THE VALUE OF CAPABILITIES AND NETWORKS IN TECHNOLOGY START-UPS
YANFENG ZHENG UNIVERSITY OF WISCONSIN-MADISON Dept. of Management and Human Resources 975 University Avenue, Room 2261 Madison, WI 53706-1323 (608) 265-4843
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JING LIU CORNELL UNIVERSITY Dept. of Economics 404 Uris Hall Ithaca, NY 14853 (607) 255-2868
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GERARD GEORGE LONDON BUSINESS SCHOOL Regents Park, Sussex Place London NW1 4SA UK +44 (0) 20 7000 7000
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Contact Author: Gerry George
An earlier version of this paper received the Irene McCarthy Award for Best Paper at the Babson Entrepreneurship Conference, 2006.
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THE VALUE OF CAPABILITIES AND NETWORKS IN TECHNOLOGY START-UPS
ABSTRACT Empirical evidence suggests that an organization’s set of capabilities is a resource that influences performance. Similarly, firms seek out alliance partners to access information and other complementary assets. Most empirical studies confirm that a firm’s network characteristics impact its performance. However, both the capabilities and networks literatures tend to explore these issues independently and often focus on the static impact of internally-developed capabilities and externally-sourced network resources on performance. We examine the temporal changes in the relative impact of these resources by investigating the dynamic relationship between capabilities, networks and firm valuation with panel data from 156 biotechnology start-ups and a minimum distance estimation technique. We find that the marginal effect of network characteristics on firm valuation decreases while internal capabilities become more critical over time.
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The intersection of entrepreneurship and strategy research includes the investigation of factors that influence a start-up firm’s performance and the sustenance of these performance gains over time. Achieving superior performance in technology-intensive start-up firms relies on the successful development and deft execution of idiosyncratic capabilities such as product development capabilities (Cockburn, Henderson & Stern, 2000) or firm-level absorptive capacity (Cohen & Levinthal, 1990) among others. A capability is defined as a combination of organizational processes that confers upon management the ability to reliably achieve outputs of a particular type (Dosi, Nelson & Winter, 2000). The organizational capabilities literature is consistent with the resource based view in that the firm possesses a bundle of resources and that performance ultimately hinges on the deft deployment of these valuable internal resources. From an external perspective, the network and alliance literature posits that since information and other strategic resources are unevenly distributed in the market, firms need to reach outside their organizational boundaries to secure the resources critical to maintaining its competitive position (Burt, 1992). It is perhaps more imperative for start-up firms that tend to be resource-constrained and have limited access to industry information channels. As such, in order to perform well, start-ups have to occupy a favorable position embedded in the industry network to access information, resources, knowledge and even prestige (Powell, Koput & Smith-Doerr, 1996; Stuart, Hoang & Hybels, 1999). Taken together, the two theoretical perspectives have engendered significant academic discourse. Nonetheless, the vast majority of empirical studies position their empirical contributions and theoretical discussion using only one perspective. Eisenhardt and Schoonhoven (1996), in a study of partnering behavior of semiconductor start-ups, noted that the lack of social and strategic explanations would lead to an impoverished explanation of partnering behavior and its impact on firm outcomes. Even among the few studies that consider both network relationships and internal capabilities (Florin, Lubatkin & Schulze, 2003; Lee, Lee & Pennings, 2001), these studies are cross-sectional in nature, thereby limiting our ability to infer temporal causality. To address this issue, in this study we adopt an integrative and dynamic approach using both social and strategic explanations to investigate the relationship between internal
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capabilities, external networks and the valuation of the start-up firm, a key performance indicator for technology start-ups. Our study makes three contributions to the literature. First, we account for the complex relationship between internal capabilities, external networks and performance and offer an integrative view. Though empirical evidence suggests that both externally-acquired and internally-developed resources augment a start-up’s performance, our understanding of their relationship is still fragmented and the dynamism in the relative impact of these types of resources on a firm’s market value has not been systematically investigated. We address this critical gap in the literature by examining the relative temporal effect of organizational capabilities and network relationships on valuation in a sample of biotech start-ups 1 . Second, by analyzing the time varying effects, we also make significant methodological contributions. We obtain more accurate coefficient estimates of the influence of a startup’s internal capability and its external networks on valuation by using a dynamic panel model (Hsiao, 2003) and an advanced estimation technique --- minimum distance estimation (Chamberlain, 