WHAT ARE YOU LOOKING AT? A RIVALRY NETWORK EXTENSION TO

COMPETITIVE DYNAMICS

Sruthi Thatchenkery University College London s.thatchenkery@ucl.ac.uk

Riitta Katila Department of Management Science and Engineering

Stanford University rkatila@stanford.edu

The dissertation from which this paper draws was the winner of the STR Division Outstanding Dissertation Award in 2018 and the Best Paper Award at the Smith Competitive Dynamics Conference. We appreciate the thoughtful feedback from seminar audiences at Boston University, Harvard Business School, McGill University, New York University, Tulane, and University of North Carolina, and audiences at the Academy of Management, DRUID, INFORMS, Strategic Management Society, and West Coast Research Symposium. We thank Gautam Ahuja, Charles Eesley, Kathleen Eisenhardt, JungYun Han, Martin Kilduff, Jason Rathje, Ron Tidhar and Bala Vissa for helpful discussions, feedback, and comments. Rae Sloane, Aisha Shafi, Hannah Warner, Deanna Lee, and Melissa Du provided research assistance. The generous research support of the National Science Foundation Graduate Research Fellowship for the first author is gratefully acknowledged.

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WHAT ARE YOU LOOKING AT? A RIVALRY NETWORK EXTENSION TO COMPETITIVE DYNAMICS

ABSTRACT

We examine how a firm’s positioning in rivalry networks relates to the firm’s awareness of

opportunities and is moderated by the firm’s motivation and capability to act on those

opportunities. Using a data set on 121 enterprise infrastructure product firms over an 18-year

period, we find that monitoring from positions that span structural holes is positively associated,

and monitoring of peripheral competitors is negatively associated with a significant competitive

action: a firm’s product introductions. We also find that firms that are motivated to compete -

that are themselves targets of intense monitoring - can amplify the benefits of structural holes

spanning. More competitively capable firms, meanwhile, can more effectively thwart the risks of

peripheral competitor monitoring and turn the risks into an asset. Overall, our findings contribute

to competitive dynamics research by incorporating a networks perspective. We also add to

increasing evidence that strategies that isolate organizations from competition can backfire,

especially in innovative industries.

Keywords: competitive actions, competitor identification, competitive dynamics, product

introductions

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According to the Awareness-Motivation-Capability (AMC) framework (Chen and Miller,

2012; Ferrier, Smith, and Grimm, 1999), competitive awareness is a prime determinant of the

firm’s ability to effectively engage competitors. Anecdotal evidence suggests that limited

awareness – i.e. “missing” or underestimating competitive threats – may hurt performance (WSJ,

2018). Empirical research offers further insight, emphasizing the importance of awareness of

“local” competitors (such as leader-follower pairs or strategic groups within an industry), and its

links to improved market share (Ross and Sharapov, 2015; Smith et al., 1991). Awareness of the

second-largest firm’s product moves, for example, protects market leaders from dethronement

(Ferrier et al., 1999), and the lack of such competitive actions hampers the firm’s ability to

respond to competition (Chen and Miller, 2012). Overall, this stream makes a strong case for

monitoring1 local competitors to benefit short term performance.

Despite these insights, the focus on local competitors seems to limit the insights that the

AMC framework can provide. First, while research on competitive dynamics emphasizes

pairwise comparisons of rivals such as leader-follower dyads, as noted above, several scholars

have recently urged research to examine positioning within “structures of competition” created

by the broader network of competitive relationships among rival firms (Chen and Miller, 2012;

Hoffmann et al., 2018). Firms vary in precisely which competitors they choose to monitor, and

extant research on networks points in the direction - but does not directly examine - that such

variance in monitoring may allow some firms to adopt network positions that allow them to “see

further” and to escape rigidities and network lock-in (e.g., Burt, 2010). We address this gap by

expanding the AMC analysis to incorporate the firm’s position in the broader network of

competitive relationships that we label rivalry networks.

1 We draw from Ruef (2002: 431) to use the term monitoring to describe purposeful awareness of particular competitors, i.e. “directed ties [that] involve a unilateral monitoring of discourse and activities on the part of other actors.” Competitor monitoring can also be labeled as competitor identification.

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Second, although AMC work emphasizes the benefits of competitive awareness, there is

reason to believe that not all awareness is beneficial, and we address this gap. For example,

monitoring of particular competitors in rivalry networks can be unhelpful or even harmful

because the information obtained may be incomplete or untrustworthy. Similarly, the influence

of spanning structural holes is an open question as there are arguments for either negative (Tsai,

Su, and Chen, 2011) or positive (Chen and Miller, 2012) influence. Overall, whether all

awareness in rivalry networks is productive, and what happens if the firm pays attention to the

“wrong” competitor(s), is not yet understood. Altogether, to address these unexamined questions

on competitive awareness, we focus on product introductions as one of the primary ways in

which the focal firm can improve its competitive stance (Miller and Chen, 1994), and ask: How

is monitoring of competitors in rivalry networks associated with firm product introductions?

We investigate the research question using a panel of public firms in enterprise

infrastructure software. Infrastructure software is used to manage and maintain critical

information technology assets in a corporation, including cybersecurity and system management.

We build a novel, hand-collected dataset on 121 infrastructure firms, their competitor

monitoring, and 8,502 product introductions over an eighteen-year period from 1995 to 2012. A

core strength is our comprehensive coverage of the entire population of U.S. firms that

developed infrastructure software during the study’s timeframe, including gathering data on the

complete rivalry network (cross-referenced with analyst calls and fieldwork) to avoid sample

bias. We also compile fine-grained measures of actual competitor awareness and in-depth hand-

collected measures of product introductions. Another strength is our effort to enhance causal

inference by careful examination of whether endogeneity is present, and the use of a government

intervention (the U.S. v. Microsoft antitrust ruling) to instrument for competitor monitoring. We

develop an understanding of the mechanisms underlying our quantitative results with first-hand

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interviews with 21 industry informants. In particular, these interviews suggest several

mechanisms through which monitoring’s insights can be distilled into new products.

There are several contributions. First, we introduce positioning in rivalry networks to

offer a more accurate and nuanced view of competitive dynamics. The introduction of rivalry

networks extends the view of competitive awareness within the awareness-motivation-capability

(AMC) framework. Controlling for intensity of competition in the sample firm’s markets and for

inter-firm heterogeneity, we find evidence that spanning structural holes in rivalry networks is

positively tied with the focal firm’s ability to alter its competitive stance. Intriguingly, we also

show that not all awareness in rivalry networks is positive: attempts to add differentiation by

monitoring competitors in the periphery of the network exacerbates competitive inertia and is

negatively tied with the firm’s ability to compete (i.e. introduce products). We also find that

firms that are motivated to compete – i.e. firms that are themselves targets of intense monitoring

– particularly benefit from spanning structural holes while more competitively capable firms are

better able to thwart the risks of monitoring peripheral competitors. Overall, our findings

contribute to competitive dynamics research by expanding the theoretical reach of the awareness-

motivation-capability (AMC) framework to examine broader patterns in awareness across an

industry and the implications for competitive action.

THEORETICAL BACKGROUND

Prior Work on Competitor Awareness and Competitive Actions

Competitive actions – i.e. “externally directed, specific, and observable competitive

moves initiated by a firm to enhance its relative competitive position” (Smith, Ferrier, and

Ndofor, 2001: 321) are central to competitive dynamics literature. Research in competitive

dynamics shows that a firm’s ability to carry out competitive actions – in particular service or

product portfolio upgrades - is central to improving performance, including market share, and the

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lack of such competitive actions hampers the firm’s ability to respond to competition (Ferrier et

al., 1999; Smith, Ferrier, and Grimm, 2001a). Sears, a retail chain on the brink of bankruptcy is a

case in point. “Unless we believe we will receive an adequate return on investment, we will not

spend money on capital expenditures to build new stores or upgrade our existing base simply

because our competitors do,” wrote the company’s CEO in 2007. In the next 10 years, Sears lost

the competitive battle to rivals like Target, Home Depot and Wal-Mart that embraced

competitive actions of rivals such as e-retailing together with upgrades to physical stores (WSJ,

2018). Altogether, the firm’s competitive actions and its response – or lack thereof – to rivals’

actions are at the heart of competitive dynamics research.

