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Small firms' choice of business strategies.

According to the standard industrial organization paradigm, firms employ numerous business strategies such as product development, marketing, research, and innovation to gain a competitive advantage over their rivals. Empirical work on firm strategies has focused largely on research and development (R&D) among large publicly-traded manufacturing firms [10]. Two recent developments have increased interest in the choice of business strategies by small firms. First, a growing body of evidence suggests that, after several decades of decline, the employment share of small firms has been increasing through the 1970s and 1980s [16]. With the greater awareness of small firms' contribution to job creation, researchers and policy-makers are increasingly focusing on the sources of small business growth [6]. Second, economic development programs across the U.S. are focusing on small businesses for promoting regional growth [17]. Success of such programs requires a better understanding of factors influencing small firms' choice of business strategies. Business strategies adopted by small firms are likely to be different from business strategies adopted by large firms due to factors such as economies of scale and differences in organizational structure. Studies have shown that small firms engage in small scale R&D work although they may not have a formal R&D department [13]. This informal R&D may include a greater reliance on the acquisition of technical knowledge and die adoption of new production technologies from external sources such as trade publications, scientific journals, and government agencies [14]. Moreover, small firm strategies may be sector-specific. For example, wholesale and retail sector firms may stress quality and attractiveness of their products for higher sales growth while financial sector firms may stress planning.

While small firms adopt diverse business strategies to gain competitiveness, little empirical evidence is available on factors affecting the choice of these strategies. Evidence on the determinants of knowledge acquisition and product innovation is available [141, but determinants of many other strategies such as planning and quality have not been examined. The purpose of this paper is to present empirical evidence on the choice of business strategies by small firms. The paper differs from previous empirical work in several ways. First, whereas most previous studies consider only formal R&D activity, we examine a wider range of business strategies such as planning and product quality. Second, we examine the effect of managerial human capital on strategy choice. While human capital has been shown to have an important effect on firm performance [4], its effect on business strategy has been largely neglected. Third, our data include a large proportion of retail, wholesale, and service firms in addition to manufacturing firms; this is important because a large share of all small businesses are in the non-manufacturing sectors.

II. A Conceptual Model of Strategy Choice

Much of the empirical work on firm-strategy determinants relates expenditures or other measurements of strategy adoption to firm size, human capital, market structure and other variables that capture revenue and cost differentials across firms [10; 14]. This approach assumes that returns to a particular strategy may differ depending on firms' abilities to use that strategy. The abilities are, in turn, determined by firm characteristics that affect efficiency and access to resources.

Arora and Gambardella [2, 371] develop a model of business strategy choice among biotechnology firms in which firm returns are linked to firm size, arguing that "larger firms may have better financial resources, they may have higher market power, or they may have some sort of economies of scale which raises the payoffs to all or some strategies in a systematic manner." Feder, Just and Zilberman [8] review the extensive literature on technology adoption in the agricultural sector in which farm size and human capital are the key variables affecting farmers' adoption behavior. These variables influence farmers' capacity to deal with the inherent uncertainty of yield changes associated with new technology.

While arguments relating the level or intensity of strategy adoption to firm characteristics are most well developed in the case of R&D, these arguments apply to other strategies as well. Pratten [18] concludes from a survey of small businesses that scale-related problems exist especially in marketing. Similar arguments suggest that a planning strategy may yield higher returns to firms with higher endowments of human capital and a technology strategy may bring higher returns to firms belonging to a sector with a favorable technological regime [3].

A simple portfolio selection model provides a formal framework for analyzing the determinants of business strategies [19]. Suppose the net pay-off ([[pi].sub.1]) per unit of discretionary capital for the representative firm employing a new strategy at level y at unit cost r is given by:

[[pi].sub.1] = [[pi].sub.1](y,r) + [functions of y,s,h,z]. (1)

The first component of the pay-off function is the mean return common to all firms and the second component is firm-specific, dependent on the level of strategy and firm characteristics such as size (s), human capital (h), and other variables such as market structure (z).

