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DOI: 10.1177/014920639802400205 1998; 24; 235 Journal of Management

Laurence G. Weinzimmer, Paul C. Nystrom and Sarah J. Freeman Measuring Organizational Growth: Issues, Consequences and Guidelines

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Journal of Management 1998, Vol. 24, No. 2, 235-262

Measuring Organizational Growth: Issues, Consequences and Guidelines

Laurence G. Weinzimmer Bradley University

Paul C. Nystrom Sarah J. Freeman

University of Wisconsin-Milwaukee

Although the literature contains an impressive volume of studies attempting to identify determinants of organizational growth, researchers have recently noted important inconsistencies in findings. They may be explained, in part, by the variety of approaches used to measure growth. Our study provides a critical review of the literature to identify issues regarding the measurement of growth. We examine alternative approaches in order to assess the consequences of using inappropriate measures. Consequently, we consider three concepts as well as three different measurement formulas. Based on comprehensive data from 193 firms in 48 industries for 20 periods, results from comparative regression analyses reveal that the significance of relationships between determinants and organizational growth, as well as amount of explained variance, depend on the specific approaches used to measure growth. Finally, we provide some guidelines to help researchers select appropriate techniques for measuring organizational growth.

A recent study of managers found sales growth to be the most commonly identified measure of overall organizational performance (Hubbard & Bromiley, 1995). Of course, managers can choose from a bewildering array of performance measures, including accounting-based return rates such as ROI, various stock- market measures, cash flows, and growth rates. Researchers confront similar choices concerning how to operationalize performance. Studies have considered numerous variations in performance measures (Lenz, 1981; Steers, 1975; Venkatraman & Ramanujam, 1986).

Organizational growth is inherently a dynamic measure of change over time, which creates an opportunity for researchers to use several different formulas, even when examining only one concept such as sales growth. The study of organizational growth has received considerable attention over the past several decades,

Direct all correspondence to: Laurence G. Weinzimmer, Foster College of Business Administration, Bradley University, Peoria, IL 61625, (309) 677-3478.

Copyright © 1998 by JAI Press Inc. 0149-2063

235



236 LAURENCE G. WEINZIMMER, PAUL C. NYSTROM, AND SARAH J. FREEMAN

producing many approaches to assessing the amount of growth that a firm has experienced. However, one can find very little discussion regarding appropriate measures of organizational growth (Birley & Westhead, 1990). Even a cursory review of the literature reveals that researchers have used a myriad of approaches to measure organizational growth, along with some awareness that inconsistencies in these measures can potentially impede theory development. Therefore, confu- sion often arises as to what researchers mean when they discuss organizational growth, in terms of how they conceptualize growth (e.g., sales versus employees) and the way they operationalize it with formulas (Whetten, 1987).

Several critics of organizational studies have questioned whether researchers devote sufficient attention to ensuring that their measurement constructs are appropriate (Churchill, 1979; Mitchell, 1985; Podsakoff & Dalton, 1987). Researchers who seek to advance theory need to define clearly what they are measuring so that they fairly replicate and extend previous research. Systematic approaches to measurement techniques can lead to superior operationalizations and, consequently, yield better theory development (Venkatraman & Ramanujam, 1986).

Although organizational growth has remained a central area of research in organization theory and strategy, researchers have found many inconsistencies regarding the objective factors leading to organizational growth (Birley & West- head, 1990; Davidsson, 1991; Kazanjian, 1988; Whetten, 1987). Such inconsistent findings may be due, in part, to variations in sample characteristics. For example, when examining predictors of growth, one may expect different results from samples of new ventures versus Fortune 500 companies, from manufacturing firms versus service fh'ms, and from public versus private organizations. However, some studies with similar sample characteristics have still yielded inconsistent results regarding predictors of organizational growth. For example, using samples of start-up firms from high-tech industries, Eisenhardt and Schoonhoven (1990) found mixed results regarding the positive relationship between growth and top management team size, while Feeser and Willard (1990) found strong support for this same relationship. Similarly, using general samples of firms, Hamilton and Shergill (1992) found a significant positive relationship between growth and related diversification, whereas Varadarajan and Ramanu- jam (1987) did not. Therefore, we think such inconsistencies are also likely to be due partially to inconsistencies in the measures of organizational growth being used.

This paper examines the extent to which the use of alternative organizational growth measures influences the outcomes of analyses. Before assessing consequences of using alternative measures, we identify critical issues and practices that have led to inconsistencies in the approaches used to measure growth. Using determinants of organizational growth identified in previous literature, we then compare results from regression models to assess the consequences of using different measures of growth, in order to examine the impact that these measurement inconsistencies have had on theory development. Finally, we propose some guidelines to selecting an appropriate measure of organizational growth based on data characteristics.

JOURNAL OF MANAGEMENT, VOL. 24, NO. 2, 1998



MEASURING ORGANIZATIONAL GROWTH 237

Issues Regarding the Measurement of Growth

Inconsistencies in the measurement of organizational growth arise from two central sources. First, past studies have used different concepts of growth. Many researchers use change in sales as the indicator of growth, but others use indica- tors such as employees or assets, and these measures assess quite different theoretical concepts. This issue raises two concerns: (1) do different concepts of organizational growth affect theory development by influencing the relationships found between determinants of growth and alternative concepts; and (2) are certain concepts of growth more appropriate, based on the phenomenon of interest and sample characteristics?