The AMC framework pays particular attention to three factors that enable firms to

undertake competitive actions: awareness, motivation, and capability (Miller and Chen, 1994).

The first driver of competitive action is competitive awareness – i.e., identification and

monitoring of a set of rivals. The key idea is that awareness increases the focal firm’s chances of

outcompeting a rival by helping the focal firm understand the rival’s strategic priorities, its

vulnerabilities, and its general patterns of competitive behavior (Chen and Miller, 2012).

Building up knowledge of rivals through competitive awareness thus enables the firm to plan its

strategy and launch its own attacks effectively.

The AMC perspective generally emphasizes a positive view of competitive awareness.

Studies suggest that when competitive awareness is low, firms fall prey to competitive inertia

and engage in fewer competitive actions such as product introductions (Miller and Chen, 1994).

Conversely, high awareness goes hand in hand with more competitive actions such as new

products (Chen, Su, and Tsai, 2007). As noted above, however, the focus of empirical work is

typically on “local” competitors within well-understood competitive clusters. An exemplar is

Giachetti and colleagues’ (2016) study of U.K. mobile phone manufacturers that showed how

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rapid incorporation of local rivals’ product features into the focal firm’s phones increased the

quantity of phones sold, and, as such, provided evidence of awareness’ ability to reduce

competitive inertia. Overall, this stream emphasizes the benefits of awareness but has paid less

attention to positioning in rivalry networks and on its potential pitfalls.

An emerging stream of AMC studies has suggested that there is a need to examine the

structural patterns of awareness, i.e. the network structure of competitive relationships beyond

firm-rival dyads. However, most extant AMC work that has started to examine networks has

examined ties to collaboration partners rather than purely competitive relationships. For

example, Gimeno (2004) studied how rival airlines select alliance partners and Madhavan and

colleagues (2004) examined how rival steel producers entered into strategic alliances with one

another. But this empirical work on collaboration does not help us answer questions about

positioning in rivalry networks – i.e. questions that we address in this paper.

A second gap in prior work is that the handful of studies that have started to examine the

network structure of competition (rather than collaboration) have inferred competitive ties from

overlap in output markets (i.e., product overlap in geographic markets or product segments), but

direct measures of monitoring are missing. For example, Hsieh and Vermeulen (2014) defined a

competition tie to exist between two drug producers who manufactured active ingredients in the

same category. In another insightful study on major U.S. airlines, Tsai, Su, and Chen (2011)

created a competition network using overlap in airline routes. However, when product overlap is

used as a measure, collaborative and competitive relations may be mixed, and inferences about

purely competitive relationships become difficult.

In contrast, we isolate competitor monitoring relationships that are idiosyncratic to each

firm, focused on purposeful and unilateral monitoring of a rival (such that asymmetry can exist

between a pair of firms), and form a rivalry network through aggregation of these monitoring

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relationships. Drawing from prior work on networks and innovation (Ahuja, 2000; Kumar and

Zaheer, 2018), we propose that two types of positioning in rivalry networks are particularly

relevant for the competitive awareness that we study: monitoring across structural holes and

monitoring the periphery of the network.

Figure 1 about here

The AMC framework also emphasizes that the performance effect of awareness is likely

to be moderated by two characteristics of the focal firm: its competitive motivation and its

competitive capability, and so we incorporate these two aspects in our model as well. Increased

motivation to compete is importantly triggered by the intensity with which competitors attack the

focal firm (Ferrier, 2001). That is, rivals’ competitive actions provide the motivation for the focal

firm to consider the rival(s) to be in direct competition with the firm (Chen et al., 2007). In

contrast, such motivation may be missing if the firm is shielded from competition. Firm’s

capability to compete is in turn defined by the extent of the firm’s competitive experience

(Miller and Chen, 1994). The importance of motivation and capability is echoed in research on

networks; for example, Burt (2010: 29) argues that “sloth” (i.e. lack of motivation) and

“incompetence” (i.e. lack of capability) are reasons why, at the individual level, managers differ

in the extent to which they act (or not) upon opportunities provided by network connections.

Figure 1 outlines our research framework that integrates insights from network research into the

awareness-motivation-capability perspective on competition.

Research Context: Infrastructure Software

Our research context, software, provides a relevant setting to examine the linkages

between positioning in rivalry networks and competitive action. Exploratory interviews in the

industry (details provided in methods) provided us a preliminary understanding of the process of

competitor monitoring in these firms. They also helped us understand how the firms turned

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insights from monitoring into action – to product development and introductions of products on

the market to improve the firm’s competitive stance. Details of this process, as described below,

provide a foundation for the hypotheses and for the measures that we present in the next section.

First, the interviews confirmed that competitive awareness was central to the top

executives we interviewed. A recurring theme was that identifying relevant competition was a

key task and influenced the kinds of competitive actions that were implemented. An experienced

executive explained that “how [executives] respond to the competition, once they figured out that

there is a competitive issue,” was crucial for the firm to decide. “What kind of strategic moves do

they make? Is it building up their product offerings? Their product portfolio. Or is it a matter of

cutting their prices so they're the cheapest ones out there to buy from?” One typical pathway for

this process was an executive roundtable that raised awareness of a new competitor or re-

confirmed focus on an existing one. One interviewee pointed out that top executives in her

company often debated which competitors posed the most potent threats, and “it was good

because it led us to consider a richer set of product features and capabilities.”

The interviews also confirmed that firms in software commonly reacted to competitive

pressures by introducing new products to improve competitive position vis-à-vis rivals (Chen,

Lin, and Michel, 2010; Young, Smith, and Grimm, 1996). Several interviewees described to us

how competitive awareness trickled down to product development through resource allocation

decisions. When a particular product development project, and a competitor, was prioritized by

the top executives, it got more resources. As one product manager explained:“[Our product] was

the CEO’s pet project because he wanted to compete with [a particular competitor]…We got our

own design team, our own engineering team, and everyone else had to share.” We also learned of

several examples of projects that were initiated because the top team was personally vested in

beating a particular competitor. A product manager traced the origins of her project as follows:

“The CEO and a couple other top executives at our company looked at [a new competitor’s first

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product] and were like, ‘Dude why don’t we have this, we could totally do this.’ …Not because this was something we were already doing, or, like, something we needed to do. I think he saw [the new competitor] and thought we could just swoop in and grab most of the market…. So he wanted [a similar product] done, and it was done.” We also asked our interviewees how they identified and monitored competitors. They

told us that they routinely followed market analyst reports and attended Gartner (and other major

industry) events and trade shows to follow relevant competition. One interviewee told us that

particularly in the 1990s and early 2000s, “retail sales and industry reports” were the “lifeline of

data” and were used by the top management teams to analyze “the products of the firms that were

closely behind or closely ahead of us in market share.” More recently, a CTO whom we

interviewed illustrated, “One of our marketing people puts together a few competitive feeds every

week. Like a competitive digest. What competitors have been up to. He shares it with product and

engineering and with the top people.” When asked whether there was any connection between the

feeds and the firm’s technology, he said that the idea to apply machine learning to a specific area

of web interfaces (the company’s second product) could be possibly attributed to exposure to

different ideas from the two communities.

Other interviewees also mentioned that customers told them about new competitor

offerings as well as about features that the focal firm was missing in its own portfolio. One

interviewee described to us how his analysis of customer survey data motivated the executive

team to rethink the product portfolio. “It made us appreciate a feature in another firm’s product

that we had previously thought as irrelevant.” As a consequence, the executive team decided to set

aside resources to beat the competitor by developing a new product.

We also asked what mistakes our interviewees had made in monitoring their competitors.

A board member recounted the time his company had lost a major deal with an enterprise

customer. The customer offered to walk the focal firm through a “side-by-side comparison” of

the firm’s and the competitor’s product “that caused us to start spending a lot more money on the

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user interface. And that was a redirection of the R&D organization.” When asked why the firm

didn’t modify the product sooner, he said, “we were observing [the other firm that won the

contract], but were very dismissive of it as competitor. We missed the fact that, even though they had

fewer features [than us], the CFO's office could get [the product] up and running with very little

training.” Interviewees also noted that because rivalry was intense and there were many

competitors, top executives needed to “prioritize” and focus on the most relevant ones. “You

would drive yourself nuts trying to develop against the entire field,” said a former CEO. Another

interviewee offered that paying attention to “wrong” competitors rapidly undermines the firm's

strategy, “You may spend a lot of time looking at the competition but you do not have a good way of

filtering or of prioritizing your competition… So if you keep focusing on the wrong thing, you're

missing the market.” Given the significance of competitive awareness, and its suggested links to

product introductions, we now proceed to discuss our hypotheses, method and results.

