A central premise in the interfirm network literature is that to develop new products and improve innovation performance, firms rely on their network position (Ahuja, 2000; Burt, 1997; Koka and Prescott, 2008; Zaheer and Bell, 2005). To achieve these outcomes, firms employ one of two opposing network positioning strategies. When firms occupy a central position, they have access to reliable information directly shared by their connected partners (Ahuja, 2000; Koka and Prescott, 2008). When firms operate in a structural hole, they gain access to non-redundant and diverse information by acting as a broker between other unconnected firms. (Burt, 1992; Zaheer and Bell, 2005). Firms do not stay solely in one position but shift between opposing positions (central to a structural hole and vice versa) to enhance their innovation performance (Ahuja *et al.*, 2012; Gilsing, Cloodt, and Roijakkers, 2015; Mazzola, Perrone, and Handfield, 2018). This strategy enables firms to exploit complementary advantages and counterbalance drawbacks between two network positions.

Although firms can take deliberate actions to adjust their network positions for greater advantages in their performance, they also face inertial pressures to maintain the status quo. This is especially relevant when industry conditions favor routinized trust-based collaboration processes that are more efficient, cost-effective, and productive than initiating new collaborations that are more uncertain in their outcomes (Jackson and Roger, 2007). Interfirm network scholars assume that environmental conditions beyond a firm’s control influence network position shifts, with variations in how these shifts occur hinge on industry context (Casanueva, Castro, and Galán, 2013). As industry conditions evolve, inertial pressures from firm age, status, and routines of interfirm behavior solidify firm network positions (Gulati, 1995; Kale *et al.*, 2000; Kim *et al.*, 2006). Since firm inertia discourages adopting new behaviors, we focus on whether firms would shift to positions that could improve performance despite well-established, well-performing collaborations with existing partners.

To tackle this conceptual puzzle, we focus on the routinized regime of an industry’s life cycle and how network shifts may be influenced by these industry conditions. Technology regimes represent dominant innovation patterns during each life cycle stage, distinguished by entrepreneurial and routinized regimes (Agarwal and Audretsch, 2001). During the entrepreneurial regime, startups and their founders commercialize novel inventions, introduce disruptive technologies, and conceive pioneering business models. These firms operate in industries characterized by rapid changes, uncertainty, and a loose regulatory environment. When the industry matures, the routinized regime emerges. Incremental and predicTable innovation occurs, led by established firms emphasizing efficiency and optimization as a more stringent regulatory framework governs the industry. Firms standardize their search activities for new knowledge. Routinized collaboration processes with trusted partners play a more significant role in rapid innovation and commercialization than in the entrepreneurial regime (Faems, Janssen, and van Looy, 2007; Balland *et al.*, 2012; Gilsing *et al.*, 2015). This implies that firms prefer to maintain a specific network position instead of shifting between the two network positions frequently. Although previous studies have argued that certain network positions provide firms with opportunities to increase their innovation performance, these studies did not explicitly address whether these opportunities depend on conditions related to an industry’s stage in its life cycle (e.g., Mazzola, Perrone, and Kamuriwo, 2015).

We investigate this network-positioning puzzle empirically using a sample of semiconductor firms and their network positions over 17 years (1991 to 2007). We found that firms were more likely to move to central positions instead of structural holes and that shifts to central positions led to higher innovation performance. Our findings indicate that firms adjust their network positioning pattern to improve greater collaboration efficiencies needed to compete in highly competitive industry conditions.

We contribute to the interfirm network literature in two ways. First, our study addresses how firms assess shifts in their network positions during the routinized regime of an industry’s life cycle when inertia and cost efficiencies can impede these shifts. Second, our study reveals that firm innovation performance improves even while maintaining a central position during the routinized regime. In contrast to a frequent position-shifting strategy, firms that maintain a central position can still improve performance while adapting to industry changes. Managers of these firms can leverage their central positions to compete effectively during the routinized regime of their industries.
# Theory and Hypotheses

## Network positions in a firm’s interfirm network

In an interfirm network, firms can be described in terms of the following positions: a structural hole or network centrality. A structural hole in a firm’s ego network exists when partners are connected to the ego firm but not each other (Burt, 1992). A network rich in structural holes means access to unique information flows from many partners who are not connected to each other (Ahuja, 2000). Firms with many structural holes in their network have the advantage of accessing new and diverse information (Uzzi, 1997). Using their access to various new information, ideas, or knowledge, firms can recombine this diverse information and knowledge to develop new technologies or products (Koka and Prescott, 2008). Firms in a structural hole can control the flow and access of information and broker this content between unconnected firms (Burt, 1997).

Although firms can access diverse information and knowledge, positioning in a structural hole has drawbacks. First, occupying structural holes limits a firm’s ability to transfer tacit knowledge between new partner firms since the ties are weak and may not have enough trust to facilitate the sharing of potentially sensitive information (Koka and Prescott, 2008). Second, since bridging ties across structural holes lacks significant past coordination experience, technology development costs increase since they require more effort to integrate the newly acquired diverse and novel knowledge into each partnering firm. These added costs burden the new product development process and lower the overall efficiency of producing innovations (Katila and Ahuja, 2002). Finally, while structural holes facilitate exploratory learning, firms unprepared to balance it with existing knowledge already available within their network may not be able to fully exploit and commercialize the opportunities (Levinthal and March, 1993).

In contrast, a central network position offers specific knowledge accumulation and coordination benefits. First, centrally positioned firms can access more reliable information and knowledge from their interdependence with other directly connected partners than those on a network’s periphery (Koka and Prescott, 2008). Interdependence in a dense network allows firms to build close working relationships, enhance partnership quality, and increase resource commitment levels among connected partners. Over time, this coordination produces similar routines and processes followed by each connected firm, further facilitating information and knowledge transfer (Uzzi, 1997). This allows centrally positioned firms to coordinate work better as they leverage the incoming flow of information and resources. With each successive transaction, a centrally positioned firm gains valuable experience in managing interfirm relationships and monitoring the behavior of its partners more effectively (Kale and Singh, 2009).

By forming extensive direct ties, a centrally positioned firm can strengthen its network status associated with the prestige, power, and legitimacy that accrue with the position (Ozmel, Reuer, and Gulati, 2013). When firms evaluate new potential collaboration partners, they cannot always confirm beforehand whether their new partners can fulfill expectations. However, centrally positioned firms can lend their high status to the prospective partner by signaling that they bring high-quality resources and better opportunities, which reduces the potential partner’s risk of adverse selection (Shipilov, Li, and Greve, 2011). Thus, centrally positioned firms can facilitate new collaborations more easily than non-central firms. At the same, centrally positioned firms need to recognize the drawbacks of their network position. They risk only accessing information based on the scope of their direct partners. Because they cannot easily access a wider pool of sources, some information duplication may occur, and the usefulness of the available information may be limited (Katila and Ahuja, 2002). To avoid this, firms may manage their network structure by strategically evaluating their partners’ alliance characteristics (Greve, Rowley, & Shipilov, 2014; Gulati, 2007). For instance, in consumer electronics, Samsung and Sony leverage their centrality or structural holes to innovate and bring new products to market (Greve, Rowley, & Shipilov, 2014). These findings emphasize the importance of grasping the benefits and tradeoffs of different network configurations, positions, and relationships for accessing critical resources, fostering innovation, and creating value.

