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Dual Signals: How Competition Makes or Breaks Interfirm
Social Ties
Denis Trapido,
To cite this article:
Denis Trapido, (2013) Dual Signals: How Competition Makes or Breaks Interfirm Social Ties. Organization Science 24(2):498-512. http://dx.doi.org/10.1287/orsc.1120.0740
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Vol. 24, No. 2, March–April 2013, pp. 498–512
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Dual Signals: How Competition Makes or Breaks
Interfirm Social Ties
Denis Trapido
The Paul Merage School of Business, University of California, Irvine, Irvine, California 92697-3125, dtrapido@uci.edu
R
esearch has documented the benefits of social ties across boundaries of competing firms but has not specified when competition enables such ties or when it damages them. Ninety semistructured interviews sought to elicit answers to this question from leaders of drug development companies in the San Francisco Bay Area. The informants reported withholding social ties from counterparts in competing companies if these companies affirmed to them the goal conflict aspect of the competition relation; they reported social connectedness to individuals in competing companies if these companies affirmed to them joint professional affiliation, the other necessary aspect of competition. Unique quantitative data on competition and social relations in the Bay Area’s drug development industry confirmed this pattern for weak social ties (acquaintance). Strong social ties (friendship) were not affected by any examined organizational or interorganizational factors.Key words: competition; acquaintance; friendship; signals; collegiality; conflict of goals; drug development History : Published online in Articles in Advance April 27, 2012.
Introduction
It is puzzling that economic competition can affect social ties across organizational boundaries in opposite ways. In some cases, senior managers avoid social relations with colleagues in competing organizations or develop antagonistic relations. But in other cases, competition between organizations has the opposite effect: it stim-ulates competitors’ social ties. All situations between these extremes are also possible.
Theory and research do not offer a ready explana-tion of such different outcomes. Scholarship on the origins of relations among competing industry mem-bers has focused on economic ties, i.e., relations that serve as means to obtainment or exchange of economic resources. Typical examples are joint ventures (Pfeffer and Nowak 1976), investment syndicates (Sorenson and Stuart 2001, 2008), and alliances (Gulati 1995, Beckman et al. 2004, Ozcan and Eisenhardt 2009). Stud-ies designed to explain economic competitors’ social ties, i.e., interpersonal relations defined by noneco-nomic content, such as acquaintance and friendship, are lacking.1 This knowledge gap is conspicuous in view of the solid evidence of the benefits of social ties to competing organizations. The executives’ social ties to colleagues in competing organizations improve their organizations’ performance because of efficiency gains from better coordination and resource allocation (Ingram and Roberts 2000) and from informal transfer of knowl-edge (Saxenian 1994, Almeida and Kogut 1999).
To develop an explanation of the varying effect of competition on social ties, this study used semistructured
interviews with leading figures of competing companies in the San Francisco Bay Area drug development indus-try. The interviews invited the drug developers to explain why they extended social ties to colleagues in some competing companies but not in others. The informants suggested a dual-signal mechanism of the emergence of ties: they reported withholding social ties from col-leagues in competing companies if they sensed that these companies affirmed the goal conflict aspect of the com-petition relation; affirmations of joint professional affil-iation, on the contrary, elicited social ties. I tested this mechanism with original relational data that cover nearly two-thirds of the active drug development companies in the region. This quantitative analysis reproduced the pat-tern of dual signals suggested by the respondents’ narra-tives only for weak ties (acquaintance). Strong social ties (friendship) were not affected by any examined organi-zational or interorganiorgani-zational factors.
Theories of Origins of Ties
A limited number of established theoretical logics have guided the study of the origins of economic and social ties between organizations. The logic of resource dependence expects organizations to establish economic ties that enable beneficial combination or exchange of resources (Pfeffer and Salancik 1978, Foa and Foa 1980, Davis et al. 1990). Resource dependence has informed a rich literature on the formation of business alliances, which has explored various types of resources obtained through interorganizational connections, notably status
498
(Stuart 1998, Stuart et al. 1999) and dynamic capabil-ities (Anand et al. 2010). The long-established, consis-tently supported logics of the formation of social ties are homophily, i.e., the tendency of similar actors to pair (Verbrugge 1977, Kandel 1978, Mark 2003), and indirect connections, which generate ties due to per-sonal introductions (Heider 1958, Taylor 1970, Obstfeld 2005). The theory of social foci (Feld 1981, 1982; Sorenson and Stuart 2008) critically reexamined the role of homophily and indirect connections, suggesting that most ties emerge as a result of people’s coinvolvement in social settings such as organizations or physical space.
These four accepted logics of tie formation do not explicitly specify how competition between organiza-tions affects personal social ties between them. The logic of resource dependence explains ties by reference to their economic utility, which makes it poorly applica-ble to social ties: by definition, full-fledged social ties may exist without having economic content. The three common logics of the formation of social ties were designed to explain the ties’ origins rather than spec-ify the effect of competition on ties. This effect can only be inferred indirectly. As competition necessarily involves a similarity of organizational features (products, markets, technology, size, location), executives of com-peting organizations are more likely to share personal similarities than executives of noncompeting organiza-tions. Competition also makes the executives more likely to have a history of joint involvement in social settings such as educational programs, firms, and professional associations. Because these social settings involve mul-tiple people, executives of competing organizations are likely to share multiple connections to third parties who may introduce them. The three explanations thus imply that competition facilitates social ties. The empirical sta-tus of this inferred implication is, however, uncertain. The explanations evidently fail to account for instances when social relations between companies’ senior figures sour as competition between the companies intensifies.
The other aspect that limits the applicability of the logics of homophily, indirect connections, and shared settings to social ties among competitors is these log-ics’ inability to account for the possible nonsymmetry of ties. Whereas the resource-dependence paradigm read-ily embraces asymmetric economic relations, viewing them as products of asymmetric resource needs (e.g., Stuart 1998), the theories of social ties were designed to account for symmetric relations. These theories apply poorly to situations when company A extends a tie to a competitor, but the competitor reciprocates with another type of tie or none at all.
Table 1 summarizes the problems with the application of the established theories of ties to understanding the effect of competition between organizations on interor-ganizational social connections.
Table 1 Competition and Social Ties Between Firms: Applicability of Theories
Theory explains
How competition Asymmetric Theory Social ties affects social ties ties
Resource No No Yes
dependence
Homophily Yes Implicitly No Transitivity Yes Implicitly No Shared settings Yes Implicitly No
The Empirical Context: The San Francisco
Bay Area’s Drug Development Industry
Because existing theory provides insufficient guidance in answering the question that motivates this study, I used grounded theory-building techniques (Glaser and Strauss 1967, Uzzi 1997) to inductively develop the answer in the context of the San Francisco Bay Area’s drug devel-opment industry.
