Businesses without glamour? an analysis of resources on performance by size and age in small service and retail firms

BUSINESSES WITHOUT GLAMOUR? AN ANALYSIS OF RESOURCES ON PERFORMANCE BY SIZE AND AGE IN SMALL SERVICE AND RETAIL FIRMS CANDIDA G. BRUSH Boston University

RADHA CHAGANTI Rider University

Research on factors influencing performance in new and small companies is extensive. Earlier work found that strategies (e.g. cost, quality, differentiation, etc.) affected performance contingent on industry conditions, the environment, and the entrepreneur’s background. Although this work provides a solid basis for understanding differences in entrepreneurial performance, some firms are limited in their choices of strategy due to size, age, or industry. Often these firms are in industries where entry barriers are low and competitive advantages are easily imitated. Small service and retail businesses operate in sectors where these conditions are apparent. Comprising more than 50% of all small firms, they require minimal start-up investments but face intense competition. Lacking the “glamour” of high innovation/high growth firms, service and retail companies are at the “end” of the value chain, their fortunes rising and falling as a result of the direct influence of the owner-founder. Hence, performance variation may be better explained by the capabilities of the firm or individual competencies of the owner-founder, that is the resource-base and resource combinations, rather than strategy. The strategic importance of an organization’s resources and capabilities is the foundation of resource-based theory. Resources are tangible and intangible assets tied to the firm in a relatively permanent fashion. Their combinations are heterogeneous and form the basis for product/market strategies. Studies of resources, strategies, and performance are emerging in the entrepreneurial area. Research shows that

EXECUTIVE SUMMARY

Address correspondence to Dr. Candida G. Brush, Boston University, 595 Commonwealth Avenue, Boston, MA 02215. Journal of Business Venturing 14, 233–257  1998 Elsevier Science Inc. All rights reserved. 655 Avenue of the Americas, New York, NY 10010

0883-9026/99/$–see front matter PII S0883-9026(97)00103-1

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various resources in concert with different strategy types can lead to above average performance over the business life cycle, and that combinations of resources are related to survival. Yet the vast majority of work focuses on high growth, high tech, or manufacturing businesses. Less is known about the relationships of resources to performance in less “glamorous” sectors. In these small service and retail businesses, we speculate that resources, in particular human and organizational resources, may play a greater role in explaining performance than strategy. Further, as other authors have suggested, it is expected that the combinations of these resources will vary across age and size. This study examines the influence of human and organizational resources on performance in a sample of 195 service and retail firms operating in central New Jersey, using a structured questionnaire. All companies utilized a focus strategy (either focused cost or focused differentiation) and employed a minimum of 3 to a maximum of 100 employees. All measures had theoretical and/or empirical precedent and were tested statistically for reliability. We used factor analysis to reduce the independent variables to: two human resource variables (owner resources and commitment), one organizational resource variable (comprised of planning, systems, and staff skills), and one strategy variable (focused cost and focused differentiation). Control variables were business age, business size, environmental benignness, and industry growth. The dependent variable performance was measured in two ways: net cash flow and log of growth in employees over 3 years. The study first examined whether strategy or resources had a greater influence on performance. Results showed that strategy influenced performance less than human and organizational resources both individually and interactively. The influence of owner resources (background and attitudes) on net cash flow was stronger than on growth, where the only significant variable was industry (market) growth. To analyze effects of resources on performance by size, we divided the sample by size groupings, selecting the smallest (maximum five employees) and largest quartiles (minimum 16 employees), which were comprised of 55 and 50 companies, respectively. These analyses showed that owner resources, commitment, and organizational resources contributed positively to net cash flow in very small firms; however, interactive effects of these resource combinations were negative. For instance, owner resources and organizational resources together, and organizational resources and commitment together, resulted in less positive cash flow than when analyzed separately. This implies that different resource combinations can have negative influences in these very small firms. We examined age effects in the same manner as size—dividing the sample into age group quartiles and conducting an analysis only for very young (fewer than 5 years) and very old (minimum 19 years) groups, which comprised 54 and 52 companies, respectively. These analyses showed that although growth was more rapid among the youngest firms, there were no distinctive resource-based correlates to growth in either age group. Substantive increases in formalized systems and procedures were not apparent among the oldest of these companies compared with the youngest, contrary to previous work showing the evolution of these over business life cycles. Results of this study are applicable only in the context of service and retail firms, and, readers should note this sample was nonrandom and geographically concentrated. Our purpose was not to predict, but describe associations between resources and performance. This study shows that, for firms in competitive industries at the end of the value chain, type of strategy is less important than resource combinations for certain types of performance. Human and organizational resources are associated with more positive cash flow, whereas industry and market factors are related to growth. These results imply that firms seeking growth are best served by selecting and entering growth markets and industries. On the other hand, if strong positive cash flows are the primary objective, attention to combinations of resources is more important. For instance, owner-founders having a strong business and managerial background, and industry experience will need less formalized systems, whereas those owner-founders with weaker managerial resources might benefit from more formalized procedures and skilled staff.  1998 Elsevier Science Inc.

INTRODUCTION Investigations of factors explaining performance are well documented in the entrepreneurial literature. Earlier studies show that strategies affect performance outcomes