## Dynamics of network positions in a firm’s interfirm network

studies on interfirm network dynamics have revealed that the benefits of firms’ network positions can be temporary when their network is in flux. As a result, firms derive more value from their positions when they understand the impact of the network structure and its evolution over time. From this perspective, interfirm network dynamics are driven by conscious agency, where firms intentionally create structures that help them by establishing or selecting connections with other specific firms, forming or breaking network links, or strengthening or weakening relationships (Ahuja *et al.*, 2012). Firms form interfirm networks to reduce costs, minimize risks, and secure access to necessary resources (Gulati, 1995). Such intentional actions affect how networks evolve. Through ego-structural changes, firm centrality can shift. These actions can also open new or close existing structural holes throughout the network. When shifting between positions of centrality and structural holes, firms intend for the resulting benefits to outweigh any disadvantages (Mazzola *et al.*, 2018). Moreover, the firm attributes, such as social status, complementary resources, risk reduction strategies, and strategic directions, influence the dynamics of interfirm networks (e.g., Shipilov, Li, and Greve, 2011). Firm preferences for trusted partners and their pursuit of power and control also impact network dynamics (Hernandez and Shaver, 2019).

Although the network dynamics literature has raised the possibility of network position shifts, we lack extensive empirical research on firm-shifting strategies and their performance implications. Research to date has focused on the position shift itself but has overlooked other considerations, such as environmental changes, that affect how and why firms shift their positions in their network structures. For example, as industry conditions change, firms operate with established norms and practices, rendering firms more resistant to change as they become more deeply entrenched in legacy structures (Hannan & Freeman, 1984). Likewise, network ties persist because of existing routines and behaviors that develop in the context of an interacting set of entities (Kim *et al.*, 2006). Because of inertia, interfirm interactions are unlikely to change rapidly because of how firms cooperate with other network partners. At best, any changes will be incremental as firms delay major adjustments in their alliance behaviors, decisions, actions, processes, and communication patterns (Nickerson and Zenger, 2002).

Network scholars also argue that content or social processes flowing through network structures encourage the persistence of specific network structures (Ahuja, 2012). At the interfirm level, firm status, age, tie formation history, or market dominance can contribute to the inertia that prevents changes in network structures (Kim *et al.*, 2006). At the network structural level, interfirm routines and norms developed among connected firms strengthen the continuity of ties between them (Gulati, 1995). If a dynamic shift occurs between two network positions, the disruptive change to a firm in one position can intensify the resistance to change due to inertia in interfirm processes, actions, and communications routines. This resistance can reduce the willingness of firms to integrate newly accessible knowledge or resources with existing knowledge or resources already developed through their current network structure. Given that changes in the industry environment and the resulting inertia restrict whether and how firms enact new behaviors, we posit that the expected benefits of shifting network positions predicted by existing theory are unlikely to last. Firms may resist a shift since the perceived benefits do not outweigh the costs of overcoming the status quo. These dual scenarios for network shifting and persistence become clearer when analyzing network dynamics against the backdrop of a changing industrial environment.

## Shifts of network positions in the routinized regime of high-technology
        industries

Industry life cycle research has documented a consistent pattern of industry evolution in four typical phases – introduction, growth, maturity, and decline – and each phase has distinct characteristics affecting firm operations (Agarwal, 1998; Agarwal and Audretsch, 2001). In high-technology industries, the innovation processes that operate in these phases can be divided into two distinct technological regimes: entrepreneurial and routinized (Agarwal and Audretsch, 2001). The entrepreneurial regime takes place during the introduction and growth stages, during which firms innovate using unique knowledge to produce one-of-a-kind products that are new to the market. The routinized regime occurs when the technical knowledge of the industry is mature and stable. Firms innovate using routines that optimize development and production processes since product-driven innovations are more difficult to generate during this regime.

Firms seek innovations by standardizing search activities and codifying their technological knowledge base during the routinized regime (Gilsing *et al.*, 2015). Firms seek new collaborations to ensure the reliable development of new products for mass markets. This has a stabilizing effect on the broader industry ecosystem. These collaborations reduce market uncertainty, increase confidence among investors, and provide customers, other stakeholders, and the public with a steady supply of products they value (Talay, Seggie, and Cavusgil, 2009). If firms engage in new collaborations for rapid commercialization, they need to establish new routines that allow for the collaboration to innovate in rapid, cost-effective, and predicTable ways (Faems *et al.*, 2007). Thus, firms are more likely to increase collaborative connections based on trusted relationships already in place or form collaborations that minimize transaction costs (Gulati, 1995). One way firms can achieve this is by collaborating with partners who are situated closer to them geographically or have similar knowledge and skills rather than with new firms (Balland *et al.*, 2012).

During the routinized regime, firms in central positions are more likely to have the market power and industry standing to manage a growing portfolio of collaborative relationships more efficiently. These firms can promote efficient collaboration processes by ensuring that other partners comply with both established industry recipes and new standards. If partners fail to comply, these firms have sufficient influence to enforce sanctions against misbehaving partners (Burt, 1992). Given these conditions, some firms seek to establish a stronger centralized network position, while others are pushed to peripheral positions (Gilsing *et al.*, 2015).

