models suffer a deficiency, because the determinants precipitating growth are, at best, implied. Additionally,
because our study did not discriminate between growing and
declining firms, where growth could be either positive or
negative, findings may also contribute to the organizational
decline literature.
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