1984; Jakubson, 1991), thereby adding a temporal dimension to theories of resources in entrepreneurial firms. Third, we capture the financial valuation of a start-up company. Valuation is a fundamental measure of success for growth-oriented entrepreneurial firms. While most popular press prescriptions articulate strategies to increase valuation, this study proffers empirical evidence for the relative impact of both internally-developed and externally-sourced resources on the firm’s value. THEORY AND HYPOTHESES The central tenet of the resource-based view is that a firm needs to secure valuable, rare and inimitable resources in order to sustain a competitive advantage (Barney, 1991). Thus resource-based arguments assume that superior performance stems from managing the deployment of internal resources, for example, talented personnel (Zahra & Nielsen, 2002) or human resource management capabilities (Becker & Gerhart, 1996). These firm-specific resources, when deployed individually or collectively, contribute to the firm’s performance by increasing its efficiency and effectiveness in achieving 1
A start-up is defined as an independent business venture that is eight years or younger.
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organizational goals. Further, these resources are hard to acquire through the factor market and the causal link between them is complex and renders decryption efforts ineffective (Barney, 1986). The capabilities literature extends and complements the resource based view with the proviso that in order to achieve superior performance, the focal firm not only needs to secure idiosyncratic resources but also needs to develop corresponding capabilities (Helfat & Peteraf, 2003; King & Tucci, 2002; Nelson & Winter, 1982). For start-ups to impress their stakeholders such as investors who have the power to ascribe a particular economic value to the start-up, they need to demonstrate superior capabilities in generating proprietary knowledge or in developing and commercializing new products (Baum, Calabrese & Silverman, 2000; Stuart, 2000). A capability is also likely to exist independent of external perceptions of the market value ascribed to such resources. A start-up firm will be able to compete and survive if it possesses the requisite current internal capabilities to achieve its performance goals. While internal capabilities encompass several functional or organizational processes, we consider two specific capabilities of high technology firms: absorptive capacity and innovative capabilities. A firm’s absorptive capacity reflects its ability to acquire and assimilate knowledge (Cohen & Levinthal, 1990; Zahra & George, 2002). Studies in the biotechnology and pharmaceutical industries find evidence which suggests that absorptive capacity drives the firm’s ability to innovate and is a critical explanatory factor of performance (e.g., DeCarolis & Deeds, 1999). Absorptive capacity is often captured by a firm’s scientific human capital. Using a sample of biotech firms, Liebeskind and colleagues (1996) found that a firm’s ability to assimilate and create new knowledge stemmed from its “star scientists”. These scientists embody a start-up firm’s capabilities to acquire and assimilate scientific knowledge and were strong predictors of securing funding through venture capital or an initial public offering of stock (Baum & Silverman, 2004; Zucker et al., 2002, 1998). Therefore, we anticipate that a firm’s absorptive capacity is likely to be a fundamental driver of firm value. We distinguish between absorptive capacity and innovative capability of the firm. Innovative capability is the ability to generate new knowledge and create innovative products. Though a firm’s absorptive capacity and its ability to innovate are likely to be correlated, they are nevertheless
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independent. A firm’s innovative capabilities rely on its structures and processes to combine knowledge bases or transform previously assimilated knowledge (Kogut & Zander, 1992). For example, Lee, Lee and Pennings (2001) contended that owning a significant number of patents could be interpreted as a manifestation of the focal firm’s innovative capabilities. They also predicted and found a positive effect of innovative capabilities on firm performance using cross sectional data on 137 new technology ventures in Korea. Equity analysts and investors often evaluate the innovative capability of biotech firms to arrive at a valuation for subsequent rounds of investment (Gompers & Lerner, 2004). Therefore, we posit that: Hypothesis 1a: Absorptive capacity will positively influence valuation of a high technology start-up firm. Hypothesis 1b: Innovative capabilities will positively influence valuation of a high technology start-up firm. Besides the internal capabilities outlined above, the burgeoning network theory and strategic alliance literatures offer an alternative perspective that highlights the external network’s impact on performance. Some scholars contend that a firm’s performance cannot be understood without taking into account the network structure in which it is embedded (Gulati, Nohria & Zaheer, 2000). The overall network structure of the industry and the firm’s position within this network are now understood to have profound implications for firm performance. While multiple benefits have been associated with a firm’s external network, there are two fundamental benefits that accrue to the focal firm: first is the ‘transferred’ benefit and the second is the ‘perceived’ benefit, corresponding to the “pipes” and “prism” metaphor proposed by Podolny (2001). In Podolny’s view, a tie between two network actors can be interpreted as a pipe conveying resources/information between those two actors. In addition, the presence (or absence) of a tie between two actors is an informational cue on which others rely to make inferences about the underlying quality of one or both of the actors (Podolny, 2001:34). Drawing on this insight, we focus on two network characteristics -- network efficiency and ascribed network status that largely capture the two benefits respectively. First, the transferred benefit stems from the basic premise that knowledge, entrepreneurial opportunities and other strategic resources are unevenly distributed across a variety of players within the
