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Government as Network Catalyst: Accelerating Self-Organization in a Strategic Industry

Governments have long standing interests in preventing market failures and enhancing innovation in strategic industries. Public policy regarding domestic technology is critical to both national security and economic prosperity. Governments often seek to enhance their global competitiveness by promoting private sector cooperative activity at the inter-organizational level. Research on network governance has illuminated the structure of boundary-spanning collaboration mainly for programs with immediate public or non-profit objectives. Far less research has examined how governments might accelerate private sector cooperation to prevent market failures or to enhance innovation. The theoretical contribution of this research is to suggest that government programs might catalyze cooperative activity by accelerating the preferential attachment mechanism inherent in social networks. We analyze the long-term effects of a government program on the strategic alliance network of 451 organizations in the high-tech semiconductor industry between 1987 and 1999, using stochastic network analysis methods for longitudinal social networks.


Introduction
Governments have long standing interests in science and technology for at least two primary reasons. First, technology is critical to the continued prosperity of nations that seek to secure a high standard of living for their citizens, fueled in part by dramatic enhancements in consumer goods, telecommunications technology, automobiles, optoelectronics, and computers (Salter and Martin 2001). Second, technology is critical to national security and the development of weapon systems, such as intercontinental ballistic missiles, unmanned aerial vehicles, and rapidly evolving applications of artificial intelligence (Mowery 1998). For these reasons, among others, nations often seek to enhance their competitiveness through science and technology (S&T) policy (Taylor 2016).
In a recent notable example, China has rapidly increased national research and development (R&D) spending over the past two decades, invested heavily in U.S. firms to acquire technology and intellectual property, often engaging in illicit transfers, and expanded intelligence collection on innovation targets. Together these efforts form the elements of a strategy to fuel economic growth and enhance military capabilities (Deutch 2018). This type of technology-oriented strategy is by no means new, and many developed nations invest heavily in national systems of science and technology. Nation states seeking to compete in the international system often attempt to gain advantage through scientific and technological development (Taylor 2016). The U.S. has maintained coherent national science and technology policies focusing on military strength and domestic prosperity since at least World War II (Bush 1945;Smith 2011).
A unique example from 1987 occurred when the United States intervened in the global high-technology market during a critical period of potential domestic market failure through a government sponsored non-profit research consortium, called Sematech (Browning, Beyer, and

Background
Today the global high-technology sector is structured as a global network of cooperative interactions between organizations. The sector did not always resemble a network but was once dominated by organizations in relative isolation. In the semiconductor industry, which is the backbone of the computing and microelectronics industries, a unique innovation trajectory took hold in the latter half of the 20 th century, referred to as Moore's Law. Named after former president of Intel, Moore suggested that the number of transistors packed onto an integrated circuit would double roughly every two years (Epicoco 2011). This non-linear innovation trajectory reached a point during the 1970s and 1980s where firms increasingly began to cooperate on research and development (R&D) in order to remain competitive (Hagedoorn 2001). As the technological landscape shifted toward cooperative behavior between organizations, the Japanese conglomerate system of networked organization, called Keiretsu, was in a favorable position and rapidly achieved market dominance (Lincoln, Gerlach, and Takahashi 1992). Industry leaders and U.S. lawmakers were extremely worried about the loss of majority global market share, which occurred in the mid-1980s, in an industry that was a U.S. invention and dominated by U.S. firms since its inception.
In order to counteract the threat from Japan, the U.S. congress and industry leaders crafted the government sponsored non-profit research consortium, Sematech, abbreviated from Semiconductor Manufacturing Technology. Through the Department of Defense (DOD) and the Defense Advanced Research Projects Agency (DARPA), the consortium received matching funds of $100 million per year for a ten-year period between 1987 and 1996 (Browning and Shetler 2000). The consortium began with participation by fourteen U.S. firms, which accounted for roughly 80% of total U.S. manufacturing capacity, and excluded foreign participants (Browning, Beyer, and Shetler 1995). Members to the consortium contributed one percent of sales, or one million dollars per year, and contributed human capital to the fabrication facility in Austin, Texas called Fab One (Browning and Shetler 2000). The primary objectives of the consortium were to improve manufacturing process quality and to innovate along the miniaturization trajectory of Moore's Law. Leadership and governance were established by the members, and DOD played a very hands-off role (Beyer and Browning 1999). In 1996 Sematech opened membership to foreign firms and DOD matching funds ended, but the consortium continued to function as a non-profit organization.