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(profitability, growth, or return on asset (ROA)) contingent on industry conditions (McDougall and Robinson 1988; McDougall et al. 1994; Carter et al. 1994), environmental characteristics (Covin and Slevin 1989, 1990), and the entrepreneur’s background (Stuart and Abetti 1988; Sandberg 1986; Feeser and Willard 1990). Previous work adopted various definitions of strategies, many derived from Porter’s (1985) work: e.g., cost, quality, and technology (Fombrun and Wally 1989); cost leadership, innovativeness, quality, and product scope (Chaganti et al. 1989); or cost, differentiation, and focus (Green, Jolly, and Srivastava 1990). Results show that particular types of strategies impact a variety of performance outcomes—profitability (McDougall and Robinson 1990), survival (Cooper, Dunkelberg, and Woo 1988), growth (McDougall and Robinson 1988), and nonfinancial measures (Stuart and Abetti 1988). Collectively this research provides a foundation for understanding differences in performance of entrepreneurial ventures depending on strategy, the industry, and the entrepreneur(s). However, some firms are limited in their choices of strategy due to industry, size, and age (Porter 1985; Wright, Smart, and McMahan 1995), and may be less likely to implement or articulate a distinctive competitive strategy. Small or new firms may be unable to achieve significant economies of scale or scope, or serve a broad target market (Porter 1985), preventing them from pursuing cost leadership or differentiation strategies. For these firms, it is recommended that focus or niche strategies are more appropriate because entry barriers are lower (Wright et al. 1995), and frequently there is a lack of differentiation among customers. These firms may prefer to focus on niche markets made up of geographic, customer, or product segments (Carter et al. 1994), particularly if they are operating in fragmented industries. In contrast, the vast majority of previous research documenting factors and strategy types influencing performance examines firms that are either high growth, high tech, or manufacturing based, and measures performance using profitability and ROA (McDougall and Robinson 1988; McDougall et al. 1994; Sandberg 1986; Feeser and Willard 1990; Fombrun and Wally 1989). Consequently, the existing findings on relationships between strategy and performance may not fully apply in the context of small retail and service businesses. Service and retail segments of the economy are sectors where these conditions are apparent. These sectors comprise more than 50% of all small firms (The State of Small Business 1995) and they remain popular as choices for entrepreneurs because of the option of working at home and low start-up costs. For instance, one can start a small accounting or landscape service, or open a secondhand sporting goods shop with comparatively few assets, minimal financial investment, a small technology base, and few employees. These businesses lack the “glamour” of the high growth, high innovation businesses and operate at the “end” of the value chain where the fortunes of the business often rise or fall depending on the direct influence of the owner-founder. Referred to as the “economic core” (Kirchhoff 1994), service and retail businesses face few barriers to imitation. Therefore, the advantages leading to performance variation in these firms may lie in the unique firm capabilities or individual entrepreneur’s competencies for service delivery. These sectors are intensely competitive and notorious for their high business failure rate (The State of Small Business 1995), making it difficult for these businesses to experience the fast explosive growth or profitability of high tech or manufacturing businesses. We believe performance variations in small service and retail firms may be better explained by examining the antecedents to strategy, namely the resources and capabilities on which strategies are predicated, instead of the stated strategies. The resource-

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based view of the firm provides a theoretical foundation for exploring this idea. In this view, organizations are comprised of heterogeneous bundles of resources (Barney 1991; Conner 1991; Peteraf 1993): physical, financial, human, reputational, organizational, and technological (Hofer and Schendel 1978; Grant 1991; Dollinger 1995). A firm’s strategic decisions hinge on ways to use existing resources and means to acquire or internally develop additional unique resources (Wernerfelt 1984; Andrews 1987). Innovation in combining or deploying resources can lead to a competitive advantage or superior rents (Grant 1991; Barney 1991). As firms grow in size, they reorganize their resources, acquire new ones, some become specialized, and others may become idle (Penrose 1959). Over stages of organizational development, changing resource combinations often require different management practices for continued success (Miller and Friesen 1984; Kazanjian 1988). Although the entrepreneurship literature recognizes the crucial importance of resources on survival, strategy, and performance (Cooper and Dunkelberg 1986; Katz and Gartner 1988; Cooper 1981), with few exceptions (Chandler and Hanks 1994; Mosakowski 1993; Cooper et al. 1994) the empirical research investigating the role of resources and their relationship to strategy performance is comparatively limited. Furthermore, the balance, combinations, and influence of different resources over organizational life stages is understudied (Mosakowski 1993; Day 1992). This study investigates the influence of particular types and combinations of resources on performance small firms operating with focus strategies. Following a resource-based view of organizations (Penrose 1959; Barney 1991; Conner 1991), we argue that in small service and retail firms operating in localized niche markets, owner-founder human resources and organizational resources will be related separately and together to different performance outcomes more strongly than strategies of cost or differentiation. We explore the extent to which combinations of human and organizational resources vary depending on size and age of the business. The following sections present a selective review of relevant literature, describe the study sample and method, present results of statistical analyses, and discuss the implications.

BACKGROUND The strategic importance of an organization’s resources and capabilities is the foundation of resource-based theory. Resources are defined as all tangible and intangible assets that are tied to the firm in a relatively permanent fashion (Penrose 1959; Wernerfelt 1984). Resources and their combinations are heterogeneous in nature and ideally are a source of competitive advantage (Barney 1991; Grant 1991). Product-market strategies are dependent on resources, and the suggested starting point for strategy formulation is a resource assessment (Andrews 1987). A resource profile can be matched to optimal product-market activities (Hofer and Schendel 1978). The relationship between resources, strategy, and performance is posited as a “fit” whereby firm and environmental contextual variables moderate the strategy-performance relationship (Wright et al. 1995; Chandler and Hanks 1994). Entrepreneurship scholars have studied resources in the analysis of strategy and performance relationships. Chandler and Hanks (1994) examined the impact of fit between specific strategies and firm capabilities tailored to the chosen strategies. Those manufacturing companies with higher resource levels and broader capabilities grew faster than those with lower resource levels and narrower capabilities. Cooper, Gimeno-

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Gascon, and Woo (1994) found that human capital, particularly education of the ownerfounder, was related to growth, but management “know-how” had a less significant impact. Further, they found that industry knowledge and financial capital contributed to both growth and survival. Another investigation of human resources showed that commitment and determination of the owner-founder was related to personal satisfaction and the continuation of the venture (Cooper and Artz 1995), but this study did not consider the influence of strategy. Mosakowski (1993) posited that certain types of resources vis-a`-vis strategies (low cost, differentiation, focus) would lead to above average performance over the business life cycle. Although she did not measure resources directly, she argued that resources are an integral part of understanding performance, and that unique combinations are associated with different strategies. In sum, this work shows that various resources are related to different types of strategies. More recently, studies about the relationships of resources and their combinations to survival and performance have emerged. Ropo and Hunt (1995), in a case study of capability changes and relationships to opportunity structures, found a tight interplay of individual and organizational characteristics over time. In particular, individual attitudes were related to organizational structures and policy changes. Similarly, Westhead (1995) found that the background (experience) of the founder and the venture worked together to influence survival and growth in high tech firms over a 6-year time span. Mullins (1996) argued that individual decision-making competencies of the entrepreneur strongly affect organizational processes, providing a foundation for competitive advantage and growth. In an application of Kirchhoff’s (1994) “dynamic capitalism typology,” Greene and Brown (1997) suggested that resources (human, physical, social, financial, organizational) will combine differently depending on innovation rates and growth. For firms in the “economic core,” which is comparatively less innovative and slower growing (i.e., retail and service businesses), they posited either high or low human resources, and low organizational resources will characterize these businesses (Greene and Brown 1997, p. 166). In sum, the importance of individual resources (human capital) and organizational resources, and their interrelationships are of increasing importance to survival and continued success in entrepreneurial ventures. Shifting to the context of interest, we believe that small retail and service firms in niche markets lack choices of strategy types because of inability to achieve significant economies of scale or scope, or to serve a broad target market (Porter 1985), and therefore more often pursue focus or niche strategies in particular local markets (Wright et al. 1995). Consequently, there is a higher likelihood of strategy imitation resulting from “tactical” moves needing only short-term investments that are low cost and easy to implement (Smith, Grimm, and Gannon 1992; Porter 1985). Moreover, there are fewer barriers to strategy imitation in these niche markets based on technology, legal patents, capital, or operating scale. In fact, both high and low performance firms may profess and attempt similar strategies, e.g., focused quality, focused innovation, focused lower cost. Superior performance may lie less in attempted strategies, and more in the underlying resource components that determine effective strategy execution. For example, following current research, an owner-founder’s human capital such as experience (Mullins 1996; Westhead 1995; Cooper et al. 1994) and commitment (Cooper and Artz 1995), or a firm’s organizational resources comprised of employees expertise, systems, and policies (Ropo and Hunt 1995) may be key to effective execution and performance. Hence the “rents” enjoyed by high performance firms would accrue from competitive advantages derived from superior resource combinations rather than attempted competitive