The history of the industry began in the 1950s when Syntex, a Mexican pharmaceuticals company, started relocating its operations to the Stanford Research Park. It moved to Palo Alto, California, in 1964 and existed independently until Roche acquired it in 1994. Syntex became a major incubator of the region’s scientific and entrepreneurial expertise. Alejandro (Alex) Zaffaroni, the executive vice president of Syntex, left the com-pany to found ALZA, the first pharmaceutics (more pre-cisely, drug delivery) start-up in the region, in 1968. Zaffaroni became a legendary serial entrepreneur, subse-quently founding Affymax, Affymetrix, Alexza, DNAX, Maxygen, SurroMed, and Symyx, as well as mentoring founders of many other companies.
The industry received a second impetus from the suc-cess of Genentech. Founded in 1976 by venture cap-italist Robert Swanson and biochemist Herbert Boyer, Genentech had grown to 10,000 employees by the sec-ond half of the 2000s. Former Genentech employees founded tens of start-ups, creating a vibrant drug devel-opment community around the company’s headquarters in South San Francisco and elsewhere in the area. This growth made the local drug development cluster the largest in the world, which it has since remained. The dense web of local ties was essential to the cluster’s suc-cess (Powell et al. 2012).
The drug development industry has an extraordinar-ily long, expensive, and low-profit product cycle. In the mid-2000s, it took an average of 14 years and $800 mil-lion to develop a drug candidate into a marketed product approved by the Food and Drug Administration (FDA) (BayBio 2005, p. 18). Of drug candidates that enter clin-ical trials, 80%–90% fail the trials (Pisano 2006). As a result, at any given time, most drug development compa-nies generate no revenues, and few are profitable. In fact, the biotechnology industry has been operating at a loss
ever since it emerged. Public biotech companies taken as a whole were slightly profitable in the mid-2000s only because of the success of Amgen, but including private companies would drive profit figures to the red (Pisano 2006). The low profitability has not prevented the indus-try from expanding and succeeding scientifically, nor has it stalled the growth of investment in it.
The intensity of competition between drug develop-ment companies varies on a wider continuum than in most industries. On the low end, there is hardly any competition except abstract common interest in capital. On the high end, competition is an all-or-nothing con-test. Whereas in most industries nothing prevents simi-lar competing products from generating revenues, safety concerns make the FDA reluctant to approve new drugs unless they are superior to existing ones. Companies may therefore be challenged to beat competitors on time or quality lest their products never reach the market.
Data Collection
I extracted records of drug development companies in the San Francisco Bay Area from the Genetic Engi-neering News (GEN) Biotechnology Database. The San Francisco Bay Area was defined as the urban area reaching from Marin County and Richmond in the north to San Jose in the south. Three months before the first interview, the GEN database listed 316 biotechnology companies in this region, most of them not involved in drug development. I narrowed the list down to 125 com-panies that (1) had drug development operations (alter-natives include clinical trials services, lab services, and agricultural applications) and (2) worked on innova-tive products (as opposed to entering new markets with existing drugs).
I conducted 90 semistructured face-to-face interviews, 77 of them with senior executives, over a period of 14 months. The structured part of the questionnaire was filled during the interviews; I wrote summaries of the informants’ explanations of social ties to competitors during or immediately after the interviews. At the end of each interview, the informant was asked to refer the interviewer to potential informants in other drug development companies. Each potential informant was emailed an interview request and up to two additional requests if there was no response. The average number of referrals was 3.5, of which, on average, 0.94 led to an interview. As interviewing progressed, the proportion of people already interviewed among the new referrals steadily increased. The interviewing stopped when all referrals had been interviewed or remained unrespon-sive after three emails. At that point, 78 drug develop-ment companies, 62.4% of the target population, had been covered. In 9 companies out of 78, there were two informants. For these companies, the social ties of the informant in the more senior formal position entered the analysis. Table 2 presents the basic characteristics of the sample.
Table 2 Characteristics of Bay Area Drug Development Companies and Informants
Total informants 90
Senior executives (C-level, presidents, vice presidents) 77 Of them, company leaders (CEOs or presidents) 53
Nonsenior executives 2
Senior scientists 11
Women 14
Of them, company leaders 6 Population of companies 125 Of them, those that ceased to exist during interview period 12 Total companies covered by interviews 81 Of them, drug development companies 78 Companies covered by interviews as % of population 62.4
Among companies active throughout interview period 63.7 Among companies that ceased to exist during 50
interview period
Competition and Its Effects on Ties in
Company Leaders’ Narratives
Forms of Competition in Drug Development: Area Contact and Indication Contact
The interview respondents were asked if their company had competitors.2 All but one answered affirmatively, and they were then asked to explain why the companies that they had in mind were competitors. Two forms of competition in the industry recurred in the informants’ answers: working in the same area (area contact) and developing treatment for the same medical condition (indication contact).
The notion of “our area” or “areas” (sometimes also “our niche”) was central to the companies’ identity, and its meaning was subjectively clear to the informants— they defined “area” by the similarity of the expertise required to develop drugs and the similarity of the indications, i.e., medical conditions that the drugs are designed to treat. For example, to explain why a certain company is a competitor, an executive said, “We are in the same area with them; we both work on autoimmune diseases.”
Area contact between two companies arises in the pre-clinical and early pre-clinical stages of drug development. In these stages, developers often test drug candidates for multiple indications. It does not become clear until late in clinical trials if a candidate is effective against any indication. As drug candidates progress through clini-cal trials, most show no effect and fail. Then, unless the companies have other development programs in the same area, area contact between them disappears. Alter-natively, clinical trials may lead two companies to focus on related but different indications, e.g., breast cancer and liver cancer. Competition as a result of area con-tact then remains, but the products will not compete on the market. The third option is that the two companies focus on drugs designed to treat the same medical condi-tion. Then they enter into indication contact and become potential market competitors.
There are two consequential differences between area competition and indication competition. The first differ-ence is in the deliberateness of entry. Entering a drug development area is always a conscious, strategic deci-sion. Founders make it when they start a company; later, the company may start projects in new areas (nearly two-thirds of the companies in this study have done this). In contrast, entering an indication is largely beyond the companies’ control. Discovery of a promising indication is a rare success, only partly determined by deliberate efforts; largely, it is a matter of chance. Serendipitous discovery of indications, famously exemplified by peni-cillin and sildenafil citrate (Viagra), is common. Second, indication competitors are few: among the companies in the sample, there were, on average, over 10 working in each area but only 2 working on each indication. This makes indication contact a personalized, focused setting, in which competitors are more keenly aware of each other than in area contact.
Notably, area contact and indication contact do not differ in the potential to generate competition: the matrices of area contact and indication contact are equally strongly correlated with the matrix of reported competition relations. Even if counterintuitive, this is understandable. Competition for a number of important resources in drug development—capital, labor, intellec-tual property, and patients in clinical trials—depends weakly, if at all, on whether the companies are work-ing in the same general area or on the same indication. It is even common for companies in the same area to become a greater competitive nuisance than indication competitors, e.g., if they hoard patents to restrict area competitors’ access to technologies.