HYPOTHESES

Extending the awareness-motivation-capability framework, we propose hypotheses

linking product introductions and positioning in rivalry networks. We propose effects of

spanning structural holes (H1), of monitoring peripheral competitors that are sparsely monitored

by others (H3), and their interaction (H5). We also propose that competitive motivation (H2) and

competitive capability (H4) moderate the effects of structural holes spanning and monitoring

peripheral competitors, respectively. Figure 2 provides an illustration.

Structural Holes Spanning and Focal Firm Product Introductions

The first type of network positioning that is likely to be relevant for competitive

awareness is the focal firm’s ability to span structural holes, i.e. the extent to which the

competitors that the focal firm monitors are (or are not) monitored by each other (see Figure 2a).

In the arguments that follow, we propose that spanning structural holes in rivalry networks is

related to early awareness and a push for the firm to respond to competition through product

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introductions, for several reasons.

Insert Figure 2 about here

First, brokerage positions in rivalry networks provide early awareness of diverse

information about competitors, which can help the firm spot opportunities to pre-empt rival

actions. A firm that spans structural holes in its networks is more likely to gain faster access to

relevant pieces of knowledge about its potential competitors–e.g., early awareness of a number

of different competitive threats and early signals of where the industry is going–compared to

firms that span few structural holes (Burt, 2010). Note that although Tsai and colleagues (2011)

argue that dense networks (rather than sparse networks with structural holes) facilitate getting to

know a particular rival better, our argument focuses on implications for more broadly-targeted

competitive actions. We argue that spanning structural holes is likely to differentiate the

information gained relative to other firms, creating early opportunities to pre-empt rival actions.

Second, knowledge that can be gained from spanning structural holes is likely to enhance

awareness of opportunities that can be responded to specifically by altering the firm’s product

portfolio (Galunic and Rodan, 1998; Schumpeter, 1934). This is because knowledge that can be

gained expedites the firm’s ability to recombine distinct pieces of knowledge into new

combinations. In contrast, a firm who monitors competitors within a dense local cluster (i.e.

tightly-connected competitors) may find it difficult to spot opportunities to outcompete rivals

through product introductions because it lacks unique early access to diverse information.

Moreover, if the focal firm is part of a dense cluster of competitor monitoring, i.e. where the

firm’s competitors have many connections to each other, the new information that reaches the

focal firm also reaches other firms who monitor the same competitors, and is likely to push the

firm to alternative competitive actions, such as price wars.

Third, spanning clusters of competitors increases the usability of information that can be

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extracted by the focal firm. Unlike alliance relationships, competitors do not work to solve

problems together. Firms typically do not make any effort to help competitors learn from them

and instead are likely to try to block or distract the monitoring efforts. If, however, a firm

monitors a competitor that in turn is part of a cluster of firms (who monitor one another), there

are likely to be more independent confirmations of the same information within each cluster and

more opportunities to understand aspects of information that may be more tacit. In other words,

monitoring competitors that are part of clusters adds scrutiny, more viewpoints and thus more

usability to the information that can be extracted. Overall, spanning clusters helps the firm

assemble and act on diverse and usable information about competitors in a timely manner.

Hypothesis 1. An increase in the degree to which a firm spans structural holes in rivalry networks is positively related to product introductions.

Contingent Effects of Structural Holes Spanning

Hypothesis 2 focuses on the moderating effect of competitive motivation on structural

holes spanning, drawing from the awareness-motivation-capability framework. Prior work

suggests that a firm’s managers will be more motivated to carry out competitive actions if the

rivals engage the focal firm, forcing the firm to defend its turf (Chen et al., 2007; Ferrier, 2001).

As a consequence, we expect that the influence on product introductions by the focal firm’s

spanning of structural holes is altered depending on the firm’s motivation to compete.

In particular, we expect that motivation will positively moderate the relationship between

spanning structural holes and product introductions. As noted above, firms in brokerage positions

are better positioned to make more sophisticated competitive actions such as new product

introductions rather than simpler actions such as price changes. However, because these more

complex actions also require more incentive to carry them out (in addition to information), we

expect that firms who are more motivated to compete are more likely to take advantage of

information advantages of network positioning and actually implement these more complex

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competitive actions. We thus propose that firms are particularly motivated to take advantage of

the knowledge gained through brokerage positions by carrying out a new product move when

they themselves are more motivated to compete.

Hypothesis 2. The positive relationship between the focal firm’s spanning of structural holes in

rivalry networks and product introductions is positively moderated by its competitive motivation: the greater the competitive motivation, the stronger the positive relationship

between structural holes spanning and product introductions.

Peripheral Competitor Monitoring and Focal Firm’s Product Introductions

Hypothesis 3 examines the effects of monitoring competitors in the periphery of the

network, i.e. competitors that are monitored by the focal firm but only sparsely monitored by

other firms in the same market (see Figure 2b). A peripheral competitor’s “marginal” status in

monitoring networks implies that fewer firms are using its product or competitive behaviors as

templates, and there are fewer replications and repetitions of its ideas by others. Unlike in

collaboration networks where a peripheral partner is likely to provide a differentiating factor for

the focal firm (Rodan and Galunic, 2004), it is unresolved whether monitoring a peripheral

competitor in a rivalry network similarly facilitates competitive action.

First, unlike alliance networks which feature active cooperation to facilitate knowledge

transfer, competitor networks transmit information that may not be properly vetted. In particular,

the more peripheral the monitored competitor is, the less the focal firm (i.e. the monitor) can take

cues from (or "read into") how other firms react to that competitor, e.g. whether the ideas are

worth learning from or responding to in the first place. Peripheral firms are also missing out on a

"selection process" that pulls intensely monitored competitors to respond to others’ monitoring

by potentially improving their ideas and products. By contrast, a peripheral firm is left with

minimal reaction. Thus, unlike within clusters of competitors where monitoring and the ensuing

extra scrutiny by other firms may help the focal firm better understand information gleaned from

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a competitor, such monitoring and vetting is by definition missing for peripheral firms. In

extreme, monitoring of peripheral competitors may mislead. A CEO related a story of a

peripheral competitor who, “baited [one of our other competitors] into following their lead, where

their lead is nothing more than a dead-end alley…they got them to commit resources and marketing

resources and other resources [to a product development project] because of their [misleading]

claims.”

Second, monitoring a peripheral competitor may create a counterproductive distraction.

As explained by our interview evidence, peripheral firms that are viewed as doing something

“new and different” can become a tempting distraction (Chen, 1996) that prompts the focal firm

to divert R&D resources away from the core business and towards new ideas that may not be

relevant. This creates the opportunity for a peripheral firm to further engage in judo strategy by

luring the focal firm into “combat on [the peripheral firm’s] terms” while avoiding “tit-for-tat”

battles in the established areas of the market in which the focal firm is more entrenched (Yoffie

and Kwak, 2002: 11). An interviewee recalled an instance in which his firm had chased a

peripheral competitor: “to catch up [with a peripheral competitor] you pull all these resources off

what you were doing before…off supporting your current customers. So now you're trying to run two

businesses…You're basically toast because you're in no man's land straddling those two worlds.”

Thus, monitoring peripheral competitors may create a distraction that makes it more difficult to

effectively carry out competitive actions, especially when in conflict with the firm’s existing

resource commitments. Altogether, because peripheral targets of monitoring may yield less

trustworthy information, and because the knowledge that can be gained from such firms is likely

harder to absorb into the firm’s own competitive strategy and product moves, we hypothesize:

Hypothesis 3. An increase in the degree to which firms monitor peripheral (rather than prominent)

competitors in rivalry networks is negatively related to product introductions.

Contingent Effects of Peripheral Competitor Monitoring

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Given that monitoring a peripheral competitor is a challenge, a question arises about

when firms would engage in monitoring of such marginal actors in the first place. Competitive

dynamics literature and the awareness-motivation-capability framework suggests a possible

answer: not all firms have an equal capability to deal with competitive threats, and some may be

more capable than others.