Literature Review
This article relies on three broad sources of literature to motivate hypotheses regarding potential effects of government action on inter-organizational networks. The first section applies network governance theory to conceptualize cross-sector inter-organizational cooperation. The second section applies the literature on complexity science, which specifies a unique relational ontology well-suited to the study of networks and highlights specific mechanisms of action. The third section applies the concept of network interventions to suggest why, when, and with what consequences decision makers might choose to intervene in social networks. Combining these sources of literature suggests how governments might catalyze network emergence.
Network Governance: As government has become less hierarchical and less centralized over the last few decades, the study of cross-sector interorganizational networks has become far more common (Koliba et al. 2019). The New Public Management movement and the emergence of the "hollow state" (Milward & Provan 2000) described a situation where governments increasingly rely on market-based mechanisms to achieve public ends. For example, contracting for products and services (Brown, Potoski, and Van Slyke 2018) and public-private partnerships (Hart 2003;Hodge & Greve 2007) provide viable alternatives to traditional hierarchical withinorganization service delivery, i.e. the "make or buy" decision (Brown & Potoski 2003;Johansson 2015). Similarly, policy networks and collaborative governance regimes emerged to tackle wicked public problems in a variety of areas from natural resource management (Berardo and Scholz, 2010) and environmental protection (Emerson & Nabatchi 2015), to emergency management (Kapucu 2006), and public health (Provan & Milward 1995).
The common thread between these alternative modes of public problem resolution is that they involve cooperation between organizations rather than vertical integration within a single bureaucracy. Sometimes these cooperative inter-organizational linkages are merely a series of dyads with little to no cross-connection. This might be the case with bilateral contracts and public-private partnerships. However, as the number of cooperative linkages between organizations increase, there is a greater probability that a complex network structure emerges. Jones, Hesterly, and Borgatti (1997) suggest that the transaction costs of cooperation between organizations increase their structural embeddedness, which in turn produces the need for governance strategies to adapt, coordinate, and safeguard an evolving network of exchanges. In these scenarios, traditional government approaches to address market failures and support innovation may prove ineffective or counterproductive. Klijn and Koopenjan (2015:11) define governance networks as "more or less stable patterns of social relations between mutually dependent actors, which cluster around policy problems, a policy programme, and/or a set of resources and which emerge, are sustained, and are changed through a series of interactions". Governance, as an activity of government agencies within such networks, is defined as a "set of conscious steering attempts or strategies of actors within governance networks aimed at influencing interaction processes and/or the characteristics of these networks". Further, the more specific activity of management, is defined as "all the deliberate strategies aimed at facilitating and guiding the interactions and/or changing the features of the network with the intent to further the collaboration within the network processes".
These definitions suggest that public problems tend to generate complex patterns of social interaction which might be enhanced or mitigated to achieve broader public ends.
Network governance emphasizes the structural properties of social networks, including constructs like trust, reciprocity, status, prestige, and broader cultural values (Powell 2003).
Network governance suggests complex public problems cannot be addressed by individual organizations or government agencies alone, shifting attention to the structural properties of networks rather than management or influence of incentives at the actor level (Agranoff 2006).
As such network governance often crosses the boundaries of public, private, and non-profit sectors. However, organizations party to governance networks are neither strongly managed by government agencies, nor are they fully adversarial in the market sense of competitive advantage (Ansell and Gash 2008). Rather, actors remain autonomous but are non-trivially dependent on access to the heterogeneously distributed resources of their network partners.