How Competition Attracts Social Ties: “Our Colleagues”
The informants were aware of the question motivating the study, and their narrative explanations of social ties to competitors came at various points during the inter-view. The interviewer’s oral introduction of the study prior to the interview was one common trigger. Expla-nations also came as reactions to two open-ended ques-tions deliberately designed as triggers. The first question asked the informants to describe their relations with a competing company that their own company cooperated with, and the second question asked the informants to describe their relations with a company that they would “probably never cooperate” with. Unlike the term “col-laboration,” which is reserved for contractual relations, in the industry, “cooperation” refers to informal mutual help based on social ties, such as information sharing.
Most interviewed drug developers portrayed positive social connectedness to area competitors and indica-tion competitors as the norm. They used the expression “our colleagues” to refer to competitors with whom they maintained social relations. The term was used in its
literal meaning to denote membership in the same pro-fession. It conveyed the salience of shared professional identity in the perception of the competitor. The markers of shared professional identity in the informants’ narra-tives varied. A typical identity narrative referred to joint adherence to the noble ethos of the industry. As a pany’s president summarized, “We have a sense of com-munity. We feel that our business has to do with moral issues, so competition does not get as bitter and personal as [in electronics or Internet].” Several informants men-tioned that they and the leader of a competing company both had Zaffaroni as their “mentor.” Some expressed sympathy toward competing colleagues and admiration for their work.
When Area Contact Inhibits Social Ties: Targeted Challenge by Newcomers
When drug developers explained reluctance to socially connect to area competitors, their narratives had a com-mon theme: they mentioned a competitive challenge that they perceived as deliberately targeted at their company. The informants’ common pejorative expression describ-ing such a challenger, “bandwagon jumper,” referred to a newcomer company that joins an area it considers “hot,” or promising, and thereby directs its competition at the incumbents in the area. Regardless of the newcomer’s intentions or the actual competitive threat, incumbents tended to perceive the newcomer’s entry as a competi-tive move targeted specifically at them. Newcomers, in contrast, experienced no such deliberate challenge from the incumbents; as a result, their collegial relations with the incumbents were not compromised by area contact.
The newcomers’ and the incumbents’ different percep-tions of area contact and the effect of these perceppercep-tions on social ties recurred in the informants’ narratives. Victor Genetics, a company in a small noncompetitive area, provides a telling illustration. According to its chief executive officer (CEO), “[Victor’s] niche is not com-petitive at all because [companies in it develop] new technology 0 0 0 0 When a technology is so young, you are most likely to end up having complementary results and products.” Victor Genetics never succeeded in taking a product to the market. Yet its reputation is good, and it keeps attracting generous investment, largely because of the consensus that its expertise is hard for any poten-tial newcomer to match. The absence of direct com-petitive challenge did not prevent Victor from suing a new entrant to the area. At the time of the interviews, this recent lawsuit was widely discussed in the industry. A leader of another company recounted, “Victor Genet-ics knew that they would lose the case, and they did. But that company was smaller and weaker than Victor, and the litigation ruined it.” Two informants independently confirmed the assessment of the CEO of Victor Genet-ics that there was no competitive threat in the area. That a won lawsuit ruined the newcomer company suggests
that it was not a formidable competitor either. The attack on it was an incumbent’s disproportionately aggressive reaction against a newcomer who had entered the area, not a solution to a real problem.
In another situation, an area incumbent reported avoiding positive social relations with the newcomer but no negative relation. This is a more typical, less drastic response to what is perceived as a targeted challenge. A founder of Skylark Biosystems reported, “There was a venture capitalist on our board who used what he learned at Skylark to help found another company 0 0 0 0 Now we don’t talk to people in that company.” The new company indeed had a recent program in the same area with Sky-lark. However, the report that it was founded to develop a competing drug candidate was inaccurate: its two ear-liest drug candidates were in a different area. It is thus unclear whether the information leakage by the venture capitalist indeed occurred; it may be a suspicion mag-nified by the anxiety of facing an unexpected compet-itive challenge. Again, as in the previous example, the antagonism was asymmetric: the president of the newer company reported having a friend and an acquaintance at Skylark.
The interviews thus showed a dual effect of area con-tact on social connectedness. On one hand, comember-ship in an area created positive collegial relations. On the other hand, when executives perceived a competitive challenge by a new area entrant, targeted specifically at their company, their reactions ranged from withholding positive social relations to hostility.
When Indication Contact Inhibits Social Ties: Competition for Status
When indication contact arises between two companies, they have typically been in area contact for years, and the distinction between the area incumbent and the new-comer has faded. Because of the remoteness of the entry into area contact and the large role of chance in creating indication contact, the notion of deliberate challenge by newcomers rarely applies to indication competitors in the San Francisco Bay Area drug development industry.3 The informants pointed out a different mechanism that damages social ties to indication competitors, which involves status competition.
Because indication competitors are few and their suc-cess may inflict severe damage, company leaders closely observe indication competitors’ moves. The informants reported reluctance to socially connect to indication competitors who, in their observation, use competition with their company to claim peer recognition, or status. The following examples give a sense of the variously phrased reports of competition for recognition or status. When asked why he had no informal relations with the indication competitor he had named, a senior executive explained, “We promise little; we don’t tout our achieve-ments. [They] like to make much noise about everything
they do.” A senior scientist of a relatively old and suc-cessful company summarized his answer to the same question: “We are proud if we do good science. [They] are proud if they beat us.” A CEO of a company working on multiple sclerosis commented, pointing at the build-ing of the indication competitor across the parkbuild-ing lot: “We don’t like them because they are arrogant. They never miss a chance to show off.” A university profes-sor, involved with multiple drug development compa-nies, observed, “I have seen many personal relationships in biotech destroyed by struggles for respect.”
The interviews thus suggested that indication contact, similar to area contact, has a dual potential to facilitate as well as inhibit social ties. Although indication con-tact was normally marked by positive collegial relations, company leaders reported reluctance to extend social ties to indication competitors who, in their perception, regarded competition with their company as a means of gaining status within the industry.
A Theoretical Conceptualization:
Social Ties as Reactions to Dual Signals
The effects of competition on social ties that fea-tured in drug developers’ narratives manifest a pattern, which will henceforth be referred to as dual signals. This section specifies how dual signals account for the dual socially bonding and socially alienating effect of competition.
