A resource-based perspective on the dynamic strategy-performance relationship: an empirical examination of the focus and differentiation strategies in entrepreneurial firms

Elaine Mosakowski

Small and newly rounded firms have received increasing attention in strategic management research. Several scholars have formulated stage models of new firm development (e.g., Greiner, 1972; Kazanjian, 1988; Drazin & Kazanjian, 1990; Miller & Friesen, 1984; Van de Ven, Hudson, & Schroeder, 1984). Others have examined the strategies and strategic planning systems of primarily small, and sometime newly rounded, firms (e.g., Robinson & Pearce, 1983, 1984; Bracker & Pearson, 1986; Bracket, Keats, & Pearson, 1988; Miller, 1983; Shan, 1990; Covin & Slevin, 1989). Nonetheless, little research has combined these developmental and strategic streams (Cooper, 1979; Mintzberg & Waters, 1982; Boeker, 1989; Feeser & Willard, 1990).

Understanding how organizational development, firm strategies, and economic performance interact would shed light on how firms establish competitive advantage. To address this, we adopt a resource-based perspective, which focuses on the creation or acquisition of unique, rare, or specialized resources (Penrose, 1959; Rubin, 1973; Lippman & Rumelt, 1982; Wernerfelt, 1984; Barney, 1986a; 1991; Teece, 1980; 1982; 1986a; 1986b; Dierickx & Cool, 1989; Conner, 1991). The resource-based perspective provides an explanation for how a firm’s resources–which relate to firm strategies (Hatten & Hatten, 1987; Wernerfelt, 1984; Barney, 1986a)–influence its subsequent performance.

This research shares the extant strategy research’s concern with the strategy-performance relationship. We also examine two strategic choices that have been frequently examined in strategic management research–namely, the focus and differentiation strategies. We use a resource-based perspective to study the relationship between the focus and differentiation strategies and firm performance as one approach to delving into the dynamics of this relationship. Unfortunately, these dynamics have often been overlooked (Ginsberg, 1988). Much strategy research implicitly assumes a long-run, equilibrium perspective by ignoring the timing of when costs are incurred and revenues are generated (Tuma & Hannan, 1984). For example, discount rates are seldom used in strategy research, even though averaging variables over 4 or 5 years is a common practice.

In this research, we employ the resource-based perspective to examine the timing of the costs and revenues associated the focus and differentiation strategies. To do so, we study the strategy- performance relationship over a brief, but critical, period in a firm’s developmental process–the time after a firm has undergone an initial public offering of stock (IPO). This period is especially illustrative of firm dynamics since strategies often change in response to the influx of capital from the IPO and performance sights may now shift to the shorter term. This combination of characteristics–changing strategic postures and an emphasis on short-run performance–provides a window into the immediate performance outcomes associated with the changes in a firm’s unique or specialized resources.

In the next section, we develop a theoretical framework drawn from a resource-based perspective. From this framework, we propose a model of the strategy-performance relationship and describe the research design that was used to estimate this model. Finally, we present and discuss the results of this estimation.


A resource-based perspective examines the economic returns to resources that a firms either owns, acquires, or develops. For a resource to generate above-normal returns (rents) and be a source of sustainable competitive advantage, Barney (1991) asserts that it must be valuable, rare or unique among a firm’s competitors,(1) imperfectly imitable, and that strategically equivalent substitutes are either rare or imperfectly imitable.

Consider a simple example of the evolution of a firm’s resources. Entrepreneurial talent, defined here as the ability to identify untapped business opportunities (Kirzner, 1973), may be a resource held by the firm at its rounding that satisfies the conditions Barney set forth as necessary for sustaining competitive advantage. Naturally, the nascent firm may also already hold other resources that meet these conditions, such as particular physical, human, or organizational assets, as well as the firm may acquire or develop additional resources that complement the firm’s original cache of resources as it grows. Some resources may become specialized to others–in essence, producing a bundle (or bundles) of co- specialized assets (Teece, 1986a; 1986b; Conner, 1991;Barney, 1991). In the example of a firm possessing the entrepreneurial talent for identifying business opportunities, the firm may need to acquire complementary managerial talent for building an organization that can exploit these opportunities (Hambrick, 1987), which may in turn call for the development of certain individual and organizational resources. As Wernerfelt (1984) has suggested for a multibusiness firm–but which may also apply to a single business context–the essence of a firm’s strategic decisions is how to use its existing resources and how to acquire or internally develop additional unique resources.(2)

To study a firm’s evolution of its resource base, we propose a very simple dynamic logic that links the investment in resources with a firm’s economic performance. When a firm invests in a resource, it initially incurs the costs of the investment. Because of these costs, firm performance during the period in which the investment was made may appear to suffer. Subsequently, if the firm succeeds in creating or acquiring a resource that meets the conditions outlined by Barney (1991) or Conner (1991), its performance may be higher than the performance of firms that have not made this investment.

It is certainly possible that what appear to be valuable, unique or specialized,imperfectly imitable, and poorly substitutable resources may not generate returns. Resources unrelated to a firm’s strategy–for example, a piece of original artwork hanging in the lobby of a firm’s corporate offices–are not likely to be sources of competitive advantage. In addition, certain physical, human, and organizational capital may actually diminish a firm’s performance. By definition, resources include all tangible and intangible assets that are tied in a relatively permanent fashion to the firm (Caves, 1980; Wernerfelt, 1984)–and as such include both a firm’s strengths as well as its weaknesses. Investments may be well- intentioned ex ante but generate negative returns ex post–for example, capital investments in which the realized returns are negative or at least fall significantly below the projected returns (Bower, 1989) or organizational characteristics, such as culture, which may cause a firm to conceive of and implement strategies that are ineffective or inefficient ( Barney, 1986b).

Although unique or specialized resources are not inevitably tied to economic success, our interest lies in understanding how the timing of the development or acquisition of these resources generally relates to firm performance. We acknowledge the considerable uncertainty associated with adding to a firm’s cache of valuable, unique or specialized, imperfectly imitable, and poorly substitutable resources, but assign some positive and not insignificant average probability to whether a firm will succeed at developing or acquiring these resources, conditional on an attempt at such a development or acquisition. In other words, we assume that in general firms will take steps that improve their performance, but not necessarily that maximize it. This is consistent with an assumption of boundedly rational behavior (Simon, 1957). We acknowledge that this assumption may be overly “optimistic” in certain contexts, such as firms undergoing significant performance problems, which may lead to actions that generally decrease performance (Staw, 1981; Brockner, 1992). Nonetheless, we expect that the post-investment performance of firms holding valuable, unique or specialized, imperfectly imitable, and poorly substitutable resources will be, on the average, positive.