As Provan and Kenis (2008) suggest, network governance has tended to display three generic modalities: the participant governed model, the lead organization model, and the network administrative organization (NAO) model. The first is a decentralized model having no lead organization, while the second is a centralized network model where a dominant organization leads a network of inter-organizational relationships. In contrast to both, the NAO represents a middle ground scenario, where a new organization is established to govern the network of relationships, but which is external in some sense to the industry it is designed to govern. In the private sector context, the NAO is often a non-profit organization specifically designed to coordinate activities between private sector organizations (Human and Provan 2000). As a nonprofit, the NAO is not a direct competitor with members of the broader inter-organizational network. The NAO may be conceptualized as a type of organizational intermediary or "collaborative platform" designed to facilitate cooperative behavior between organizations (Ansell and Gash 2018). Wardenaar, de Jong, and Hessels (2014) suggest the network governance approach provides a useful conceptualization of strategic research consortia in science and technology policy. In this article, we conceptualize the government sponsored research consortium, Sematech, as a mode of network governance closely related to the NAO model, where Sematech has administrative capacity with respect to a sub-network within a broader global network of alliances (see section two for details about Sematech). This conceptualization suggests that government intervention may provide the capacity necessary to enhance the network effectiveness of organizations on rapidly changing technological landscapes. By focusing on the structural properties of social networks, the network governance approach may represent a more sustainable long-term strategy for achieving public ends in science and technology networks.
Complex Systems: Complexity theory suggests attention to unique generative mechanisms that manifest at the systems level. Thurner, Klimek, and Hanel (2018:22) define complex systems as "co-evolving multilayer networks". This definition of complexity suggest that complex systems are understood specifically as overlapping and interacted sets of dynamic networks. Ladyman, Lambert, and Wiesner (2013) point to the concepts of numerosity and interaction, where numerosity simply refers to the fact that complex systems involve numerous elements, actors, or agents, while interaction simply means that these elements are all interacting with each other in a non-trivial way. Miller and Page (2009:44) point to the concepts of selforganization and emergence, where "localized behavior aggregates into global behavior that is in some sense disconnected from its origins". Self-organization is compatible with decentralization in network governance. In contrast to top down administration by central agencies, the local partnering behavior between organizations gives rise to a less centralized and more adaptive structure of interaction where network structure exhibits properties of self-organization such as reciprocity, closure, and homophily (Robins, Lewis, and Wang 2012). Kauffman (1993) suggested that complex systems can be characterized as fitness landscapes. In such systems, organizations cooperate to summit peaks of fitness on technological landscapes, where recombination of technology produces novel innovations. A feature of evolution on such landscapes is called "catalytic task spaces". While the original concept was designed to describe chemical interactions that give rise to biological processes, the notion can be generalized to technology landscapes, where organizations combine their respective resources and recombine existing technologies to produce new innovations. The catalytic task space may be thought of as a local cluster within the landscape where recombination occurs at an accelerated rate. Kauffman's (1993) notion of a catalytic task space fits well with the idea that government might act as a network catalyst. The Oxford English Dictionary defines a catalyst as "A substance which when present in small amounts increases the rate of a chemical reaction or process but which is chemically unchanged by the reaction; a catalytic agent". In this sense, if governments create local clusters of cooperative behavior, such clusters may catalyze a broader process of cooperation on the landscape. While, the metaphor breaks down over whether the government remains unchanged in the process, the rate of increase in a chemical reaction maps on well to the notion of increased cooperative behavior in, for example, government sponsored consortia.