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industry. This situation propels firms to rely on external information sources through alliance partners to complement internal knowledge or other functional capacities such as distribution networks (Hagedoorn & Schakenraad, 1994; Powell, et al., 1996). Thus the efficiency of a firm’s external network becomes critical because increasing the number of alliances without considering partner diversity will create a less efficient configuration that provides less diverse information capabilities at a greater cost than a smaller non-redundant set of ties (Baum, et al., 2000). A highly redundant configuration may even prevent a firm from obtaining novel information critical to its adaptation by limiting the number of links to firms in touch with emerging innovations (Uzzi, 1996). For biotechnology firms, network efficiency has an especially significant implication. Biotechnology is a rapidly changing and knowledge fragmented arena (Argyres & Liebeskind, 2002; Powell, et al., 2005). To be competitive requires not only developing effective in-house capabilities but also access to a variety of emergent opportunities and scientific ideas in an efficient way. Because network efficiency is linked to improved knowledge transfer, increased resource flow and access to business opportunities, we anticipate network efficiency to positively impact firm valuation. Second, relationships between firms serve not only to transfer information, knowledge or other strategic resources (pipes), but also imply status and prestige (prism). When faced with uncertainty and incomplete information about the focal firm, affiliations with prestigious partners may significantly increase the visibility and reliability of low status players (i.e., the start-up firm). Linkages with high status players could confer status to the focal firm and generate an “endorsement effect”. Stuart, Hoang and Hybels (1999) theorized that this endorsement effect may represent an assessment of quality and reciprocity within the relationship and thus allows outside stakeholders to ascribe value to these relationships based upon these social cues. They empirically tested this proposition with 301 U.S. biotech start-ups and found those firms having affiliations with prominent partners will outperform those without such relationships in terms of time to initial public offering of stock and raising more capital at their initial public offerings. Therefore, partnerships with high-status players will boost the start-up’s perceived
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status. As a result, the benefits of network ties for a start-up firm may likely translate into perceived financial value. Therefore, we posit that: Hypothesis 2a: Network efficiency will positively influence valuation of a high technology start-up firm. Hypothesis 2b: Ascribed network status will positively influence valuation of a high technology start-up firm. Our next question follows from hypotheses 1 and 2: what happens to the relationship between internal capabilities, external networks, and valuation when viewed through a temporal lens? The majority of studies implicitly assume a static relationship, and to our knowledge, we know of no studies that examine the impact of both internal capabilities and network characteristics on valuation using a dynamic temporal approach. Phrased differently, studies tend to assume that the relative effect of internal or external resources will remain constant over time. In this section, we hypothesize that the relative importance of capabilities will increase over time. First, if we assume that the capability (absorptive capacity and innovative capabilities) levels of the start-up remain unchanged, its impact on valuation may increase over time due to the fact that the external stakeholders may attribute greater value to the existing capability base because it has increased the probability of firm survival. The longer a firm survives, outsiders are more likely to perceive a greater degree of reliability to the firm’s activities (Hannan & Freeman, 1984) and may attribute such survival to its internal capabilities. Some researchers have argued that manifestations of scientific capabilities such as patents or publications act as credible signals of the firm’s capabilities and serve to increase the stakeholders’ perception of firm value (e.g., DeCarolis & Deeds, 1999). It is also possible that in uncertain markets, only after a threshold number of transactions can an outside stakeholder make more accurate evaluations of a firm’s quality (Nayyar, 1993). While studies posit that the status effect may vary with uncertainty (Podolny, 2001; Sanders & Boivie, 2004; Stuart, et al., 1999), our study extends this reasoning to suggest that when social cues are controlled for, true accounts and representations of internal capabilities, be it positive or negative, will become increasingly salient over time.