The next step in addressing how performance dynamics relate to firm strategies requires linking strategies with unique or specialized resources. We argue here that tying strategies to firm resources may help resource-based scholars solve the dilemma of how to empirically examine what Mahoney and Pandian (1992, p. 373) describe as “the rich connections between uniqueness and causal ambiguity (Lippman & Rumelt, 1982)” that is so central to the resource-based view. Instead of attempting to eliminate the potentially unresolvable causal ambiguity underlying each firm’s strategy, a large-sample empiricist may focus directly on the unique or specialized character of the firm’s strategy.

Various scholars of business strategy and organizational theory have highlighted the “focus” or “specialist” strategy as a critical strategic choice (Porter, 1980; 1985; Hannan & Freeman, 1977; 1989; Caroll, 1985). This strategy refers to the size of the customer group or segment that a firm serves and is closely tied to the size of the competitive niche in which a firm operates. We assert that when firms focus, they possess know-how and other assets that are unique or specialized to its product market segments. For example, a focused firm’s sales force often develops a specialized understanding of the needs of its market segment and relays information about these needs to other functional areas, such as R&D and service. One can easily imagine a complex web of assets that become specialized and co-specialized to a firm’s market niche. In many industries including computer software, the fact that few firms develop multiple focus strategies is consistent with our assertion that the focus strategy often requires highly specialized resources.(3) Therefore, existence of the focus strategy may indicate resources that are unique or highly specialized.

When a firm first adopts a focus strategy, its performance may be lower than the performance of other firms because it will incur the costs of developing the unique or specialized resources involved. Subsequent to this adoption period, however, the focused firm will generally outperform other firms because of the returns accruing to these resources.

Hypothesis 1: During the period when an entrepreneurial firm adopts a focus strategy, its performance will, on the average, be lower than the performance of other firms.

Hypothesis 2: After the entrepreneurial firm has put a focus strategy in place, its performance will, on the average, be higher than the performance of other firms.

It is perhaps interesting to note that, in the context of entrepreneurial firms,these predictions derived from a resource- based perspective may be seen as contradicting the predictions that would follow from other discussions, especially those relying heavily upon Porter’s (1980; 1985) topologies. Wright (1987) has argued that entrepreneurial firms should adopt a focus strategy since the costs of erecting barriers to entry in a niche market are lower than the costs of erecting barriers to entry in a more general market. Thus, he would predict that, during the period of adopting a focus versus general strategy, entrepreneurial firms choosing the focus strategy will perform better than firms choosing a more general strategy. In addition, hypothesis 2 generally contradicts Porter’s (1985) characterization that the focus strategy alone is not a source of competitive advantage. “Narrow focus in and of itself is not sufficient for above-average performance (Porter, 1985, p. 15).” Instead, Porter argues that the focus versus broad decision simply represents a matter of degree as to which market segment in an industry a firm will target–not a source of competitive advantage. In fact, our characterization of the focus strategy is considerably closer to Porter’s earlier discussion in which he argued that the focus strategy requires that the skills and resources of a firm be specialized to its particular target segment, and hence may enhance the efficiency or effectiveness of the firm (Porter, 1980, pp. 38-41).

The differentiation strategy refers to the way in which firms make their products different from those of competing firms; industrial organization economists often describe differentiation as a way to reduce competition (Chamberlin, 1933) by creating barriers to entry (Porter, 1980; 1985). As for the focus strategy, however, we relate the differentiation strategy to a firm’s resources and posit that adopting the differentiation strategy requires that firms hold unique or specialized resources. Examples of resources that generate rents because they are unique or specialized and that are commonly cited in work by scholars working from a resource-based perspective–brand names, in-house knowledge of technology, and skilled personnel (Wernerfelt, 1984, p. 172)–are often employed by the firm in differentiating its products. Many firms in the computer software industry, for example, differentiate their products on unique technical features (such as specialized user interfaces or connectivity/compatibility features), brand names, control over a defacto standard, service customized to specific user groups or products, or some combination of these.

Because of the costs of investing in resources that form the basis of a differentiation strategy, performance by firms establishing a differentiation strategy will be lower than the performance of other firms. After this strategy development period, we expect that these resources may generate rents so that the differentiated firm will generally outperform other firms.

Hypothesis 3: During the period when an entrepreneurial firm adopts a differentiation strategy, its performance will, on the average, be lower than the performance of other firms.

Hypothesis 4: After the entrepreneurial firm has put a differentiation strategy in place, its performance will, on the average, be higher than the performance of other firms.

Note that Hypotheses 3 and 4 are generally consistent with Porter’s (1980; 1985)view of the differentiation strategy. This view argues that the immediate costs incurred in erecting entry barriers associated with the differentiation strategy would be greater than the costs of not erecting these barriers to entry. This would result in a performance dip when the differentiation strategy is adopted; however, the decreased competition that these barriers to entry produce will yield above-normal profits for the differentiated firm.

Finally, note that all the above dynamic arguments represent simplifications of the actual cost and revenue patterns resulting from these two strategies. After the initial costs of establishing a strategic posture, firms may need to invest in so-called “resource maintenance.” For example, if a resource is technological know-how, firms may need to invest in the protection of its intellectual property rights with patents or copyrights, trade secret protection, legal battles, and organizational arrangements that mitigate the leakage of technological secrets (Teece, 1986b). Another example of resource maintenance is the investment necessary for “keeping” perishable resources. For the differentiation strategy, a firm’s reputation and goodwill may erode without continued advertising and other types of goodwill-generating activities. For the focus strategy, a firm’s specialized know-how about its customer base may become obsolete over time as the needs of this customer base change. Nevertheless, the proposed dynamic framework may be seen as a simplified starting point for understanding the dynamics of costs and revenues associated with these strategies.

In the next section, we build a model of this simple dynamic framework.

Models of Performance

The previous discussion described the dynamics of costs and revenues associated with two strategies and changes in these strategies. To be consistent with this,we propose dynamic models of performance. The alternative–using a static model in which the independent variables and dependent variables are all measured at one point in time–would be inappropriate for this research question since static models necessarily assume that the independent and dependent variables have reached an equilibrium (Tuma & Hannan, 1984).