A second problem contributing to inconsistencies in the measurement of growth stems from the use of alternative formulas. These formulas range from using average percentage change to simply taking the difference between the first and last years of observation. Again, two concerns become apparent: (1) do different formulas used to measure growth affect theory development by influencing relationships between determinants of growth and alternative formulas; and (2) are specific formulas more appropriate, given alternative sources of data? An enumerated summary of the literature (see Appendix A) attests to the variety of concepts and formulas used to measure growth. Studies identified in Appendix A emerged from an exhaustive review of the literature from 1981-1992. The jour- nals reviewed include: Academy of Management Journal, Administrative Science Quarterly, American Sociological Review, Entrepreneurship Theory and Prac- tice, Journal of Business Venturing, Journal of Management, Journal of Manage- ment Studies, Journal of Small Business Management, Organization Science, and Strategic Management Journal. Studies selected include any research examining predictors of organizational growth, regardless of sample characteristics such as firm size or type of industry(ies).

Practices in Conceptualizing Organizational Growth Whetten (1987) noted that size is an absolute measure, whereas growth is

defined as a relative measure of size over time. Therefore, discussions regarding conceptualizations of organizational size prove valuable when seeking to identify appropriate concepts of organizational growth. Studies measuring organizational growth have been criticized for focusing on a single dimension of change in size, rather than using multiple dimensions (Birley & Westhead, 1990). Kimberly (1976) identified several size measures used in the literature, including employees, assets, capacity, and sales. Although these measures may be correlated empirically, they do differ conceptually. For example, the number of employees working in a set of organizations may not exhibit the same pattern of change over time as the sales volume of those organizations because of improvements in process efficiency. Kimberly (1976) prescribed a contingency approach, and suggested using number of employees for service organizations and assets for manufacturing organizations. Regardless of the type of organization, he maintained that researchers should use a theoretical rationale for selecting any measure of size.





Shifting attention from size to growth, Appendix A shows that the majority of the 35 studies (83%) identified in a literature review used sales (or revenues) as a concept of growth and nearly three-quarters of these studies used sales as their only measure of growth. Both Child (1973) and Kimberly (1976) contended that organizational size cannot be measured by simply considering sales, and they urged that multiple concepts be considered. Other common concepts used to measure growth found in our literature review included employees (17%) and assets (8%). To assess the degree to which different concepts influence outcomes, our study measures growth by incorporating all three approaches commonly used in previous literature: growth in sales, employees, and assets.

Practices Used to Operationalize Organizational Growth

Of the 35 studies of organizational growth identified in a literature review and described in Appendix A, only 22 reported an identifiable formula for their measure of growth. Three of the 35 studies did not report formulas because they used subjective self-reported measures of growth obtained from organization members, an approach subject to both systematic bias and random differences in interpretation. Nineteen of the 22 studies reporting identifiable formulas used manipulations of first-year (to) and last-year ((f) size to measure growth. Specifi- cally, six studies measured growth as a ratio of last-year to first-year organization size ((f/to); another five studies measured growth as the difference of first-year size and last-year size divided by first year size ([(f- t 0] / to); another five studies measured growth as the difference between first-year size and last-year size divided by length of the study ([tf - t 0] / n); and three studies measured growth by subtracting first-year size from last-year size ( ( f - to). Thus, of the 22 studies reporting mathematical relationships to measure growth, 19 (86%) analyzed growth as some difference between first-year and last-year sizes.

Logic suggests that using only first-year and last-year sizes does not fully capture the growth rate of organizations because it does not identify any behaviors of an organization during the middle periods of the study. For example, an organization may be experiencing consistent, predictable decline over most observations but experience a growth spike in the final period of observation, as seen in series A of Figure 1. Using the two data-point approach, a researcher would miss fine- grained fluctuations and conclude that the growth rate of this organization has been positive over the last five periods. Additionally, when variation in growth is high, this approach may identify growth rates of organizations incorrectly. For example, series B of Figure 1 shows an organization experiencing moderate growth, but use of only first and last period observations would lead a researcher to conclude that this firm had been declining over the last five periods. Moreover, the problem of high variation could be amplified if the data are heteroscedastic. The first-and-last year approaches ignore valuable information concerning the middle years of a study, and thus fail to capture the dynamic properties of growth. This may result in either weak models and/or misspecified results and interpretations.

Only three studies from our literature review used the middle years of their observation periods (Grinyer, McKiernan, & Yasai-Ardekani, 1988; Hamilton & Shergill, 1992; Miller & Friesen, 1983) and their models employed organizational





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Organizational Size Changes Over Time

growth as just one of many variables used to measure organizational performance. Paradoxically, none of the studies seeking exclusively to explain organizational growth has used a continuous measure of change in size over time. Two of the studies using information from the middle years of observation fit an OLS regression line of a size measure over time, taking the beta coefficient as a measure of growth. The other study took an average of the annual change in sales over the period of study. While both of these approaches recognize changes in size during the middle years of a study, use of a beta coefficient is better because it dampens the effect of any significant outliers. Figure 2 plots quarterly sales from 1987 to 1991 for a firm from our sample. A substantial outlier occurs in period 5, and this outlier dramatically influences the average percentage change in growth, yielding a growth rate of 62.8% over the twenty quarters. By contrast, a beta estimate yields a growth rate of -3.2%. When one examines the data plot, one sees that the beta estimate produces a much more accurate measure of growth than does the average percentage change.

Finally, it is important to distinguish between absolute growth and growth rates. Formulas common to the growth literature, (~-- to) and ([~,-- to] / n), measure absolute growth, the actual difference in terms of organizational size from one observation to another. Conversely, the ([(f- to] /to) and ~1 measures examine growth rates or relative changes in size. Therefore, the size of an organization has an effect on its measured growth. Using absolute growth, a large firm would be likely to realize greater growth, in terms of size, compared to a small finn (e.g., a firm with $2 billion





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in sales is more likely to increase sales by $100 million in a given time period than a firm with $200 million in sales). Conversely, using growth rates, a small firm would be likely to realize greater growth, in terms of percentage changes, compared to a large firm (e.g., a $100 million increase in sales represents 5% growth for the $2 billion f'Lrm, but 50% growth for the $200 million firm). Therefore, depending on the formula used to measure growth, organizational size would have a positive impact on absolute growth and a negative impact on growth rate.