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While the preceding argument centered on the perception of internal capabilities by outside stakeholders, internal capabilities may become increasingly relevant for valuation even without the social cues. For instance, by investigating the histories of Borders and Barnes & Noble, two major book superstores, Raff (2000) demonstrated that both firms were able to develop their unique capability base by marginally refining their initial competencies. A capability developed early in the life cycle may yield performance benefits in the future. For instance, the customer database (used by Borders bookstore) may have had a marginal initial impact since the firm dealt with fewer products and operated within a limited geographic area. However, with increases in scale of operations and the geographic dispersion of store locations, the benefits from such an information management capability get multiplied. Continual refinements to the capability set may also lead to significant performance increases over time. In a study of patenting and licensing capabilities at a university technology transfer office, George (2005a) found that incremental changes to routines and processes underlying these capabilities provided a positive compounding effect on performance by reducing costs over time. Therefore, we anticipate that a start-up firm’s internal capabilities, whether unchanged or even incrementally changed, may have an increasing impact on valuation. More specifically, we anticipate both absorptive capacity and innovative capabilities to have an increasingly positive impact on firm value over time. A firm’s capacity to acquire and assimilate knowledge is likely to be cumulative because the size of the knowledge corpus increases with experience and possibly new human capital additions to the scientific team. That is, a firm may be able to leverage greater value from its enriched knowledge base than was feasible when the knowledge base was small. Similarly, innovative capabilities are also likely to increase in effect over time. The ability to innovate is also likely to benefit from experience gains because firms may search for new ways to exploit their existing knowledge base while searching for new product areas (Katila & Ahuja, 2002), which is likely to have positive implications for firm value. Therefore, we posit that: Hypothesis 3a: The positive effect of absorptive capacity on valuation of a high technology start-up will increase over time.