Complexity concepts have been applied by public policy and administration scholars, including nonlinearity (Morçöl 2012;McGee and Jones 2018), self-similarity, feedback (Eppel 2017), self-organization (Comfort 1994;Berardo and Scholz 2010), fitness landscapes (Rhodes & Dowling 2018), and preferential attachment (Carboni & Milward 2012). However, there has been insufficient attention to the nature of complex systems and the application of statistical modeling of social networks in the public policy and administration literature (Robins, Lewis and Wang 2012). Observations regarding decentralization of governance and increasing cross-sector cooperation abound, yet extant concepts from complexity theory remain underutilized given their potential for describing these very situations. This article applies a set of complexity concepts to understand network governance generically, as well as specific mechanisms of complex systems to motivate hypothesis regarding government intervention in inter-organizational networks.
The specific mechanism of interest in this research is preferential attachment. Preferential attachment describes a process where network actors tend to seek out connections with already well-connected actors (Barabasi and Albert, 1999;Newman, 2001) and where popularity tends to generate increasing popularity (Robbins, Lewis, and Wang 2012). The concept has many permutations and precursors such as the Mathew Effect described by the sociologist Robert K. Merton (1968), i.e. "the rich get richer, and the poor get poorer". This process leads to a positively skewed distribution of network connections. Such networks often follow a power law distribution and are characterized as scale-free when preferential attachment is present, and the network is continuously expanding (Barabasi and Albert 1999). However, the question of whether many social networks are actually scale-free has come under criticism (e.g. Broido and Clauset 2019) and is beyond the purpose of this article. We apply the concept of preferential attachment to suggest that organizations within inter-organizational networks tend to seek and form alliances with already well-connected organizations.
Although preferential attachment is a prominent concept in network analysis, its application to the field of public policy and administration has been limited. Literature has discussed preferential attachment as a structural feature of social networks (Robins, Lewis and Wang 2012;Weare, Lichterman and Esparza 2014). Sun and Cao (2018) used preferential attachment to understand the structure and function of Chinese research and development (R&D) policy networks, finding that government agencies were more powerful as a result of preferential attachment. They argued that policymakers could capitalize on preferential attachment to strengthen interagency collaborations. Similar studies on R&D policy networks found that repeat participation within the network (Protogerous, Caloghirou, and Siokas 2010) and a broader process of self-organization (Biggiero and Angelini 2015) were factors leading to preferential attachment within policy networks. Lake and Wong (2009) suggested that preferential attachment could enhance political power but also pose challenges when powerful actors are removed from centralized networks. Concerns have also been raised regarding network stability (Carboni and Milward 2012) and hindrances to the flow of information (Lyles 2015). Schilling and Fang (2014) proposed that "moderately hubby" network forms prevent instability, while  suggest improving administrative support in order to build trust between less centralized actors.
R&D alliance networks in the high-technology sector are like other social networks in many respects, featuring common processes such as reciprocity, homophily, and preferential attachment. Preferential attachment may occur for functional or institutional reasons.
Cooperative extensions of the resource-based view of the firm (RBV) suggest that firms seek out partnerships in order to gain access to heterogeneously distributed resources which might be applied for joint advantage on competitive landscapes (Eisenhardt and Shoonhoven 1996;Das and Teng 2000). Thus, firms will tend to seek out partnerships with well-connected firms in order to gain access to their deep resource portfolios. Preferential attachment also highlights the relevance of social resources and social capital (Lin 1999), as well as drivers of alliance formation related to cognitive and institutional processes (Inkpen and Tsang 2005;Powell et al. 2005). For example, Stuart (1998) showed that prestige is a factor driving alliance formation in the semiconductor industry. For these reasons, we hypothesize that, in the semiconductor industry, organizations with many alliances will tend to form even more alliances, i.e., a preferential attachment process drives network evolution. is relevant to the current study. Valente (2012:49) defines network interventions as "the process of using social network data to accelerate behavior change or improve organizational performance". Building on network theory developed in the health sector, Valente suggests that governments might intervene to stimulate the emergence of a network, to alter the structure of an existing network, or to even break up an existing network. As Valente states, "When network data indicate that the network is nonexistent, too fragmented, too centralized, or otherwise dysfunctional, there is a need for network change. The interventionist should use induction or alteration techniques to create a network amenable to change. Once the network is built or restructured, identification and segmentation tactics can be used to accelerate change." Valente (2012,52) The basic propositions that Valente advance is that understanding network structure is important and governments might take specific modalities of intervention in order to achieve public ends. These include, among others, adding or deleting nodes, adding or deleting links, and rewiring existing links. One of the most common types of intervention strategies involve identifying key players in order to elevate them to leadership positions for policy implementation (Valente et al. 2015). We take a different approach, suggesting that government action, via Sematech, represents a type of link addition intervention, where the consortium establishes network ties between existing firms in an incipient network. Scott (2016) suggests that these types of collaborative network interventions have not been systematically examined in policy studies. Scott shows that participation in government sponsored collaborative groups enhances further collaborative ties between organizations by reducing the transaction costs of cooperation in the network. We suggest a similar logic applies to Sematech as a mode of network intervention and governance, where the consortium lowered cooperation costs that enabled large scale resource combination.