In particular, we propose that the firm’s competitive capability is particularly relevant in

rivalry networks in which ties may provide information that is not always fully vetted. One of

our interviewees explained that competitors “will tell you stuff …all the time that actually doesn't

work. But that's where you've got to know the market and the products better.” Peripheral

competitors (that others do not pay attention to) can be valuable targets of monitoring and a

source of potential differentiation, but, as noted above, the knowledge that can be learned carries

the risk that it is less tested, has faced less scrutiny, and is possibly less readily integrated into the

firm’s existing competitive strategy. By having more competitive capability, firms can

potentially mitigate these risks of untested knowledge.

Hypothesis 4. The negative relationship between the firm’s monitoring of peripheral

competitors in rivalry networks and product introductions is positively moderated by its

competitive capability: the greater the competitive capability, the weaker the negative relationship between monitoring of peripheral competitors and product introductions.

Thus far we have examined types of positioning in rivalry networks (structural holes,

peripherality) separately, but have not discussed how they can influence product introductions in

combination. These dynamics are germane because firms are likely to derive their ideas for

competitive action from multiple sources and may monitor peripheral competitors while also

spanning structural holes in rivalry networks.

In hypothesis 5 we propose that the two types of network positioning are complements,

such that spanning structural holes will mitigate the drawbacks to monitoring peripheral

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competitors. As noted above, peripheral firms (as actors that are distant from the ‘core’ of a

network) can be sources of particularly novel ideas. If a firm combines spanning structural holes

with monitoring peripheral competitors, the benefits to gaining diverse and particularly novel

information may begin to outweigh the drawbacks of that information coming from less vetted

sources. In other words, the firm is adopting a network position that maximizes both the diversity

and uniqueness of the information gleaned through competitor monitoring, allowing the strengths

of the two types of network positioning to build on each other (Ruef, 2002). In contrast,

monitoring peripheral firms while not spanning structural holes is likely to be particularly

harmful, as firms must deal with the drawbacks to receiving unvetted and possibly distracting

information from peripheral competitors while also receiving redundant information from

competitors connected to each other within a single cluster. We propose:

Hypothesis 5. The negative relationship between the firm’s monitoring of peripheral competitors

and product introductions is mitigated by the firm’s spanning of structural holes.

METHODS We test the hypotheses on a novel hand-collected dataset of 121 public U.S. firms in the

enterprise infrastructure software industry between 1995 and 2012. Infrastructure software forms

the backbone of enterprise computing and serves critically important functions for enterprise

clients. Prototypical examples of firms in this industry include Computer Associates (network

and system management), Symantec (security), and Forte Software (application development).

Infrastructure software products are typically used to manage and maintain complex information

technology (IT) systems, encompassing a wide range of functions such as data backup, virus

protection, and system performance.

Enterprise infrastructure software is a particularly relevant context for the study. The

industry is neither too concentrated, such that competitors would be few and identical across

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firms, nor too fragmented, such that competitors would be treated as interchangeable (as is the

case of commodities markets) or too difficult to identify. This balance between concentration and

fragmentation provides rich but tractable variation in the data. We also chose public

infrastructure software firms because, as one of our interviewees noted, it is easy for firms that

have gone through an IPO to become complacent about competition, making competitor

monitoring and competitive action (vs. inertia) a significant strategic decision for our sample

firms (e.g. Chen and Hambrick, 1995). The setting was also appropriate because unlike the

coopetitive logics in many craft industries (Hoehn-Weiss and Pahnke, 2018), our setting is

representative of many technology-intensive industries in which competitive actions can be

isolated from coopetitive ones - providing clarity of argument and testing (Gulati and Singh,

1998). Finally, the setting is relevant because product introductions are key to competitive

advantage. Enterprise customers have come to expect sophisticated and robust infrastructure

tools to manage their increasingly expansive and critical enterprise IT systems. As the CEO of

security software firm McAfee explained, “Discounting will not win competitive business of this

scale, you need superior solutions” (McAfee, 2006).

We began our sample in 1995 to coincide with the transition from centralized to

distributed (aka “networked”) computing. This transition marked a fundamental shift in the

technical architecture of enterprise IT and created the need for more sophisticated infrastructure

tools. We ended the sample at the time when the industry underwent its next major technology

shift, the advent of cloud computing in 2012. The core strength of our data is its comprehensive

coverage of the entire population of public U.S. firms that developed software in the five

enterprise infrastructure software markets for all three operating systems since the widespread

adoption of distributed computing. Comprehensive data on the full population is particularly

important for accurately documenting rivalry networks, and for avoiding sampling bias.

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Our primary dataset is a hand-collected longitudinal study of 121 firms, supplemented by

in-depth fieldwork. In order to better understand how monitoring is related with changes in the

firm’s product portfolio, we interviewed individuals in the software industry (both enterprise and

consumer software). The 21 interviews were unstructured but all featured the following open-

ended questions: “What does the product development process look like in your firm? Who is

involved (different functions, different hierarchical levels)? Who do you see as a rival? How do

you find out about new competitors? Tell me about a competitor that you wish you had stopped

paying attention to.” We refrained from asking leading questions that would either support or

negate our hypotheses.

Sample Construction

Because infrastructure software is not distinguished from other types of software in standard

industrial classifications, we took several steps (outlined below) to identify the firms that

operated in the industry. We also took care to triangulate between multiple sources to improve

the coverage and to create a comprehensive dataset.

We started by compiling a list of all public software firms in the United States. Consistent

with prior work, we defined a “software firm” as any firm with either a primary or secondary2

classification under the SIC code of “prepackaged software” 7372. Between 1995 and 2012,

there were 1,206 public software firms in the U.S. After excluding the 390 firms that developed

products only for consumers (to focus on enterprise software), we compared each firm’s product

portfolio with the list of Gartner Research’s IT Glossary (a standard industry source) that

provides a comprehensive list of infrastructure software product categories. Gartner Research’s

list has been found to provide a detailed and accurate description of the industry (Pontikes,

2012). We classified a firm as an infrastructure software company if the majority of its product

2 The most common primary classifications for infrastructure software firms with 7372 as a secondary classification were 7371 (programming services) and 7373 (integrated computer systems).

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portfolio matched the Gartner keywords.3 We triangulated this information with The Software

Catalog (an annual listing of software products) to ensure a comprehensive sample. We also

went to two industry experts for suggestions of companies to include. Cross-validation of these

sources yielded a final sample that consists of 121 firms and 823 firm-year observations between

1995 and 2012. Our firms exhibit expected patterns of regional concentration typical of software,

with 31% of the sample firms headquartered in the San Francisco Area, and 11% and 9%

headquartered in the Los Angeles and Boston areas, respectively.

Data Sources

Competitive actions: Product introductions. We used several sources to build the

dataset. For product introductions, we assembled data using a "literature-based innovation output

indicator" method (Coombs, Narandren, and Richards, 1996); specifically, through a careful

examination of company press releases. Press releases are the most common way in which

enterprise software firms announce new products and the standard source for product data in the

industry (Thatchenkery, 2017). We searched LexisNexis using the combination of the names of

our sample firms and product-related keywords (e.g., new product, announce, launch, release,

version) to identify potentially relevant announcements (Li et al., 2013b). Our initial search

returned over 118,000 press releases. Next, we used text analysis in Python to filter out

duplicates (i.e. the same press release issued to multiple newswires) as well as announcements

about unrelated topics such as new executive hires or international expansions. This automatic

text analysis returned roughly 42,000 unique articles with possibly relevant information about

new products.

We then engaged in a painstaking manual review of the 42,000 articles. The first author

categorized all articles and a team of trained coders conducted a second, independent review of

3 We primarily relied on the 2012 version of the glossary but cross-referenced against descriptions of infrastructure software categories found in older Gartner reports and found them to be consistent.

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the entire sample. We first read through the headlines only and identified about 9,000 as relevant

to our firms' product introductions for further inspection and categorization. We then reviewed

those articles in more detail and, for those that were about product releases, recorded each

product’s release date, name, version number, and a brief description. Infrastructure software

products were distinguished from other software products (e.g. services, applications) by

comparing product descriptions with Gartner keywords. Inter-rater reliability was 92%,

indicating very high agreement and accuracy of the coding. Disagreements were resolved

through discussion between coders. Overall, our careful content analysis of over 118,000 press

releases yielded data on 8,502 unique infrastructure software products.