Consequences of Using Alternative Concepts and Formulas

To examine the effects of these alternative measures on the development of organizational growth theory, we developed and analyzed a base model contain- ing 13 determinants of organizational growth identified in previous empirical studies. See Appendix B for the variables, their rationales, and citations. Studies have found that industry characteristics, organizational strategies, and characteristics of top management teams influence organizational growth. Identification of possible predictors of organizational growth was based on the same exhaustive review of the literature from 1981-1992 as that identifying measures of growth. We drew potential predictors from any research examining predictors of organizational growth, regardless of sample characteristics such as firm size or type of industry(ies). Selection of independent variables represented the most commonly used predictors of growth that could be measured using secondary data sources. The specific measures of these independent variables appear in Appendix C.





Additionally, several control variables were considered, including organizational slack, organizational size, and organizational age. Slack was found to be significant in explaining growth and remained in our analyses. Organizational size and age, however, were not significant in explaining growth, nor did their presence change the significance or direction of any other independent variables. Therefore, they were dropped from subsequent analyses.

Data Collection Methods Data were collected from multiple sources: Standard & Poors'

COMPUSTAT II data tapes, Standard & Poors' Corporate Records, Predicast' s F&S Index, Moody's Industrial Manual, Moody's OTC Manual, Moody's Unlisted OTC Manual, and Dun and Bradstreet's Reference Book of Corporate Managements. Included in the sample were all publicly held firms on COMPUSTAT II that reported quarterly figures for sales, assets, advertising expenditures, and research-and-development expenditures for 20 consecutive quarters from the first quarter of 1987 to the fourth quarter of 1991, and that reported top management data. Power analysis (Cohen, 1977) was performed to identify a sufficient sample size. An average effect size of .125 was calculated based on previous empirical studies found in the organization theory and strategy literatures. In addition, a conservative estimate was used for 0~ (.05), and it was assumed that all independent variables and control variables would be significant in explaining growth concurrently. Based on these estimates, it was determined that a sample of at least 186 firms was needed to achieve a conservative power estimate of 80%.

While many previous studies of organizational growth have been constrained by sampling methods, such as limiting the types of organizations to be examined (e.g., new ventures) or studying only a single industry, our sampling method did not discriminate by type of fLrrn, age of firm, or primary industry of operation in order to increase generalizability. This yielded a sample of 193 firms from 48 industries, based on primary SIC codes. Annual sales ranged from $3 million to $51 billion (mean = $473 million), number of employees ranged from 5 employees to 296,000 (mean -- 15,200), and assets ranged from $4 million to $38 billion (mean = $376 million). Age, measured as the difference between the first year of the study (1987) and the date of an organization's formal incorporation (cf. Shan, Singh, & Amburgey, 1991) ranged from 3 years to 116 years (mean = 34 years).

Time Period Because organizational growth is a time-dependent variable, length of the

observation period becomes an important issue. This study used quarterly data from 1987 to 1991. This five-year period was selected for two reasons. First, the Department of Commerce reclassified SIC codes in 1987 to reflect major techno- logical changes within and between industries, thereby making comparisons between pre- and post-1987 industry-level data both difficult and suspect. Second, a five-year period has been the time frame most widely used in previous research examining organizational growth. In our survey of 35 studies measuring growth, time frames varied from static one-year measures to 11 years (see Appendix A).





Specifically, five studies considered time frames of 18 months or less, three studies considered time frames of two years, eight studies had time frames of three years, one study had a four-year time frame, 11 studies had a time frame of five years, three studies had time frames of greater than five years, and five studies did not report the time frames used.

Measurement of Organizational Growth In order to compare different measurement formulas, this study considers

three different operationalizations of growth. Specifically, this study uses a beta estimate (B) to capture middle periods of observation, and two variations of a two data-point approach. The first formula for measuring organizational growth involves a beta estimate (6') from an OLS model to measure growth, where size is regressed on time using 20 quarterly data points. Makridakis, Wheelwright, and McGee (1983) suggested that a minimum of 15 observations is required to fit a regression line to time-series data properly. This study regressed sales, employees, and assets over 20 time periods and used the resulting beta coefficients as measures of organizational growth. The use of quarterly data may introduce the influence of seasonality to each time series. Consequently, when measuring growth with a beta coefficient of quarterly data, an indicator variable was used to control for the effects of seasonality. Additionally, industrial-organization economists have found that firm size may influence growth (Evans, 1987). Therefore, growth rates were 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 & Beard, 1984; Grinyer et al., 1988).

Real Growth. In order to measure the real growth of an organization, effects of time must be factored out. For example, if a researcher uses change in annual revenues as a measure of organizational growth, the effect of inflation should be taken into account. If a researcher uses any variables affected by inflation, such as sales (in dollars) or assets, unadjusted growth rates would be artificially high. Actual unit sales may not increase over time; the do|lar value of a sales increase may be due exclusively to inflation. Moreover, samples from several studies in our literature review included finns from different time periods. When firms in a sample are all observed over the same time period, descriptive statistics may be inaccurate (e.g., mean sales), and yet the relative effect sizes of predictor variables should be maintained. However, when a sample consists of firms representing different periods of observation or different lengths of observation, the effects of inflation will not only distort growth measures, but may also influence the effects of explanatory variables.

Of the 35 studies identified in Appendix A, only six explicitly acknowledged inflationary effects. Twelve studies apparently did not consider inflation when measuring growth rates, based on the fact that their data sources, such as COMPUSTAT growth rates, do not adjust for inflation; 16 studies did not report any information as to whether inflation had been factored into the growth rate or not; and one study focused exclusively on employee growth, which does not require inflation adjustment.