The dynamic models we propose are:

[Average_Revenue.sub.t2,t3] = [a.sub.0] + [a.sub.1] [Revenue.sub.t1] (1)

+ [a.sub.2] [Size.sub.t1] + [a.sub.3] [Age.sub.t1]

+ [a.sub.4] [Static_Focus.sub.t1] + [a.sub.5] [Adopt_Focus.sub.t2,t3]

+ [a.sub.6] [Static_Differentiation.sub.t1]

+ [a.sub.7] [Adopt_Differentiation.sub.t2,t3]

+ e

[Average_Net_Income.sub.t2,t3] = [a.sub.0] + [a.sub.1] [Net_Income.sub.t1] (2)

+ [a.sub.2] [Size.sub.t1] + [a.sub.3] [Age.sub.t1]

+ [a.sub.4] [Static_Focus.sub.t1] + [a.sub.5] [Adopt_Focus.sub.t2,t3]

+ [a.sub.6] [Static_Differentiation.sub.t1]

+ [a.sub.7] [Adopt_Differentiation.sub.t2,t3]

+ e

Specifying the temporal structure of the relationship between firm resources and performance is naturally problematic under the assumptions of a resource-based model. Biggadike (1979) demonstrated that new ventures often take considerable time to become profitable–on the average, about eight years. However, as Barney(1991) notes, resources yield rents only as long as they remain suited to their environment. Some unexpected occurrence in a firm’s rapidly changing environment may obsolete previously valuable resources.

Ideally one would want to observe performance after a period that is sufficiently long for the resource to generate rents yet not so long that environmental changes have made it inconsequential. We balance these concerns by observing the strategy performance relationship over a three-year period. We begin this three-year period with the first year after a firm has undergone the IPO (t0=year of IPO; t1=1 year after IPO; etc.). Starting our observations one year after the IPO allows a short time for firms to adjust their strategies after the resource influx of the IPO. This three-year period is modelled as follows.

In Equation 1, the dependent performance variable is a firm’s revenues averaged over two years (times t2 and t3), controlling for revenues measured one year prior (time t1); in Equation 2, the dependent performance variable is a firm’s net income averaged over times t2 and t3, controlling for net income at time t1.In this way, Equations 1 and 2 control for a lagged measure of the dependent variable: we refer to this general form hereafter as a “lagged dependent variable” model.

Lagged dependent variable models offer the advantage that, by controlling for prior performance, they provide a more rigorous test of the effects of the strategy variables than if no lagged dependent variables were included. This rigor results since, in effect, lag dependent variable models control for omitted variables that are correlated with prior performance. This is consistent with the focus of this research on the development or acquisition–that is, change in–resources since lagged dependent variable models implicitly control for other, unspecified resources that are already generating returns reflected in a firm’s prior performance. We chose the lagged dependent variable specification in place of a dependent variable that is a change or percentage change in the level of the dependent variable since this allows us the flexibility of estimating (instead of assuming) the extent to which performance depends upon prior performance.

Note that in Equations 1 and 2 the dependent variable is the average of revenues or net income over two years. We chose a two- year average in order to increase the reliability of annual measures of performance drawn from accounting data. With accounting data, firms may be able to distort the timing of when certain revenues and costs are reported to enhance investor confidence. For example, January shipments may be reported for December, and December costs reported for January. In addition, if substantial revenues and costs occur infrequently, a one-year snapshot of performance may not be an accurate reflection of overall firm performance. For example, under Department of Defense contracts, firms may sell software contracts only once every few years. Therefore, by averaging performance over time, we are in effect smoothing out temporal fluctuations–whether due to intentional reporting distortions or artifacts of annual reporting practices.

The independent variables in both equations are identical. We control for a firm’s size and age at time t1. Size and age provide a control for a firm’s stage in the developmental process, as well as for possible constraints on a firm’s strategic options. For example, small or newly rounded firms may have little choice but to focus on a small segment of the product market because of limited financial or organizational capital.

The variables representing the two strategic choices–the Focus and Differentiation strategies–appear in the equations in two ways. First, we examine the effect of a static form of the strategy–that of holding a Focus and/or Differentiation strategy–that suggests the effects of being a Focus and/or Differentiation firm. To capture this, we create variables that indicate whether or not a firm has held–and continues to hold–a Focus and/or Differentiation strategy beginning in at least year t1. The coefficients estimated on these static variables will suggest the performance difference between, on one hand, firms that have consistently held a strategy and, on the other hand, firms that have either fluctuated or have consistently not held this strategy.

Second, we examine the effect of a dynamic form of the strategy– that of Adopting a Focus and/or Differentiation strategy. These “Adopt” variables suggest the effects of becoming a Focus and/or Differentiation firm. This is measured by creating variables that indicate whether or not a firm has adopted a Focus and/or Differentiation strategy during the time that coincides with our performance variables, t2 and t3. The coefficients on these dynamic variables will indicate the performance difference between firms that have adopted a particular strategy during this period and those that have either consistently held this strategy, consistently held another strategy, or adopted another strategy during this period. One can see that the dynamic form of the strategy variable is, by definition, mutually exclusive of the static form of the strategy variable since in order for a firm to adopt a strategy in years t2 or t3 it must, by definition, not have consistently held the strategy beginning in year t1.

However, note that, in our representation of the Focus and Differentiation strategies, we implicitly assume that these two strategies are not mutually exclusive. Instead, following Porter (1985), we consider the focus and differentiation strategies as independent choices. In other words, a firm may choose a broad differentiated strategy, a focus differentiated, a broad undifferentiated, or a focus undifferentiated strategy in our model. In addition, we do not include other strategic choices in our model, most notably, what Porter (1980; 1985) describes as the cost leadership strategy. This is done since we restrict our focus to computer software firms in this study and many of these firms do not engage in manufacturing activities–thus, process R&D is difficult to define. In addition, firm-level cost data are not available. We assume that this omission will not bias our results since we do not follow Porter’s assumption that cost-leadership and differentiation are mutually exclusive strategic categories (Hill, 1988; Murray, 1988). Instead, we view this omission as similar to the omission of other rent-generating resources. As mentioned above, this is controlled for, to some extent, by controlling for prior performance in the tagged dependent variable model.

We propose the two equations corresponding to two performance measures since the theoretical framework described offers no clear guidelines as to the form performance effects may take. Theoretically, it makes sense to consider the “costs” of investing in unique or specialized resources and the “revenues” generated by these resources. However, “costs” include opportunity costs, which may appear as decreased revenues, and “revenue” changes may influences a firm’s investment in other activities, which appear as costs. As a result, we cannot easily distinguish economic costs from economic revenues with the use of accounting data. Therefore, we direct our hypotheses equally to both dependent variables, Revenues and Net Income, and do not expect a priori that specific hypotheses will hold for one equation but not the other.