Firms with established collaboration processes are likely to be positioned centrally in their interfirm networks, and this positioning reflects connections with partners that have strengthened over time (Gulati, 1995). The value of a firm’s central network position is associated with interfirm trust, power, and status established among its connected partners. Centrally positioned firms can derive long-term value from their collaborations by developing sTable interfirm routines and enforcing supporting norms. If centrally positioned firms shift to a structural hole, they abandon not only structural benefits (such as access to a large amount of reliable information) but also positional benefits gained from interfirm routines, processes, and communication patterns they established over time. The shift will impose greater costs on firms than remaining in the central positions since additional efforts will be needed to overcome the inertia that resists changes in mature high-technology industries (Prashant and Singh, 2009; Kim *et al.* 2006). For these reasons, we predict the following:

```
**Hypothesis 1a.** *During the routinized regime of a high-technology industry, firms currently in a
              structural hole network position are**more
              likely**to shift to a more central network
              position in the future.*
```

```
**Hypothesis 1b.** *During the routinized regime of a high-technology industry, firms currently in a
              central network position are**less
              likely**to shift to a structural hole network
              position in the future.*
```

## Dynamics of network positions and firm performance

Research on technology management shows the benefits of simultaneously exploring new opportunities and exploiting existing ones. When firms develop the capacity for this, they are more likely to improve their innovation performance (O’Reilly and Tushman, 2008). The innovation process involves two main stages: 1) idea generation and 2) development and commercialization (Ahuja and Lampert, 2001). During the idea stage, firms focus on problem-solving and creating/recombining technological ideas, often seeking knowledge distant from their existing base (Hargadon and Sutton, 1997). During the development and commercialization stage, firms aim to leverage knowledge similar to their existing base to bring new product solutions to market. This balanced approach of exploring new frontiers while exploiting existing knowledge positively impacts innovation outcomes (Katila and Ahuja, 2002). Rothaermel and Deeds (2004) found that exploration alliances, centered on basic research and identifying new technologies, foster the generation of novel technologies and product concepts, leading to a rise in products under development. Meanwhile, exploitation alliances, concentrating on design, engineering, prototyping, and marketing, support the innovation’s development and commercialization, resulting in more products entering the market. We apply the network structural holes and centrality position concepts to these exploration and exploitation alliances.

Firms that occupy structural holes are more likely to access new knowledge from distant sources required for exploratory alliance opportunities (e.g., Ahuja, 2000; Rothaermel and Deeds, 2004). Structural holes connect firms to other firms and industries that can provide non-overlapping information needed to commercialize new technologies (Burt, 1992). Firms that span structural holes can act as technology intermediaries. In this role, they improve their market position by becoming more aware of new threats and opportunities, improving business efficiency, opening new access to information, and identifying and responding to industry trends (Zaheer and Bell, 2005). This bridge to new, non-duplicated information and knowledge exploration increases the likelihood of successful technology development (Nickerson and Zenger, 2002).

Firms that occupy a more central position benefit from the close interactions with existing partners over operational routines associated with exploiting opportunities based on existing knowledge (e.g., Ragatz *et al.*, 2003; He *et al.*, 2014). High centrality provides three benefits for promoting innovation through exploitation alliances: knowledge collection, accumulation, and scale (Boumgarden, Nickerson, and Zenger, 2012). First, centrally positioned firms can accumulate a wide range of information to improve their ability to monitor their external environment and find new knowledge. Knowledge accumulation can lead to better recognition and integration of new ideas into existing operations, promoting further innovation (O’Reilly and Tushman, 2008). Second, centrally positioned firms can improve their economies of research scale (Ahuja, 2000). By having reliable collaborators in place, new search costs for external resources remain low, and new technology development processes work through existing suppliers, patents, technologies, and prospects (Ragatz *et al.*, 2003; Billiteri *et al.*, 2013).

Effective sharing of information and knowledge from one organizational structure to another depends on how firms manage the capacity of their existing knowledge, processes, and routines. When firms acquire new knowledge from an external organizational structure, these details are shared, disseminated, and synthesized through their previously established knowledge repositories, processes, and routines. The extent to which these internal structures synthesize externally acquired knowledge determines the scope and capacity of firms to execute technological development and innovation (Prashant and Singh, 2009). That is, firm innovation performance is primarily a function of its diverse organizational routines, processes, and knowledge to integrate novel information and knowledge acquired from others (Koka and Prescott, 2008).

Based on these principles of firm knowledge management, centrally positioned firms have several advantages that influence their innovation performance. First, central firms can reduce search costs and increase knowledge transfer efficiency. When firms have multiple direct ties or highly connected ego networks, they can access information at shorter distances. For instance, central firms can easily reach and select suppliers that provide the best knowledge and capabilities for successful new product development in ways that less centrally positioned firms cannot (Ragatz *et al.*, 1997). Second, by accessing new complementary knowledge less likely to conflict with their knowledge base, central firms can lower the performance risk of unsuccessful technology acquisitions, enact reliable development routines and processes, and increase the likelihood of bringing new products to market on time (Phelps, 2010). Third, central firms collaborate with reciprocity and trust by sharing common third-party partners in their dense network. This structure further reduces information asymmetry between partners and allows central firms to monitor any deviant behavior of their partners easily, resulting in a lower risk of opportunism in the alliances.

During an industry’s routinized regime, firms are likely more concerned about cost-effective alliance collaborations, given general cost-containment and pricing pressure to stay competitive. When firms evaluate new opportunities–either originating from existing alliances or new partners–these opportunities need to satisfy internal cost-benefit evaluations. While new exploratory alliances could lead to promising breakthroughs, these opportunities require more upfront costs to ensure cost-effective collaborations and rapid commercialization. At the same time, the inertia that favors existing relationships and their defined collaboration routines and processes increases the intrinsic costs of establishing new relationships.

When entering an alliance with a new partner, both parties must establish their collaboration processes to ensure the alliance operates effectively, as the new alliance entails significant investments in financial and management resources. As the number of new alliances increases, firms need additional resources to support them. This imposes a higher sunk cost for each additional partnership (White and Lui, 2005). Centrally positioned firms already have multiple partners in place. Rather than entering new alliances, these firms can reduce new sunk costs by strengthening ties with existing partners. As sunk costs decrease, the overall cost-effectiveness improves for firms, especially when evaluated across multiple alliances. In contrast, firms in structural holes need to establish new partnerships and incur additional costs associated with this effort before they can acquire any exploratory knowledge through their new partners. At the same time, by moving to structural holes, firms may need to forego additional opportunities to deepen their existing alliances and chances to amortize their sunk costs over repeated engagements (Shimizu and Hitt, 2005). We argue that in the long run, the shift to a more central position has a positive influence on performance. For these reasons, we predict:

```
**Hypothesis 2.** *During the routinized regime of a high-technology industry, firms that shift to
              central network positions are more likely to generate higher innovation
              performance.*
```
# Methods

## Research context and background of the semiconductor industry

We tested our hypotheses using data from the global semiconductor industry from 1991 to 2007. We chose this context because of its high prevalence of interfirm alliances and rapid development of new technologies and business models (Stuart, 1998). Firm collaboration is critical due to the complexities of producing the final product. The semiconductor chip manufacturing process comprises a series of specialized tasks, including designing, manufacturing, testing, packaging, and distribution that no single firm can execute alone. Thus, interfirm collaboration is necessary to complete the production process successfully. Moreover, the industry is characterized by a high level of technological complexity and rapid innovation, requiring firms to update their knowledge and skills to remain competitive continuously. In this regard, interfirm cooperation has been proven to be a productive mechanism for accelerating innovation and enhancing the industry’s overall competitiveness.