Let [[pi].sub.2] be the pay-off function for the representative firm under the old strategy This function is independent of the firm's characteristics and environment. The firm can choose pay-off function [[pi].sub.1], [[pi].sub.2], or a combination of the two. Suppose the firm has K units of discretionary capital and let k be the amount allocated to the new strategy, such that k K. The firm's total pay-off [pi] is given by

[pi] = k[[[pi].sub.1](y,r) + [functions of y,s,h,z] + (K - k)[[pi.sub.2].


The problem for the firm is to maximize its total pay-off by choosing optimal values for y and k:


The first-order conditions for maximization require that


These conditions imply a two-stage decision process: first, the firm decides the optimal level of the new strategy, y*, by solving (4). In the second stage, the firm will employ the strategy if

[[pi].sub.1](y*,r) + [functions of y*,s,h,z] > [[pi].sub.2]. (6)

Thus, for each firm there is an optimal level of the new strategy determined by its characteristics. Our empirical model below uses a reduced form of the firm's decision process to test hypotheses regarding the effects of firm size and human capital on strategy choice. In the rest of this section, we focus on these hypotheses and discuss additional factors (z) affecting strategy adoption.

As noted earlier, explanations for linking a firm's choice of strategies to its size focus on economies of scale. For example, the Schumpeterian hypothesis suggests that higher R&D expenditures may be associated with large firms due to the presence of scale economies for R&D inputs [1]. Larger firms have better access to scarce inputs and a higher capacity to bear risks [8]. These considerations suggest a positive effect of firm size on expenditures and time allocated to competitive strategies.

A similar logic suggests a positive effect of human capital on strategy formulation and implementation. Human capital enhances both the returns and risk-bearing capacity associated with business strategies [19]. Firms with higher levels of human capital have a better command over technical information for implementing strategies. Such firms also have higher allocative abilities and, hence, are more efficient in utilizing the knowledge they acquire.

Business strategies are likely to vary across industrial sectors because of differences in market structure factors such as concentration, product type, and technological regime. For example, in information-intensive sectors such as finance, insurance and real estate, firms may place greater emphasis on formal planning whereas retail stores may stress product appearance to attract customers.

Besides size, human capital, and market structure, a firm's choice of strategies is likely to be affected by its ownership structure. Ownership structure has been shown to affect firm behavior by influencing a firm's access to credit markets. Evans and Leighton [7] suggest that lack of access to sufficient start-up funds restricts entrepreneurial choice. Asymmetric information problems and bankruptcy costs which give rise to capital constraints are especially important in the case of independent and individually owned firms. Sociological studies suggest that ownership characteristics such as gender and minority status may also affect firms' choices of business strategies [5; 15].

III. Empirical Results

Data on competitive business strategies and firm characteristics were obtained through a survey of small businesses, defined as firms employing less than 500 employees, in 25 rural counties in Georgia.(1) Table I reports details of four leading business strategies adopted by 895 firms.(2) For each strategy, we use the ordinal scale given in the table as an observable indicator of the intensity of strategy adoption by the firm. While objective measures of strategy adoption (e.g., expenditures) are preferable, such data are rarely available for small firms and manager-response measures similar to ours have been used in previous studies [14].


The four strategies are planning (USEPLAN), a product strategy (ATTRPROD), adoption of new technology (USETECH), and emphasis on quality (QUALITY). Each of these strategies is of critical importance for small business survival and success. For example, in a longitudinal study that followed German business start-ups, Kayser [12] found that start-ups that emphasized planning tripled their sales in the first seven years whereas sales declined after four years for poorly prepared start-ups. An international comparison of small business performance suggests that the flexibility of small businesses to adopt quality and technology strategies is an important source of small business growth [16].

Table II reports our empirical measurements for the determinants of strategy choice discussed in section II. Firm size is defined as the total number of employees (EMP). The sample of 895 firms available for regression analysis has a mean size of eight employees with a standard deviation of 15.3.


We include years of schooling (EDN) and hours on the job (HRS) of the owner/manager to account for human capital effects. Education has been used in most of the previous studies [8]. The HRS measure is important since it indicates how much of the owner/manager's work effort is available for operating the firm.