Competitor Awareness: Monitoring networks. Building on and extending prior work,

we used 10-K filings as a source for competitor monitoring (Hoberg and Phillips, 2016;

Lewellen, 2013; Li, Lundholm, and Minnis, 2013a), and verified the accuracy of this source

using analyst calls and fieldwork, as described in detail below. All public U.S. firms must file a

yearly 10-K report that updates shareholders on the company’s strategy, structure, and

performance. We examined the mandatory “competition” section in Item 1, in which the firm

describes the competitive conditions it faces, including specifically naming competitors. Details

about this data source are included below. We defined a unilateral monitoring “tie” to exist each

year the focal firm lists another as a competitor in its 10-K. Ties are directed and thus sample

firms may monitor firms that do not monitor them in return.

If a firm offered products outside infrastructure software (such as software services), we

only included competitors that were listed in the 10-K’s section on infrastructure software to

maintain consistency across the firms in our sample. We also focused on public competitors in

10-Ks. This was important so that variation in monitoring across organizations reflects differing

beliefs about which particular competitors are relevant, not differences in how easy they are to

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notice in the first place (Miller and Chen, 1994). In a sensitivity analysis, we assessed the impact

of all competitors (public and private listed in the 10-K) with consistent results. Correlation

between the measures based on all competitors versus public competitors is high (ρ=0.88).

10-Ks are an appropriate source for data on competitors, for several reasons. 10-K

listings are (1) comprehensive records (due to mandatory nature and incentives tied to SEC

filings), (2) shown to more accurately capture monitoring than more traditional SIC-based

measures, and (3) particularly relevant to keep track of monitored competitors in the software

industry (based on our qualitative field work, and examination of analyst calls). We discuss each

of these issues in detail below.

First, 10-K filings are particularly appropriate for capturing the range of competitors that

are monitored. Li, Lundholm, and Minnis (2013) note that 10-K filings capture “competition

from many different sources that are hard to identify empirically [otherwise], such as…potential

new entrants.” (Li et al., 2013a: 402). Hoberg and Phillips (2016) similarly note that 10-K’s are

informative regarding “firms that managers themselves perceive to actually be rivals” (Hoberg

and Phillips, 2016: 1448). This comprehensiveness and accuracy are at least partly due to the fact

that firms have an incentive to be accurate in how they describe competition in the 10-K filings

because the qualitative descriptions of the firm and its business in the SEC filings are

consequential for the firm. Research finds that investors respond to changes in the textual

portions of the 10-K even after controlling for changes in financial results (Brown and Tucker,

2011). Given careful investor attention to the text of the 10-K filing, then, it is critical for firms

to not leave the impression that they do not understand the competitive environment. The

regulation governing the 10-K filing also requires that firms not include names of competitors

that would be “misleading” to investors, reducing the likelihood that firms would include

competitors that they do not genuinely believe to be relevant. Thus, firms are incentivized to

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document the particular firms that they monitor as competitors accurately and comprehensively

in their disclosures.

Second, an analysis of 10-Ks has been shown to create valid measures of competition,

even surpassing several traditionally-used measures. Research in finance and accounting has

found that measures of competition based on textual analysis of SEC filings including the

competition section of 10-Ks are more accurate measures of competition over more

traditionally-used SIC code based measures (Hoberg and Phillips, 2016; Lewellen, 2013; Li et

al., 2013a; Rauh and Sufi, 2012). For example, Rauh and Sufi (2012) found that listed

competitors in the firm’s SEC filings provided a 40% improvement in explanatory power over

more traditional SIC-based measures. Kim, Gopal, and Hoberg (2016) provided similar evidence

of measures of product market competition based on textual analysis of 10-Ks in the information

technology industry. Another case in point is Dedman and Lennox's (2009) survey of managers

in a cross-industry sample of firms that found little relation between managers’ perceptions of

their competitive environment and traditional economic measures of competition such as

industry concentration or numbers of firms in a market (that have been traditionally used as

proxies of relations with competitors; Greve and Taylor, 2000). Altogether, management’s

discussion of competition in 10-Ks allows us to create measures that meaningfully capture

variation in who firms in enterprise software monitor as their competitors.

Third, we also carefully verified that the competitor listings in the 10-Ks were an

appropriate source for our industry setting, i.e. infrastructure software. The first step was

comparison to analyst calls. We compared the competitors listed in the 10-K to the competitors

mentioned in analyst calls. For 32 sample firms, we inspected transcripts of the year end call that

aligned with the filing of the 10-K for at least three years per firm. Competitors mentioned by

executives in analyst calls were consistently found listed in the 10-Ks, which provides

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independent confirmation that the 10-Ks are a comprehensive source of data on monitoring.

As a second step, our expert interviews and examination of the 10-Ks indicated that

naming competitors is the norm in infrastructure software. Firms in our sample listed a minimum

of one competitor and an average of 6-7 competitors in the 10-K. These numbers are highly

consistent with prior work on competitor identification, which finds that managers focus on

between two and nine competitors (Clark and Montgomery, 1999; Porac et al., 1995).

As a third step, we validated the use of 10-Ks as a data source by interviewing current

and former executives in enterprise software. During interviews, we showed each executive a list

of firms in enterprise software, and asked the executive to rate each firm on a scale of 1 (not a

competitor) to 10 (intense competitor). We also asked if there were any competitors that were not

listed but were relevant to that particular firm. Overwhelmingly, comparison of these surveys

with each firm’s 10-K filings confirmed that executives viewed the competitors (that their firm)

listed in the 10-K as significant competitive threats and we did not find any instances of major

competitors that were omitted. Furthermore, our executive interviews also confirmed that top

executives, including the CEO, were involved in both competitor monitoring and product

development strategy in infrastructure software firms. Interviewees also confirmed that

competitive analysis in public firms that we study was typically done “across a 12-month

timeframe” matching the frequency of 10-K reports that we observe.

As a final step, we compared the use of 10-Ks as a data source on competition monitoring

with collaboration ties in enterprise software, and excluded collaborations to focus on purely

competitive ties. Our interview data indicated that because of the nature of enterprise software, it

was typical to ally with partners in other parts of the enterprise IT ecosystem such as platform

owners, with software firms in complementary markets, and with software service firms. For

example, Symantec, a leading security software firm, entered into partnerships with

24

complementors such as a database firm Informix, platform owner IBM, telecommunications firm

AT&T, and a professional services firm KPMG. In contrast, collaboration with other security

software firms was less common. We verified these patterns by excluding partnerships listed in

10-Ks, and by cross-checking a subset of our monitoring data against alliance data downloaded

from SDC Platinum. Overall, the evidence that we gathered showed that within infrastructure

software, the 10-Ks’ lists of competitors were accurate representations of monitoring of the

firm’s competition.4

Finally, we collected data on executive team characteristics with a comprehensive search

and triangulation of several sources: LexisNexis, Thomson ONE, Compustat, SEC filings, and

LinkedIn (Smith et al., 1994).5 We use triangulation of multiple sources, particularly proxy

statements and Compustat, to identify executives in our sample firms because it has been shown

to provide an accurate list (Hambrick, Humphrey, and Gupta, 2015). We also collected data on

firm and competitor financial indicators, including S&P index membership, firm size, financial

performance, R&D expenditures, and merger and acquisition activity from Compustat, Thomson

ONE, CapitalIQ, and SDC Platinum.

Measures

Dependent variable. We measured product introductions using new infrastructure

software products introduced by each firm yearly, collected from press releases. Product

introductions are an appropriate measure of competitive actions (Lee et al., 2000; Young et al.,

1996; Ndofor, Vanevenhoven, and Barker, 2013; Young et al., 1996), particularly for public

firms in our setting. As one of our interviewees noted, “If you’re a public company, you need to keep

4 One of our expert interviewees noted that industries differ to the extent that competitors are listed in 10-Ks, but the norm in infrastructure software is to provide an accurate list of specific competitors. 5 The SEC requires that public firms disclose their principal executive officers, including “any other officer who performs a policy making function,” in major filings (Securities Act of 1933, Rule 501(f), 17 C.F.R. § 230.501(f)). Prior work has used SEC filings as a source of individuals at a firm who are making executive-level decisions, including about competition strategy.