Because this study examines two variables influenced by inflation (sales and assets), their values are discounted for inflation based on the gross domestic prod- uct (GDP) price deflator (Economic Report of the President, 1993). We chose the GDP price deflator, rather than the CPI deflator, because our sample contains a diverse representation of industries. Therefore, using a modified version of an ordinary-least-squares beta coefficient, regressing organizational size over time, organizational growth is operationalized here as:

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The second formula examines only the difference of the first period of observation from the last period of observation divided by a constant ([(f- toi l / n) to measure growth, where ~e represents organizational size in the final period of observation, t o represents organizational size during the initial period of observation, and n represents the number of periods of observation. Note that this measurement approach accounts for all variations of either (tf - t 0) or ([(r- to] / n) because dividing the dependent variable by a constant in a regression equation will produce identical results. The third formula examines the difference between last and first period divided by the first-period observation ([(f- to] / to). Data used for the two-period measures have also been adjusted for inflation when using sales growth and asset growth.

Results

Correlations

To assess results emanating from alternative concepts and formulas of organizational growth, this section first examines correlations. We then employ an OLS multiple regression model to examine the relationships between organizational growth and potential predictors when using different concepts and formulas.

Table 1 shows that the three growth concepts all correlate at the p < .001 level when using the same formula; the median r is .70. However, if all of





Table 1. Descriptive Statistics And Correlations For Growth Measures a

Variable Mean Std Dev 1 2 3 4 5 6 7 8

1.

2.

3.

4.

5.

6.

7.

8.

9.

Sales (1~) 1.13 2.60

Employees ([~) .92 13.95 .80***

Assets (13) 1.06 3.17 .84*** .71"**

Sales 46.32 103.56 .15" .16" ([tf- t 0] / n)

Employees .32 1.05 ([tf- to] / n)

Assets 160.51 501.40 ([tf-- tO] / t/)

Sales .98 1.32 ( [ ~ - to] / t 0) Employees .18 .68

([tf- t 0] / to) Assets .77 .41

([tf- t0l / t 0)

.22**

.35*** .36*** .37*** .70***

.09 .07 .21"* .80*** .43***

.70*** .51"** .63*** .08 .23*** .02

.75*** .89*** .65*** .10 .38*** .06 .57***

.65*** .51"** .79*** .11 .25*** .11 .70***.60***

Notes: an = 193

p < .05; **p < *** * .01; p< .001

these formulas accurately measure growth, we should expect very high correlation coefficients between formulas within each concept. This is not the case for the ( [ ( f - t 0] /n ) formula, where the correlation between employee growth and asset growth was only .43. Moreover, when comparing correlation coefficients between formulas, it can be seen that the sales and assets measures using the ( [ t f - t 0] / n) formula (an absolute measure of growth) do not correlate with their ([tf- t 0] / to) counterparts. Note that the 13' measure on fine-grained sales data produces significant correlations with all but one other measure. The ( [ ( f - t 0] / n) measure for coarse-grained employee data also exhibits significant correlations with other measures, although their magnitudes (median r = .36) are lower. In general, correlations between the absolute ([~e- to] / n) and relative ( [ t f - to] / t o and 13') measures of growth are fairly low. Despite the strong correlations among the three concepts using two of the three different formulas (not [tf- t 0] / n), multiple regression models discussed below reveal some distinct differences.

Table 2 shows the correlations among explanatory and control variables. Sixty-one of the 91 correlations (67%) have very low absolute values, with r < • 10. Eighty-five of the 91 correlations (93%) have absolute values of r < .20. Even the largest six correlations seem to be modest, with absolute values falling in the range between .21 and .40. Consequently, when sequentially testing for multicol- linearity among each of these variables, the presence of correlated variables did not influence significance or direction for the relationship between organizational growth and any predictor variables.

JOURNAL OF MANAGEMENT, VOL. 24, N 0. 2, 1998



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Comparison of Alternative Concepts of Growth Tables 3 to 5 show the results of applying an OLS multiple regression model

to examine whether alternative concepts of growth yield different results, in terms of both significance and explained variance. Each of the three concepts of growth (sales, employees, assets) was measured using all three formulas for growth, and then regressed on a set of variables identified by other scholars as contributing to organizational growth (see Appendix B).

These analyses reveal that using alternative concepts of organizational growth yields different results. The ability of this set of independent variables to explain variance in organizational growth depended on the concept being examined as the dependent variable. Consider the concept of sales growth first. Using the beta measure, these independent variables explained 42.8% of variance in sales growth. The same independent variables explained only 29.2% of the growth in number of employees and 28.3% of asset growth.

T a b l e 3, Regress ion Resul ts for Three Al terna t ive Formulas of

Growth Using Sales Concep t a

Independent Variables b

Models

[~' t f - tol n t f - tol t o

Environmental Dimensions Munificence .19*** .06 .10*

Dynamism -.05 - .02 .00

Concentration .08 .06 .19*

Entry Barriers .11 * .00 .09

Strategy Characteristics Diversification .07 - .07 -.01

Aggressiveness .28*** -.03 .25***

Acquisitions .22*** .19** .09

Managerial Attributes Industry Heterogeneity .13" * .08 .06

Functional Heterogeneity .34*** .07 .18**

Top-Management-Team Size .26*** .39*** .20**

Company Tenure .00 .15 - . 12

Top-Management-Team Age - . 11" .07 .20**

Composition of Board .05 - . 17"* .14"

Control Slack .13** .04 .16'*

R 2 .469 .247 .355

Adjusted R 2 .428 .188 .305

F 11.26"** 4.51"** 7.02***

Notes: * p < .05, **p < .01, *** p < .001, aN = 193, bse¢ Appendices B and C




Table 4.