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strategies. Furthermore, consistent with Mosakowski’s (1993) speculations, firms of different sizes and ages likely would have different combinations of human and organizational resources correlated to performance. In sum and contrary to previous findings for manufacturing and high tech firms, we believe that in small retail and service ventures pursuing focus strategies in niche markets, we believe their articulated competitive strategies may not explain interfirm performance differentials as fully as the resources on which strategies are based (Barney 1991; Peteraf 1993; Wernerfelt 1984).

HYPOTHESES Resources This study will focus on human and organizational resources as the core dimensions underlying performance of the small service and retail firms operating in local markets. Whereas financial, physical, and social resources are also important to any new and small venture, this study examines only human and organizational resources because, as noted earlier, these are essential to success in these industry sectors. According to prior research, human resources comprise a broad range of aspects: the owner-founder’s achieved attributes (Becker 1964), background in family characteristics, education, and experience (Cooper 1981; Westhead 1995), reputation (Dollinger 1995), attitudes and motivations (Birley and Westhead 1990), goals (Davidsson 1989), and competencies (Chandler and Jansen 1992). In general, studies revealed that businesses were more successful when the owner-founders possessed greater amounts of human resources. Owner-founder’s industry experience is documented as an influence on performance (Cooper and Gimeno-Gascon 1992; Westhead 1995), whereas Cooper et al. (1994) found education was positively related to growth. In service and retail businesses, these human resources are likely to be helpful in better identifying or prospecting for customers, building a more efficient distribution, and selecting the right vendors. The present study selected two types of human resources: first, the owner-founder’s human capital, as comprised of industry experience, business education, and second, the owner-founder’s attitudes toward running their businesses. Prior research finds owner-managers’ attitudes to be influential. For example, owners’ organizational commitment was strongly associated with firm performance (Thompson, Kopelman, and Schriesheim 1992; Cooper and Artz 1995), whereas goals and motivations explained better business results (Cooper and Artz 1995; Filley and Aldag 1978). Following this logic, we focused on three types of attitudes: owner-manager’s (1) commitment to the organization, (2) desire for balance in personal/business demands, and the extent to which (3) building relations with customers and employees is valued compared with revenues and profits.

Commitment Cooper (1993) points out that commitment and determination (behavioral characteristics) are understudied and deserve more attention than they have received. Commitment is an indicator of “how” the owner-founder approaches the business (Vesper 1990; Timmars 1989), a criterion applied by investors in evaluating funding choices. Hence level of commitment may be related to performance outcomes.

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Balance Not all businesses are founded/acquired with economic goals as the end. Indeed, some entrepreneurs have noneconomic goals, pursuing only certain performance thresholds (Cooper 1993). Filley and Aldag (1978) note that comfort level or personal achievement may be primary objectives for some owner-founders. It is recognized that not all firms will be “gazelles,” but many firms will be “lifestyle” businesses (The State of Small Business 1995). Lifestyle concerns of achieving balance between work and personal life figure prominently among motives propelling men and women to start and manage their own businesses (Fisher, Reuber, and Dyke 1993). Hence in small service and retail businesses, a desire for balance in personal and work life demands may be correlated positively with performance outcomes.

Building Relations This is another attitude that is not highly studied. Cooper (1993) notes that predictors of success may well vary by type of venture; for instance engineering skills will be important in certain technology businesses, whereas these would not be related to performance in a dress shop. Following this logic, retail and service businesses generally have direct contact with customers, and in small firms, the owner/manager works closely with employees. We speculate that in these sectors, a positive attitude toward building relationships with key employees, and close contact with customers to gain information about their preferences will be important. Turning to organizational resources, Chandler and Hanks (1994) observed that high performance ventures developed resource-based capabilities that were tailored to the specific types of business strategies adopted. Others have found firms’ resources important to business performance but identified these more broadly: relationships and alliances (Tomer 1987), planning and control systems (Bracker and Pearson 1986; Cragg and King 1988; Miller and Cardinal 1994), structures (Hofer and Schendel 1978), and routines, culture, and knowledge assets such as employee skills (Barney 1991; Dollinger 1995). Presumably management systems, skills of employees, and organizational routines are central to achieving efficiencies in operations, effectively reaching customers, and providing superior levels of service. Whereas multiple types of organizational resources were correlated with performance in the past, we examined three types of organizational resources that we believe will distinguish high and low performers in service and retail businesses: (1) use of long-range planning horizon for key decisions, (2) formalization defined as use of regular written reporting, and (c) employment of trained or qualified operating staff. We believe these three types of organizational resources will be related to various performance outcomes in this context.

Strategies For purposes of this study, the product-market strategies of cost leadership and differentiation refer to focused cost leadership and focused differentiation (Carter et al. 1994; Green et al. 1990; Chaganti et al. 1989). Both are forms of focus or niche strategies where specialized markets or geographic areas are served (Carter et al. 1994). Following our earlier discussion, we do not expect strategies to have a strong impact on performance.