While periodic technological innovations, such as the emergence of next-generation microchips, have been a major driver for industry evolution, the semiconductor industry has been in a mature, routinized regime since the mid-1990s (Smithsonianchips, 2019). Historically, the industry grew at an annual rate of 30 percent, but between 1995 and 2004, the annual growth rate slowed to six to 19 percent. From 2005 to 2014, the annual growth rate dropped to seven to ten percent due to an overall decline in average prices. End-user markets, such as computers and telecommunications, have matured since the mid-1990s, increasing pricing pressure on major original equipment manufacturers (OEMs) and semiconductor components vendors. This pricing pressure, coupled with overproduction, has forced semiconductor firms to reduce their product costs and shorten production times. As a result, the industry is known for producing standardized low-cost products. With large suppliers investing heavily in R&D to create better single-chip solutions and increasing capital spending to reduce the cost per integrated chip, the industry experienced an accelerated decrease in overall revenue and growth margin in the late 1990s and 2000s. In addition to structural challenges, the semiconductor industry underwent significant consolidation through mergers and acquisitions (M&As). This allowed firms to rapidly access new markets, technologies, and capabilities while eliminating potential competitors. In the 1990s, M&A deals expanded market share and product portfolios for certain firms. For instance, Texas Instruments acquired Silicon Systems in 1997, and Motorola acquired General Instrument in 2000. In the 2000s, high-profile M&A deals included Hewlett-Packard and Compaq’s merger in 2002, ATI Technologies’ acquisition by Advanced Micro Devices in 2006, and Freescale Semiconductor and NXP Semiconductors’ merger in 2015 (CNNMoney, 2002; Sorkin, 2006; NXP, 2015). These transactions resulted in fewer, larger firms with more resources and capabilities to develop products in-house. These remaining firms increased their market share and consolidated the industry. Since 2007, the top 25 semiconductor companies have accounted for more than 77 percent of the global market share, with the world’s top five companies, including Intel, Samsung, TSMC, SK Hynix, and Micron, accounting for 43 percent of total sales in the industry as of 2017 (Alsop, 2022; Howley, 2022). The increased in-house technological capabilities reduced interfirm alliances and technology licensing agreements (McCarthy and Aalbers, 2022).

## Sample

To construct our analytic sample, we gathered data from multiple sources. For interfirm collaborations, we obtained data from the SDC Platinum Alliance/Joint Venture database, which has global alliance information since 1985 compiled from filings with the Securities and Exchange Commission (SEC) and its international counterparts, corporate news, trade publications, and other publicly available sources. The SDC database contained 2,400 alliance agreements in the semiconductor industry (SIC 3674) between 1991 and 2007. To process the raw data, we began by selecting interfirm networks based on the types of long-term alliance agreements, such as manufacturing, R&D, marketing, licensing, and sales services. These alliance agreements represent approximately 80% of all transactions. Next, we filtered out transactions that did not have a “completed/signed” status and error transactions involving only one recorded party. We also dropped duplicate transactions by cross-referencing alliance announcements in Factiva. Finally, we converted multiple alliances involved in a single transaction into a set of bilateral alliances (Ahuja, 2000). After these data-cleaning steps, we yielded 3,001 alliance agreements by 766 firms headquartered in 35 countries over 17 years from 1991 to 2007. We formed our network variables from these different alliance agreements. We obtained firm-specific data from Refinitiv’s Eikon (formerly Thomson Financial), and patent data from the US Patent and Trademark Office (USPTO)’s website. From the main analytic sample, we constructed two different datasets to test our hypotheses. We analyzed the first Hypothesis as a panel dataset and the second Hypothesis as a cross-sectional dataset. Since each dataset has unique features and variables, we explain the details separately in the following sections and summarize the steps in Figure 1.

<figure><img src="image_1.png" /><figcaption>figure 1 — Data handling and analysis steps</figcaption></figure>

## Panel dataset – overview

Our panel dataset comprised 766 firms over 17 years (1991-2007). The panel is unbalanced because these firms did not enter into repeated annual agreements with each other. Partner firms in the panel dataset primarily operated in the semiconductor & related devices industry (SIC 3674, 57 percent); the remaining partner firms represented nearly 30 other industries. The top industries included electronic equipment and components (SIC 36, 9 percent); industrial machinery (SIC 35, 8 percent); business services (SIC 73, 8 percent); and measuring, analyzing, and controlling instruments (SIC 38, 2 percent).

### Panel dataset – Variables

We created two dependent variables for our panel dataset: a firm’s eigenvector centrality and structural holes. These are established variables in interfirm network research, and we followed procedures similar to those used in other studies (Shipilov and Li, 2008). First, we calculated the *eigenvector* *centrality* as: , where represents the eigenvector centrality of firm *i* at year *t*: *N*<sub>it</sub> signifies the set of firms that are connected to firm *i*at year*t*, λ is the eigenvalue of the adjacency matrix of the network, υ<sub>jt</sub> refers to the centrality value of firm *j* connected to firm *i*at year*t*(Ozmel *et al.*, 2013). In essence, the eigenvector centrality measures a firm’s influence within its network and the extent to which it can access information and resources from its connections to other central firms (Koka and Prescott, 2008). Secondly, we measured *structural holes*based on the formula (1-constraint score): , where *sh*<sub>it</sub> denotes the structural holes value of firm *i* at year *t*; is the proportional ties invested by firm *i*in the relationship with firm *j*at year*t*; *p*<sub>ijt </sub>is the proportional ties by firm *i* invested in the relationship with firm *q* at year*t*; and *p*<sub>qjt</sub> is the proportional strength of firm *q*’s relationship with firm *j* at year*t*. This calculation captures the extent to which a firm’s direct ties are disconnected, accounting for the extent to which a firm is constrained by other firms connected to one another in its network. Firms with higher structural hole values are less constrained and have more access to disconnected firms (Shipilov and Li, 2008; Zaheer and Bell, 2005). We first created 17 binary adjacency matrixes (one per year, 1991-2007) for all bilateral alliances among the 766 sample firms to construct the centrality and structural holes variables. We then analyzed the dyadic data using the network-analysis software UCINET VI to calculate the network variables (Borgatti, Everett, and Freeman, 2002). To compare over time, we normalized the two variables using each variable’s average and standard deviation estimated by their respective population at time *t*. Thus, for firm *i* at time *t,*we used the following formula to compute the dependent variables:

For our primary independent variables, we used the normalized centrality (and structural holes at time *t*-*1*. We included year dummies to control for period effects, such as macroeconomic conditions and other industry or network characteristics that may have changed during our study period. In Figure 2, we illustrate changes in the ego network structure of an exemplary firm, Advanced Micro Devices, Inc. (AMD). In these network maps, the size of all nodes is proportional to the firm’s eigenvector centrality. Figure 2a shows that in 1993, AMD was in a peripheral network position with a low centrality score based on its alliance with only seven firms. In 1995, the firm became a more central actor with 21 alliances (Figure 2b). By 1997, AMD had cemented its network centrality with its many direct connections with other firms (Figure 2c).