MEASURING ORGANIZATIONAL GROWTH

Regression Results for Three Alternative Formulas of Growth Using Employee Concept a

247

Models

Independent Variables b ~' t f - to/ n t f - to / t O

Environmental Dimensions Munif icence .09 .00 .09

Dynamism - . 14* - . 11 - . 0 9

Concentration .13 ' .00 .09

Entry Barriers .03 - . 02 .05

Strategy Characteristics Diversification .06 - . 09 .07

Aggress iveness .12" - .05 .16"*

Acquisi t ions .22** .19'* .20***

Managerial Attributes Industry Heterogenei ty .06 .14* .11 *

Functional Heterogeneity .07 .12* .36***

Top-Management-Team Size .39*** .31 *** .24***

Company Tenure .12* . l I .04

Top-Management-Team Age - . 10 - . 14* - . 12*

Composition of Board .04 - . 0 6 .08

Control Slack .11 * .04 .13**

R 2 .344 .185 .338

Adjusted R 2 .292 .121 .286

F 6.67*** 3.65*** 6.49***

Notes." * p < .05, **p < .01, *** p < .001, aN = 193, bSee Appendices B and C

The sales model finds eight of the thirteen independent variables to be significant predictors of organizational growth. Those variables that the sales model fmds highly significant (p < .001) are also significant in the asset model, but only three of them are significant in the employee model. Variables with moderate significance levels in the sales model (i.e., entry barriers and TMT age) fail to achieve significance in the employee or asset models. While the sales and asset models are similar in terms of which independent variables are significant, the employee model is quite different; it includes several independent variables that failed to achieve significance using the other two concepts, and one of them-- industry dynamism--was in the wrong direction.

Using the ([tf- to] / n) formula, the sales model found only four independent variables significant (adjusted R E = 18.8%), and one of these four was in a direction opposite to that predicted by theory. The employee model found five inde-





Table 5. Regression Results for Three Alternative Formulas of

Growth Using Assets Concept a

Independent Variables b

Models

~' t f - to/ n t f - to/t O

Environmental Dimensions Munificence .15"* .01 .10 Dynamism - .06 .05 - .02

Concentration .04 - . 12* .07

Entry Barriers - .04 -.03 .00

Strategy Characteristics Diversification .00 - .06 .00

Aggressiveness .23*** -.09 .20***

Acquisitions .25*** .27*** .23***

Managerial Attributes Industry Heterogeneity .11" .05 .05

Functional Heterogeneity .26*** .07 .26***

Top-Management-Team Size .27*** .30*** .13'*

Company Tenure .02 .17** -.07

Top-Management-Team Age .05 -.01 - .09

Composition of Board .09 - . 10 .14"*

Control Slack .14"* .01 .13"*

R 2 .335 .223 .321

Adjusted R 2 .283 .162 .268

F 6.42*** 3.65*** 6.02***

Notes: * p < .05, **p < .01, *** p < .001, aN = 193, bSee Appendices B and C

pendent variables significant (adjusted R 2 = 12.1%), and three of them were not significant in either the sales or asset models. The asset model found four variables significant (adjusted R 2 = 16.2%); however, the relationship between asset growth and industry concentration was in the wrong direction. Only TMT size and acquisitions were significant predictors in all three models.

Finally, using the ([tf.- to] / to) formula, the three concepts produced similar outputs in terms of explained variance. However, the sales model finds seven of the thirteen independent variables to be significant predictors of growth (adjusted R 2 = 30.5%), the employee model finds six of the independent variables to be significant (adjusted R = 28.6%), and the asset model found only five of the independent variables significant (adjusted R 2 = 26.8%). While each model had a unique set of significant independent variables, all of the variables that were significant among the three models were in the predicted directions. Therefore,





although some similarities were apparent, significant predictors of different growth concepts varied across all three measures of growth.

Comparison of alternative formulas of growth. Table 3 rcveals that the three alternative formulas used to opcrationalizc sales growth produced different results, in terms of significance, direction, and explained variance. First, note that the ability of these independent variablcs to explain variance in sales grow~ varied with thc formula used for the dependent variable. Recall that using the 13 measure, thcsc predictors explained 42.8% of sales growth. By contrast, only 18.8% of sales growth was explained when using the ([tf- to] /n) measure; 30.5% of the sales growth was explained using the ([tf- t 0] / t 0) measure. Thus, the independent variables drawn from the literature as determinants of growth were able

I

to explain growth better by using thc B measure than by using any of the measures more commonly employed in previous litcraturc.

More worrisome, independent variables bchaved differently depending on thc particular formula used for sales growth. Different independent variablcs wcrc significant depending on the formula used. Furthermore, composition of the board of directors was not significant using 13' to measure sales growth, but would have bccn interpreted as being significant in contradictory directions using the other two approachcs.

Table 4 shows that alternative formulas for employee growth also produced different rcsults in terms of significancc and explained variance. Using thc B' measure, these independent variables cxplaincd 29.2% of the variance in employee growth. Considerably less variance was explained using the ([tf- to] / n) formula (adjusted R 2 = 12.1%), while the ([tf- to] / to) formula allowed 28.6% of the variance in employee growth to bc cxplained. Similar to findings for the sales conccpt, diffcrcnt formulas used to mcasurc employee growth resulted in variation in thc significance of certain variables. Specifically, dynamism, concentration, aggressiveness, industry and functional hctcrogcncity, company tenure, TMT agc, and organizational slack wcrc all significant using somc formulas and not significant using other formulas.

Finally, Table 5 reveals that using altcmativc formulas to mcasurc asset growth produced disparate results in terms of significance of predictors and explained variance. As seen using the other conccpts, the 13' measure allowed thc highcst percentage of variance in asset growth to bc explained (adjusted R 2 = 28.3%). The ([tf- to~ / n) formula again resulted in considerably less explained variance (adjusted R ~ = 16.2%), while thcsc predictors explained 26.8% of the variance in asset growth when using the ([tf- t 0] / t 0) formula. Once again, each formula produced a uniquc sct of significant predictors. Only TMT size and acquisitions were significant across all models using the three formulas. Addition- ally, using the ([~e- to] / n) formula found a ncgativc relationship between industry concentration and asset growth--oppositc of the direction predicted by thcory. In general-, the predictors that have bccn use d most cxtcns~vely in previous literature on organization growth do a better job of explaining relative growth (([tf- t 0] / to) and 13') than they do explaining absolute growth ([tf- t 0] / n), regardless of the growth concept used.