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Hypothesis 3b: The positive effect of innovative capabilities on valuation of a high technology start-up will increase over time. In contrast, we anticipate that the impact of network characteristics on valuation will decrease over time. First, the positive information or resource “pipe” effect of a firm’s network is likely to vary over time for the following reasons. First, ceteris paribus (for instance, network size) the transferred benefit from a network will decrease. Over time, firms may emphasize non-network based information sources such as public conferences, databases to complement the information transferred through interorganizational networks. For example, Haunschild (1994) speculated that the Fortune 500 companies might seek information on acquisitions from outside their board of director networks such as public media, which potentially downplayed the importance of their interorganizational network. Network efficiency may also decay because the benefit derived from information provided by these sources is likely to be transient and impermanent (Powell et al., 2005). By investigating 249 Italian television productions teams, Soda, Usai and Zaheer (2004) found that current structural holes rather than past ones have a larger positive effect on network performance. Given that most inter-firm relationships last longer than a year, the transient benefit of bridging an “information gap” may lapse or be rendered less valuable in a dynamic environment. Second, the value of endorsement or the associated halo effect is also likely to taper off as firms establish their own image in the marketplace and the linkages with prestigious partners may no longer be as critical as it was in the early stages of their life. Stuart et al. (1999) speculated that the endorsement effect may decrease or even evaporate after the young biotech firms had an initial public offering of stock. Grounded in signaling theory, Sanders & Boivie (2004) developed similar hypotheses and tested them empirically with 184 publicly traded Internet firms. Absent the historical record and/or objective data to verify, it is difficult for outside stakeholders to make judgments about the venture’s true value. In such situations, relationships with prestigious players could serve as a certification mechanism. Nevertheless, once the start-up develops its own reputation through frequent interactions with outside stakeholders or with the media, the endorsement or halo effect of network partners may decrease in its effect on firm
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valuation. We do not predicate that the absolute levels of network efficiency or ascribed network status per se will decrease over time but rather its impact on valuation. Our arguments speak to the marginal effects of such variables on firm valuation at different points of time. So, even though it is likely that start-up may streamline its network structure and be able to connect with more prestigious partners, the relative impact on valuation may decline as the novelty or importance of such relationships decrease over time. Therefore, we propose: Hypothesis 4a: The positive effect of network efficiency on valuation of a high technology start-up will decrease over time. Hypothesis 4b: The positive effect of ascribed network status on valuation of a high technology start-up will decrease over time METHOD Sample The biotechnology industry refers to the manipulation of genetic material through recombinant DNA technology, cell fusion and monoclonal antibodies. Under this definition, the industry covers the R&D and products in the human diagnostics and therapeutics segment (Powell, et al., 1996; Stuart, et al., 1999). The biotech industry represents an ideal research setting to test our hypotheses for several reasons. Studies have documented that forming strategic alliances has become a common practice in the biotech industry (Powell et al., 2005). Virtually every biotech firm was involved in at least one partnership with another biotech firm, pharmaceutical company or non-profit research institution. Also, firm capability in terms of assimilating state-of-art scientific knowledge and R&D is a critical factor for success. The coexistence and importance of internal capabilities and external networks for biotech start-ups make it an ideal research context for our study. Additionally, the past two decades have witnessed a phenomenal increase in interest in the biotechnology and pharmaceutical industries among both the academic communities and policy makers. As a consequence, data on biotech firm such as performance and partnering behavior have become readily available. In this study, we took multiple approaches to identify sample firms. In the first step, we checked Bioscan, a comprehensive biotechnology industry directory, to identify firms that were active in human
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diagnostics and therapeutics and founded between 1984 and 1992. We decided to choose firms born in this period of time because we were unable to validate information for older firms and obtain new firms’ information due to our panel data design. We also collected firm level data such as location and number of employees from Bioscan. This procedure yielded 247 US-based dedicated biotech firms (DBF). We complemented our data with other sources including The GEN Guides to Biotechnology Companies, LEXIS-NEXIS, SEC filings and company websites. Patenting information was collected from the NBER patent database, which provides comprehensive coverage on three million US patents issued since 1969 to 1999 (Hall, Jaffe & Traijtenberg, 2001). For alliance and valuation data, our primary data source was rDNA.com (formerly Recombinant Capital), a fee-based data access service and a leading biotechnology data provider. By matching data with each source, the final sample reduced to 156 DBFs. We tracked these firms for the first eight years of their life. From which, we created a 1245 firm-year panel data for model estimation2. To ensure the validity of our sample, we first compared our sample with those firms dropped due to lack of information on dimensions such as geographic area and firm size. Then, we compared our descriptive statistics on firm size and other variables with other biotechnology studies (Powell, et al., 1996; Stuart, et al., 1999) and found no significant differences. Dependent Variable Previous studies have documented that most DBFs sustain losses until product success is imminent (DeCarolis & Deeds, 1999; Powell, et al., 1996; Stuart, et al., 1999). In this context, accounting measures of profitability are ineffective indicators of performance. The conventional method is to treat other measures including patents, products under development as performance indicators (Baum, et al., 2000; Stuart, 2000). To test our hypotheses, we measure valuation as the total market value of the equity stock of the company. When fresh investments are made in the start-up, the investors revalue the firm, thereby providing us with a dynamic picture of firm value. The data are available for publicly traded firms because their stock is subject to frequent valuation by investment analysts. For privately held firms, rDNA.com compiled comprehensive valuation histories based on the start-up’s private placement or 2
Only three firms did not survive their 8th year.