The Duality of Competition: Collegiality and Goal Conflict
An established tradition in organization research (McPherson 1983, Burt 1992, Bothner et al. 2007, Ingram and Yue 2008) defines competition as the sepa-rate pursuit of the same scarce resources. This definition, which I adopt, regards competition as a relation. Com-petition is rarely zero-sum because it generates shared benefits such as legitimacy and mutual learning (Barnett and Hansen 1996). Scarcity of the pursued resources, a defining feature of the competition relation, nevertheless implies that both parties of the relation are unable to obtain resources in desired amounts. Competitors’ goals are, to a varying extent, incompatible, and goal con-flict is an inherent component of the competition rela-tion. A collegial relation is the other integral (albeit not sufficient) relational component of competition. The collegial relation exists because of joint profession-related affiliations—working on similar problems; joint belonging to professional associations; or sharing simi-lar expertise, standards of excellence, business practices, and professional identity.
By definition, competitors necessarily share a goal conflict relation and a collegial relation. Yet the extent to which the competitors affirm each of the two aspects of the relation may vary. The drug development executives’
comments reflected this variation. Joint belonging to a professional community, sharing the same knowledge, and adherence to the same professional standards are collegial aspects of competition. By mentioning these or similar aspects to explain why they had social ties to counterparts in a competing company, the informants were reporting that this company affirmed the collegial element of the competitive relation. When the informants explained why social ties to a competing company were absent or antagonistic, they mentioned how this com-pany affirmed the goal conflict aspect of competition, typically by targeting the competitive challenge at the informant’s company or by staking its status on surpass-ing the informant’s company in competition.
I will use the term “signals” to refer to affirmations of either of these two aspects. Specifically, a signal of goal conflict affirms to a competitor the incompatibil-ity of the signal origin company’s and the competitor’s goals; a signal of collegiality affirms the signal ori-gin company’s and the competitor’s shared professional affiliation.
A remarkable feature of interorganizational signals examined here is that they may travel without being intentionally communicated. Instead, firm leaders may obtain them by monitoring competitors. Such active obtainment of information, although underplayed in sig-naling models in economics (Riley 2001, Spence 2002), is a central insight of sociological theories of the mar-kets (White 1981, Porac et al. 1995, Podolny 2005). As White (1981, p. 518) summarized, “[W]hat a firm does in a market is to watch the competition in terms of observables.” Sociological theories of the markets imply that, because information about competitors may travel as a result of organizations’ monitoring efforts rather than as a result of intentional signaling, signals are not necessarily actions. They may be any observable
Figure 1 The Theoretical Model of Dual Signals
Company X
Company A competing with X Company B competing with X
Top executives Collegial signal at t0
Goal conflict signal at t0
Social tie between company leaders at t1
Note. To improve readability, the signals sent by Company X and the ties received by its executives are disregarded.
attributes of competitors that organizations’ leaders mon-itor and that affect these leaders’ behavior.
Figure 1 summarizes the effect of dual signals on social ties between executives of competing companies that the interviews suggested. The figure shows that signals of collegiality elicit social ties from recipients of the signals to individuals in the signal origin com-pany; signals of goal conflict inhibit such ties. The figure also conveys that signals are an intercompany phenomenon, usually shaped by competing companies’ top management teams. The social ties that the signals affect, in contrast, are interpersonal: they are necessar-ily between specific individuals within companies. Note that both social ties and signals may be asymmetric.
The effects of dual signals summarized in Figure 1 may also be presented as a pair of hypotheses.
Hypothesis 1. A company executive is likely to have social ties in a competing company to the extent that the competing company affirms the collegial aspect of competition.
Hypothesis 2. A company executive is unlikely to have social ties in a competing company to the extent that the competing company affirms the goal conflict aspect of competition.
Mechanisms
Existing theory specifies mechanisms that may under-lie this dual effect. Homophily, i.e., the tendency of perceived similarity to stimulate social bonding, offers a simple explanation of the positive effect of colle-gial signals on social ties. By definition, collecolle-gial sig-nals increase the signal recipients’ awareness of shared professional affiliation with the source of the signals. Because shared professional affiliation is a type of simi-larity, the increase in perceived similarity resulting from collegial signals elicits social ties. If the collegial signals
are asymmetric, so will be the ties that they elicit. To the extent that homophily attracts executives to the same people and into the same settings, collegial signals may also trigger the other two mechanisms of tie forma-tion discussed above, the transitivity mechanism and the shared settings mechanism.
Research also offers insights into the mechanisms that underlie the damaging effect of signals of goal conflict on social ties. Labianca et al. (1998) showed that per-ceived conflict damages social ties by eliciting delib-erate avoidance of contacts in organizational units that individuals perceive to be in conflict with their unit. Such avoidance may be more common when the per-ceived conflict is between organizations rather than units because there are fewer interdependencies between orga-nizations that necessitate contacts.
The two particular signals of goal conflict that fea-tured in drug developers’ accounts are known to elicit negative reactions that are poorly compatible with social ties. A newcomer’s entry into a niche creates “competi-tive tension,” i.e., anxiety combined with keen awareness of which competitor is its source, among the executives of the companies already operating in the niche. The executives react to competitive tension with intensified attacks on the newcomer’s business (Chen et al. 2007). Such reactions to intruders may be at odds with eco-nomic rationality because they result from an impulse to protect the firm’s identity rather than from prag-matic considerations (Livengood and Reger 2010). Sta-tus rivalry, the other common signal of goal conflict in drug development, makes the parties avoid infor-mation originating from rivals who threaten their sta-tus (Menon et al. 2006) and withhold information from such rivals (Pettit 2011). Such reluctance to exchange information is particularly likely to harm weak relations because information exchange constitutes a larger part of their content.
Testing the Effect of Dual Signals on
Social Ties
The structured section of the interviews in drug devel-opment companies generated systematic data for test-ing the effect of dual signals on social ties. To support the logic of dual signals presented above, the test must establish all of the following: (1) informants in com-panies exposed to collegial signals are more likely to report, at a later point in time, social ties in companies where the signal originated; (2) informants in compa-nies exposed to signals of goal conflict are less likely to report, at a later point in time, social ties in companies where the signal originated; (3) these effects persist net of the control variables that may simultaneously cause the signals and the ties; and (4) these effects are not due to the causal precedence of the ties over the signals.
Measures
The intercompany signals and the social ties reported by the informants entered the analysis as 78 × 78 net-work matrices. The analysis also includes attributes of network members, i.e., variables in the usual sense. The exponential random graph models used to analyze the data (see the statistical model subsection) can estimate the effects of variables as well as networks. “Covariates” is a convenient covering term referring to variables and matrices.
The Dependent Matrices: Networks of Reported Social Ties. To record the networks of intercompany social ties, each informant was given the full list of 125 drug devel-opment companies in the region and was asked to indi-cate those where he or she had ties. This method copied that of Ingram and Roberts (2000), who captured interor-ganizational friendships of one senior executive from each organization in a regional industry. If the infor-mant reported a tie, 1 was entered in the matrix cell at the intersection of the ego’s row and the alter’s col-umn; 0 was entered otherwise. The analysis models two types of ties, acquaintance and friendship. The distinc-tion between the two reflects tie strength and makes sure that friendship involves personal affection. The question-naire instructed the informants to consider the two types of ties as the same, except that friends “regularly spend time together for other reasons than work.” To consider whether the effect of signals depends on whether or not friendship predated economic competition, I replicated all analyses with friendship that began after the infor-mants joined their current companies. Making this dis-tinction for acquaintance was impossible because most informants could not recall when their acquaintance relations began.