We combine insights from governance networks, complexity theory, and network intervention to advance the hypothesis that governments might catalyze cooperative behavior in policy relevant sectors by stimulating the process of preferential attachment around a set of policy relevant target firms in an emerging network. By creating a task space around a set of target organizations, governments might catalyze a broader process of preferential attachment. In the present case, members of the government sponsored research consortium, Sematech, are the policy relevant targets of intervention.

Data, Methods, and Variables
Data Sources: Data on network connections between firms were gathered from two principal sources. Data from 1986-1989 were gathered from public announcements compiled through press releases and other public news announcements. Data from 1990 to 1999 were gathered from the Strategic Reviews annual reports from the semiconductor market research firm IC Insights. The reports include strategic profiles for firms in a given year. Network connections between firms are operationalized by each focal firm's list of partners and their business relationships, including co-development agreements, cross-licensing agreements, equity investments, joint ventures, marketing agreements, and mergers. In the business literature, these types of relationships are characterized as strategic alliances (Eisenhardt & Schoonhoven 1996).
The alliances represent reciprocal exchange of resources, including financial resources, social and human capital, technology, and information. The data also included firm level data on sales and headquarters location. Sematech members were identified by contacting Sematech. The strategic alliance data were used to create undirected binary networks for each year between 1987 to 1999. These time periods were chosen because the implementation of Sematech started in 1987, the maturity of Sematech began roughly in 1991, and the post-DOD sponsorship era of Sematech occurred with DOD-exit in 1996 (Browning & Shetler 2000).

Stochastic Network Analysis Methods:
We use two different stochastic network analysis methods, which we attempt to model in parallel form: 1) exponential random graph modeling (ERGM) of a network of 283 organizations; and 2) stochastic actor-oriented modeling using RSiena (SIENA) of a network of 451 organizations. We use these two methods to fit the structure of the data in two separate time periods. Because the early alliance data between 1987 and 1989 included only announcements, which did not also include data on ongoing alliances, we are unable to track the evolution of the network during these years. Thus, for the early period we used ERGM on a pooled network that consisted of all ties formed between 1987 and 1990.
Conversely, data from 1990 forward included ongoing alliance information, which allowed for the use of SIENA to model the network dynamics over time. We include analogous modeling terms in both approaches to maintain continuity between the two different methods.
ERGM is a method for estimating the probability of tie formation between nodes in a network (Lusher, Koskinen, and Robins 2013). Ties that form within a network are not independent of one another creating dependencies that are not appropriately accounted for with traditional statistical methods, such as logistic regression (Butts 2008, Snijders 2011  SIENA models were originally developed for directed networks based on the assumption that a tie's existence is determined by the sender of the tie. Recent developments have extended these models to non-directed networks, such as those found in the semiconductor industry as well as in a wide variety of governance networks. As discussed by Snijders and Pickup (2016), non-directed networks require adjustments to the timing process and the choice process. 1 We rely on a one-sided opportunity process and a mutual choice process to model the dynamics of the semiconductor industry. We selected this combination because a one-sided initiative with mutual confirmation "is in most cases the most appealing simple representation of the coordination required to create and maintain non-directed ties" (Snijders and Pickup 2016, p. 233). We believe this methodological choice best approximates the process of strategic alliance formation in practice, e.g. co-development agreements as a mutual contract representing a mutual pooling of resources but for strategic objectives (e.g. Eisenhardt and Shoonhoven 1996). Firm entry and exit (i.e., composition change) in the semiconductor industry is controlled for in the model through structural zeros (see Ripley et al. 2019).