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growing and the way to do that is to…create new products.” As noted above, we carefully cross-

referenced product descriptions with keywords from the Gartner IT Glossary in order to identify

enterprise infrastructure software products and excluded all consumer products and enterprise

application products. We also only included products that were confirmed to have shipped (i.e.

we excluded planned releases that never materialized). To qualify as new, we counted brand-new

products only (e.g., 1.0) and excluded ports of existing products to new operating systems.6

Independent variables. We measured structural holes spanning by the extent to which a

focal firm monitors competitors that do not monitor each other, presenting a bridging opportunity

for the focal firm. We used Burt's (1992) constraint measure of structural holes7 and reverse

coded the measure by multiplying by negative 1 in order to examine the effects of less

constrained network positions, i.e. structural holes spanning. In an alternate version of the

variable, we removed, per Burt (2010), from the network those competitors with only one or only

two indegree connections (i.e. competitors that only 1-2 firms in the entire network are

monitoring) to ensure that brokering opportunities are occurring between clusters of competitors

rather than between isolated competitors. Results using this alternative measure are highly

consistent with our main results (available from authors).

We measured monitoring of peripheral competitors by examining whether the competitor

listed in the focal firm’s 10-K was also listed by other firms in the focal firm’s market(s) and

used lack of monitoring by other than the focal firm as a measure. In line with Greve and Taylor

(2000) and prior work’s measure of “outlier” competitors (Reger and Huff, 1993) we calculated

6 During the sample timeframe, there were three major enterprise server operating systems (Windows, Linux, and UNIX). Multi-homing, i.e. releasing a version of each product for every platform, is typical for our sample firms. 7 Constraint is measured as !" = ∑ %&"' + ∑ &")&'))*"*' +,' where Ci is the constraint of firm i, pij is the proportion of firm i's total ties invested in competitor j, and piq and pqj are defined analogously for competitors j and q. The lower end of the theoretical range approaches but does not reach zero when every monitored competitor is disconnected from every other monitored competitor. The upper end of the theoretical range exceeds 1 in dense networks. While this is not a concern in our sparse monitoring networks, we test an alternate measure that standardizes constraint (i.e. divides by the maximum possible value) (Burt, 2004) with consistent results.

26

peripheral competitors as: ∑ -./

where dj is the number of firms in the focal firm’s markets that

listed competitor j in their 10-K, aggregated across all competitors listed by the focal firm.8

Because the primary measure is dependent on the number of firms listed in the 10-K, we also

tested a proportional measure, calculated as the proportion of monitored competitors that were

listed as a competitor by the focal firm and at most one other firm in the same markets. We also

tested a measure based on monitored competitors’ size (i.e. firms below a certain threshold in

sales) (Chen et al., 2007). Results were consistent.

Because competitive motivation is triggered by the intensity with which competitors pay

attention to the focal firm (Ferrier, 2001), we measured firm competitive motivation using

indegree centrality: c/(m-1) where c is the number of public infrastructure software firms that

listed the focal firm as a competitor (i.e. the number of inward ties to the focal firm) and m is the

total number of firms in the industry (i.e. the number of nodes in the network).

Because a firm competitive capability is defined by the extent of the firm’s competitive

experience (Miller and Chen, 1994), we used number of years since the firm’s initial public

offering as a measure. Firms that have been public for longer have more competitive experience

to draw upon (private-firm competitive experiences are likely to be significantly different;

Hoehn-Weiss and Karim, 2014; Hoehn-Weiss and Pahnke, 2018). As a robustness check, we

also tested alternative measures of competitive capability including firm size (since larger firms

have more resources with which to compete), with broadly consistent results.

Controls. We included several controls. Because firm diversification may influence

8 If the focal firm is the only firm to list competitor j, dj takes a value of 1. The upper end of the theoretical range is m, where m is the total number of competitors monitored by the focal firm, and would only occur if every monitored competitor was not monitored by anyone else in the focal firm’s markets. The lower end of the theoretical range approaches but does not reach zero when all monitored competitors are prominent i.e. monitored by a high number of other firms. We also tested the results with an alternative measure of this variable that only included monitored competitors in the same markets as the focal firm. Results were broadly consistent.

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product introductions by creating internal opportunities for cross-pollination (Ahuja and Katila,

2001), we controlled for firm scope, measured as the number of infrastructure software markets

in which the focal firm developed products. We also controlled, in alternative tests, for firm

monitoring intensity (measured as the number of competitors monitored by the focal firm divided

by the maximum possible number of competitors i.e. the firm's standardized outdegree

centrality), with consistent results. As an additional measure, we also controlled for firm size by

number of corporate employees yearly (in thousands), logged to mitigate skew, and, by the

firm’s annual revenues (in millions of U.S. dollars), inflation-adjusted, with consistent results.

Because firm scope was highly correlated with the other measures, we used scope as the primary

control.

Because firms with declining performance may be less likely, or, perhaps, in contrast,

more likely to introduce new products, because performance may influence urgency to out-

compete rivals, we controlled for firm performance, measured as return on sales (Young et al.,

1996). In a sensitivity test, we alternatively controlled for firm growth (measured as number of

employees in year t divided by number of employees in year t-1) and revenue growth (measured

as revenue in year t divided by revenue in year t-1), with consistent results.

Because investment in R&D is likely to influence new products, we also controlled for

firm R&D intensity, measured by dividing R&D expenditure by total sales annually. In a

sensitivity analysis we used R&D expenditures (inflation-adjusted and logged), with consistent

results.

We controlled for top executive team turnover of each firm because prior research has

tied top management team composition to competitive agility (Hambrick, Cho, and Chen, 1996).

We measured turnover by the number of executives who joined or departed the executive team

yearly. We counted joining and departing as separate events because team size and roles were

28

not fixed and not all executives who departed were replaced. As in prior work, we defined the

firm’s top executives as those employees listed as executive officers in the firm’s proxy

statements, cross-validated with Compustat (Hambrick et al., 1996).

Because firms with products in multiple markets are likely to face more competition, and

likely to enact more competitive moves, we controlled for competitor density by the total number

of public firms that developed products in the focal firm’s market(s). This measure aggregated

competitors that launched competitive actions across all the firm’s markets, but competitors that

overlapped with the firm in more than one market were only counted once. We also tested

several alternatives, including a logged version of the measure. Because firms that are similar in

size may exert stronger competitive pressures (Chen et al., 2007), we alternatively controlled for

density of similarly-sized competitors in the firm’s markets, counting a firm as a competitor if its

annual revenues were in the same quartile as the focal firm’s. We alternatively controlled for

density of large competitors as the number of competitors in the firm’s markets with annual

revenues over $1 billion, because large firms may exert more intense pressure than small firms

do. Results are consistent across all measures.

We also controlled for any unobserved market effects with five market segments in which

our sample firms operated. We included controls for five standard markets based on Gartner’s IT

Glossary—developer tools, integration and middleware, database management, security, network

and system management—setting each binary variable to one if the firm had at least one product

in that market in a year and zero otherwise. Firms in the sample entered and exited market

segments during the time period of our study, creating variation over time.

We also controlled for three geographic regions with high numbers of enterprise software

firms, i.e., San Francisco, Boston, and Los Angeles (Orange County) because knowledge

spillovers within a region can enhance product development (Owen-Smith and Powell, 2004).

29

Region effects drop out from the fixed effects Poisson regressions that we report in the tables but

are included in sensitivity analyses (e.g., random effects or negative binomial regressions).

We controlled for macroeconomic variation as well as possible year-to-year fluctuations

in technologies which may influence product opportunities by including unreported year fixed

effects. Lastly, we included a two-year lagged dependent variable (i.e. new products in year t-2)

to control for time-variant firm heterogeneity and to further enhance causal inference (Heckman

and Borjas, 1980). Because standard errors can be inaccurately reduced in models with lagged

dependent variable, we also ran a model excluding the lagged dependent variable, with

consistent results. Furthermore, the use of a two-year rather than one-year lag helps reduce the

potential bias on standard errors. Standard errors were highly consistent across models with or

without the lagged dependent variable.

Statistical Method

Because our dependent variable is a count variable, our main analysis used fixed effects

Poisson models. Fixed effects models help control for any baseline (i.e. time-invariant)

heterogeneity between firms, and were preferred over random effects by the Hausman test

(Hausman, 1978). We also ran several alternative specifications as robustness checks. Because

fixed effects models drop any firms with two or fewer observations or firms that do not exhibit

variation in the dependent variable over time, we also report random effects Poisson results.