### Panel dataset – Dynamic Model specification

We employed dynamic models to analyze the relationships between centrality<sup>*t*</sup> (structural holes<sup>*t*</sup>) and structural holes<sup>*t-1*</sup> (centrality<sup>*t-1*</sup>) for firm *i* at time *t*. Specifically, we examined the following relationships:

where and , and and are vectors at time *t* and *t-1*, respectively; X is a vector of year dummies; α, β and γ are parameter vectors; *c*<sub>*i*</sub> represents unobservable firm heterogeneity; and is the idiosyncratic error term. In this equation, the current centrality and structural holes values may depend on past values of these network positions (endogeneity), may be correlated with the unobserved heterogeneity factor *c*<sub>*i*</sub>, or the error term may be autocorrelated over time. As a result, analyzing our panel dataset using standard pooled OLS estimators and panel fixed effects would lead to biased and inconsistent results. To properly account for dynamic effects and unobserved heterogeneity in our panel data, we employed regression models with the system generalized method of moments (System GMM) (Arellano and Bover, 1995). System GMM estimation offers several tools to overcome the limitations of conventional panel regression models. The most important advantage is the estimation’s ability to use a set of “internal” instruments, such as past values of centrality and structural holes, contained within the panel to address endogeneity concerns. This feature eliminates the need for external instruments. The estimation also includes firm fixed effects to account for unobservable firm heterogeneity and is robust to firm-specific patterns of heteroscedasticity and serial autocorrelation. System GMM is robust to sample gaps and data loss in unbalanced panels, because the orthogonal deviations method subtracts the average of all available future values of the variable. Regardless of the number of gaps, only the last observation is unavailable for analysis. To conduct our analysis, we followed Roodman (2009) and used the -xtabond2- command in STATA 16. We specified all variables as endogenous variables (gmmstyle) except for the year dummies, which we used as exogeneous variables (ivstyle). To address the potential overfitting of instrumental variables caused by instrument proliferation, we used the “collapse” option that combines instrument vectors into one column through addition. To enhance the efficiency and consistency of the estimates, we included lag 2 as the nearest lag and lag 17 as valid lag for dependent variables of all models (Roodman, 2009).

## Cross-section dataset - overview

From the full panel dataset of 766 firms, we selected only publicly traded semiconductor firms (SIC 3674), resulting in a sample of 137 semiconductor firms headquartered in 12 countries. We drew from all public firms within the original pool of 766 to prevent any potential selection bias (Phelps, 2010). We created a cross-sectional dataset of public firms because of firm-specific data availability in their annual reports.

### Cross-section dataset - Variables

We created our *innovation performance* dependent variable by using the number of granted patents for firm *i* in year *t*(the year of the patent application) between 2010 to 2012. We chose a 3-year window for our analysis to capture the period in which most patent applications for firm *i* are examined and granted (Ahuja, 2000; Phelps, 2010). Thus, we measured innovation performance for 2010 as the cumulative number of granted patents from 2008 to 2010, for 2011 as the cumulative number from 2009 to 2011, and so on. This approach accounts for the lag between innovation activities—such as R&D, network positioning dynamics, and resource acquisition—and outcomes, such as patents or new products. We constructed two independent variables, *shifts to centrality* and *shifts to structural holes*, using a procedure similar to Mazzola *et al.*’s (2018) study. We counted the number of shifts for firm *i* from time *p*to time *t*. A shift occurred when Centrality<sup>*t*</sup> was larger than Centrality<sup>*p*</sup> and Structural Holes<sup>*t*</sup> was smaller than Structural Holes<sup>*p*</sup> at the same time, since firm *i* moved more toward a central position than structural holes at time *t.* Thus, we coded the *shifts to centrality* variable 1 if Centrality<sup>*t*</sup> > Centrality<sup>*p*</sup> and Structural Holes<sup>*t*</sup> < Structural Holes<sup>*p*</sup> and 0 otherwise. Likewise, if Structural Holes<sup>*t*</sup> is larger than Structural Holes<sup>*p*</sup> and Centrality<sup>*t*</sup> is smaller than Centrality<sup>*p*</sup> at the same time, firm *i* shifted more toward structural holes than a central position at time *t*. We coded the *shifts to structural holes* variables 1 if Structural Holes<sup>*t*</sup> > Structural Holes<sup>*p*</sup> and Structural Holes<sup>*t*</sup> < Structural Holes<sup>*p*</sup> and 0 otherwise. We then counted how often firms shifted positions (toward more central or structural holes) during the 17 years. Figure 3a shows the frequency distribution of 137 firms. 118 firms out of 137 (about 86 percent) shifted at least once. Figure 3b displays the distribution of firms that shifted to a more central position; 103 firms out of 137 shifted to a more central position at least once. Figure 3c exhibits the distribution of 102 firms that shifted to structural holes.

In terms of control variables, we created several to account for alternative explanations. Since younger firms are more likely to shift network positions than older firms, we controlled for *firm age* as the difference between the firm’s founding year and 2007. Larger firms may have more resources leading to higher innovation performance than small ones, so we included *firm size* as the natural logarithm of the number of average employees during 1991-2007. A firm’s patent stock reflects its depth of technological resources, research & development (R&D) ability, and absorptive capacity (Phelps, 2010), thus we accounted for *patent stock* by using the natural logarithm of the number of patents obtained in the 10 years prior to 1991. R&D expenditures contribute to a firm’s ability to create new knowledge. Thus, we controlled for R&D*expenditures* as the natural logarithm of the average of R&D expenditures during 1991-2007. A firm’s R&D partnership strategy contributes to its technical knowledge and innovation, so we controlled the number of *R&D alliance*during 1991-2007. In the semiconductor industry, manufacturing and non-manufacturing firms commonly have different business models. Manufacturing firms engaged in chip fabrication, foundries, lithography, etching, packaging, and assembly and testing often keep close connections to semiconductor fabrication processes, shaping their innovation strategies and capabilities. On the other hand, non-manufacturing firms, including fabless and design automation and tools providers, prioritize design development, intellectual property development, or strategic partnerships. To control for these distinctions, we coded manufacturing firms as 1 and non-manufacturing firms as 0 within our analysis. Indirect network ties may affect a focal firm’s performance; we measured the number of *indirect ties* using UCINET. Finally, we included *nationality* and coded it 1 if the firm was United States-based and 0 otherwise. We summarized the construction details for each variable in Table 1.