Model Terms and Variables:
In order to establish continuity across the two modelling techniques, we employ parallel modelling strategies with analogous terms between the two approaches. Both the ERGM and SIENA models employ three types of effects: structural effects, 1 These new options arise because the formation of a tie can longer be based solely on the utility considerations of the sender of the tie. In non-directed networks, senders and receivers cannot be differentiated and thus one must decide how to take into account the utility of each actor. According to Snijders and Pickup (2016) the timing process for non-directed networks has two possibilities: one-sided initiative (where a single actor is randomly chosen); or two-sided opportunity (where an ordered pair of actors is randomly selected). More importantly, the choice process has three options: dictatorial (where one actor's utility determines the tie); mutual (where both actors must agree on the tie; in the sense that it is beneficial to both); and compensatory (where a combined objective function is established for each pair of actors).
actor attribute effects, and homophily effects. We provide generic variable names for analogous model terms across ERGM and SIENA models described in Table 1 as well as a brief description of their function.
For structural effects in the ERGMs, the variable of interest for our first hypothesis is the geometrically weighted degree distribution, gwdegree. This term captures the skewness of the distribution, where a negative estimate indicates a highly skewed distribution (Levy 2016, Hunter 2007). This term serves as a measure for a general preferential attachment process in the early period of the alliance network. The gwdegree term, as parametrized, captures an antipreferential attachment process and thus a negative coefficient provides support for H1 (Hunter 2007). For the structural effects in our SIENA model we rely on a popularity effect, called inPop, to capture the tendency for actors to form ties with others who already have many ties.
The popularity effect operationalizes the process of preferential attachment hypothesized in H1.
Unlike the gwdegree term, a positive coefficient on inPop indicates preferential attachment.
For node attribute effects in the ERGM we use the nodefactor term; and, in the SIENA model we use the egoPlusAltX term for undirected networks. These both permit the estimation of categorical variables on the probability of tie formation. The key variable of interest is whether the node is a Sematech member during time t. We take this term as our basic proxy for Sematech induced preferential attachment, as it models the tendency for certain organization types to be nominated more than others (i.e., is there preferential attachment to certain node types). These Sematech terms serve as our proxy measures for the test of H2. In the ERGM model there is no change in Sematech membership for the pooled networks between 1987-1990. For the SIENA models Sematech membership changes from year to year.
Control variables include main effects for country headquarters, specifically focusing on USA and JPN headquartered firms, as well as homophily effects of country and Sematech membership. We also control for other self-organizing processes prevalent in networks, such as transitivity (i.e. the tendency for actors to form subgroups or triads where all three nodes are connected). In the ERGM this is captured by geometrically weighted edgewise shared partner distribution term, called gwesp; and, in the SIENA model this is captured by the transTriad term.