Recall that only six of the 36 studies examining organizational growth acknowledged the problem of inflation and adjusted for it. The analyses reported in this section conservatively adjusted for inflation as noted earlier. But if, in real- ity, a measure did not adjust for inflation, even more disparate results would likely be generated by alternative formulas.

Discussion

Concepts of Growth Although alternative concepts of growth may be correlated empirically, they

assess different theoretical concepts. Results from a comparative regression analysis showed that the significance of relationships between independent variables and organizational growth, as well as the amount of explained variance, depended on the specific concept of growth utilized. A set of independent variables commonly used in the literature explained sales growth (42.8%) better than it explained either employee growth (29.2%) or asset growth (28.3%) using the 6' formula. However, differences in explained variance diminished when testing the three different growth concepts with the other two formulas, especially ([tf- to] / to).

The sales model finds the majority of independent variables to be significant and to work in the directions predicted by the literature in explaining organizational growth. This seems less surprising when one recalls that the independent variables were taken from existing literature, where the majority of studies used sales growth. The highly significant predictors in the sales model (p < .001) were also significant and worked in the predicted directions in the asset model. The influence of several of the variables with moderate significance levels exhibited less consistency in strength across the three concepts used for growth. Therefore, since different concepts of growth exhibit different relationships with determinants, theoretical justification should be established for selection of appropriate concepts. Researchers studying sales growth and those studying growth in the number of employees are likely interested in very different concepts.

Formulas of Growth The literature review summarized in Appendix A revealed that seven differ-

ent formulas have been used to operationalize growth, ranging from use of a beta coefficient over time to simply taking the difference between the first and last year of sales. We were able to group the seven approaches into three formulas because several approaches differed only in that they were divided by a constant, which yields identical results in a regression equation.

There were also several other considerations in the measurement of organizational growth, such as selection of an appropriate time frame, adjustment for the effects of inflation, and standardizing for size of fh'rns in the sample. Even after conservatively controlling for all of these concerns, results from the empirical analysis revealed that alternative formulas for measuring growth produced different results in terms of significance and direction of relationships, and in the amount of variance explained. Variance in relative growth was explained more





fully than was variance in absolute growth. In addition, between the two measures of relative growth, the 13' measure produced better results. For example, sales growth was explained more fully using the B measure (42.8%) than by using either the ([tf- to ] /n) measure (18.8%) or the ([tf- t 0] / t 0) measure (30.5%). Additionally, the 13 measure yielded results which are the most consistent with previous theory compared to either of the formulas based on just two data points. Using the B formula, all statistically significant independent variables exerted influence in the direction predicted by theory. The 13 measure's superiority seems to derive from its observing firm behaviors in the middle years of an observation period, whereas the other two formulas ignore all the middle years, and this places increased weight on those two end data-points.

Using either the ( [ t f - to] / n) or ( [ t f - to] / to) formula resulted in failure to find significance for several predictor variables. Each formula produced a unique set of significant predictors across all three concepts. Additionally, the more fine-grained the data, the greater the difference between the beta measure and the two data-point measures. That is, differences produced by the three formulas were greater for sales growth than for the coarser-grained employee growth or asset growth. Therefore, it can be concluded that results of the regression models depend on the type of formula used to measure organizational growth. We conclude that using a two data-point approach to measuring organizational growth with the sales concept is inadequate, especially given the fine- grained fluctuations of sales. Therefore, many findings from previous research on determinants of organizational growth appear suspect and deserve replication.

Limitations This study had several limitations which should be noted. First, given the

need for sufficient statistical power, combined with the need for longitudinal data to test different growth measures across multiple industries, the only viable method for data collection involved the use of secondary data sources listing publicly traded firms. Therefore, we considered only objective measures and not perceptual measures.

Second, although we studied 48 industries, some other industries such as banking, insurance, and utilities were not studied because organizations in these industries do not report sufficient data to capture the independent variables selected for this study. Therefore, one ought to be cautious when attempting to generalize findings from this study.

Third, availability of appropriate data for this study was limited to post-1987 data. In 1987, the Department of Commerce reclassified the Standard Industrial Classification (SIC) system due to competitive changes in specific industries. Therefore, it would be difficult and potentially misleading to compare pre-1987 industry characteristics with post-1987 industry characteristics for industries that were reclassified. Consequently, longitudinal data-analytic techniques such as ARIMA modeling were not applicable, as they require a minimum of 50 longitudinal observations (Makridakis et al., 1983).





Guidelines

This section develops guidelines to selecting appropriate measures of organizational growth.

Concepts of Growth

Given the inconsistent findings across models, which concept or concepts of growth are appropriate? First, it is important for researchers to select a concept of growth based on some theoretical rationale. Kimberly (1976) suggested that the research question being investigated and the types of firms in the sample should drive the selection of one concept over another when examining size; it seems reasonable that similar considerations should determine the conceptual approach used to examine growth. For example, when a researcher is studying manufacturing firms, which tend to be capital intensive, then assets may be an appropriate concept for growth. When a researcher is studying service organizations, which tend to be more labor intensive, then employees may be an appropriate concept for growth.