Therefore, Hypotheses 1 and 3, respectively, predict that [a.sub.5] and [a.sub.7] will be less than zero in both equations, and hypotheses 2 and 4, respectively, predict that [a.sub.4] and [a.sub.6] will be greater than zero in both equations. We now turn to the research methodology used to estimate these equations.

Research Methodology

Our sample consisted of 86 entrepreneurial firms in the computer software industry. A firm was included in the sample if it met all of the following three criteria: 1) the firm attempted an initial public offering (IPO) of stock on any U.S. stock exchange during 1983 or 1984; 2) the firm completed the IPO in or before 1984; and 3) the primary business of the firm was computer software according to the information given in the list of firms filing to go public that was published in the Institutional Investor publication. The years 1983 and 1984–two boom years in high-technology IPOs–were chosen in an effort to reduce the selection bias of only sampling successful firms. During these boom years, firms with no full-time employees and no products attempted and completed initial public offerings of stock. Thus, the sample represents firms with diverse likelihoods of success. The criterion that the firm must have completed the IPO by 1984 was necessary to collect data over several years.

To hold industry specific factors constant, we studied only firms in the computer software industry. In related research (Mosakowski, 1991), we also studied computer hardware and hardware/software firms that met these criterion, resulting in a total sample of 122 firms. However, since our focus in this paper lies in understanding how differences in inputs–i.e. resources–relate to differences in output–i.e. performance–we chose to strictly control for the intervening technological processes that translate inputs into outputs. In other words, it is possible that a particular type of resource may be important for the success of software firms but not for the success of hardware firms. Therefore, we restrict our sights to a single technology and a single product market–computer software.

A list of firms fitting these criteria was compiled from the Institutional Investor publication. We collected longitudinal data on the 86 computer software firms attempting to go public in 1983 or 1984 that completed the IPO. Annual data were obtained from the prospectuses filed with the Securities and Exchange Commission (SEC forms S-1 or S-18) and annual disclosure forms (SEC form 10-K). Financial data and data on product market strategies were collected from these SEC documents. Because these firms are relatively small with limited product offerings, in most cases a detailed discussion of a firm’s products, services, and its targeted customers was presented in the body of the reports. Occasionally, this information was omitted (coded as missing data) or included as an appendix to the report (coded with the same techniques as information included in the body of the reports). All information was self reported, but subject to the due diligence procedure of the IPO filing. A firm’s officers are held legally accountable for disclosing this information accurately. Thus, we took the discussion of products and services as accurate and significant.

The data were coded by knowledgeable coders, who had at least one- and-one-half years each of experience in the computer industry. For a subsample of firms, two coders were used to check the inter-rarer reliability of the coding schemes. In addition to the two focus and two differentiation strategy variables we describe below, we also collected financial data from firms’ income statements and balance sheets, as well as data on firms’ formal organizational structure, outside contracting and licensing relationships, size, age, and product diversity from product and organizational information.

From these self-reported descriptions of product strategies, we measured two types of focus strategies that were common in the computer industry at this time: a vertical markets focus and a customer needs focus. The vertical markets firm focuses on the basis of customer types (Abell, 1980)–in particular on the industry in which the customer resides. Examples include firms selling computer software to health care providers, aerospace manufacturers, and hotels and restaurants. In many cases, vertical markets producers satisfy rather general customer needs (such as data base management or computer graphics) with products that are specialized to the applications, conventions, or other features of a specific industry environment. In addition to a vertical markets focus, firms may focus on specialized customer needs (Abell, 1980) that cut across customers’ industry classification. Examples of firms focusing on the customer needs dimension are firms selling computer software for artificial intelligence, encryption, and image or language processing applications.

Similarly, we included two dimensions along which firms commonly differentiate their products in the computer industry: technological and service dimensions. Since product technology is evolving rapidly in computer software (Inmon, 1987)–resulting in increasingly sophisticated, fast, powerful, and user-friendly software–technological change is so integral to this industry that a firm’s technological advantage affects not only the success of its current products, but also the success of future products. Because of product complexity, specialization, and co- specialization, customer services (such as on-line support, on-site installation and support, and customized software design) also contribute to a firm’s performance.

To study the performance ramifications of the existence or adoption of these four strategic choices, we used dichotomous variables to represent the choices. We assumed these choices are independent–in other words, the choices are not mutually exclusive of each other, but are independent of each other. The independence assumption implies, for example, that a vertical markets firm may or may not choose to focus on specific customer needs within its vertical market.

For the vertical markets focus strategy, we created a dichotomous variable to indicate whether or not a firm had established sales of products to at least one vertical market beginning with the first year after the IPO and ending with the third year after the IPO (the “Static_Vertical_Markets” variable coded as “1” indicates such a consistent vertical market focus). This variable was created from annual self-reported product descriptions, including whether a firm’s products were specialized or tailored to a particular industry.

When a firm did not have a consistent vertical markets focus in at least one of its product lines, we examined whether the firm adopted a vertical market strategy during either the second and third years after the IPO. If this occurred, the Adopt_Vertical_Markets variable was coded to be a “1”; otherwise, it was “0.” Note that the Adopt_Vertical_Markets variables can be “1” only if the Static_Vertical_Markets variable is “0.”

In our data on the customer needs focus, a three-point scale was developed from the annual self-reported product descriptions to indicate the extent to which firms specialized their products to customer needs. Firms selling computer software that are highly specialized to particular applications, such as artificial intelligence, image or language processing, and encryption, were coded as highly specialized (code = “3”). Firms selling software for applications such as computer-aided design, process control, computer-aided manufacturing, or statistical analysis were coded as somewhat specialized (code = “2”). Firms selling software for applications such as word processing, presentation graphics, and databases that were targeted to the needs of most customers were coded as unspecialized (code = “1”).

Based on the annual values of this variable, we created a dichotomous variable to indicate if, from years one through three after IPO, the firm offered products that are highly specialized (the “Static_Customer_Needs_Focus” variable was coded to “1” if the firm was coded as a “3” on the three-point scale described above). A “Adopt_Customer_Needs_Focus” variable was coded to “1” if the “Static_Customer_Needs_Focus” variable was “0” and the firm moved to the level “3” of specialization during the second or third years after the firm’s IPO; otherwise, this “Adopt” variable was set to “0.”