Besides EDN and HRS, which measure manager-specific human capital, we include the number of years the firm has been in operation (AGE) to account separately for industry-specific human capital accumulated within the firm. In theoretical firm growth models, firm age captures the learning process as the firm adjusts output and size to match its estimated efficiency level [11]. Part of this adjustment is likely to be achieved through different strategy choices over the firm's life cycle.

Previous studies have used industry dummies and concentration ratios to capture the effect of market structure on firm behavior. In the present case, we use sectoral dummy variables for mining, construction and manufacturing (MCM), services (SER), finance, insurance and real estate (FIRE), and wholesale and retail trade (the omitted category).

Four sets of dummy variables represent ownership structure. The first set includes sole proprietorships (PROP), partnerships (PART), and corporations (the omitted category). The second set indicates whether the firm is a single, independent establishment (INDEP) or a multiple-establishment firm (the omitted category). The third and the fourth sets indicate whether the owner/manager is a female (GENDER) or a minority individual (MINORITY).

Given the ordinal categorical nature of the strategy adoption measurements, we employ the ordered probit framework for estimation [9]. Regression results for the four strategies are reported in Table Ill and Table IV. Table III gives coefficient estimates, P-values for associated t-statistics, and results of a chi-square test for model fitness. Table IV gives marginal probabilities of dependent variables with respect to changes in the explanatory variables. For continuous explanatory variables, the marginal probabilities are partial derivatives of the probability of a firm choosing the cth (c = 1, . . . , C) or higher category of a dependent variable with respect to the explanatory variables.(3) For dummy explanatory variables, the marginal probabilities represent the change in probability of a firm choosing the cth or higher category of a dependent variable as dummy variables change from one to zero. In the discussion below, we adopt a 10 percent significance level for two-tailed t-tests of coefficient estimates.

The hypothesis that firm size has a positive and significant effect on the choice of business strategy cannot be rejected for any of the four strategies. The marginal probabilities for firm size are reported in the first row of Table IV. USEPLAN has the highest estimated marginal probability


of 0.09 with respect to firm size, followed by USETECH (0.08), QUALITY (0.07) and ATTRPROD (0.06). Table IV also reports in parentheses the elasticities of the dependent variables with respect to the levels of continuous variables. For USEPLAN the elasticity with respect to firm size is 0.19. Therefore, holding all other explanatory variables at their mean values, a one percent increase in firm size results in a 0.19 percent increase in the probability of a firm placing moderate or greater emphasis on the regular use, modification and updating of business plans. The elasticity of USETECH with respect to firm size is 0.14. Thus, for a one percent increase in firm size, the probability of an "average firm" placing important or critical emphasis on the use of new technology increases by 0.14 percent. The result for USETECH is consistent with earlier evidence from Link and Bozeman [14] that larger firms have a higher propensity to acquire technical knowledge and adopt new technology. ATTRPROD and QUALITY have nearly identical elasticities of 0.16 with respect to firm size.

Firm age has a significantly negative influence on the degree of emphasis on USEPLAN and ATTRPROD strategies. USETECH and QUALITY strategies are not significantly influenced by firm age. USEPLAN and ATTRPROD have elasticities of -0.11 and -0.05 with respect to AGE. The finding that older firms do not stress planning as much as younger firms has interesting implications. Since earlier studies [12] have shown that firms which emphasize planning tend to grow faster, the relative lack of planning by older firms may partly explain their lower growth rates [6].

Education of the owner/manager (EDN) has a positive and significant effect on all strategies. This result, while new to the business strategy literature, agrees with similar evidence on the technology adoption behavior of farm firms [8; 19]. USEPLAN and QUALITY have relatively large elasticities of 0.78 and 0.51 each with respect to level of education, followed by USETECH (0.47) and ATTRPROD (0.32). The positive effect of education on USEPLAN and USETECH is particularly interesting since planning and technology adoption are key factors contributing to better firm performance [4; 12].