Additionally, Birley and Westhead (1990) criticized past studies of growth because researchers only considered unidimensional concepts. In our review of the literature shown in Appendix A, 71 percent of all studies used a single conceptual dimension to measure growth. Since sales data are important to both manufacturing and service organizations in the for-profit sector, a combination of sales data and one of the other concepts may provide additional insights for a researcher. Moreover, the use of sales data may be more appropriate than the other two concepts because a firm can realize growth in sales dollars without achieving any significant change in employees or assets, For example, a firm may take advantage of a price-inelastic market by increasing prices such that sales increase signifi- cantly while employees and assets remain the same. This was evidenced by the Disney Corporation when it raised prices by almost 50% with almost no change in attendance. Its price increase ultimately resulted in a 59% increase in sales, while employee and asset growth remained low. Similarly, increases in productivity can lead to sales growth without parallel increase in employees or assets.

Moreover, sales data may be more appropriate in studies including organizations from different industries. Some industries will be capital intensive, while others are more employee intensive. These differences may not simply reflect a distinction between manufacturing and services industries, however. Some service industries are capital intensive, such as the airline and trucking industries. Some manufacturing industries are employee intensive, such as the garment industry. Therefore, fluctuations in sales may be a more neutral measure of growth, compared to asset or employee growth, with respect to inter-industry studies. Similarly, it is sometimes difficult to determine whether a f'u'm should be considered as a manufacturer or a wholesaler, as in the case of organizations that outsource production, such as Liz Claiborne or Nike.

Formulas to Measure Growth

The results of this study suggest that certain independent variables will be significant or not significant predictors depending on the specific formula used to operationalize organizational growth. Our results should not be interpreted as





proving the inherent superiority of the ~' formula. However, when sales growth is conceptually appropriate, then ~l is the superior formula because it captures the finer-grained fluctuations intrinsic to sales changes. On the other hand, it is less likely that employee growth and asset growth would experience these short-term fluctuations. Analyses also revealed that the ( [ ( r - to], / to) formula produced percentages of explained variance similar to those for ~ for employee and asset grow,th. Therefore, given that the ([(f- t 0] / t 0) formula is more parsimonious than the 13 formula and involves easier data collection, the former approach may be a sufficient measurement technique when a coarse-grained concept, such as employee growth or asset growth, is the appropriate approach conceptually.

ff However, when fine-grained concepts such as sales are used, a measure, or vari- ant of it, will be necessary to capture fluctuations in the middle periods of observation. Finally, the ( [ ( f - t 0] / n) formula, which is the most commonly used approach in the literature, always produced inferior results in terms of explained variance, and sometimesproduced results inconsistent with theory.

Conclusions

Inconsistencies exist across studies that examine organizational growth (Whetten, 1987). These inconsistencies seem due partially to the lack of coherent discussion about an appropriate definition of organizational growth (Birley & Westhead, 1990). Our study presents empirical evidence that several measurement issues must be addressed in order for researchers to arrive at better agree- ment about what causes organizational growth. Scholars initiating future studies can use findings from our comparative research to select an appropriate concept and formula for measuring organizational growth.

The purpose of this study was to identify, compare, and assess the consequences of using alternative approaches commonly found in the literature for measuring organizational growth. While we have shown that inconsistencies in measurement approaches have contributed to a lack of consensus in the organizational growth literature, this study also has implications for future research in other areas of theory development, involving changes in sample characteristics, independent variables, dependent variables, and modeling. While this study analyzed firms from a broad set of industries and focused on relatively large corporations, future research could focus on smaller companies or on entrepreneurial firms. Such a project would most likely examine different variables within categories of environmental, strategy, and management determinants. For instance, in place of nationwide measures of environmental attributes, it may be more reasonable to analyze attributes of a local community or market niche. Strat- egies such as vertical integration and franchising could replace those of diversification and acquisitions studied here. In place of top-management-team characteristics, one might analyze personal characteristics of entrepreneurs as predictors of growth (Weinzimmer, Fry, & Nystrom, 1996).

The suggestions about measurement advanced in our study apply equally well to the organizational decline literature. Our study did not discriminate between growing and declining organizations; that is, organizational growth could





be either positive or negative. Therefore, researchers interested in studying the impact of certain variables on organization decline or tumarounds will likely find that the specific measurement approach chosen affects their empirical results.

Future studies could also link into the accumulating literature on downsizing (Freeman, 1994) by focusing on the concept of employment and generating a different set of independent variables than those that have dominated studies of sales growth. Using a contingency approach, as suggested by Kimberly (1976), studies of employment changes could focus on samples of service industries or relatively labor-intensive firms.

Industrial-organization economists have found firm size and age to be significant predictors of organizational growth. Storey (1994) identified three studies that found a positive relationship between firm growth and size, and six studies that found a negative relationship between firm growth and size. While we did not find empirical evidence to suggest these relationships, a sample consisting of very young, small firms combined with a sample of large firms may be able to show that the relationship between growth and size (or age) is dependent on the specific growth measure used. This could be accomplished by comparing results using relative ([tf- to] / to) and absolute ([tf- to] / n) measures. Additionally, it may be interesting to compare results of the 1~' measure standardized for size (relative growth) versus not standardized for size (absolute growth). We suggested that organizational size would have a positive impact on absolute growth measures and a negative impact on relative growth measures.

When all of the data for years through 1999 become available, a sufficient number of periods ( > 50) will exist to apply ARIMA modeling (Makridakis et al., 1983). Other new modeling approaches might involve development and testing of a two-stage model in which employment changes lag sales changes by one or more periods. Finally, one could model sales growth as a penultimate variable that leads to changes in some other well-know measures of firm performance such as ROI (Hubbard & Bromiley, 1995; Venkatraman & Ramanujam, 1986).

Acknowledgment: The authors would like to acknowledge an anonymous reviewer for helpful suggestions that justify the use of sales as the most appropriate concept of growth.





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258 LAURENCE G. WE1NZIMMER, PAUL C. NYSTROM, AND SARAH J. FREEMAN

Appendix C Operationalizations of Independent Variables

Industry munificence. Data used for industry-level munificence were collected from sales reported by all firms for a specific 4-digit SIC code, rather than relying only on firms in our sample. Industry-level sales data were regressed over time and the corresponding standardized coefficient (13') was used as a measure of industry growth (cf. Dess & Beard, 1984; Keats & Hitt, 1988).