Intercompany Competition: Networks of Area and In-dication Contact. The measures of competition between drug development companies (and of its split into dual signals) were designed to capture the informants’ accounts of area competition and indication competition summarized above. The area contact covariate is a sym-metric adjacency matrix coded as 1 if the row and the column company have ever simultaneously developed products in the same area and as 0 otherwise. Because no formal classification of diseases (such as the ICD-10) or pharmacological compounds intends to group drug indi-cations into areas, the grouping task required an original solution. A postdoctoral scholar in pharmacology at a major research university was hired to assist with this task. He distinguished 24 areas of drug development, including 11 areas mentioned by the informants, and categorized the indications into these areas. Only five indications remained uncategorized. Indications associ-ated with two areas were included in the area with which the association was stronger. Two drug develop-ment professionals, both with Ph.D.’s and years of sub-sequent drug development experience, were then given
alphabetized lists of indications and areas and inde-pendently categorized the indications again. The values of weighted Cohen’s kappa, a measure of agreement between each new rater and the original rater, were 0.66 and 0.71, indicating substantial agreement just short of the 0.75 threshold of “excellent agreement” (Fleiss et al. 2003, p. 609). Table 3 shows all area categories and lists the indications within two selected categories for illustration.
In the indication contact matrix, 1 is assigned to pairs of companies that have been simultaneously developing a drug for the same indication; 0 is assigned otherwise. Only drugs that entered clinical trials were taken into consideration; preclinical projects, which sift through thousands of drug candidates, are too preliminary for the notion of indication contact to be meaningful. Taken formally, companies working on the same indication are necessarily working in the same area. To capture the sub-stantive difference between area contact and indication contact, the measures make sure that indication contact
Table 3 Areas of Drug Development and Examples of Grouping Indications into Areas
Areas of drug Examples of indication grouping development (occurrence in data shown in parentheses)
1. Allergy 3. Autoimmune diseases—Failure of 2. Antibiotics organism to recognize its own tissue 3. Autoimmune resulting in pathologic
diseases immune response: 4. Cancer
5. Cardiovascular Asthma (13)
diseases Lupus erythematosus (1) 6. Dermatology Multiple sclerosis (6) 7. Endocrinology Myasthenia gravis (1) 8. Gastrointestinal Psoriasis (3)
diseases Rheumatoid arthritis (10) 9. Gynecology Unspecified (2) 10. Hematology
11. Immunology 12. Inflammation
13. Medical devices 19. Pain—Unpleasant sensory and 14. Metabolic emotional experience associated
disorders with tissue damage: 15. Nephrology
16. Neurology Chronic (1) 17. Obesity Fibromyalgia (1) 18. Ophthalmology Injection painkiller (1) 19. Pain Local anesthetic (1) 20. Psychiatric Patch (1)
disorders Topical anesthetic (1) 21. Pulmonary Transdermal medication (1)
diseases Unspecified (8) 22. Tissue
regeneration 23. Vaccines 24. Viral diseases
Note. The complete grouping of indications into areas of drug development is available in the electronic companion (at http://dx .doi.org/10.1287/orsc.1120.0740).
does not count as area contact. Two companies may have both types of contact only if they have worked on the same indication and on another project in the same area.
The Dual Signal in Area Contact: Initiated vs. Non-initiated Contact. As mentioned, the incumbents in drug development areas tend to perceive entry into their area as an affirmation of goal conflict. To capture these sig-nals, I created a nonsymmetric area contact matrix where initiation of area contact, i.e., entering the area later than the other company, is coded as 1, and 0 is entered other-wise. I also constructed a nonsymmetric matrix where, on the contrary, entering the area earlier than the other company is coded as 1, and 0 is entered otherwise; this matrix captures the collegial aspect of competition that joint area membership affirms when it is not coupled with a perception of challenge by a newcomer. The ties in these two nonsymmetric matrices are not mutually exclusive because a dyad member may initiate contact in one area and have the other dyad member initiate contact in another area. These and all other independent matri-ces were transposed relative to the dependent matrimatri-ces. The transposition ensured that the analysis captured the effect of exposure to signals (as opposed to sending sig-nals) on establishing social ties.
The Dual Signal in Indication Contact: Status Com-petition vs. No Status ComCom-petition. The extent to which a company competes for status with its indication com-petitors is captured by the interaction between the matrix of indication contact and the matrix of reported status competition. The effect of indication contact on social ties in the absence of status competition is captured by the main effect of indication contact in the mod-els that include this interaction term. The indicator of status competition combined two items measured on a five-point Likert scale (Cronbach’s alpha = 0076). The informants were asked to evaluate the applicability of the statements “Being better than this company has been a matter of prestige for us” and “We competed for a respected position in the industry” to every competitor they had identified. The 1-to-5 scale was converted into a 0-to-4 scale to ensure that the lowest score (“strongly disagree”) is equivalent to the absence of status com-petition. The sum of the scores for the two items was entered in the adjacency matrix at the intersection of the informants’ rows and the competitors’ columns.4 All companies not named as competitors have zeroes in the corresponding matrix cells. Again, the transposition of these matrices ensures that the analysis examines the effect of status competition reported by an informant in one company on reactions of informants in a dif-ferent company. This effect exists insofar as the affir-mation of status competition is signaled between the companies.