In the annual descriptions of these computer firms, differentiation on the basis of service was measured with a four-point scale to represent the highest level of services mentioned.(4) Firms offering virtually no pre-or post-sale service were coded with a “0.” Firms offering telephone support services were coded with a “1.” Firms offering on-site support and installation services were coded with a “2.” Firms offering customized programming and product design were coded with a “3.”A “Static_Customer_Service” variable was coded to “1” if the firm consistently offered customer services rating level 2 or above beginning in the first year after IPO. If this “Static Customer_Service” variable was set to “0” and the firm moved to level 2 or above during the second or third years after IPO, a “Adopt_Customer_Service” variable was set to “1”; otherwise, this “Adopt”variable was set to “0.”

The measure of technological differentiation was created in a fundamentally different manner than were the two focus variables and the customer service differentiation variable. Note that the two focus and the customer-service differentiation variables were coded from self-reported descriptions of firm behavior in their product markets. For technological differentiation, we rely upon an indicator of firms’ efforts to differentiate their products technologically–namely, their R&D spending to Sales ratio in the prior year. Therefore, we base our measure of a firm’s technological differentiation in year t1 on the firm’s R&D spending in year t0 (the IPO year), assuming the effort in one year will translate into behavior in the next.(5) We create a dichotomous variable “Static_R&D_Differentiation” that indicates, based on a median split of R&D Spending per Sales Revenue dollar, whether a firm was in the top half during the IPO year and remained in the top half of R&D spending during subsequent years–i.e. was consistently a top R&D spender (coded as “1”). We also coded an “Adopt_R&D_Differentiation” variable to be “1” to indicate when a firm that was not consistently in the top half of R&D spending moved into this position in years t2 or t3 (based on R&D spending in years t1 and t2).

For the control variables, we coded total assets from a firm’s balance sheet as a measure of firm size. Since the size variable had a highly skewed distribution, we transformed it with natural logarithm function. Age was calculated with the number of months that lapsed between when the firm was incorporated (or rounded, if no incorporation date was given) and the start of a firm’s first fiscal year after the IPO. The dependent variables, Revenues and Net Income, were coded from annual income statements, with Net Income representing profits after taxes. These performance variables were averaged over years two and three after the IPO for the dependent variable. The lagged values of these dependent variables were measured in year one after the IPO.

Descriptive statistics on these variables were calculated for the total sample of 86 firms and appear in Table 1.

To estimate Equations 1 and 2, we used a two-staged least squares procedure. The two-staged least squares estimation may be necessary because, for ordinary least squares to be efficient, the correlation between disturbances and regressors should be zero. This may not be the case when a lagged dependent variable is present. To correct for this possibility, the two-staged least squares procedure creates an instrumental variable for the lagged dependent variable (Markus, 1979). In other words, instead of including the observed lagged dependent variable in the equation, an estimate of this lagged dependent variable is included. As part of this two-staged lease squares procedure, we analyzed the residuals, which resulted in the elimination of two outlier cases.

In the next section, we examine the results of this research.



Table 2 reports estimates of Equations 1 and 2.

For both equations, we estimate coefficients on the lagged dependent variables that are not statistically significant. Thus, performance in year one does not appear to have a significant effect on performance averaged over the two subsequent years. (Note that, when we estimated a simpler lagged dependent model in which the dependent variable was performance only in the subsequent year, the coefficients on the lagged dependent variables were positive and less than one.)However another control variable–Size–appears to have an important influence on a firm’s subsequent performance. The coefficients estimated on the Size variable (represented by the natural logarithm of total assets) are positive and statistically significant in both equations. Age, on the other hand, exhibits either no statistically significant effect (for Net Income performance) or a negative and statistically significant (for Revenue performance).


Recall that Hypothesis 1 predicted that the adoption of a focus strategy would result in a performance decrease during the period when the change occurred. This prediction translates into a negative coefficient on the “Adopt Vertical Markets Focus” and the “Adopt Customer Needs Focus” variables in Table 2. Our predictions are partially supported for the Adopt Vertical Markets Focus variable. In the Revenue equation, we estimate the coefficient on this variable to be negative and statistically significant; in the Net Income equation, this coefficient is negative but not statistically significant. For the Customer Needs Focus, our results do not support Hypothesis 1. The coefficients on the “Adopt Customer Needs Focus” variable in both equations are not statistically significant. In the Revenue equation, the coefficient is negative; in the Net Income equation, the coefficient is positive.

Hypothesis 2 predicted that firms with an established vertical markets or customer needs focus would outperform other firms–in other words, we would expect a positive coefficient on these variables. Our estimates of Equations 1 and 2 generally support this hypothesis. For the Vertical Markets Focus variable, the coefficients are positive in the two equations–for Net Income performance, we estimate a positive and statistically coefficient on the Vertical Markets Focus variable; for Revenue performance, we estimate a positive, but not statistically significant, coefficient. For the Customer Needs Focus variable, the coefficients are once again positive in the two equations–for Net Income performance, the coefficient on the Customer Needs Focus variable is positive, but not statistically significant; for Revenue performance, the coefficient is positive and statistically significant. Overall,we find relatively good support for Hypothesis 2.

Similar to Hypothesis 1, Hypothesis 3 predicted that firms adopting a differentiation strategy–with a Customer Service and/or Technological basis–would experience a performance downturn. We find no support for this hypothesis. The coefficients on the “Adopt Customer Service Differentiation” variable are positive and statistically significant in both the Net Income and Revenue equations. The coefficients on the “Adopt R&D Differentiation” variable are positive in both equations; only for Revenue performance is this coefficient statistically significant. Overall, four out of the four possible estimated coefficient fail to support Hypothesis 3.

Hypothesis 4 predicted that established Service and Technological Differentiation strategies would enhance a firm’s performance. This prediction is generally supported by our results. The coefficient on “Service Differentiation” is positive and statistically significant for Net Income performance, and it is positive, but not statistically significant, for Revenue performance. The coefficient on R&D Differentiation is positive and statistically significant in both the Net Income and Revenue equations.

In the following section, we discuss these findings.

Discussion and Conclusion

A relatively clear pattern emerges in the results presented above. For the four strategy variables that indicate an established strategic posture–the Vertical Markets Focus, the Customer Needs Focus, the Customer Service Differentiation, and the R&D Differentiation strategies–our predictions generally support the hypothesis that firms that hold these strategies will outperform other firms. For the eight coefficients we estimated–four variables times two equations–all eight are positive and five out of the eight are statistically significant. Thus, the suggestions of the resource-based perspective that certain types of resources– and it follows, certain types of strategies associated with these resources–will lead to above-normal performance is consistent with our findings. Thus, to the extent that these strategies represent valuable, rare or unique, inimitable, and non-substitutable resources, our findings have supported a central argument of the resource-based view–namely, that these resources will be a source of competitive advantage.