<table><caption>Table 1 — Variables</caption><tr><td>MeasureOperationalizationSourcesCentralityNormalized value of eigen vector
            centrality: (centralityit-mean of centralityit)/standard deviation of centralityitOzmel
            et al., (2013); Shipilov and Li, 2008)Structural HolesNormalized value of structural
            holes (1-constraint score): (structural holesit-mean of structural holesit)/standard
            deviation of structural holesitShipilov and Li, (2008)Innovation performanceNumber of
            granted patent between 2008 and 2010 (3-year window)Ahuja (2000); Phelps(2010)Patent
            stockaLn (number of patent obtained 10 year prior to 1991)Vanhaverbeke et al. (2009)Firm
            ageaLn (difference between the firm’s founding year and 2007)Control for experience
            effectsFirm sizeaLn (average of employees during 1991‑2007)Control for economies
            of scaleNationality0-1 dummy variable for United StatesAhuja (2000)R&D
            expendituresaLn (average of R&D expenditure during 1991-2007)Phelps
            (2010)Manufacturing0-1 dummy variable for manufacturing Control for firm
            characteristicsR&D allianceNumber of R&D alliances during 1991‑2007Control for
            R&D partnershipsIndirect tiesNumber of indirect ties of firm i from time p to time
            tControl for the influence of indirect tiesShifts to structural holesNumber of shifts to
            structural holes of firm i from time p to time tMazzola et al. (2018)Shifts to
            centralityNumber of shifts to centrality of firm i from time p to time tMazzola et al.
            (2018)Year effects0-1 dummy variable for each year, 1991 base yearControl for
            time-related factors</td></tr></table>

<table><caption>Table 2 — Descriptive statistics and correlation of panel data set — * p<0.05</caption><tr><td>VariablesObsMeanS.D.MinMax(1)(2)(3)(4)1. Centralityt3,001.014.955-.8838.3651.0002.
            Structural Holest3,001-.002.996-1.3793.7860.382*1.0003.
            Centralityt-12,189.0561.054-.8838.3650.233*0.162*1.0004. Structural
            Holest-12,189.1051.027-1.3793.7860.217*0.364*0.403*1.000</td></tr></table>

<table><caption>Table 3 — Descriptive statistics and correlation of cross-sectional data set — * p<0.05, <sup>a</sup> Natural logarithm</caption><tr><td>VariablesObsMeanS.D.MinMax(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)1.Patent
            (3-year)137237.92618.781044742.Patent stocka1371.5871.62607.8380.237*3.Firm
            agea1373.218.77205.0750.1090.397*4.Firm
            sizea13711.4254.5154.5920.9380.351*-0.007-0.0375.Nationality137.606.4901-0.0880.020-0.026-0.235*6.R&D
            expendituresa13717.2573.8872.91822.5030.1100.0280.0660.100-0.0397.Manufacturing13755.32876.04124380.1200.1530.1120.168*-0.0140.1658.R&D
            alliances137.737.44201-0.182*-0.023-0.069-0.021-0.0400.169*0.0359.Indirect
            ties1375.56912.67611150.0270.0850.0710.1360.0120.185*0.759*0.05210.Shifts to structural
            holes13755.32876.04124380.1200.1530.1120.168*-0.0140.1651.000*0.0350.759*11.Shifts to
            centrality1371.2411.141060.232*0.224*-0.0160.1340.0130.184*0.425*-0.1360.291*0.425*</td></tr></table>
# Results

In Table 2 and Table 3, we provide descriptive statistics and correlations for the panel and cross-section datasets, respectively. We observed low to moderate correlations between all variables in Table 2 and 3. The VIF for each variable was below 1.53, so we concluded that multicollinearity was not a significant concern in our models.

## Regression Model results for the panel dataset

In Table 4, we report the System GMM estimates with all relevant diagnostic tests. In Table 4, Models 1 and 3 show the baseline models that include only the year dummies. Models 2 and 4 show the effects of lagged network positions (Structural Holes<sup>*t-1*</sup> and Centrality<sup>*t-1*</sup>, respectively) on current network positions (Structural Holes<sup>*t*</sup> and Centrality<sup>*t*</sup>, respectively). This analysis uses a starting point of 1992 as time *t*, but the independent variables are calculated based on data from one year prior (*t* – 1). As a result, there are no observations for the independent variables at *t* – 1 in 1991. Thus, the number of observations for Models 2 and 4 is 2,189, calculated by subtracting 812 observations (1991 panel) from 3,001 (the total number of observations). Our system GMM estimation involved testing for second-order serial correlation in the first differenced residuals using the Arellano-Bond test. Additionally, we used the Hansen over-identification test to ensure that the instruments used in the analysis were robust and not correlated with the error term. As Table 4 shows, both tests did not reject their stated null hypotheses.

With Hypothesis 1a, we predicted a positive effect of structural holes in time *t-1* on centrality in time *t*. Model 4 reveals that a high structural holes value in the previous year (Structural Holes<sup>*t-1*</sup>) increases the likelihood that a firm will shift to a more central position in the current year (Centrality<sup>*t*</sup>), supporting Hypothesis 1 (β=0.238, *p*<0.05). With Hypothesis 1b, we predicted a negative relationship between centrality in time *t*-1 and structural holes in time *t*. Model 2 shows that centrality in the previous year (Centrality<sup>*t-1*</sup>) was not statistically significant on structural holes in the current year (Structural Holes<sup>*t*</sup>). Thus, we did not find support for Hypothesis 1b. We reran our GMM models using a two-year lag (Centrality<sup>*t-2*</sup> and Structural Holes<sup>*t-2*</sup>) as a robustness check. The results were highly consistent with those reported in Table 4. To test the robustness of the findings, we ran a fixed effects regression analysis. As shown in Table 4, Models 5 and 7 show the regression of Structural Holes<sup>*t*</sup> and Centrality<sup>*t*</sup> with only the year dummies, Models 6 and 8 display the effects of lagged network positions. The estimated coefficient for Structural Holes<sup>*t-1*</sup> is significant and consistent with the prediction of Hypothesis 1, consistent with the GMM results.

## Regression Model results for the cross-sectional dataset

Since our dependent variable–the number of granted patents–is a count variable, we used a negative binomial Model to analyze the cross-sectional dataset. We chose the negative binomial Model over the Poisson Model because of overdispersion in the data. In Table 5, we present our results.