Because our dependent variable exhibits signs of over-dispersion, we also ran fixed effects

negative binomial regressions, with consistent results (available from the authors). While most

firms introduce at least one new product each year, a few firms introduce none, and so we also

verified our results with zero-inflated Poisson models, which produced consistent results.

While we controlled for as many relevant factors as possible in our models, there may be

unobservable variables, such as firm quality, that also influence new products and thus could bias

30

our results. We attempt to reduce potential bias in several ways. First, as noted above, we

included firm fixed effects in all models, to control for unobserved, time-invariant variation

between firms. We also included several variables to control for time-variant firm characteristics

and a lagged dependent variable. To further account for unobserved firm heterogeneity, we ran a

model in which we included a presample products variable (Blundell, Griffith, and Van Reenen,

1995) —that is, we controlled for products introduced by the firm three years prior to the study

period. Because it is plausible that firms that have been active in product development in the past

will continue to be so, the presample variable accounts for such unobserved heterogeneity that

may otherwise influence the results (Heckman and Borjas, 1980).

Causal Inference

To further facilitate causal inference, we lagged all independent and control variables by

one year. We also examined the extent to which potential endogeneity exists in our sample by

running Durbin and Wu-Hausman tests for both of our main explanatory variables (Durbin,

1954; Hausman, 1978; Wu, 1973). Although the tests (reported below) confirmed that

endogeneity is not present and our main results’ estimates using observed data are consistent

with our instrumental variables results, we added an instrumental variables analysis out of

abundance of caution, and tested several alternative explanations for our findings (detailed

below).

Through instrumental variables analysis we attempted to control for unobserved factors

that are simultaneously related to how firms monitor competition and to new products (e.g.

“high-quality” firms may monitor more selectively and introduce more products). Running a

two-stage instrumental variables analysis that we document below allows us to provide more

assurance that differences in monitoring of particular competitors are specifically related to

differences in new products.

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We instrument for competitor monitoring using an interaction between a major regulatory

event and firm visibility. The regulatory event is the landmark United States v. Microsoft Corp

antitrust case, in which Microsoft was found to have obstructed competition in software. A few

of our interviewees mentioned that firms were pushed to consider competition more thoughtfully

and more broadly following the case. In particular, U.S. v. Microsoft was the first major

regulatory action taken against a software firm, which led to concern over increased enforcement

in the future (Liebeler, 2002).

However, the effects of the Microsoft case were not equal among all software firms.

Rather, it was apparent from field evidence that more visible firms were likely to feel more

vulnerable to increased antitrust enforcement. Because inclusion in an S&P index draws

increased attention from investors and other stakeholders (Aghion, Van Reenen, and Zingales,

2009). to account for intensified effect due to visibility, we followed Aghion and colleagues

(2009) and Clay (2002) and measured visibility with a firm’s membership in a major Standard &

Poor’s stock index (i.e., the S&P 1500 that includes S&P LargeCap 500, S&P MidCap 400, and

the S&P SmallCap 600). Greater attention to a firm’s stock draws greater attention to the firm

itself, increasing visibility. We measured Member of S&P 1500 as a binary variable set to 1 if the

firm is a member of the S&P 1500. S&P index membership tracks a firm’s visibility but is

unlikely to be correlated with product performance because inclusion is not based on

expectations of strong future performance, but rather the extent to which a stock contributes to a

balanced representation of the overall economy (Standard & Poor's, 2013).

Because we expect S&P index membership (i.e. visibility) to be related to monitoring of

competitors in the years following the Microsoft antitrust ruling, we instrument our explanatory

variables with an interaction between the focal firm’s S&P index membership and the timing of

the Microsoft ruling in June 2000 (Post-Microsoft ruling measured as a binary variable set to 1 if

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the year is after 2000). Thus, the interaction of these variables (which serves as the instrument)

takes a value of 1 when the year is after 2000 and the firm is a member of an S&P 1500 index.

Because our dependent variable is a count of new products, we ran instrumental variables

Poisson regressions (reported in table A3). The first stage uses the instrumental variables and

control variables to predict the potentially endogenous variable using a linear OLS model. The

second stage uses the predicted values of the endogenous variable to predict counts of new

products, using a Poisson model.

RESULTS

Table 1 reports descriptive statistics and correlations. The average firm in the population

develops products in 1-2 markets, and faces about 36 other firms across markets. Out of these 36

potential competitors, the focal firm monitors about 6 on average. Our data are thus consistent

with prior work that shows that executives monitor a limited number of competitors, and

specifically about 6-7 competitors on average (Clark and Montgomery, 1999). As expected,

spanning structural holes is relatively uncommon in the data (constraint measure is 0.32) which

indicates that an average firm monitors clusters of competitors that are moderately connected

rather than completely disconnected. Of the competitors that the firm monitors, on average 14%

are “peripheral” i.e. mostly ignored by other firms in the same markets.9 It is also noteworthy

that 67% of monitoring ties in our data are unidirectional, i.e. the focal firm monitors a

competitor that does not monitor the focal firm in return. The average firm that we study releases

2-3 new products per year.

All three measures of competitor monitoring exhibit high variation. Among explanatory

variables that are included in regression models simultaneously, correlations are mostly low to

moderate. Variance inflation factors (VIFs) for most independent variables (Menard, 2001),

9 Of the monitoring ties that are bidirectional, there is no pattern of peripheral firms monitoring other peripheral firms (i.e. dyads of network isolates are not common in the data).

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including our hypothesized variables of peripheral monitoring and spanning structural holes10,

were less than the recommended cut-off value of 5.0. The only exceptions were controls for firm

performance, firm R&D intensity, and competitive density. We tested the results with and

without these controls, with consistent results. To address potential multicollinearity between

main effects and interaction terms, we mean-centered the variables (Cronbach, 1987).

Regression analysis

Firm fixed effects analysis. We first ran a Hausman test to determine whether fixed

effects or random effects models were more appropriate (Hausman, 1978). The Hausman

specification test revealed systematic differences in coefficients when estimating fixed versus

random effects, indicating that fixed effects were appropriate. Fixed effects Poisson panel

regression results are reported in Table 2 models 1-6 and random effects in model 7. Results for

control variables (Model 1) are in line with expectations. More diversified, higher-performing,

and more R&D intensive firms introduce more new products.

Table 2 and figures 3-5 about here

Hypothesis 1 predicted that spanning structural holes in competitor monitoring is

positively-related to product introductions. The coefficient is positive and significant across

models, supporting hypothesis 1. A one standard deviation increase from the mean in spanning

structural holes (which roughly equates to one additional structural hole) yields one additional

product per year.

Hypothesis 2 predicted that competitive motivation would strengthen the relationship

between structural holes spanning and product introductions. The interaction is positive and

significant across models, supporting hypothesis 2. Because point significance in non-linear

10 VIFs for structural holes spanning and peripheral competitors are 3.73 and 3.47, respectively, indicating the collinearity is unlikely to affect our results. Moreover, our results are robust to a stricter measure of spanning structural holes that specifically excludes ties between peripheral competitors (Burt, 2010).

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models does not always imply significance over the whole range of data (Hoetker, 2007), Figure

3 plots the effect of structural holes on new products for two levels of firm competitive

motivation. The results demonstrate that spanning structural holes has a significantly more

positive effect on new products at high levels of competitive motivation: at high levels, a one

standard increase in structural holes spanning (i.e. an additional structural hole) yields roughly

three additional products per year (3.2 products). At low levels of competitive motivation,

however, additional structural holes do not yield any additional products.

Hypothesis 3 predicted that monitoring peripheral competitors is negatively-related to

product introductions. The coefficient is negative and significant in the full model, supporting

the hypothesis. A one standard deviation increase from the mean in monitoring peripheral

competitors, which roughly equates to adding two competitors that no other firm is monitoring,

decreases product introductions by roughly half a product per year (0.4 products), or roughly one

fewer product every two years.

Hypothesis 4 predicted that competitive capability would amplify the relationship

between monitoring peripheral competitors and product introductions. The coefficient on the

interaction term is positive and significant, supporting hypothesis 4. Figure 4 plots the effect for

two levels of competitive capability. The results demonstrate that monitoring peripheral

competitors has a particularly negative influence on product introductions among the least

experienced firms, but a mildly positive influence for more experienced firms. At low levels of

competitive capability, a one standard deviation increase in monitoring peripheral competitors

decreases product introductions by 0.4 products per year. At high levels of competitive

capability, however, a one standard deviation increase in monitoring peripheral competitors

increases product introductions by 0.5 products per year.