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Business Performance Owner-founders seek diverse outcomes from business ownership, and different types of human and organizational resources are likely to be important for different types of performance outcomes (Cooper 1993). Therefore, we selected multiple performance criteria. Previous research uses performance measures ranging from market- and accounting-based growth measures to survival, and composite assessments of success by the entrepreneur (Brush and Vander Werf 1992). For our sample of small service and retail businesses operating in local niche markets, we chose two types of indices: (1) financial soundness as shown by net cash flow (NCF) and (2) firm growth judged by change in employee size. The NCF criterion is appropriate because cash flow problems have caused many small and new businesses to fail (Dambolina and Shulman 1988; Dun and Bradstreet 1992; Longnecker, Moore, and Petty 1996). Similarly, young and small businesses that find it feasible and necessary to add to their workforce (grow in size) not only promote general economic prosperity, but are more likely to survive over the long-term (Kirchhoff and Phillips 1988). Because we expected these small service and retail businesses to have different objectives, and, given that age, size, and type of firm can have different performance correlates, we chose to use two performance outcome measures (Cooper and Gimeno-Gascon 1992). Further, the types of resources that contribute significantly to success are likely to differ across the two types of performance, e.g., firm growth may demand ownermanager’s human resources as well as organizational resources, whereas better levels of cash flow may be associated with other resource configurations. (See Figure 1 for a graphic depiction of our conceptual framework.) Following the arguments presented above, we hypothesize that: H1: For small service and retail firms operating in niche markets, human resources of the owner-founder and the firm’s organizational resources would be more significantly related to firm performance compared with product-market strategies. H2: Further, for these firms, relationships of human resources and organizational resources to firm performance would differ across different performance criteria.

Effects of Firm Size and Age Size and age may strongly impact organization’s resources and performance (Aldrich and Auster 1986; Venkataraman and Low 1994). Penrose (1959) notes that as firms grow in size, they reorganize their resources, and these new combinations often require different management practices to achieve success (Miller and Friesen 1984). Mosakowski (1993) speculates firms of different sizes and ages will likely have different combinations of human and organizational resources correlated to performance. It appears that these size and age effects sometimes may be positive and sometimes may be negative. Typically, very young ventures suffer from liabilities of newness (Stinchcombe 1965) in that they are deficient in resources like financial capital and expertise, and have less developed internal systems and external relationships with customers and vendors. Prior research (Churchill and Lewis 1983) also suggests that in early life-stages, firms can benefit from increased formalization and systems. Although, very young firms may enjoy the advantage of owner-founder’s commitment and involvement, and enhancing performance, on the other hand, a small firm’s size can limit its access to financial, human, and organizational resources, thus handicapping its performance (Cooper and Dunkelberg

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FIGURE 1 Conceptual framework.

1986). Alternatively, the very small firm may be more flexible and responsive, thus enhancing its performance. That is, although it is clear that size and age can significantly impact the resource-performance connection, the direction of effect is uncertain. Accordingly, H3: For small service and retail firms operating in niche markets, firm size and age will significantly impact the relationship of resources to firm performance.

The following section explains the sample, measures for variables, and the statistical procedures.

METHOD Sample Data were collected by interviewing 279 small firms in central New Jersey, using a structured questionnaire. These firms employed a minimum of three employees and a maxi-

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mum of 100 employees, and the owner-founders were known personally to students in one of the author’s senior-level strategic management classes offered between fall 1992 and fall 1995. Although these businesses were not selected randomly, interviewers knew the firms’ owner-founders and operations well. There were no high tech businesses in the sample. Among the 279 firms, 195% or 69.8% were service and retail firms. This sample distribution of firms across industry sectors was not significantly different from that in the state of New Jersey as a whole, where 66.5% of all small firms employing five to 99 employees were service and retail establishments (x2 5 0.54, p , 0.9). Firms were defined as operating in niche markets if at least 80% of their sales were made to customers within 100 miles and at least 80% of their sales were made directly to customers. By this criterion, all of the 195 service and retail firms in our sample were operating in niche markets.

Measures Performance Financial soundness is gauged by NCF during the last 3 years measured on a 5-point scale (5 5 very positive, 1 5 very negative), and firm growth, measured as changes in employment, is the logarithm of percent change in total employment during the last 3 years. This study used logarithm of 3-year employment growth to minimize the exaggerating effects of small denominators for the very small-sized firms. Intercorrelations among the performance criteria were not overly high: NCF and growth 5 0.131, (p , 0 .07). Although multi-item measures of each criteria are preferred, there are precedents for using single measures of multiple criteria (see Cooper and Gimeno-Gascon 1992, Appendix 12C). In addition, there is precedent for use of subjective measures as a useful means to measure performance (Stuart and Abetti 1988; Brush and VanderWerf 1992). Furthermore, it has been argued that top managers constantly interpret and select their own environments (Morgan 1986; Daft and Weick 1984; Lado and Wilson 1994) and base their actions on enacted environments that make sense to them. Therefore, perceived measures would be relevant.

Resources Based on prior research relating entrepreneurs’ personality and background variables to firm performance, we used four categories of human resources: (1) owner-founder’s business background consisting of prior managerial work experience, previous business ownership, business education, and completion of undergraduate degree (measured as 1 5 yes, 0 5 no) (Cronbach a 5 0.59); (2) owner-founder’s industry experience was measured in years worked in the same industry, outside of the present firm; (3) organizational commitment of the owner-founder was assessed on a 5-point scale, using the 15item instrument designed by Mowday and Steers (1979); the items were reworded to apply to business owners; (4) business attitudes of the owner/founder were measured by agreement/disagreement with a 5-point scale of seven items: “flexibility in work is a motivation,” “entrepreneur’s environment comprises of family, social, and business issues,” “personal success is balance between personal, family, and business demands,” “employee relations are key to success,” “my management style is people oriented,” “team spirit is vital to business,” and “business performance is satisfying to customers.”

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Factor analysis of the seven items produced two factors; the first factor included the first three items, and we labeled it “balance” (Cronbach a 5 0.60). The second factor was comprised of the remaining four items, and it was labeled “relational attitude” (Cronbach a 5 0.74). To confirm the validity of all the variables in our study, we ran a factor analysis on all individual items together. Four factors were produced, explaining 62.5% of the total variance all having eigenvalues of 1.0 or greater (with the component items for each factor loading at 0.50 or higher on the pertinent factor and not loading higher than 0.30 on the remaining factors). Experience and background grouped into a single factor, whereas the seven business attitude items broke down into two factors, reproducing the balance and relational attitudes found earlier. Commitment remained a separate and single factor. Because our interest was in comparing the differential effects of human resources relative to organizational resources, we chose to combine these variables into a single “human resources” variable. To test the validity of this combination, we performed the reliability test. This produced two human resource variables: (1) owner resources, consisting of industry experience, prior managerial experience, business education, and balance and relational attitudes (Cronbach a 5 0.62) and (2) owner commitment (Cronbach a 5 0.86). The first one refers to the resources involved in prior preparation,and the motivations that the owner-founder brings to the enterprise. Commitment is selfexplanatory. Aggregating the different types of human resources does reduce the richness in interpretation of relationships, yet it is appropriate in the context of the present study because our primary interest is to study more global relationships. At the same time, the individual variables constituting the aggregated variable of the owner resources category were rooted in prior research and were found to be valid in the present sample. Three types of measures were used to assess the level of organizational resources: (1) use of “long-range planning” reflected in the time-horizon used for managing five areas: cash flows, sales, business expansion, new product and new market entries (on a 5-point scale where 5 5 2 years or longer, 1 5 3 months or less; (2) “staff skills” were defined as employment of trained staff in five areas: accounting, budgeting, sales, purchasing, and computer areas (measured with 1 5 yes, 0 5 no); (3) “reporting” referred to the use of regular written reports for monitoring five items: cash, sales, costs, advertising and promotion expenses, and profits. On the 3-point scale, the anchors were 3 5 regular written reports frequently were used, 1 5 no reports were used. We performed another factor analysis on these three types of resources to reduce the data. This resulted in two factors which explained 69.7% of variance (items comprising each factor loaded on that factor at 0.50 or higher and no higher than 0.3 on the remaining factor, and the minimum eigenvalue for each factor was 1.00). As with the human resources variables, we sought to combine all of the organizational variables into a single organizational resource variable, provided the reliability was acceptable. The combined organizational resource variable produced an a of 0.63 and thus was used as an independent variable in all statistical procedures.