<table><caption>Table 4 — Results of system GMM and fixed effect analysis — Robust standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01; Two-tailed test..</caption><tr><td>GMM ModelsFixed Effect ModelsRobustness
            checkModel 1Model 2Model 3Model 4Model 5Model 6Model 7Model 8Structural HolestStructural
            HolestCentralitytCentralitytStructural HolestStructural
            HolestCentralitytCentralitytStructural
            Holest-10.1760.238**-0.085**0.074**(0.243)(0.119)(0.029)(0.032)Centralityt-10.1540.224-0.003-0.090**(0.098)(0.177)(0.025)(0.027)Year
            19920.0810.166*-0.0410.1230.0830.041(0.078)(0.096)(0.090)(0.087)(0.081)(0.086)Year
            19930.0290.054-0.074-0.0150.022-0.051-0.021-0.096(0.078)(0.096)(0.080)(0.087)(0.085)(0.101)(0.091)(0.110)Year
            19940.0450.008-0.0060.078-0.075-0.291***0.029-0.108(0.091)(0.103)(0.091)(0.095)(0.093)(0.110)(0.099)(0.120)Year
            1995-0.043-0.071-0.118-0.058-0.219**-0.446***-0.105-0.245**(0.088)(0.096)(0.079)(0.083)(0.097)(0.114)(0.104)(0.124)Year
            19960.1600.260**-0.0630.106-0.023-0.283**0.046-0.034(0.102)(0.108)(0.080)(0.080)(0.101)(0.119)(0.108)(0.130)Year
            19970.0830.039-0.0350.057-0.135-0.386***0.149-0.008(0.101)(0.110)(0.105)(0.103)(0.105)(0.125)(0.112)(0.136)Year
            19980.215*0.1900.0730.143-0.087-0.297**0.213*0.191(0.115)(0.123)(0.106)(0.100)(0.111)(0.131)(0.118)(0.143)Year
            19990.1240.073-0.037-0.031-0.207*-0.395***0.0420.003(0.108)(0.135)(0.096)(0.106)(0.114)(0.136)(0.121)(0.148)Year
            20000.1210.137-0.0040.131-0.289**-0.558***0.127-0.052(0.116)(0.139)(0.099)(0.106)(0.116)(0.139)(0.124)(0.152)Year
            20010.194*0.248**0.0840.061-0.327***-0.509***0.1630.083(0.110)(0.124)(0.110)(0.099)(0.117)(0.140)(0.124)(0.152)Year
            20020.199*0.1760.0190.166-0.293**-0.580***-0.034-0.105(0.113)(0.134)(0.103)(0.119)(0.120)(0.144)(0.128)(0.157)Year
            20030.0900.0590.0160.057-0.410***-0.765***-0.006-0.228(0.112)(0.124)(0.095)(0.094)(0.125)(0.150)(0.133)(0.163)Year
            20040.2030.126-0.058-0.045-0.419***-0.743***-0.180-0.439**(0.124)(0.160)(0.091)(0.083)(0.134)(0.164)(0.143)(0.179)Year
            20050.1690.1100.1010.049-0.521***-0.754***-0.171-0.485**(0.130)(0.146)(0.106)(0.103)(0.144)(0.177)(0.153)(0.194)Year
            20060.296**0.292**-0.121-0.135-0.510***-0.882***-0.458***-0.576***(0.142)(0.148)(0.078)(0.103)(0.165)(0.202)(0.175)(0.221)Year
            20070.0810.166*-0.0410.1230.022-0.051-0.021-0.096(0.078)(0.096)(0.090)(0.087)(0.085)(0.101)(0.091)(0.110)Constant-0.196***-0.094-0.026-0.0660.150**0.483***0.0090.166*(0.066)(0.068)(0.073)(0.059)(0.071)(0.083)(0.075)(0.091)Observation
            (n)3,0012,1893,0012,1893,0012,1893,0012,189Hansen tests of overidentifying restriction
            (p-value)0.0130.2040.4560.344AR (1) (p-value)0.0000.0010.0000.001AR (2)
            (p-value)0.8030.2050.6910.118R20.0170.0400.0120.029</td></tr></table>

<table><caption>Table 5 — Negative binomial model of innovation performance — Standard errors in parentheses. *p < 0.1, **p < 0.05, ***p < 0.01; Two-tailed test. — <sup>a</sup> Natural logarithm.</caption><tr><td>Innovation performanceNegative Binomial ModelsInnovation performanceProbit Models-
            Robustness checkModel 1Model 2Model 3Model 4Patent
            stocka0.324***0.250**0.011-0.048(0.115)(0.108)(0.090)(0.096)Firm
            agea-0.356-0.3120.0940.160(0.220)(0.223)(0.186)(0.199)Firm
            sizea0.165***0.103**0.0150.012(0.037)(0.040)(0.030)(0.032)Nationality-0.145-0.217-0.135-0.097(0.340)(0.319)(0.280)(0.296)R&D
            expendituresa0.099***0.081**0.067**0.059*(0.035)(0.035)(0.031)(0.033)Manufacturing-0.511-0.5200.488*0.711**(0.336)(0.328)(0.286)(0.315)R&D
            alliances-0.032-0.035*-0.012-0.012(0.020)(0.020)(0.019)(0.019)Indirect
            ties0.002-0.000(0.003)(0.003)Shifts to structural holes0.2140.239(0.164)(0.178)Shifts to
            centrality0.467**0.333*(0.183)(0.187)Constant2.339**2.484**-1.035-1.605*(0.991)(0.975)(0.872)(0.949)N137137137137Wald
            chi 253.58***65.97***11.7120.60**Log-likelihood-751.25-745.06-59.23-54.78Pseudo
            R20.0340.0420.0900.158</td></tr></table>

Model 1 includes only the control variables, followed by Model 2 with the shifts to structural holes and shifts to centrality variables included. Regarding the coefficients for our control variables, we find that patent stock is statistically significant in all models, showing that a firm’s existing patent portfolio is positively related to the number of patents granted over the study period. We also see a positive and statistically significant coefficient for firm size, suggesting that larger firms may have greater innovative capacity due to their superior resource base. Finally, R&D expenditures are positively related to patenting activity in all models, and the coefficients are statistically significant and in the expected direction. In terms of our independent variables, we observe in Model 2 a positive association between a firm’s shifts to centrality and innovation performance (β=0.434; p<0.05), which supports Hypothesis 2. We conducted robustness checks by including an innovative performance dummy variable in our analysis. This variable takes a value of 1 if a firm developed at least one patent during the observed period and 0 otherwise, providing insight into the firm’s ability to introduce innovation (Hoetker, 2007). To analyze this dichotomous variable, we employed a probit Model (Table 5). Model 4 presents the results, which are consistent with our main findings. We also removed the company with the outlier 4,474 patents from the dataset, reran the regression, and plotted the results in Figure 4. These results reinforced the robustness of our main findings, even with outliers included in the sample.