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venture capital investment history. Valuation histories for both the publicly traded companies and private start-ups were computed in the same manner and were drawn from the same rDNA.com data source. To ensure consistency of the valuation data, we cross-checked a sub-sample of the firms with other data sources: Compustat for publicly traded firms and Venture Economics for privately held firms. Both sources confirmed the reliability of the rDNA valuation data used in this study. We used a logarithmic transformation to control for the skewness of the distribution. Independent Variables A firm’s internal capabilities are typically operationalized as either the human capital base or the size of its intellectual property portfolio. To be consistent with the literature, we compiled two measures to capture internal capabilities. Absorptive capacity is measured as the number of employees with advanced (PhD or MD) degrees in the firm at year t. This measure is similar to other measures such as the number of R&D staff. We use the scientific human capital in the firm as representative of a start-up firm’s capacity to acquire and assimilate knowledge (Baum & Silverman, 2004; Zucker et al., 2002, 1998). Given that these firms do not have prior routines or established processes for acquiring external knowledge, the scientists are likely to reflect a true capacity to absorb new knowledge. Innovative capabilities is measured by the number of patents weighted by citations received by the start-up firm prior to year t, consistent with other studies (Hoang & Rothaermel, 2005; Lee, Lee & Pennings, 2000). We determined the date of each patent by its application date, since the filing date (rather than grant date) more accurately reflects the ending of a specific invention endeavor. Patents are often used as indicators of innovative output. In the biotechnology industry, patents are intermediate outcomes and are reflective of a firm’s ability to generate new inventions. We constructed two measures to capture the corresponding network characteristics. First, we measured Network efficiency with a Herfindahl index of heterogeneity in types of partners (Baum, et al., 2000). Network Efficiencyit = [1 - ∑ij (PAijt)2]/NAit
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where PAitj is the proportion of all startup i’s alliances that are with partner type j at time t, and NAit is startup i’s total number of alliances at time t. A start-up with six alliances, two with pharmaceutical firms, two with hospitals, and one with universities would score [1- (2/5) 2+(2/5) 2+(1/5) 2]/5 = 0.124. We operationalized Ascribed network status with the number of agreements 3 with prestigious partners defined as large pharmaceutical companies, major research universities and prestigious non-profit research institutions. Following a common practice in alliance research (Bae & Gargiulo, 2004; Podolny, Stuart & Hannan, 1996), both network-based variables are counted on a 3-year backward moving window rather than an annual basis to account for the duration of each alliance. We also conducted sensitivity analyses to ensure that our results were robust to different specifications such as two and four year time windows. Controls Firm size: Ceteris paribus, larger firms may possess greater levels of slack resources such as administrative support that the firm can deploy to achieve higher performance (George, 2005b). Size may also pose as a constraint on the firm’s ability to form or manage alliances (Park, Chen & Gallagher, 2002). Smaller firms typically cannot maintain several strategic partners simultaneously and could possibly have reduced chances of learning through these network relationships. We use the log value of number of non-PhD full-time employees as an indicator of size. Firm Age is also a variable of interest. Firm age may embody important learning mechanisms that need to be controlled in our study. If we take the interaction approach, it is also necessary to include this variable to make a meaningful interaction (Jaccard & Turrisi, 2003). We use the number of years from the date of incorporation as a measure of age. Technological field: Though firms in our sample are all biotechnology firms, they competed in different niches within this industry. To account for sectoral differences, we included indicator variables representing participation in various segments of biotechnology. Following Stuart et. al. (1999), we
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Note that we do not normalize the number with the network size since our theory about borrowed status actually is on an absolute metric, not a relative one, such as efficiency.