Strategies Two types of focus strategy variables were included: (1) focused cost leadership, which was measured by respondents rating four items: i.e., prices of product/service, labor

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costs, productivity, and material costs relative to those of key competitors (Cronbach a 5 0.64); (2) focused differentiation was ascertained by owner-managers of firms rating six items: product/service quality, product/service innovation, customer service, product/service variety, product/service image, and advertising expenditures relative to their key competitors (Cronbach a 5 0.86). Both strategies were measured on a 5-point scale (5 5 much higher, 1 5 much lower). To ascertain the validity of these two strategies, we factor analyzed these 10 items. Results were satisfactory, with two factors resulting (eigenvalues of 1.00 or higher loading on the expected items explaining 57.7% of the total variance). Factor loadings for individual items were at least 0.50 on the pertinent items and did not exceed 0.40 on the remaining items. Because our interest was to examine the impacts of human and organizational resources relative to strategy on performance, we combined the two focus strategies into a single strategy variable (Cronbach a 5 0.62). Thus, we examined the validities of the study’s variables with the help of both factor and reliability analyses. Furthermore, when aggregated variables were used to represent human resources, organizational resources, and strategy, we checked for acceptable Cronbach alphas.

Controls Research shows industry environment influences performance directly and indirectly through the strategy-performance linkage (Chandler and Hanks 1994; McDougall et al. 1994; Sandberg 1986). Therefore, we included two industry variables as controls: industry growth measured as the rate of market sales growth during the preceding 3 years, and the benignness versus hostility measure developed by Covin and Slevin (1989) (Cronbach a 5 0.74). As mentioned earlier, firm size (measured by number of employees) and age (years since founding) have significant effects on performance, and hence these were also used as control variables. Thus our statistical analysis was based on four independent variables: two human resource variables (owner resources and owner commitment), one organizational resource variable, and one strategy variable. Our dependent variable, performance, had two measures: net cash flow and log of growth in employees over 3 years. Four controls—industry growth, benignness, firm age, and firm size—were included in our statistical procedures. (Refer to Figure 1 for a graphic depiction of our conceptual framework.)

Statistical Procedure As a first step, we ran separate multiple regressions for each of the two performance criteria to explore relationships of the selected human and organizational resources, and strategies to performance in the 195 service and retail firms. Next, multivariate analysis of variance (MANOVA) investigated whether there were significant differences in resources, strategies, and performance across firm size and age groups. We used quartile-based groupings rather than median splits because we expected that more distant groups would bring out contrasts in resources and performance more sharply than comparisons among proximate groups. This follows Kazanjian (1988) who notes that the extent of organizational changes between each sequential stage of development may be marginal, whereas comparisons between the first two and last two stages may be

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more significant. Subsequently, multiple regressions were carried out for firms in each size and age quartile.

RESULTS Table 1 presents the descriptive statistics for the total sample of 195 firms and for the top and bottom quartiles on firm size and firm age, along with the univariate Fs from the MANOVA on the age and size quartiles. Table 2 presents results of the regression analyses done for the sample of 195 firms. Descriptive statistics show that the largest firms enjoyed higher levels of performance as well as higher levels of human and organizational resources relative to the smallest firms. However, these differences were significant only for the employment growth measure and the two human resource variables of owner resources and commitment. Comparisons between the two age quartiles revealed that employment growth was significantly greater among the youngest firms. The firms in the oldest quartile also enjoyed significantly greater levels of human resources. Organizational resources were not significantly different across either of the age or size groups. Hypothesis 1 posited that for small service and retail firms operating in niche markets, strategy would have less impact on firm performance than human and organizational resources. Results in Table 2 support this for both performance indices, NCF and employment growth. First, bivariate correlations with performance were nonsignificant. Second, in the regressions, strategy did not show significant relationships to NCF (b 5 1.00) or firm growth (b 5 21.27). Hence, this hypothesis was supported with respect to the limited influence of strategy on performance. However, human and organizational resources significantly impacted only the NCF performance of firms (see Table 2). The interaction terms, first, for owner resources and commitment, and second, for owner resources and organizational resources, showed a significant and negative impact on NCF. None of the resources or the related interaction terms impacted performance in employment growth. Thus, Hypothesis 1 is supported with respect to nonsignificant influence of strategy, but not with regard to the influence of human and organizational resources. Hypothesis 2 stated that the impact of human and organizational resources would differ across two performance indices. This was mildly supported in that human and organizational resources impacted NCF both individually and through the interaction effects. But, relationships were nonsignificant for employment growth.

Size and Age Effects Hypothesis 3 stated that a firm’s employment size and its age would materially impact the role of human and organizational resources on firm performance with regard to NCF and employment growth. To investigate this hypothesis, first, a MANOVA was run on firm size in total employment and age in years. For this, the 195 firms in the sample were partitioned into four size and four age groups. Firms in the two extreme quartiles for size and age were used in the analyses. There were 55 firms in the bottom quartile for size, all with five or fewer workers (labeled the smallest quartile of firms), and 50 firms in the top size quartile with 16 or more workers (identified as the largest quartile of firms). For the two extreme age groups, there were 54 cases in the bottom quartile of firms, 5 years or younger (labeled the youngest quartile), and 49 firms in the top age

0.32 0.88 1.08 36.91 15.81 16.06 5.68 0.39 0.48 0.38

3.14 4.19 15.15 14.69 29.81 2.38 4.24 2.39

SD

0.17 3.71

Mean

All Firms (n 5 195)

27.46 2.42 4.20 —

9.73 4.29

3.01 6.04

0.12 3.71

Mean

5.49 0.42 0.53 —

7.67 0.94

1.22 47.84

0.30 0.88

SD

Very Small Firms (n 5 55)