The effect of hours worked (HRS) is varied. The coefficient estimates for HRS are significant for USEPLAN and QUALITY with elasticities of 0.30 and 0.23. For the level of hours worked, these results suggest that planning and quality strategies require more time input and personal attention of the owner/manager. Identifying such effects can be useful for tailoring the recommendations of small business agencies to suit specific business situations. For example, strategies that require the owner's personal attention should perhaps be de-emphasized relative to strategies that can be delegated in small businesses in which the owner works part-time.

Sectoral and ownership dummy variables exhibit considerable variation in their effects. Service firms (SER) and finance, insurance and real estate firms (FIRE) have a significantly higher probability of placing greater emphasis on USEPLAN and a significantly lower probability of placing greater emphasis on ATTRPROD compared to wholesale and retail firms. The estimated marginal probability indicates that FIRE firms have a one-third higher probability of placing moderate or higher emphasis on the regular use, modification and updating of plans than wholesale and retail firms. Firms in manufacturing, construction and mining (MCM) sectors are less likely to use ATTRPROD strategy compared to wholesale and retail firms. Results for ATTRPROD accord with our initial suggestion that sales firms are more likely to use this strategy compared to firms in other sectors.

Independent (INDEP) and sole proprietorship (PROP) firms are less likely to use planning strategy compared to multiple-establishment firms and corporate firms. Partnership firms do not differ significantly from corporate firms in the choice of strategies. Minority status of the firm owner/manager has no impact on strategy choice while female owners/managers are more likely to emphasize ATTRPROD and less likely to emphasize USETECH compared to male owners/ managers.

IV. Summary and Conclusions

Numerous studies have investigated the determinants of R&D and technological innovation among business firms. However, R&D and technology development strategies represent only one dimension of business success. Other business strategies such as planning and adoption of technology are likely to be important, particularly for small firms. This paper presents empirical evidence on the determinants of business strategies among a sample of manufacturing, sales and service firms. Our results have three major implications for firm-strategy research and small business policy.

First, our results support the earlier emphasis on firm size as a key variable affecting strategy choice. Firm size has a positive impact on the choice of all four strategies we analyze. Assuming that these strategies have a positive effect on firm growth, these findings point to a puzzling aspect of firm growth research: while larger firms have greater propensity to adopt growth-promoting business strategies, small firms tend to have higher growth rate. One possible explanation for this puzzle is that large firms represent a self-selected sample of originally small firms that have grown to their current size by successfully adopting competitive strategies. This aspect needs further investigation.

Second, human capital plays a major role in strategy selection. The better educated the owner or manager, the higher is the probability that a firm employs strategies such as planning and use of new technology to promote firm growth. This suggests that managerial education and work commitment are key factors to be stressed in all stages of entrepreneurial and small business training programs [17].

Third, independent and sole proprietorship firms and firms owned or managed by women may require special attention with regard to planning and adoption of new technology.

An obvious extension of this study is to integrate a strategy adoption model with a model of firm growth in a dynamic framework. This would allow better control of self-selection effects and better modeling of the intertemporal aspects of strategy adoption and firm performance.