Dynamism. Dynamism was operationalized as the standard error of the regression coefficient (afllk) for the munificence equation previously described. Additionally, each measure of dynamism (DYN k) is standardized for size by dividing t~l~lk by its mean (~tyk) (cf. Dess & Beard, 1984; Keats & Hitt, 1988.

Competitive concentration. We measure competitive concentration using a four-firrn concentration ratio (cf. Eisenhardt & Schoonhoven, 1990, Pennings, 1981; Romanelli, 1989).

Entry barriers. A composite score was calculated for each 4-digit SIC Code using advertising intensity and R & D intensity (cf. Khandwalla, 1981; Scherer, 1980). Advertising intensity was measured as the ratio of industry advertising expenditures to industry sales, and R & D intensity was measured as the ratio of R & D expenditures to industry sales. These entry barrier measures may not be comparable among different industries given characteristics endogenous to different industries. Therefore, these measures were standardized before the variables were summed to calculate height of entry barriers (cf. McDougall et al., 1992). This study used the composite measure to calculate average height of entry barriers from 1987-1991. Individual variables used in this composite score were not weighted.

Portfolio-levelstrategly. We use Davis and Duhaime's (1992) entropy measure of diversification ~ to investigate the influences of related diversification from 1987 to 1991 as follows:

G DR = gZ Z Sgh ln~l S-s

= I hag Sgh

where: DR s

G H In

= Related diversification = percentage of sales = Two-digit SIC group sales = Four-digit SIC segment sales = natural logarithm

COMPUSTAT II industry-segment data were used for each company to measure diversification because industry-segment data are very compatible with entropy measure criteria, such as related diversification, unrelated diversification, and total diversification (Davis & Duhaime, 1992).

Strategic aggressiveness. We used a modified version of Fombrun and Ginsberg's (1990) continuous index using organizational resource allocations to





measure corporate aggressiveness. Data for our composite measure came from COMPUSTAT II, Standard & Poors' Corporate Records, Predicast's F&S Index, Moody's Industrial Manual, Moody's OTC Manual, Moody's Unlisted OTC Manual.

We used a composite of the average ratio of R&D expenditures to total sales, the average ratio of advertising expenditures to total sales and the average ratio of new plant and equipment expenditures to total sales over the five-year period. Measures were discounted for inflation. Organization size in terms of sales was controlled for by placing total corporate sales in the denominator of each equation.

Aggressiveness measures may be endogenous to certain industries. For example, firms in the consumer-beverage industry are likely to have higher ratios of advertising expenditures to sales compared to firms in the steel industry. Hence, industry effects were controlled by dividing finn aggressiveness measures by average industry aggressiveness measures in order to assess the degree of firm- level aggressiveness relative to competition. A scale was used with a range from zero to three, where a firm would get a score of one for each of the three measures that had a value of one standard deviation above the standardized mean for the particular aggressiveness measure. Therefore, higher values indicate more aggres- sive firms, while lower values indicate a more conservative strategic posture. This approach yielded a Cronbach's alpha of .803

Acquisition strategy. Information on the extent of acquisitions/divestitures for each organization was obtained from Standard & Poors' Corporate Records, Predicast's F&S Index, Moody's Industrial Manual, Moody's OTC Manual (D'Aveni, 1989). This is a continuous variable, ranging from -n to n acquisitions for the period of 1987-1991.

Industry heterogeneity of top managers. Top managers are defined as all officers above the level of vice president, as well as officers serving on the board of directors (Michel & Hambrick, 1992). Data for top managers were obtained from Dun & Bradstreet' s Directory of Corporate Managements. These data were not averaged. Therefore, this information was collected at the beginning of the study.

Industry heterogeneity was measured using a coefficient of variation (cf. Wiersema & Bantel, 1992). Industry dispersion was based on self-reported industry experience identified by TMT members. The coefficient of variation is scale invariant, making it sensitive to relative rather than absolute, differences and, thus, preferable to standard deviation for measuring heterogeneity (Allison, 1978).

Functional heterogeneity of top managers. Functional heterogeneity was measured by the number of functional disciplines represented by top management team members divided by the size of the top management team. Functional heterogeneity was measured by a variation of the Herfindal-Hirshman index (Blau, 1977):

9

H = 1 - ]~ (S i ) 2

i = 1





where: H = functional heterogeneity of the TMT Si = percentage of career track I where TMT manager spent more time than

any other The variable I was coded using a nine-point scale (cf. Michel & Hambrick,

1992) consisting of the following categories: production operations, research & development, finance, accounting, general management, marketing, law, administration, and personnel/labor relations. H can range from 0 to 1, where low scores indicate homogenous TMTs with one or two dominant functional areas, and higher scores indicate heterogeneous TMTs comprised of members with varied functional backgrounds.

TMT Size. This variable has been commonly measured by the number of individuals included in top management (Feeser & Willard, 1990; Eisenhardt & Schoonhoven, 1990).

Company Tenure. Company tenure was measured as the mean number o f years that top management team members had been with an organization (Michel & Hambrick, 1992).

TMTAge. Age is measured by the mean age in years of top managers (Norbum & Birley, 1988).

Board of Director Membership. Board membership is measured as the ratio of inside board membership to total board membership. An inside member of the board is any member of the top management team who serves on the board of directors.

Organization Slack. Organization slack was measured as assets/debt (Eisenhardt & Schoonhoven, 1990), based on COMPUSTAT II data tapes.

Note

1. Note that the formula used in this study differs from the formula appearing in Davis and Duhaime's (1992) article in Strategic Management Journal. The version appearing in Strategic Management Journal was incorrect due to a misprint.

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