<figure><img src="image_2.png" /><figcaption>Figure 4 — Probability of innovation performance</figcaption></figure>
# Discussion

Our study was motivated by the lack of research on the dynamics of network positions in high-technology industries and their impact on long-term performance, especially when mature industry conditions can influence these network dynamics. We tackled a conceptual puzzle that became clear after weaving together principles from the network dynamics and industry evolution literatures: why do firms shift their network positions when inertial pressure should discourage changes in firm behaviors? From our analysis of the global semiconductor industry between 1991 and 2007, our findings offer new insights into how firms shift network positions and their impact on performance outcomes. Specifically, our results show that industry maturity increases the likelihood that firms shift to a more central position and that occupying a central position has a positive effect on firms’ innovation performance in the long term.

## Implications for research and practice

This study contributes to the literature on interfirm networks in two ways. First, past research assumed a causal link between two network positions (central and structural hole) in the ego network based on dynamic shifts of network positions over time (Berends *et al.*, 2011; Gilsing *et al.*, 2015; Mazzola *et al.*, 2018). However, no studies to date examined how firms’ positioning behavior may vary with changing industry conditions. Our study reveals that during the routinized technology regime, firms kept their central network positions due in part to the inertia that prevented shifts to other positions. Second, previous interfirm network research showed the positive effects of structural hole and central network positions on corporate performance (e.g., Shipilov and Li, 2008; Gilsing *et al.*, 2006). We found firms that held their central network positions were more likely to benefit during the routinized technology regime than those that shifted between two network positions. This “hold-and-stay” network strategy firms employ to adapt to changes in their industrial environment affects innovation performance. Our findings are consistent with past network research that showed how centrally located firms benefit from higher visibility, better and more reliable information access, and enhanced innovation performance (e.g., Casanueva, *et al.*, 2013; Koka and Prescott, 2008). Other research has reported that excessive shifting between structural holes and centrality positions can harm new product development (Mazzola *et al.*, 2018). Our study supports this conclusion. In addition, our findings align with the theoretical insights of the history-friendly (HF) Model by Malerba *et al*. (2016), highlighting the importance of interfirm network interactions, including network positions and technological regimes that shape industry dynamics and firm performance. The HF Model captures innovation processes’ path-dependent and heterogeneous nature within high-technology industries, offering insights into how historical and structural factors influence competitive outcomes. The alignment between our empirical evidence and their HF Model suggests a cohesive understanding of interfirm dynamics and innovation in high-technology industries.

Our study offers clear managerial implications. Managers should evaluate the benefits of their firm’s network position regarding the external conditions associated with their industrial stage. A network strategy that calls for dynamic shifts between the two network positions may produce positive outcomes for innovation during periods of industry growth, but this same strategy may not be as effective as the industry matures. Specifically, occupying a structural hole may have a positive effect on the discovery of novel ideas and technologies. However, these network outcomes may not immediately translate into improvements in innovation performance for mature industries, which are characterized by high concentration and intense competition. Moreover, inertial pressures constrain firms from shifting positions and unlocking the complementary benefits of the two network positions. Therefore, we recommend that managers closely monitor their industry’s conditions when implementing their network strategy rather than constantly shifting between the two network positions. Our findings also show that a shift to a more central network position has a positive effect on innovation performance. Firms in central network positions can expand their close connections with partners based on trust and reciprocity to strengthen existing technological innovations and bring them to the market. Maintaining a central network position enables rapid innovation by helping firms use standardized innovation and collaboration processes to commercialize promising technologies.

## Limitations and implications for future research

Our study has limitations, and we note them as potential future research opportunities. Since this study is based on the semiconductor industry, our findings may not completely generalize to other industry contexts where firms face less pressure in technological innovation but greater pressure to expand their market as the industry matures. Future research may examine network dynamics in other mature industry settings and compare how these other settings vary in network formation and dynamics. We focused on network dynamics during the 1991 to 2007 period. Since the 2008 global financial crisis, the semiconductor industry experienced a significant decline in interfirm collaborations. While this decreasing trend does not affect our findings, it limits the external validity of our findings. Future research may investigate the relationship between network dynamics and external environmental circumstances, such as macroeconomic conditions similar to the global economic crisis, and whether these conditions produce a different pattern of shifting behavior. We used formal collaborative connections as proxies for interfirm networks of the ego firm. However, corporate scientists or teams could be connected to each other through a professional scientific community, personal friendships, or other informal ties. Although we did not integrate these ties because of data availability, future research could examine how informal networks influence the dynamics of formal interfirm networks and innovation performance.

During our study period, numerous M&As occurred in the semiconductor industry involving small and lesser-known firms or startups. Due to constraints in data availability, we did not analyze changes in the firms’ networks resulting from these M&As or their potential impact on network structure. Future research could focus on finding network changes for all firms involved in these transactions. We measured innovation performance using the number of patents; however, this approach does not consider the potential impact of variations in patent quality on innovation performance. Future research could consider incorporating measures to assess patent quality and its influence on overall innovation performance. As many firms from the alliance database were private, we tested innovation performance with a limited number of publicly traded firms to mitigate possible selection bias. Given these constraints, future research should aim to expand the sample size to encompass a wider variety of firms, including private ones.

We have focused on the impact of centrality on innovation performance at the industry level but not at the product segment level. While the mature semiconductor industry encouraged firms to place more value on central positions, firms may shift to structural holes that facilitate new knowledge creation and open new product segments within the industry over time. Future research could explore whether the performance benefits differ between the product segment and industry levels. We included nationality as a dummy variable based on U.S. and non-U.S. firms. However, this binary coding may not adequately capture the diversity and complexity of geographic contexts. The non-significance of the nationality variable in our analysis could reflect limitations in how geographic differences were operationalized rather than an absence of effects. Future research could address this limitation by employing a more granular approach to geographic representation, such as incorporating specific country or regional data. This would deepen insights into how geographic context shapes interfirm network dynamics, strategic behavior, and performance. Finally, future research could integrate intellectual capital management with interfirm network positioning and performance (Grindley & Teece, 1997). Their work on licensing and cross-licensing offers insights into managing intellectual property, particularly in high-technology industries like semiconductors. Exploring the intersection of intellectual capital management and network positioning could enable the development of a more comprehensive strategic framework. Further studies could analyze intellectual capital’s role in shaping network dynamics, providing deeper insights into firm outcomes and extending managerial implications, especially for patenting and alliances.
# Conclusion

This study provides new evidence for how industry conditions influence a firm to shift between structural holes and centrality network positions and whether these shifts lead to changes in innovation performance. Prior research emphasized the performance benefits of occupying both structural holes and central network positions, and firms increase these benefits by shifting between the two network positions dynamically. However, past research did not integrate the potential impact of industry conditions on shifting behavior and its performance impact. Our research shows that during the routinized technology regime of the high-technology industry, firms take charge and move to more central positions than structural holes, which has a positive impact on innovation performance.

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