Control Covariates. The analysis controls for the square root of the number of employees in signal ori-gin companies (the square root symmetry transformation most plausibly approximates the number of potential tie recipients, i.e., senior executives and scientists) and for these companies’ ages. All models also include matrices of similarity of size and age. These controls are nec-essary because they are correlated with the dependent and the independent covariates: bigger and older com-panies receive more signals and more ties, and there are more signals and ties between companies of similar size and age. The controls are most crucial in the mod-els that distinguish between incumbents and newcomers to drug development areas: incumbents are by defini-tion older and bigger than newcomers and hence more likely to receive ties. I also included a dummy indica-tor of the signal origin company’s partnership with a large pharmaceutical corporation, along with the respec-tive similarity effect. Such partnerships may attract ties as they increase the visibility of the company and secure its financial stability. Properties of tie reporters that may confound the results are also controlled for. Com-pany leaders (CEOs and presidents), who constitute the majority of informants, are better socially connected and more exposed to intercompany signals. The same applies to informants who have worked in the region’s two major incubators of social networks, Genentech and the group of companies founded by Zaffaroni. I further controlled for three types of social proximity that may
Table 4 Dependent Networks and Independent Covariates: Descriptive Statistics
Standard
Symmetric Binary Mean deviation Minimum Maximum
Dependent networks
Acquaintance No Yes 0033 0048 0 1
Friendship No Yes 0006 0024 0 1
Friendship established after joined No Yes 0002 0012 0 1 current companies
Attributes of signal origin companies
sqrt(Number of employees) N/A No 13046 14081 2 100
Age in years N/A No 10010 7086 2 45
Partnership with large corporation N/A Yes 0053 0050 0 1 Attributes of tie reporters
Company leader N/A Yes 0064 0048 0 1
Has worked at Genentech N/A Yes 0012 0032 0 1
Has worked for Zaffaroni N/A Yes 0017 0038 0 1
Network covariates
Business collaboration Yes Yes 0001 0007 0 1
Geographic distance (miles) Yes No 12082 8055 1 42
BayBio comembership Yes Yes 0011 0031 0 1
Area contact Yes Yes 0032 0047 0 1
Indication contact Yes Yes 0004 0020 0 1
Noninitiated area contact No Yes 0019 0039 0 1
Initiated area contact No Yes 0018 0039 0 1
Status competition reported by No No 0007 0057 0 8 informant in signal origin company
Notes. The similarity effects of company attributes and the structural network effects do not enter the analysis as separate covariates because they are automatically computed by the data analysis program (SIENA) on user request. For the mathematical definition of these effects, see Ripley and Snijders (2010, pp. 36, 62–67).
affect social ties: formal collaboration relations, the dis-tance between the companies’ offices (computed using zip code coordinates as in Sorenson and Audia (2000); distances within zip codes are fixed at one mile), and the companies’ comembership in the local professional association (BayBio). Finally, I estimated structural net-work effects that may violate the assumption of indepen-dence of observations (Krackhardt 1988). Three possible effects were examined to control for increasingly indi-rect flows of influence in networks: reciprocity, i.e., the tendency of asymmetric flows in a dyad to become sym-metric; transitive triplets, i.e., the flows via single third parties; and indirect connections, i.e., the flows through multiple third parties.
Table 4 shows descriptive statistics for the covariates. The Statistical Model
The SIENA analysis package performs Markov chain Monte Carlo (MCMC) estimation of the exponential random graph model (ERGM) for nonlongitudinal data (Wasserman and Pattison 1996, Snijders 2002, Snijders et al. 2006). The ERGM is specially designed for multi-variate analysis of social network outcomes and differs from standard regression techniques in a number of ways. First, the ERGM does not assume independence of observations (actors or dyads); instead, it enables estimating the effects of various patterns of interdepen-dence. Second, it simultaneously accepts actor attributes and network matrices as covariates. Third, because the
usual notion of sampling is not applicable to the net-work data used to estimate ERGMs, standard errors do not serve to compute the probability that coefficients are zero in the population. Instead, they assess the con-sistency of the statistics over a large number of simu-lated networks (Ripley and Snijders 2010). Fourth, the MCMC method is stochastic, which precludes the use of familiar goodness-of-fit tests such as log likelihood. To quantify the goodness of fit, SIENA implements
Table 5 Effects of Dual Competition Signals on Reporting Acquaintance Ties in Signal Origin Companies in San Francisco Bay Area Drug Development
Exponential random graph models
Model 1 Model 2 Model 3 Model 4 Model 5
Control covariates
Reciprocity 0080∗ 0084∗ 0081∗ 0079∗ 0081∗
400085 400095 400095 400095 400095 sqrt(Number of employees)
—in signal origin company 0006∗ 0006∗ 0006∗ 0006∗ 0006∗
4<00015 4<00015 4<00015 4<00015 4<00015 —similarity to tie reporter’s company 3037∗ 3036∗ 3034∗ 3044∗ 3048∗
400375 400365 400385 400385 400395 Age of signal origin company
—in years 00014∗ 00013∗ 00014∗ 00013∗ 00012∗
4000065 4000065 4000065 4000065 4000065 —similarity to tie reporter’s company 0003 0001 0004 0002 −0001
400255 400235 400235 400245 400235 Partnership with large corporation
—signal origin company 0078∗ 0077∗ 0078∗ 0078∗ 0078∗
400075 400065 400065 400075 400065 —similarity to tie reporter’s company 0002 0002 0002 0002 0002
400065 400065 400065 400055 400085 Tie reporter is company leader 0036∗ 0035∗ 0036∗ 0037∗ 0036∗
400075 400075 400065 400075 400075 Tie reporter has worked at Genentech 0023∗ 0022∗ 0024∗ 0024∗ 0023∗
400085 400095 400095 400095 400095 Tie reporter has worked for Zaffaroni 0025∗ 0021∗ 0021∗ 0025∗ 0023∗
400075 400085 400085 400085 400085 Business collaboration 0098∗ 0097∗ 0096∗ 0088∗ 0087∗
400415 400385 400375 400375 400395 Geographic distance (miles) −0002∗ −0002∗ −0002∗ −0002∗ −0002∗
4<00015 4<00015 4<00015 4<00015 4<00015 BayBio comembership −0007 −0006 −0006 −0006 −0007 400085 400095 400095 400095 400095 Competition covariates Area contact 0012∗ 400065
Noninitiated area contact 0032∗ 0029∗
400085 400085
Initiated area contact −0017∗ −0019∗
400085 400085
Indication contact 0029∗ 0029∗ 0027∗
400135 400145 400135
Status competition reported by informant 0028∗ 0028∗
in signal origin company 400075 400075
Indication contact × Status competition −0027∗ −0025∗
400135 400125 Convergence criterion (tmax< 0015) met Yes Yes Yes Yes Yes
Notes. All network covariates are 78 × 78 matrices. Standard errors are in parentheses.
∗p < 0005 (two-tailed tests).
a generalized Neyman–Rao score test (Schweinberger 2012). All models reported below meet the criterion of good convergence based on this test (tmax< 0015) (Ripley and Snijders 2010, p. 48).
The Effects of Dual Signals on Acquaintance and Friendship
The ERGMs in Table 5 examine the effects of expo-sure to collegial and goal conflict signals on reporting
acquaintance ties to people in companies where the sig-nals originated. Of the three structural network effect controls, only reciprocity is included in the reported models. No model that included transitive triplets or indirect connections met the convergence criterion, which indicates that these two controls misspecify the mechanisms of tie formation in the examined setting.
Model 1 considers the general effect of area con-tact without distinguishing its collegial and goal conflict aspects. This effect is positive and statistically signifi-cant. Model 2 demonstrates that this effect is an amal-gamation of two opposite effects: a highly significant positive effect of the noninitiated area contact and a weaker significant negative effect of the initiated area contact. Thus, entry into an area inhibits acquaintance ties from incumbents, but incumbency in an area attracts acquaintance ties from later entrants. These results are consistent with the dual-signal mechanism as drug devel-opers portrayed it: entry into an area is a signal of goal conflict, whereas incumbency affirms the collegial aspect of area contact because it involves no deliberate com-petitive challenge.