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included four categorical variables to indicate whether the start-up firm operated in any of the four segments: Genetic Engineering, Protein Engineering, Immunology and Diagnostics. Public company: A significant number of firms in our sample experienced an initial public offering of stock during their first eight years of their existence. Since a firm’s valuation is substantially different between public and private firms, we introduced a dynamic categorical variable that indicated whether a company was public in a specific year (1 = public; 0 otherwise) to control for the effect of capital market access on firm valuation. Geographic area: Researchers argue that agglomeration economies and knowledge spill-over effects may be important in developing capabilities in the biotechnology industry (DeCarolis & Deeds, 1999; Owen-Smith & Powell, 2004). The Bay and Boston Metro areas account for almost 40% of USbased biotechnology firms. Not surprisingly, geographic access may have a substantial impact on knowledge flows, such as the possibility to meet potential exchange partners and recruit key personnel to develop a capability. To control for this geographic effect, we create two categorical variables to indicate whether a start-up is located in either Bay or Boston areas. Market condition: The biotechnology industry is also sensitive to stock market conditions. Optimal stock market conditions provide easier access to resources and strong incentives to develop capabilities or networks that increase firm valuation. Conversely, an adverse market may force technologically competent start-ups to have deflated value. We use the biotechnology stock market index (Lerner, 1994) to control for the impact of market conditions on valuation. Analysis In order to empirically test hypotheses 1 and 2, we organized a firm-year dataset and used a panel regression technique. To account for the possibility that observationally equivalent firms might differ on unmeasured characteristics, either fixed-effects or random-effects generalized least squares (GLS) models are appropriate. Though the Hausman test (1978) revealed a significant difference between the fixedeffects and random-effects model, we opted to use the random-effects model because: (a) the results from both models are almost identical even in the interaction terms and the trivial differences are possibly
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manifest as statistically different due to the large panel size (Hausman, 1978; Hsiao, 2003), and (b) to be consistent with previous studies, we included fixed (categorical) variables such as geographic and segment dummies, which is only feasible in the random effects model (Halaby, 2004). To test hypotheses 3 and 4, it is not feasible to use ordinary least squares models or a similar technique on each year’s data. If we estimated coefficients using annual data spells, it would be the equivalent of treating each stage (year) as independent and ignores the possibility that the stages might be correlated. One viable option is to introduce an interaction term between age and our key independent variables in the panel regression models (Jaccard & Turrisi, 2003). The interaction approach, though straightforward, restricts the interaction relationship and holds the slope constant. To address this issue, we use the interaction term with age in a random effects model but complement the results with a dynamic panel model, estimated by a regression technique called Minimum Distance Estimation (MDE). This panel regression is an extension of traditional panel models in that it retains the benefits of controlling for heterogeneity and thus makes unbiased and efficient estimates (Hsiao, 2003). More importantly for this study, the MDE allows the coefficients of independent variables to change over time and is also able to test the latent variable’s effect on the dependent variable (Chamberlain, 1984; Jakubson, 1991). The basic specification for the dynamic panel model is:
yit = β t ,0 + β t ,1 xit ,1 + ... + β t ,k −1 xit ,k −1 + γ t Ait + δ t cit + ε it , ∀i, t Where ‘x’ denotes independent variables, vector βt stands for the coefficients of ‘x’ at time t. Similarly, ‘A’ is the network measure (in our case, efficiency or status) and γt will reveal their effect on valuation over time. A special variable is the variable ‘c’, which was introduced in our model to capture the latent firm specific effect (Halaby, 2004; Jakubson, 1991) and its coefficient vector δt is functionally equivalent to βt and γt. We separate them deliberately because they are estimated differently due to the nature of this variable. For more details on estimation, please refer to the Online Supplement to this article. RESULTS
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Table 1 reports the descriptive statistics and correlation matrix for the variables. The panel regression of the internal capabilities and network characteristics’ impact on valuation is reported in Table 2. We entered the control variables in model 1 (Table 2) and entered the relevant variables to test our hypotheses in subsequent models. The overall models were significant and the variables of interest increased the overall model fit (Chi-square = 596.9, R2 = .33, p