Lastly, a density parameter is also included in the models. In the ERGM this is the edges term; while the SIENA model is degree(density). This variable functions similarly to an intercept term in a standard linear model and captures the overall tendency for ties to form in the network. The overall density of the network edges degree(density)

Results
The results of the exponential random graph models (ERGM) used in the implementation period (1987)(1988)(1989)(1990) are below in Table 2. The model and goodness-of-fit diagnostics are in the online appendix, showing good convergence statistics. The first model shows the base ERGM with the degree distribution term (Pref.Attachment). The negative estimate indicates high skewness in the distribution (Levy 2016, Hunter 2007, which is our proxy for general preferential attachment in network tie formation. This result provides support for H1 during the early implementation period. In the second model, we add the main effect term (Sematech Effect), which captures the tie formation effect of membership in the government sponsored network administrative organization. The estimate is significant and positive, and Pref.Attachment has now lost its significance. This indicates that the main effect of Sematech accounts for a portion of the skewness in the degree distribution. In other words, to the extent that the network has a right skew, those high degree nodes can be partially accounted for by Sematech membership. The third model adds the transitivity term, control variables for country headquarters, and homophily terms for Sematech members and firms of the same country headquarters. The effect of Sematech remains positive and significant, while Pref.Attachment has reversed sign and is now significant. These results provide support for H2 during the implementation period.
Interestingly, during this period, USA firms appear to have higher propensity for alliance formation than JPN headquartered firms; yet USA firms had a negative propensity for forming ties with each other. This is consistent with the extremely competitive nature of USA firms, also indicating the USA firms have strong alliance formation tendencies with foreign firms despite the essentially protectionist nature of Sematech. Finally, Sematech members appear to have a positive tendency to form ties with each other during this period. However, the estimate is only marginally significant.  Next, we analyzed four distinct time segments, within the maturity period and the post-DOD period of the evolution of the collaboration network, using stochastic actor-oriented models in RSiena (SIENA). Model 4, 97-99. All four models indicated good convergence statistics. The absolute value of the convergence t-ratios were all less than 0.1 and the overall convergence ratio was less than 0.25 (Snijders, van de Bunt, and Steglich 2010). Goodness-of-fit diagnostics for the SIENA models are in the online appendix.  In support of HI, all four models show a positive and significant general effect on Pref.Attachment. However, our proxy for Sematech induced preferential attachment (Sematech Effect) only showed a positive significant effect in Model 1 providing partial support for H2 during the maturity period. In Model 2 the estimate reverses but is insignificant. Then, in Model 3 the sign remains negative but becomes significant. The sign becomes positive in Model 4 but is insignificant. The reversal of sign during the post-DOD period suggest that the attractiveness of Sematech membership on alliance formation were strongest during the implementation period and during the first half of the maturity period which supports H3. The reversal of significance between Model 1 and 2 may be due to several members leaving the Sematech consortium, while the increase in significance of the negative estimate in Model 3 supports H3. In summary, the results indicate strong support for H1, moderate support for H2, and moderate support for H3. Table 4 summarizes our main results. 1987:1990 1991:1993 1993:1995 1995:1997 1997:1999 Pref. Attachment + + + + + Sematech Effect + + ns -ns The control variables also showed interesting estimates. The positive and negative signs on Sematech homophily appear to correlate with the main Sematech Effect, suggesting that when Sematech members form alliances with each other, they also seek alliances with others.

Model Years
Sematech homophily is strongly negative in the immediate post-DOD period, perhaps suggesting that Sematech substitutes for alliance formation between USA firms and foreign firms. JPN firms show a higher propensity for collaboration across the time periods and a higher propensity to collaborate with other JPN firms, while USA firms only appear to show positive tie formation in the immediate post-DOD period and show a consistent negative propensity to collaborate with other USA firms.

Discussion
The study posed three hypotheses regarding alliance formation in the semiconductor strategic alliance network: H1) a general preferential attachment process is present in the distribution of network ties; H2) a government sponsored consortium, structured as a network administrative organization (NAO), stimulates this process; and H3) the effects are time dependent and differ during and after government sponsorship. The results show that preferential attachment operates in the industry network generically, but the situation regarding target organizations is more nuanced. The results indicate that NAO membership is associated with rapid tie formation in the implementation and early maturity periods of the development of the program. However, this effect appears to diminish and even reverse as the consortium proceeds through the maturity period and then enters the post-DOD period. This may be due to saturation in alliance formation as target firms reach a threshold of utility for new alliances, which supports observations regarding diminishing returns to alliance portfolio size (Oxley 1997;Lahiri and Narayanan 2013). As such, there may be limits to government sponsorship efforts aimed at firms with large alliance portfolios.