When examined separately in Model 3, the effect of indication contact on acquaintance ties is positive. The main effect of indication contact remains positive when its interaction with reported status competition is added in Model 4, confirming that, in the absence of reported status competition, indication contact facilitates acquain-tance. The negative interaction effect shows that as status competition intensifies, the positive effect of indication significantly decreases: the more status competition drug developers report with companies they are in indication contact with, the less likely leaders of these compa-nies are to name them as acquaintances. These results again reveal the dual effect of competition on social ties: shared indication contact, which affirms a collegial rela-tion, facilitates acquaintance; yet this positive effect on ties is reversed to the extent that companies signal status competition.
Model 5 includes all indicators of collegial and goal conflict signals described above. No effect differs remarkably from what it was when examined separately. This ensures that each measure reflects a unique signal and does not duplicate other measures.
All significant effects in Table 5, except those of geographic distance and goal conflict signals, are posi-tive. This agrees with the theoretical expectations for all covariates, including the controls. One effect, the posi-tive main effect of status competition in Models 4 and 5, is unexpected. It demonstrates that status competition facilitates acquaintance when unaccompanied by indica-tion contact. I return to this finding in the discussion.
I estimated ERGMs of reported friendship ties with the same independent covariates as in Table 5 and repeated the analysis for a narrower definition of friend-ship that limited the dependent network to friendfriend-ship
ties established after the informants joined their current companies. No effect was significantly different from zero in any of the 10 ERGMs of friendship. The result demonstrates that strong social ties were not influenced by the dynamics of competition between drug develop-ment companies. In fact, friendship was not affected by any examined factors, including the controls. Because this conclusion completely conveys the substantive result of the analysis of friendship, the models of friendship are not reported. They are available from the author upon request.
Direction of Causality
In the causal logic of dual signals, signals antecede social ties. To support the argument of dual signals, the analysis must make sure that the effects reported above result from this causal ordering. To ensure this, the mea-surement of the hypothesized causes must antecede the measurement of the hypothesized effects in time. The data were available to establish this antecedence for friendship. As for acquaintance, the questionnaire items that captured the signals instructed the informants to report on the period before the year of the first interview, whereas acquaintance was recorded as of the time of the interview. This separates the hypothesized causes from the hypothesized effect in time—but, given that some acquaintances are long term, this separation may or may not be sufficient to preclude reverse causal effects.
The two opposite causal orderings have differ-ent implications that can be examined empirically. First, recall that the criterion distinguishing intercom-pany friendship from acquaintance is “spend[ing] time together for other reasons than work.” Friendship so defined involves a greater amount of time spent together and greater personal affection. Other things being equal, friends (i.e., two individuals who spend time together and like each other) influence each other’s behavior more profoundly than, or at least as profoundly as, acquain-tances (i.e., two individuals who share less time and affection than friends). Therefore, if the effect of ties on signals is responsible for the correlation between the two, the correlation between friendship and signals must be at least as strong as the correlation between acquaintanceship and signals. Yet the analysis showed that acquaintance ties but not friendship ties were corre-lated with the same signals. Furthermore, it showed that acquaintance was correlated with multiple interorgani-zational and organiinterorgani-zational measures, including control variables; friendship was not correlated with any vari-able in the models. This pattern is incompatible with reverse causality. It is consistent with the flow of causal-ity from signals to ties: the signals affect weak social ties but not stronger ties, which are based on personal intimacy rather than organizational actions or attributes. The timing of the signals’ emergence can be used to design an alternative test of the direction of causality.
The questionnaire recorded when companies began working on each drug development program they had in or before the year that preceded the interview. It is thus known when every competition relation and its corresponding signal, such as noninitiated area contact, emerged. In contrast, the exact timing of the emer-gence of acquaintance ties is not known; it is only known which ties did or did not exist at the time of the interview. Therefore, the further a moment is removed from the interview, the less accurately the interview data describe the acquaintance ties at that moment.
This feature of the data creates a second implication of reverse causality. To cause signals, the ties must precede the signals in time. The older the signal (i.e., the earlier it emerged prior to the interview), the less accurately the available measure captures the tie that could have preceded and caused this signal. A less accurate, noisier measure of ties is less likely to be correlated with signals than a more accurate measure. The causal priority of ties thus implies that the co-occurrence of signals with ties (as measured in this study) approaches randomness as the age of the signal increases: the older the collegial signals, the less frequently they co-occur with ties, and the older the signals of goal conflict, the more frequently they co-occur with ties. Accordingly, the acquaintance matrix must be negatively correlated with the age of col-legial signals and positively correlated with the age of signals of goal conflict.
To examine this implication, the age of the signals was expressed in months elapsed from the emergence of the signal until the interview in the signal recipient company. For the purpose of age computation, the inter-action between indication contact and status competi-tion was split into two dichotomous matrices: indicacompeti-tion contact with no status competition (a collegial signal) and indication contact with nonzero status competition (a signal of goal conflict). The quadratic assignment pro-cedure (QAP) correlation coefficients of the acquain-tance matrix with the matrices of signal ages are given in Table 6. The age of collegial signals is positively cor-related with acquaintance: the older the collegial signals,
Table 6 QAP Correlation Between the Age of Received Signals and Acquaintance Ties Sent
Pearson correlation
Signals coefficient p
Collegial
Noninitiated area contact 0008 0007 Indication contact with no 0020 0001
status competition Goal conflict
Initiated area contact −0001 0040 Indication contact with −0007 0036
status competition
Notes. All measures are 78 × 78 matrices. The age of nonexisting signals is coded as missing.
the more likely they were to co-occur with ties. The correlation between the age of the signals of goal con-flict and acquaintance is negative but insignificant. The age of the signals thus does not dilute their correlation with ties; the correlation of collegial signals with ties even strengthens with signal age. This result does not support reverse causality. It is consistent with the causal logic of dual signals: the longer a collegial signal has existed, the more likely it is to have elicited a tie.
Methods of examining the direction of causality cus-tom designed for specific empirical contexts, such as the presented analyses of tie strength and the timing of sig-nals, are uncommon in research, possibly because they are not codified in statistical theory. Yet, when care-fully designed, such methods may be effective because of their simple logic and fit to the context at hand.
Discussion
Contributions
This project is the first deliberate attempt to explain the dual socially binding and socially alienating effect of competition on social ties across firm boundaries. An analysis of extensive narrative evidence from interviews with executives in the San Francisco Bay Area drug development industry suggested a dual-signal account of the formation of social ties between competing companies. According to the informants’ reports, col-legial signals, i.e., companies’ actions and attributes that affirmed joint professional affiliation, elicited cross-company social ties from the recipients of the sig-nals. Signals of goal conflict, i.e., companies’ actions and attributes that affirmed the incompatibility of the company’s and the competitor’s goals, hampered ties. A quantitative analysis of acquaintance ties supported the dual-signal explanation.