This pattern of results contradicts Porter’s (1985) prediction of the relationship between his generic focus strategy and firm performance. Our findings are generally inconsistent with Porter’s (1985) argument that the focus strategy by itself is not sufficient for generating above-normal returns. The disagreement between our and Porter’s (1985) views may revolve around whether one sees the focus and differentiation strategies as similar in kind as we do or as different in kind as Porter (1985) does. Representing what Barney (1991) calls environmental models of competitive advantage, Porter (1985) tends to separate a firm’s decision as to its competitive scope from its decision as to its competitive advantage. A resource-based perspective instead places greater emphasis on these two strategic choices as interdependent such that a firm’s scope/environment will be highly dependent upon–as well as will determine–its competitive advantage.

A most dramatic example of the argument that competitive scope and advantage are interdependent is presented in Henderson’s (1983, p. 4) discussion of Gause’s Competitive Exclusion Principle that “no two species can coexist who make their living in the same way.” He develops this argument to suggest that only one firm with a unique advantage can survive in any competitive arena. This emphasis on the uniqueness of a firm’s sources of competitive advantage and the relationship of this advantage to the firm’s competitive arena is also shared by many writers working within the resource-based tradition. One can easily see, however, that this directly contradicts Porter’s model in which many firms with similar resources can survive in any given competitive arena.

The other pattern that emerges in the results suggests little support for what we thought was the relatively straightforward hypothesis that the adoption of strategies would result in a negative performance effect because of the cost of acquiring and/or developing resources. Of the eight coefficients estimated on the adoption variables–four strategies times two equations–three were negative and five were positive. Of the three negative coefficients, one was statistically significant; of the five positive, three were statistically significant. Thus, the results suggest that, if anything, overall positive returns accrue to firms during the period of adopting one of these strategies.

The lack of support for our predictions about strategy adoption may be explained on methodological or theoretical grounds. The simple dynamic model we proposed separated the period of adoption from subsequent periods. It is extremely possible that our model and/or data were overly imprecise at representing this separation. In addition, the dichotomous variables were constructed so that the group of firms adopting the focus and/or differentiation strategies was implicitly compared to “other” firms, excepting firms that had established these strategies but including firms adopting other strategies. However, the rationale behind the posited negative performance effect is the act of adoption itself; yet in this study, we do not have data on the adoption of other strategies, and therefore could not better understand determinants of performance in the “other”category. Perhaps the harshest methodological criticism of this research is, however, that we did not observe the changes in a firm’s resources directly, but instead assumed a relatively simple, direct, and expedient link between a firm’s resources and its product market strategies. Naturally, firms exhibiting similar product market strategies may vary considerably in their resource caches, which represent, at least in part, the general question of strategic implementation. To address these shortcomings, future empirical research may want to explicitly examine the link between a firm’s product market strategies, its resources, and its performance over a substantial time period. This research will necessarily have to attempt to resolve the causal ambiguity as to the factors underlying firm success (Lippman & Rumelt, 1982). Nonetheless, a detailed inventory of a firm’s resources over time could shed light on the relative costs and benefits of different types of investments and how these contribute to firm performance over time.

In addition to these methodological concerns, theoretical murkiness may complicate the underlying performance dynamics. The timing of the returns to particular resources will be complicated by many factors, all of which may change over time. The most simple of all is the extent to which a resource persists over time. Both tangible and intangible assets, such as equipment and reputational assets, may perish or wear out over time. Alternatively, a firm’s early source of competitive advantages may grow over time, such that a small lead may escalate into an advantage that proves to be insurmountable (Lippman & McCardle, 1987). This suggests that future research should be directed toward the nature of path dependencies in the resource-based view (David, 1985; Arthur,1988).

In addition, returns to a resource will depend on the resource’s relationship to the other resources held by the firm so that, if a resource is more specialized to other resources of a firm, it may yield higher returns (Conner, 1991). However, when resources are highly interdependent and co-specialized, changing resources central to a firm’s strategy may cause greater overall upheaval and performance downturns that, if taken to the extreme, could underlie the liability of newness and inertia arguments advanced by population ecologists (Stinchcombe, 1965; Hannan & Freeman, 1977; 1989). In addition, the returns generated by a resource will depend on conditions in a firm’s competitive and general environment. For example, technological property rights and appropriability shifts arising out of competition (Lieberstein, 1979; Teece, 1986a; 1986b) and advances in know-how in the general technological environment (Koopmans, 1957; Barney, 1991) will affect the returns that a firm garners from a technological resource.

With the hindsight gained from this empirical study, it is our opinion that research adopting a resource-based perspective must delve deeper into issues of timing. To date, this work has addressed those factors that affect the sustainability of returns to a resource, but has been relatively silent on how various factors affect the specific temporal pattern of returns to a resource. Given the relationship of the resource-based view to economic theories of the firm (Conner, 1991), the resource-based perspective has instead emphasized which resources will generate above-normal returns in equilibrium. However, as Mueller (1990, p. 1) notes, there is another view of competition in which the concept of equilibrium does not play such a central role:

When competition is viewed as a dynamic process of new product and process creation, … what is of interest is not the constellation of prices and allocation of resources at a particular point in time but their movements over time. The perspective is that of a system in flux, of constant disequilibria evolving through time, rather than of a system in a state of equilibrium at a particular point in time.

Applying this Schumpeterian perspective to the problem at hand suggests that understanding the patterns of change and adjustment of the returns generated by a resource may be as, if not more, important than understanding the long-run stable level of returns that obtain (Geroski, 1990). For example, if a resource does not yield rents in the long run but the process of adjustment to the zero-rent state is extremely slow, substantial quasi-rents may be earned in the interim. Under certain circumstances, such as environments with rapid exogenous technological change, acquiring such a resource may be more desirable than would acquiring a resource with a low level of rents that persists in the long run but with a rapid adjustment to the equilibrium level of rents.