The results have implications for the network governance literature in public policy and administration (Provan and Kenis 2008) by suggesting preferential attachment is a property of social networks which might be stimulated, attenuated, or generically manipulated to achieve public goals. Rather, than creating new public bureaucracies, providing direct subsidies to private firms, or enacting trade barriers in the international system, this research suggests that governments might act upon social structure rather than the agents themselves. Further, this study synthesizes the literature on network interventions (Valente 2012) and complexity with network governance in order to more fully explore the theoretical underpinnings of interorganizational networks, as well as the role of governments within these networks. We conceptualize the NAO approach in the Sematech case as a tie-addition intervention, but further suggest a positive feedback loop where adding ties among existing node sets may stimulate the development of more ties. This is evident because the propensity of USA firms to form ties with other USA firms is largely negative or insignificant across all time periods, suggesting that the NAO permits cooperation between firms which otherwise are highly competitive. A tie addition strategy through alliance sponsorship may provide the needed governance structure to reduce cooperation costs and permit resource combination.
The results also contribute to the literature on strategic alliances by emphasizing the role of public policy in private sector alliance formation. While the extant literature in this area focuses chiefly on transaction costs, resource-based view of the firm, and social capital, there has been limited treatment of public policy effects on strategic alliance behavior. The results reinforce the general notion that USA firms are highly competitive, and indeed, appear to form cooperative ties with a lower propensity than JPN firms during the maturity and post-DOD periods. However, USA firms do appear to be more cooperative during the implementation period, which, as we argue, is due to the influence of the NAO during the early crisis period between 1987-1990. The norm also appears to reverse during the immediate period following DOD exit, as USA firms are forming alliances at a more rapid rate with firms of other national headquarters, i.e. higher tie formation simultaneous with negative homophily.
Limitations: The first limitation deals with the issue of missing data on ongoing alliances in the early years, limiting the analysis of that period to a pooled ERGM rather than the SIENA model. This pooling approach to the ERGM in the early years may discount information on temporal dynamics. Second, there is no definitive method for directly assessing whether preferential attachment is stimulated by select nodes. Rather, preferential attachment generally refers to the distribution of ties in a network. As such, we rely on proxy variables in the models.
Finally, as with all non-experimental designs, we cannot be confident that Sematech is causing increases in the probability of tie formation, or whether these increases would have been observed in the absence of Sematech. As such, we limit our conclusions to associational claims rather than using causal language.

Conclusion
The aim of this study is to test whether government intervention can catalyze mechanisms of network evolution in strategic industries. Specifically, the focus is on the effects of a policy program on the preferential attachment mechanism of the research and development network in the high-technology sector. Due to the importance of technology to the national interest, the quasi-public nature of technology, and the cooperative character of the semiconductor industry, theory suggests that network-based interventions may be useful policy tools in context. The general research question we have sought to address is whether government intervention might catalyze network formation around a set of policy target organizations. The results of the study provide support for the theory and suggest further research is necessary to explore the dynamics of network intervention in strategic industries.

Online Appendix -ERGM & SIENA Goodness of Fit Diagnostics
The statnet program in R was used to produce the goodness-of-fit plots for the ERGM model and RSiena was used of for the Siena models. The goodness-of-fit plots demonstrate how well networks simulated from the specified model capture "out-of-model" statistics (e.g., shared partners, degree distributions, and the triad census). These out-of-model statistics are global properties of the networks that were not directly specified in the local configurations used in the ERGM and SIENA models. In the plots below, the thick black line represents a given statistics observed value. The boxplots show the simulated networks distribution based on the model parameters. The plots show that the out-of-model statistics are well captured the ERGM. For the Siena models, the Triad Census is well fit, however, while we capture the shape of the degree distribution, we underpredict the number of actual isolates causing slight overprediction for other levels of the degree distribution.