31.09 2.44 4.40 —

24.94 36.06

3.28 6.68

0.16 3.84

Mean

5.59 0.37 0.48 —

23.71 19.05

1.06 20.54

0.31 0.82

SD

Larger Firms (n 5 50)

28.45 2.38 4.25 —

3.04 11.41

3.22 11.45

0.40 3.69

Mean

5.62 0.39 0.41 —

1.32 13.40

1.01 57.22

0.38 0.95

SD

Very Young Firms (n 5 54)

31.60 2.40 4.42 —

35.90 20.98

3.39 5.76

0.06 3.73

Mean

5.72 0.39 0.57 —

18.65 17.65

1.10 20.47

0.16 0.76

SD

Older Firms (n 5 49)

0.78 0.08 — 8.33d 7.33d 0.27 4.17e —

17.88c — 4.18b 0.06 6.01e —

26.80c 0.07

UnivF

1.12 0.00

0.20 0.58

UnivF

MANOVAs Ageb Sizea

Means and SD for Study Variables for Total Sample, Very Small, Larger, Very Young, Older Firms, and Univariate Fs from MANOVAs for Very Small versus Larger, and for Very Young versus Older Firms

Multivariate Fs: a For size quartiles (very small versus larger firms): F 5 2.85, p , 0.01. b For age quartiles (very young versus older firms): F 5 5.57, p , 0.000. c p , .10. d p , .001.

PERF Lempg NCF ENV Bengn Indgth FIRM Age Emp RESRCS Ownres Orgres Comit STRGY

Variable

TABLE 1

246 C.B. BRUSH AND R. CHAGANTI

ANALYSIS OF RESOURCES ON PERFORMANCE

247

TABLE 2 Results of Regression Analysis of Study Variables with Performance Criteria. Net Cash Flow and Logarithm of 3-Year Employment Growth (n 5 195) Net Cash Flow Variable Firm Age Emp Environment Benign Ind gth Resources Owner Res Commit Org Res Strategy Interactions OR*Commit OrgR*Commit OR*OrgR Str*OR Str*OrgR Str*OrgR Adjusted R2 F

Log of Employment Growth

Corr. Coeff.

Regression b

Corr. Coeff.

Regression b

20.07 0.06

20.14b 0.03

20.29 0.02

20.32d 20.05

0.17 0.20

0.07 0.13a

0.11 0.22

0.24a 0.24c

0.03 0.35 20.01 0.00

3.72d 3.33d 2.33c 1.00

20.01 0.04 0.16 20.05

20.67 20.82 1.09 21.27

23.12d 21.22 21.67c 0.22 21.32 20.21 0.19

1.01 20.63 20.50 0.23 1.37 20.14 0.15

3.39d

3.26d

Note: All correlation coefficients of 0.13 or higher are significant at p , 0.05 or better. a p , 0.10. b p , 0.05. c p , 0.01. d p , 0.001.

group of 19 years or older (labeled the oldest quartile). Table 1 (referred to earlier) reports on the univariate Fs for the size and age groups.

MANOVA The multivariate F for the size quartiles also was significant (F 5 2.85, p , 0.01), and univariate Fs were significant for the two human resource variables, owner resources (F 5 4.18, p , 0 .05) and owner organizational commitment (F 5 4.18, p , 0.05). The multivariate F for the age quartiles was also significant (F 5 5.57, p , 0.000). The logarithm of employment growth (F 5 26.890, p , 0.000) significantly contrasted between the 54 youngest and the 49 oldest firms. As with the size groups, the two human resource variables, owner resources (F 5 7.32, p , 0 .01) and owner commitment (F 5 4.17, p , 0 .05) differed significantly between the age groups, but differences in organizational resources were nonsignificant. These results warranted further exploration of resource performance linkages by size and age. Therefore, separate hierarchical regression analyses were done for each of the size and age quartiles. Results of these regressions are presented in Tables 3 and 4. Overall, the relationships of resources to performance are positive for the two criteria, NCF and employment growth.

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TABLE 3 Results of Regressions for the Smallest Quartile and the Largest Quartile of Firms on Net Cash Flow and Logarithm of Employment Growth Smallest Quartile (max. emp 5 5) (n 5 55) Net Cash Flow Variable Env Benign Ind Gth Resources OwnR Res Comitmt Org Res Interactions OwnR*Comit OrgR*Comit OwnR*OrgR Adjusted R2 F

LogEmGth

Net Cash Flow

Corr. Coeff.

Regression b

Corr. Coeff

20.08 0.19

20.19a 0.17a

0.10 0.45

0.04 0.41c

0.21 0.35

0.05 0.44 0.00

3.29c 4.02c 5.53c

0.05 0.28 0.08

0.73 2.06 3.37b

20.10 0.22 20.01

21.36 23.71c 23.81c 0.49 7.36d

Regression b

Largest Quartile (min. emp 5 16) (n 5 50)

20.04 23.18a 20.96 0.17 2.42b

Corr. Coeff.

Regression b

LogEmGth Corr. Coeff.

Regression b

0.15 0.08

0.15 20.15

0.07 0.01

2.67 1.50 1.29

0.03 20.34 0.22

2.86 4.69d 5.44c

22.13 20.46 21.24 0.02 1.14

23.02b 25.17d 20.17 0.38 4.77d

Note: Correlation coefficients of 0.28 and above are significant at p , 0.05 or better. a p , 0.10. b p , 0.05. c p , 0.01. d p , 0.001.

Size Effects Referring to Table 3, the adjusted R-square was significant for three of four regressions, showing the results to be interpretable. In general, relationships of individual resources to NCF performance were significant for the 55 smallest firms, whereas in the group of 50 largest firms, relationships were significant for employment growth. These differential impacts of resources on performance by size groups partially supported Hypothesis 3. Results for the individual performance criteria, NCF performance among the smallest firms shows a marginal and negative association with the control variable, benignness in the environment, but a positive and marginally significant relationship to the second control, industry growth. In addition, each of the three resource variables significantly and positively affected NCF, but the interaction term for organizational and owner resources, as well as the interaction of commitment and organizational resources showed a significant and negative impact. In contrast, for the 50 largest firms, NCF was not significantly impacted by any of the control variables, resources, or interactions among resources. Turning to employment growth, performance of the 55 smallest firms was significantly improved by a high growth market and strong organizational resources. The interaction of organizational resources and commitment had a negative effect on employment growth.