As Geroski (1990) notes, public policies–and, by implication, strategic policies–need be concerned with both the equilibrium levels of profits as well as the adjustment process by which profits reach their equilibrium levels. From a resource-based perspective, developing models of the temporal patterns of returns to a firm’s resources may contribute to our understanding of the nature of path dependencies and isolating mechanisms that serve as impediments to imitation and substitution (Rumelt, 1984). A study of the dynamics of returns to resources may provide insight into the conditions that lead to the persistence of resource heterogeneity versus those that lead to the persistence of resource homogeneity. As Barney (1991) notes, whether one assumes resource heterogeneity versus resource homogeneity determines whether a resource-based model or an environmental model of competitive advantage is more appropriate.

Acknowledgment: Funding from the National Science Foundation (under grant SRS 84-10556), the University of California, Los Angeles, and the University of California, Irvine, is gratefully acknowledged. The author would like to thank: Jay Barney, Chris Earley, Joe Mahoney, and Naren Udayagiri for their comments.


1. This view emphasizes that a resource be rare, but not necessarily unique, for it to generate a competitive advantage (Barney, 1989). A nonunique resource may still generate a competitive advantage if the number of firms possessing the resource is less than the number of firms needed to generate perfect competition dynamics in an industry (Hirshliefer, 1980).

2. It is the author’s opinion that a debate may be emerging in the literature on the conditions under which a firm may consistently acquire resources that generate rents (e.g., see Schulze’s (1992) discussion of strong versus weak forms of the resource-based view). Several authors have argued that, when factor markets are competitive, the market price for these resources will reflect the rent-stream potential of the resources (Dierickx & Cool, 1989; Barney, 1986a; 1991).

However, Mahoney and Pandian (1992) argue that when a firm’s combination of resources is unique, a bilateral monopoly situation may (depending on the outcomes of the negotiations) yield rents to the firm acquiring the resource. In an argument also dependent on a firm having a unique combination of resources, Conner (1991, pp. 134-137) demonstrates that a resource acquired in the market may generate rents when this resource is readily available in the market and it is more specialized (or linked) to a firm’s resources than to other firms’ resources. Therefore, in addition to “uniqueness” per se, “specialization” and “co-specialization” in the Williamson (1985) sense may also contribute to the generation of above-normal returns.

3. For example, in the sample of software firms studied in this article, only one firm out of the total sample of 86 held a strategy that targeted multiple vertical market segments. It is perhaps interesting to note that with the exception of this firm’s initial vertical markets business, the other vertical markets businesses were all added through the acquisition of stand-alone vertical markets firms.

4. Note that in virtually all cases, firms offering what we deem to be a higher level of service also offered the services associated with the lower levels.

5. Note that using R&D efforts in one year to reflect technological innovativeness in the next year, which is asserted to affect firm performance in the following two years, results in a two-to-three year lag structure linking R&D spending and firm performance. Empirical work suggests this may be reasonable. For example, Ravenscraft and Scherer (1982) estimated that approximately a four-year lag was the optimal lag structure for observing the impact of R&D spending on performance; however, their study included low, as well as high, technology businesses and was dominated by large, established firms. In the current research, two factors are likely to result in a shorter lag structure–the type of industry and the type of firms studied. We expect that the outcomes of R&D will influence firm performance much quicker in a rapidly changing industry and for firms with profit-and sales- streams that are not well-established. In the computer software industry, the time between product generations is often short–in the one to two year range. Therefore, we expect that a lag structure of approximately two years is appropriate. In addition, if a two-to-three year lag structure is not optimal in this context, this is likely to affect only the magnitude, but not the sign, of the estimated coefficient. Ravenscraft and Scherer (1982) found that the choice of different lag structures did not affect the sign of the coefficient on R&D spending.


Abell, D. F. (1980). Defining the business: The starting point of strategic planning. Englewood Cliffs, NJ: Prentice-Hall.

Arthur, W. B. (1988). Self-reinforcing mechanisms in economics. Pp. 9-31 in P. W. Anderson, K. J. Arrow, & D. Pines (Eds.), The economy as an evolving complex system. Redwood City, CA: Addison- Wesley.

Barney, J. (1986a). Strategic factor markets: Expectations, luck, and business strategy. Management Science, 32: 1231-1241.

—–. (1986b). Organizational culture: Can it be a source of sustained competitive advantage? Academy of Management Review, 11: 656-665.

—–. (1989). Asset stock accumulation and sustained competitive advantage: A comment. Management Science, 35: 1511-1513.

—–. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17: 99-120.

Biggadike, R. (1979). The risky business of diversification. Harvard Business Review, (May-June): 103-111.

Boeker, W. (1989). Strategic change: The effects of founding and history. Academy of Management Journal, 32.

Bower, J. L. (1989). Managing resource allocation or strategy as an outcome. Harvard Business School note, HBS 9-389-040.

Bracket, J. S., Keats, B. W., & Pearson, J. N. (1988). Planning and financial performance among small firms in a growth industry. Strategic Management Journal, 9: 591-603.

Bracker, J. S. & Pearson, J. N. (1986). Planning and financial performance of small, mature firms. Strategic Management Journal, 7: 503-522.

Brockner, J. (1992). The escalation of commitment to a failing course of action:Toward theoretical progress. Academy of Management Review, 17: 39-61.

Carroll, Glenn R. (1985). Concentration and specialization: Dynamics of niche width in populations of organizations. American Journal of Sociology, 90: 1262-1283.

Caves, R. E. (1980). Industrial organization, corporate strategy and structure. Journal of Economic Literature, 58: 64-92.

Chamberlin, E. H. (1933). The theory of monopolistic competition: A re-orientation of the theory of value. Cambridge, MA: Harvard.

Conner, K. R. (1991). A historical comparison of resource-based theory and five schools of thought within industrial organization economics: Do we have a new theory of the firm? Journal of Management, 17: 121-154.

Cooper, A. C. (1979). Strategic management: New ventures and small business. Pp.316-327 in D. E. Schendel & C. W. Hofer (Eds.), Strategic management. Boston, MA: Little, Brown.

Covin, J. G. & Slevin, D. P. (1989). Strategic management of small firms in hostile and benign environments. Strategic Management Journal, 10: 75-87.

David, Paul A. (1985). Clio and the economics of QWERTY. American Economic Review Proceedings, 75: 332-337.

Dierickx, I. & Cool, K (1989). Asset stock accumulation and sustainability of competitive advantage. Management Science, 35: 1504-1511.

Drazin, R. & Kazanjian, R. K. (1990). A reanalysis of Miller and Friesen’s life cycle data. Strategic Management Journal, 11: 319-325.

Feeser, H. R. & Willard, G. E. (1990). Founding strategy and performance: A comparison of high and low growth firms. Strategic Management Journal, 11: 87-98.