The argument and the analysis are consequential in a number of ways. First, they expand the scope of the research on the sources of interorganizational networks. Most progress in this line of research has been due to studies of economic ties. Extending the focus to social ties is not only of academic significance. It informs man-agers and policy makers interested in capitalizing on the positive effects of social ties among competitors (Ingram and Roberts 2000)—or in avoiding their organizationally harmful effects such as under- and overembeddedness (Uzzi 1997, McDonald and Westphal 2003) and socially harmful effects such as collusion.
Another theoretically consequential aspect of the dual-signal mechanism is in its explicit focus on the perception of relationships. Researchers have made recurrent calls for a shift of focus from the configura-tions of relaconfigura-tions to their meaning for the participants (Emirbayer and Goodwin 1994, Krippner 2001, Ingram and Yue 2008). Yet the paths laid out by these calls, as their authors unanimously affirmed in my improvised
email poll, remain lightly traveled (for exceptions, see Porac et al. 1995, Chen et al. 2007). The reason is partly in the difficulty of obtaining reliable empirical evidence on meaning and perception in social networks, which normally requires a combination of systematic quanti-tative and ethnographic data. But partly the reason is in the consensus that results of purely structural analy-sis are significant in their own right. By highlighting a problem where a structural analysis would fail to cap-ture the opposite consequences of differently perceived aspects of the competition relation, this study makes another case for a systematic examination of communi-cated meaning as both an antecedent to and content of network relations in business.
The finding that friendship across organizational boundaries is not affected by interorganizational signals, or in fact by any examined organizational factor, also has theoretical as well as practical implications. Organiza-tional theory and research, notably in the embeddedness tradition, have established that social relations, weak or strong, affect economic action. This study showed that the opposite influence is limited: economic action that significantly affects weak social ties may have no effect whatsoever on stronger ties. This result is consistent with the evidence that strong relations in work settings develop because of interpersonal affect (Sias and Cahill 1998). The implication for business practitioners is that when they take steps that signal a conflict of goals, they must weigh the benefits of such steps against the costs of severed or nonoccurring weak ties. Strong ties, in contrast, will persist or fail to occur regardless of usual competitive actions.
Limitations and Unresolved Issues
By showing that companies’ actions and attributes elicit ties from executives in other companies, this analysis demonstrated that such actions and attributes carry infor-mation, i.e., function as signals. The meaning of these signals to the executives was derived from the execu-tives’ firsthand reports. This is superior to nonempiri-cally assuming the meaning that observers attribute to signals. Still, more direct methods of capturing signals and their meaning are possible, which this study was unable to use. Signals are more reliably recorded as they occur, rather than retrospectively. Ideally, a researcher must also be able to capture the meaning that every recipient of a signal attaches to it, rather than extend the dominant interpretation of the signals in the industry to individual actors. The contribution of the study would be further strengthened by quantitative verification of the mechanisms that mediate the effects of signals on ties such as the perception of similarity, avoidance, competi-tive tension, and information withholding. Future studies that will not discover the effect of signals inductively, as this study did, will be able to overcome these limitations
by incorporating variables that capture the meaning of signals and the mediating mechanisms.
The other issue that this project could not bring data to bear on is the finding that status competition elicited acquaintance ties between companies that were not in indication competition. Although the ambition of the study is limited to explaining ties between companies that are objectively in competition, this incidental find-ing is unexpected. It presents a twofold puzzle. First, why do some executives signal status competition to other companies that are, by objective measures, non-competitors? In search for a possible answer, I compared companies that received signals of status competition from indication noncompetitors to those that received no such signals from them. Companies that received signals of status competition from indication noncom-petitors were significantly more prominent: larger, older, and more involved in the professional association. There were no similar significant differences for companies that received signals of status competition from indica-tion competitors. This suggests that prominent compa-nies may become models of high status that others, even if not in competition with them, aspire to emulate and surpass. The second part of the puzzle is why executives of noncompetitor companies react to affirmations of sta-tus competition by extending acquaintance ties. A poten-tial explanation may lie in the fact that signals of status competition require focused attention to be accurately interpreted by company leaders. Unless the mutual atten-tion is as focused as it is among indicaatten-tion competitors, status competition may not get conveyed with the clarity required to inhibit ties. Instead, it may reach recipients as a noisy, vague signal that merely makes the recipients aware of its sender and is sufficient to elicit weak ties. The previously unreported finding that status competi-tion does not affect social ties among companies in area contact (attention to area competitors is more dispersed because companies have far more area contacts than indication contacts) indirectly supports this explanation. The third limitation of this analysis is geographic: the results call for a comparison with other regions and industries. The geographic concentration of firms and dense interfirm social networks may have strengthened the effect of signals in the San Francisco Bay Area drug development industry. Yet the weakening of this effect in socially and spatially dispersed industries is a priori not certain. Given that signals do not need to be reciprocated to influence ties, their effect on ties may spread across large distances more easily than the effects of factors that depend on interaction, such as introduction and joint activities. This project was unable to assess whether the directed nature of signals extends the findings beyond locally embedded (or incestuous, to use drug develop-ers’ self-ironic term) industries, and leaves this task to future research.
Electronic Companion
An electronic companion to this paper is available as part of the online version at http://dx.doi.org/10.1287/orsc.1120.0740. Acknowledgments
The author thanks Mark Granovetter, Jone Pearce, Suresh Kotha, and anonymous Organization Science reviewers, whose detailed comments led to major improvements. The project also benefited from suggestions of Christine Beckman, Henning Hillmann, and Woody Powell. Data collection became possible because of Daria Mochly-Rosen’s enthusi-astic personal introductions and the support of Matt Gardner at BayBio. Early results of the project were presented at the 2008 Annual Meeting of the Academy of Management in Anaheim and at the 2009 West Coast Research Symposium on Technology Entrepreneurship in Seattle. The author grate-fully acknowledges financial support from the National Sci-ence Foundation [SES-0703310] and the Stanford Graduate Research Opportunity Fund.
Endnotes
1Here and going forward, the distinction between economic
and social ties coincides with Granovetter’s (1985) distinction between “economic action” (creation of economic ties is a type of economic action) and “social relations.” Economic ties are defined by their economic use: when such use is absent, the economic tie is absent or purely nominal. In contrast, social ties have noneconomic content that is sufficient for their full-fledged existence when economic use is absent.
2Names and details in this section have been changed or
with-held to ensure privacy.
3This is less true for global pharmaceutical corporations.
Unlike the local entrepreneurial companies considered here, global corporations routinely purchase advanced drug candi-dates or products developed by others, thus deliberately creat-ing indication contact.
4The scores were summed rather than averaged because the
analysis software allows only integers in network matrices. References
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