Age Effects In general, regressions for the age groups revealed fewer significant relationships between resources and firm performance (see Table 4). Moreover, this was true for the youngest as well as the oldest groups of firms in the sample. Hence, the part of Hypothe-

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ANALYSIS OF RESOURCES ON PERFORMANCE

TABLE 4 Results of Regressions for the Youngest Quartile and the Oldest Quartile of Firms on Net Cash Flow and Logarithm of Employment Growth Youngest Quartile (max. age 5 5) (n 5 54) Net Cash Flow Variable Env Benign Ind Gth Resources Own Res Comitmt Org Res Interactions OwnR*Comit OrgR*Comit OwnR*OrgR Adjusted R2 F

Corr. Coeff.

Regression b

LogEmGth

Oldest Quartile (min. age 5 19) (n 5 52) Net Cash Flow

Corr. Coeff.

Regression b

Corr. Coeff.

Regression b

LogEmGth Corr. Coeff.

Regression b

0.27 0.32

0.12 0.28b

20.03 0.10

20.13 0.15

20.07 0.31

0.04 0.15

0.15 0.30

0.09 0.29b

0.21 0.34 0.01

3.53b 1.97a 2.10

0.15 20.27 0.46

0.76 1.22 0.82

20.09 0.38 0.10

3.07b 0.82 1.70

20.04 0.24 0.17

20.18 0.25 0.95

22.03a 21.20 21.72 0.21 2.72b

21.54 21.18 0.92 0.28 3.51c

23.36c 1.89 0.13 0.17 2.57c

0.48 20.64 20.38 0.04 1.32

Note: Correlation coefficients of 0.27 or higher are significant at p , 0.05 or better. a p , 0.10. b p , 0.05. c p , 0.01. d p , 0.001.

sis 3 proposing significant age-based differences in the linkages of human and organizational resources to firm performance was rejected. Examining the results for the individual performance measures, NCF of the youngest firms was improved significantly by industry growth and the presence of high owner resources. Owner commitment was positively related, whereas the interaction between owner resources and commitment had a marginally negative influence. Results for the oldest group of 49 firms in the sample also indicated that owner’s resources contributed to positive NCF, whereas the interaction of commitment and owner resources detracted from high performance. Employment growth showed nonsignificant relationships to resources in both the oldest and youngest firms. However, industry sales growth was a significantly positive factor explaining employment growth of oldest firms. In summary, tests of our hypotheses yielded the following results: H1: For small service and retail firms operating in niche markets, human resources of the owner-founder and the firm’s organizational resources would be more significantly related to firm performance compared with product-market strategies. Results—partial support: supported with regard to limited influence of strategy, and partially for influence of human and organizational resources. H2: Further, for these firms, relationships of human resources and organizational resources to firm performance would differ across different performance criteria. Results—mild support: human and organizational resources influence cash flow both individually and through interactions, but relationships to employment growth were not significant. H3: For small service and retail firms operating in niche markets, firm size and age will significantly impact the relationship of resources to firm performance.

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Results—partial support: impact of resources on performance varied by size but differential effects of resources by age were not supported.

DISCUSSION We analyzed relationships of strategies and resources to the performance of 195 small low tech service and retail firms doing business in local niche markets in central New Jersey. Results provided partial or mild support for our hypotheses. Following is a discussion of the results of our statistical analyses.

Comparison across Groups Organizational resources did not differ significantly by either size or age, although human resources did vary significantly on both dimensions. That is, the largest and oldest companies compared with the smallest and youngest companies appeared to be managed by owner-founders who were significantly better prepared, deemed customer and employee relations to be essential elements of business success, and viewed success to be a balance of personal and business concerns. On the other hand, older niche firms did not exhibit a significant evolution in their organizational systems, planning horizons, and staff skills. In part, this may reflect the “hands-on” approach characteristic of smaller companies, where the owner-founder continues to take an active and controlling role in decision-making, preferring informal systems and procedures. Regarding performance, firms in the oldest as well as the largest quartiles enjoyed higher performance in NCF, but youngest firms experienced greater growth.

Resource-Performance Relationships for the Total Sample The strategy variable did not show a significant impact either individually or interactively with resources on performance indices. Whereas research is extensive in attesting to the role of strategy in performance (McDougall et al. 1994), this study suggests that this link may be more tenuous in retail and service firms. To some degree, this study parallels that findings from Chandler and Hanks (1994) who observed stronger performance relationships for resource combinations than for strategies alone. NCF relationships were significant and positive for both human and organizational resources. The better prepared the owner-founder, and better developed the organizational systems and staff skills, the more positive the NCF. On the other hand, the significant interaction effects for resources were negative, indicating that higher qualities of resources are not always better for NCF. Instead, some combinations of human and organizational resources are likely to be more productive than others (Penrose 1959). To better understand the effects of resource interactions on NCF, we compared levels of NCF for firms with high versus low levels of organizational resources. It should be pointed out that 85% of the firms stated that their NCF was at least break-even (score of 3 or higher on a 5-point scale), which means that the comparisons of NCF levels play out in terms of more versus less positive NCF rather than negative versus positive cash flows. Referring to the interaction of owner resources and commitment on NFC (note that 80% of the owners reported high or very high levels of commitment), results showed that when owner resources were high, NCF was less positive for firms whose owner’s commitment was very high compared with NCF for firms whose owners had a slightly lower commitment. In contrast, when owner resources were low, a slightly higher level

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of owner commitment did not result in more positive NCF. That is, because owner commitment was universally high, differences in owner preparation had a more significant impact on NCF than slight differences in commitment. Turning to the next significant interaction, the effects of owner and organizational resources on NCF showed that when owner resources were high, a greater emphasis on organizational resources was associated with less positive NCF. On the other hand, when owner resources were low, the emphasis on organizational resources showed a more positive NCF. Employment growth showed nonsignificant relationships to resources, and only the control variables influenced growth. Regardless of firm size or levels of resources, firms added employees only when the industry was growing. This supports previous research and provides ample evidence that industry growth determines firm performance (McDougall et al. 1994; Cooper et al. 1994). Hence, for small retail and service businesses, the selection of a growth industry and timing of entry are most important for growth in employment.