Geroski, Paul A. (1990). Modeling persistent profitability. Pp. 15-34 in Dennis C. Mueller (Ed.), The dynamics of company profits. Cambridge, England: Cambridge University Press.

Ginsberg, A. (1988). Measuring and modelling changes in strategy: Theoretical foundations and empirical directions. Strategic Management Journal, 9: 559-575.

Greiner, L. E. (1972). Evolution and revolution as organizations grow. Harvard Business Review, 50(4), 37-46.

Henderson, B. (1983). The concept of strategy, Pp. 3-26 in Kenneth J. Albert (Ed.), The strategic management handbook. New York, NY: McGraw-Hill.

Hambrick, D. (1987). Top management teams: Key to strategic success. California Management Review, 30: 88-108.

Hannan, M. T. & Freeman, J. H. (1977). The population ecology of organization. American Journal of Sociology, 82: 929-964.

—–. (1989). Organizational ecology. Cambridge, MA: Harvard University Press.

Hatten, K. J. & Hatten, M. L. (1987). Strategic groups, asymmetrical mobility barriers and contestability. Strategic Management Journal, 8: 329-342.

Hill, C. W. L. (1988). Differentiation versus low cost or differentiation and low cost: A contingency framework. Academy of Management Journal, 13: 401-412.

Hirshliefer. J. (1980). Price theory and applications, 2nd ed. Englewood Cliffs,NJ: Prentice-Hall.

Inmon, W. H. (1985). Technomics: The economics of technology and the computer industry. New York: Dow-Jones/Irwin.

Institutional investor. New York, NY, 1983-1984.

Kazanjian, R. K. (1988). Relation of dominant problems to stages of growth in technology-based new ventures. Academy of Management Journal, 31: 257-279.

Kirzner, Israel M. (1973). Competition and entrepreneurship. Chicago, IL: University of Chicago Press.

Koopmans, Tjalling C. (1957). Three essays on the state of economic science. New York, NY: McGraw-Hill.

Lieberstein, S. H. (1979) Who owns what is in your head: Trade secrets and the mobile employee. New York: Hawthorne Books.

Lippman, Steven A. & McCardle, Keven F. (1987). Dropout behavior in R&D races with learning. Rand Journal of Economics, 18: 287-295.

Lippman, Steven A. & Rumelt, Richard P. (1982). Uncertain imitability: An analysis of interfirm differences in efficiency under competition. Bell Journal of Economics, 13: 418-438.

Mahoney, Joseph T. & Pandian, J. Rajendran. (1992). The resource-based view within the conversation of strategic management. Strategic Management Journal, 13: 363-380.

Markus, G. B. (1979). Analyzing panel data. Berkeley Hill. CA: Sage.

Miller, D. (1983). The correlates of entrepreneurship in three types of firms. Management Science, 29: 770-791.

Miller, D. & Friesen, P. A. (1984). A longitudinal study of the corporate life cycle. Management Science, 30: 1161-1183.

Mintzberg, H. & Waters, J. A. (1982). Tracking strategy in an entrepreneurial firm. Academy of Management Journal, 25: 465-499.

Mosakowski, E. (1991). Organizational boundaries and economic performance: An empirical study of entrepreneurial computer firms. Strategic Management Journal,12: 115-133.

Mueller, Dennis C. (1990). Profits and the process of competition. Pp. 1-14 in Dennis C. Mueller (Ed.), The dynamics of company profits. Cambridge, England: Cambridge University Press.

Murray, A. I. (1988). A contingency view of Porter’s generic strategies. Academy of Management Journal, 13: 390-400.

Penrose, E. G. (1959). The theory of the growth of the firm. New York: Wiley.

Porter, M. (1980). Competitive strategy: Techniques for analyzing industries and companies. New York, NY: Free Press.

—–. (1985). Competitive advantage: Creating and sustaining superior performance. New York, NY: Free Press.

Ravenscraft, D. & Scherer, F. M. (1982). The lag structure of returns to research and development. Applied Economics, 14: 603-620.

Robinson, R. B. & Pearce, J. A. (1983). The impact of formalized strategic planning on financial performance in small organizations. Strategic Management Journal, 4: 197-207.

—–. (1984). Research thrusts in small firm strategic planning. Academy of Management Review, 9: 128-137.

Rubin, P. H. (1973). The expansion of firms. Journal of Political Economy, 81: 936-949.

Rumelt, Richard P. (1982). Towards a strategic theory of the firm. Pp. 566-570 in R. B. Lamb (Ed.). Competitive strategic management. Englewood Cliffs, NJ: Prentice-Hall.

Schulze, William S. (1992). The two resource-based models of the firm: Definitions and implications for research, Academy of Management Proceedings: 37-41.

Shan, W. (1990). An empirical analysis of organizational strategies by entrepreneurial high-technology firms. Strategic Management Journal, 11: 129-139.

Simon, H. A. (1957). Models of man. New York: Wiley.

Staw, B. M. (1981). The escalation of commitment to a course of action. Academy of Management Review, 6: 577-587.

Stinchcombe, A. L. (1965). Social structure and organizations. Pp. 142-193 in J.G. March (Ed.), Handbook of organizations. Chicago, IL: Rand McNally.

Teece, D. J. (1980). Economies of scope and the scope of the enterprise. Journal of Economic Behavior and Organization, 1: 223-247.

—–. (1982). Towards an economic theory of the multiproduct firm. Journal of Economic Behavior and Organization, 3: 39-63.

—–. (1986a). Firm boundaries, technological innovation, and strategic management. In L. G. Thomas (Ed.), The economics of strategic planning: Essays in honor of Joel Dean. Lexington, MA: Lexington Books.

—–. (1986b). Profiting From technological innovation: Implications for integration, collaboration, licensing and public policy. Research Policy, 15: 785-805.

Tuma, N. B. & Hannan, M. T. (1984). Social dynamics: Models and methods. Orlando, FL: Academic Press.

Van de Ven, A. H., Hudson, R., & Schroeder, R. (1984). Designing new business start-ups: Entrepreneurial, organizational, and ecological considerations. Journal of Management, 11: 87-107.

Wernerfelt, B. (1984). A resource-based view of the firm. Strategic Management Journal, 5: 171-180.

Williamson, O. E. (1985). The economic institutions of capitalism. New York, NY:Free Press.

Wright, P. (1987). A refinement of Porter’s strategies. Strategic Management Journal, 8: 93-101.

COPYRIGHT 1993 JAI Press, Inc.

COPYRIGHT 2004 Gale Group

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