[1.] Acs, Zoltan J. and David B. Audretsch. "R&D, Firm Size and Innovative Activity," in Innovation and Technological Change, edited by Zoltan J. Acs and David B. Audretsch. Ann Arbor: The University of Michigan Press, 1991, pp. 39-59. [2.] Arora, Ashish and Alfonso Gambardella, "Complementarity and External Linkages: The Strategies of the Large Firms in Biotechnology." The Journal of Industrial Economics, June 1990, 361-79. [3.] Audretsch, David B., "New-firm Survival and the Technological Regime." The Review of Economics and Statistics, August 1991, 441-50. [4.] Bates, Timothy, Entrepreneur Human Capital Inputs and Small Business Longevity." The Review of Economics and Statistics, November 1990, 551-59. [5.] _____, "Commercial Bank Financing of White- and Black-owned Small Business Start-ups." The Quarterly Review of Economics and Business, Spring 1991, 64-80. 6. Evans, David S., "Tests of Alternative Theories of Firm Growth." Journal of Political Economy, August 1987, 657-74. [7.] _____ and Linda S. Leighton, "Some Empirical Aspects of Entrepreneurship." American Economic Review, June 1989, 519-35. [8.] Feder, Gershon, Richard E. Just, and David Zilberman, Adoption of Agricultural Innovations in Developing Countries: A Survey." Economic Development, and Cultural Change, January 1985, 255-98. [9.] Greene, William H. Econometric Analysis. New York: Macmillan, 1990. [10.] Griliches, Zvi, ed. R&D, Patents, and Productivity. Chicago: The University of Chicago Press, 1984. [11.] Jovanovic, Boyan, "Selection nd Evolution of Industry." Econometrica, May 1982, 649-70. [12.] Kayser, Gunther. "Growth Problems of Young Firms," in New Findings and Perspectives in Entrepreneurship. edited by Rik Donckels and Asko Miettinen. Brookfield: Avebury, 1990, pp. 231-41. [13.] Kleinknecht, Alfred, Tom P. Poot, and Jeroen O. N. Reijnen. "Formal and informat R&D and Firm Size: Survey Results from the Netherlands", in Innovation and Technological Change, edited by Zoltan J. Acs and David B. Audretsch. Ann Arbor The University of Michigan Press, 1991. pp. 84-108. [14.] Link, Albert, N. and Barry Bozeman, "Innovative Behavior in Snuff-Sized Firms." Small Business Economics, September 1991, 179-84. [15.] Loscocco, Karyn A., Joyce Robinson, Richard H. Hall, and John K. Allen, "Gender and Small Business Success: An Inquiry into Women's Relative Disadvantage." Social Forces, September 1991, 65-85. [16.] Loveman, Gary and Werner Sengenberger, "The Re-emergence of Small-Scale Production: An International Comparison." Small Business Economics. March 1991, 1-37. [17.] Morse, George W., ed. The Retention and Expansion of Existing Businesses. Ames: Iowa State University Press, 1990. [18.] Pratten. Cliff. The Competitiveness of Small Firms. New York: Cambridge University Press, 1991. [19.] Wozniak. Gregory D., "Human Capital, Information, and the Early Adoption of New Technology." The Journal of Human Resources, Winter 1987, 101-12.

(1.) The data come from a survey of 1691 small businesses in 25 Georgia counties. The sample is a 33 percent random sample obtained from a frame of firms existing in the survey region in the first week of July, 1990. The sampling frame used yellow page telephone directories and a number of industry-specific sources such as trade directories, vertical files, customer records, government listings, and city directories. The response rate to the survey was 86 percent. Since the study includes owner/manager characteristics such as gender, education and hours of work, firms whose respondents were not the owner or manager were excluded from the analysis. Deleting incomplete observations for the variables included in the analysis left 895 observations for regression analysis. (2.) The survey had 18 business strategy questions. Rather than present regression results for the whole set of questions, we adopted a variable reduction procedure to identify the leading strategies adopted by the sample firms. First, four combinations of strategies were identified by performing a factor analysis on the responses to the 18 questions. Second, we isolated the dominant strategy in each combination by selecting the variable with the highest factor loading and highest communality. The results of this analysis are available on request. (3.) For each regressor, there are as many marginal probabilities as the number of ordinal categories of the dependent variable. Thus, for USEPLAN alone there are 60 marginal probabilities. Rather than present marginal probabilities for each category of the dependent variable, we present the cumulative marginal probability of the dependent variable being in the cth or higher category. For each dependent variable, the cth category is that for which the marginal probability reverses the sign when going from the lowest category, one, to the highest category, C [9, 704-05]. For instance, the marginal probabilities of USETECH with respect to EMP are -0.058, -0.026, 0.027, and 0.057 for categories one through four. Thus, Table IV reports 0.084 as the marginal probability of USETECH being in the third or higher category.
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Author:Kraybill, David S.
Publication:Southern Economic Journal
Date:Jul 1, 1993
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