Size Effects The differential impacts of resource combinations across size quartiles showed that among the very small firms, owner resources, commitment, and organizational resources significantly and positively contributed to NCF. In contrast, the interactive effects were negative for both combinations of owner resources and organizational resources, and organizational resources and commitment. This implies that when the owner-founder background and preparation was extensive, greater use of routines and systems may weigh against a more positive cash flow. A similar interpretation holds for the interactive influence of commitment and organizational resources. For example, some of the more experienced owner-founders, as well as those with very high commitment, may be “over” formalizing the business, therefore hurting cash flows. Managerial competencies of these owner-founders might be sufficient to earn strong cash flows, and their simple operations did not need high investments in systems and staff. In contrast, for ownerfounders lacking in experience and a balanced business perspective, simple but explicit systems compensated for personal deficiencies. Presumably by monitoring operations formally, using trained staff in areas like inventory control and accounting, and by emphasizing advance planning of sales and cash, owner-founders were better able to keep cash inflows in line with outflows. In sum, this study implies that resources and capabilities that are inimitable and relatively idiosyncratic to the firm can be a key source of competitive advantage (Barney 1991; Lado and Wilson 1994) for these small service and retail firms in the same way as found for high tech, manufacturing or large companies. But, an important difference in the context of “less glamorous firms” is that these unique capabilities are embedded in the managerial competencies of the owner-founders. The preparation of these ownerfounders and their attitudes may be relatively unique and longer lasting resources for the individual firm, whereas many of the systems instituted and types of staff skills hired can be fairly standard and easy to imitate, valuable, but not rare or costly to imitate (Barney 1997).

Age Effects Although significant results were limited to NCF, growth was significantly more rapid among the youngest firms, and there were no distinctive correlates to growth within

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each age group. Growth apparently occurred as a natural consequence of age and was significant only when age contrasts were sharp. Whereas organizational systems became more sophisticated with age, these were not significantly different between the very new ventures and the oldest. The well-established thesis regarding evolution of organizational processes and structures over a firm’s life stage (Churchill and Lewis 1983; Quinn and Cameron 1983) was not a noticeable phenomenon among these service and retail firms. These companies apparently added routines and procedures, such as reporting and staffing policies, but the progress plateaued very quickly. These owner-founders did not incrementally increase structural formalization. Several of the youngest firms appeared to have exceeded the one-person scale of operation as evidenced by the 10-employee average for the 5-years-and-under age group. This is understandable, because a gas station, a restaurant, or a nursery does entail a minimum staff even at start-up. Yet, these companies encountered limits to growth after a certain point in time. Growth requires expansion in market scope (type of customer base) or movement beyond the established location. Expansion requires multiple locations stretching the owner-founder’s competencies and the firm’s organizational resources. Most of these owner-founders were not willing or capable of taking on these added challenges. Furthermore, even those firms without the market and “plant” capacity constraints imposed by location set a ceiling on their expansion, or chose not to grow beyond a certain level (Ginn and Sexton 1990). Hence, the synchronous progression of life stages, and associated development, formalization, and institutionalization of systems and policies that is portrayed in the life cycle literature seems to be a more conspicuous phenomenon among the fast track, often high tech, or more “glamorous” firms (Eggers, Leahy, and Churchill 1994; Kazanjian 1988). It is less characteristic of smaller, niche service and retail establishments. These counterintuitive results may be explained by the simplicity of the operations in these businesses. Moreover, its possible that firms at the “end” of the value chain may require more flexibility in systems procedures and policies to meet the direct demands of customers. These intensely competitive niche markets also may cause owner-founders to “customize” products/services much more according to customer preferences, in which case formal procedures and policies would be restrictive. Furthermore, it maybe that formalized policies/procedures and structures, leading to efficiencies in high tech or manufacturing businesses, may actually be detrimental to the smallest of firms, contributing to reduced flexibility and customization in retail service sectors. Finally, this study suffers from some limitations. The sample was nonrandom and was geographically concentrated. The use of self-reporting subjects it to the common methodology problems of possible source bias. However, in the absence of publicly available secondary data on variables of interest, self-reports are a necessary source, and in fact are frequently used in entrepreneurship research. Also, many of the study’s measures such as employment growth, owner’s business background, formalization, staffing, and planning were objectively anchored and showed good reliabilities. The difficulties in obtaining objective measures of organizational capabilities is a continuing problem for those testing resource-based theory. For these, we used perceptual measures, an evaluation based on the owner-founder’s evaluations of the environment (Morgan 1986; Daft and Weick 1984). Although not as rigorous as objective measures, we believe entrepreneurs “act” on their own evaluations. Therefore we believe this is a reasonably good surrogate, but acknowledge that some measurement shortcomings exist. These results do need to be verified in large random samples drawn from disparate

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regions. Finally, the overall percentage of variance (adjusted R-squared) in performance as explained by the regressions was limited in most cases. This is less of a concern for the present study, because the objective was to identify associative rather than predictive relationships between resources and performance.

CONCLUSIONS AND IMPLICATIONS This study analyzed the influence of two types of resources, human and organizational, on firm performance among 195 small service and retail firms. First, it showed that human and organizational resources are more strongly associated with certain performance outcomes than strategy in service and retail firms. This finding is contrary to earlier work in other industries, which shows that strategies significantly distinguish among levels of performance. However, many of these studies focused on profitability and sales measures of performance (McDougall et al. 1994). Second, using more extensive measures of human and organizational resources than in previous work, we found that performance outcomes did vary with levels of resources. Human and organizational resources were associated with more positive cash flows, but industry/market factors were related to growth in employment size. This supports earlier work, indicating that performance outcomes have different correlates (Brush and VanderWerf 1992; Cooper and Gimeno-Gascon 1992; Cooper 1993). Furthermore, resources influenced firm outcomes both through main effects and interactively. Although our data did examine multiple outcomes, other important measures that might be considered in future research are profitability and sales growth. Third, our results showed strong resource effects for the performance by size (very small and larger firms), but fewer significant results surfaced depending on age of the business. In particular, NCF was significant for the smallest and largest firms. Importantly, results varied from positive to negative when interaction effects were considered, revealing that higher levels of human resources and organizational resources do not always lead to linear increases in performance. Rather the quantity of resources is less important than the combination or quality of resources relative to the opportunity. Specifically, when owner expertise and background are strong, there is less need for extensive organizational systems. This seems to support earlier discussions of “negative resources” (Stevenson and Gumpert 1985) and emphasizes the point that “unique combinations characterize each firm (Penrose 1959). There is an opportunity for theory development in expanding on types, dimensions, and uses of resources in the entrepreneurial context. Furthermore, empirical research might build on the resource combinations that are indicated in this study, by considering new contexts—high tech, manufacturing, international, or family businesses, and by including other types of resources, e.g., social, financial, physical, and technological. Finally, we studied less glamorous businesses at the “economic core”—that, although less frequently studied, contribute to significant activity in the U.S. economy. Our findings showed that for young service and retail businesses, the most important factor affecting employment increases is industry growth. However, for very small companies in the sectors, the combination of owner resources, commitment, and organizational resources was very important to positive cash flow. This research suggests resources and combinations need to be tailored to the particular busines objectives. In sum, this research demonstrates that there are certain contexts where strategy choice matters less than resources. Further, our study shows the importance of consider-

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ing resource combinations as influences on performance, highlights the need for researchers to use multiple performance outcome measures, and demonstrates the varying effects of size and age.

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