Hypothesis 5 predicted that structural holes spanning would weaken the negative

35

relationship between monitoring peripheral competitors and product introductions. The

coefficient on the interaction term is positive and significant. To aid in interpretation, Figure 5

plots the effect of monitoring peripheral competitors on product introductions at high and low

levels of structural holes spanning. The results demonstrate that the negative relationship

between peripheral competitors and new products is even stronger when monitoring dense,

tightly-connected clusters of competitors. At low levels of structural holes spanning, a one

standard deviation increase from the mean in monitoring peripheral competitors reduces product

introductions by 0.8 products per year. In contrast, at high levels of structural holes spanning the

effect of monitoring peripheral firms starts to turn mildly positive. Hypothesis 5 is therefore

supported. Random effects results for all hypotheses (model 7) are consistent.

Instrumental variables analysis. We first examined the extent to which potential

endogeneity exists in our sample. We ran Durbin and Wu-Hausman tests for both of our main

explanatory variables (Durbin, 1954; Hausman, 1978; Wu, 1973). For structural holes, the

Durbin and Wu-Hausman test statistics are both 0.24 (p=.63). For peripheral competitors, the

Durbin and Wu-Hausman test statistics are both 0.04 (p=.84). The lack of significance suggests

that bias from unobserved firm heterogeneity is not a concern for either of our main explanatory

variables and that our main results’ estimates using observed data are preferred to instrumental

variables results.

However, for comprehensiveness, we also ran an instrumental variables analysis (details

in Methods). Our field interviews and archival research documented that in the software

industry, the Microsoft antitrust case sparked an increase in attention to competition. To avoid

antitrust scrutiny, our interviewees noted that there was an increased urgency for software

markets to become more competitive, providing face validity for the instrument.

Tables A1-A2 report descriptive comparisons of product introductions and competitor

36

monitoring for the treatment and control groups in the three years before and after the Microsoft

ruling and show descriptive evidence that the trial had little effect on competitor monitoring in

the control group, while intensifying competitor monitoring for the treated firms (table A1), as

expected. Given the timing of the Microsoft case in 2001, an obvious concern with the

instrument is the influence of macroeconomic trends. It is noteworthy that the downturn in the

U.S. economy in 2001 reduced product introductions for both the treated and the control groups,

and did significantly more so for the treated relative to the control group (32% vs 20% decrease

in table A2), making our estimates more conservative. Thus, the data in table A2 potentially

reduce the concern that changes in the economy, independent of the antitrust case, would be

confounding.

Tables A1 and A2 about here

We then examined the relevance and validity of our instruments. First, we examined

whether the instrument was relevant (i.e. had an effect in the first stage) using a Stock-Yogo test

(Stock and Yogo, 2005). The F-statistic for structural holes competitors is 4.61 while the F-

statistic for peripheral competitors is 10.92. This indicates that our instruments are somewhat

weak (a typical challenge in organizations research) and so instrumental variables results should

be interpreted with caution.

Second, we examined whether the instrument was valid (i.e. uncorrelated with the error

term in the second stage). A review of the antitrust literature indicated that there was no evidence

of a systematic effect of the Microsoft antitrust case on software firms’ product development

(Page and Childers, 2007; Pitofsky, 2001). Economic modeling further suggests that, in fast-

moving technology industries, antitrust enforcement has both positive and negative influences on

innovation incentives that are ultimately likely to cancel each other out (Segal and Whinston,

2007). Our quantitative tests using Hansen’s J-statistic for nonlinear models (Hansen and

37

Singleton, 1982) provided confirmation. The test statistic for structural holes spanning is 3.46

(p=.18) and the test statistic for peripheral competitors is 6.23 (p=.04), indicating that our

instrument is valid for structural holes but not valid for peripheral competitors. In light of these

results, we ran the instrumental variables analysis only for hypothesis 1.

Table A3 about here

Two-stage instrumental variables Poisson regression results for spanning structural holes

are reported in table A3. Model 1 reports results from a first-stage linear regression. As noted

above, we instrument structural holes spanning with an interaction between the Microsoft ruling

and S&P index membership (just under 25% of the sample firms were part of S&P 1500). As

expected, the coefficients on the interaction terms between Post-Microsoft ruling and Member of

S&P 1500 are positive and significant (p=.007). Models 2-3 report second stage Poisson results,

with the instrumented value for structural holes spanning added in Model 3. The coefficient is

positive and significant (p=.02), lending further support for hypothesis 1. Altogether, although

the tests reported above (Durbin, 1954; Hausman, 1978; Wu, 1973) show that endogeneity is not

a concern in our data, our additional instrumental variables analyses provide further confidence

on our main findings.

Sensitivity analyses. One alternative explanation for our findings is that firms may be

both more conscious of opportunities to span structural holes and more likely to introduce new

products when they diversify to new markets. We therefore tested the robustness of our results to

dropping years prior to firm entries into new infrastructure software markets, by excluding one

year and two years prior to the release of a product in a new market. Results (available from the

authors) are consistent, indicating that the observed effects are probably not simply driven by

plans for expansion.

A related question is whether firms in general, as they consider a significant strategic

38

move, such as a market entry, have a tendency to strategically hide their monitoring of certain

competitors, so as not to reveal their intentions. However, hiding would make it harder for us to

find results (as new competitors that are monitored would not be listed) and thus is not a likely

explanation. Another somewhat related question is whether some firms would strategically over-

monitor lower-performing firms to appear relatively stronger to their investors (and as a

consequence low performance of monitored competitors rather than network position would

potentially explain the results on peripherality). However, again, this seems an unlikely

explanation as the correlation between peripherality and financial performance is very low,

indicating that peripheral, i.e. less-monitored firms are not necessarily low performers. In fact, in

parallel with the observation (from our first-stage models predicting peripheral-firm monitoring)

that higher rather than lower-performing firms are more likely to monitor a peripheral

competitor, these additional analyses pose an interesting question for future work of who the

peripheral firms are, including the possibility to study whether these firms are newcomers to the

network.

Another alternative explanation for our findings is that executives may be more likely to

monitor peripheral competitors or monitor competitors from disparate clusters (span structural

holes) when they are interested in acquiring those firms. In other words monitoring of non-local

competitors and product introductions go hand in hand because firms monitor acquisition targets,

and acquisitions in turn boost the product introductions of the acquiring firm (Ahuja and Katila,

2001). We tested for this alternative explanation by removing competitors that the focal firm

later acquired (within one year, within two years, or at any later point) from our measures of

structural holes and peripheral competitors. Results were again consistent.

DISCUSSION

We started the paper with the observation that despite the rich insights on awareness of

39

local competitors, the AMC perspective does not yet incorporate understanding of how

positioning in rivalry networks may influence competitive action. Our purpose was to extend the

AMC framework, and to show that not all competitive awareness is beneficial. Drawing on

research on competitive dynamics, and on a longitudinal analysis of 121 infrastructure software

firms from 1995 to 2012, a key finding is that positioning within competitive networks matters:

monitoring some competitors but not others can be a source of advantage over rivals facing the

same competitive environment. Controlling for intensity of competition in the firm’s markets,

and for firm heterogeneity, we find that product introductions are increased when firms span

structural holes in the rivalry network, and typically reduced when peripheral competitors are

added. Effect sizes are substantial. For example, adding one additional structural hole is related

to one additional product per year, while adding a peripheral competitor is related to roughly one

fewer product every two years, in a context where the average firm only introduces 2-3 products

per year. At high levels of competitive motivation the effect is even more dramatic, as an

additional structural hole yields three additional products per year. Altogether, our findings

indicate that competitive awareness is not simply a matter of who the firm decides to monitor,

but how those choices position the firm within the wider network of competitor monitoring

within an industry. These findings have implications for research on competitive dynamics.

Contributions to Competitive Dynamics

We make several contributions to research on competitive dynamics. First, we highlight

previously-unexamined variation in how firms think about competition and so offer a more

accurate and nuanced view of competitive dynamics. Most frameworks on competition imply

that ties between competing organizations form such that they aggregate into relatively

homogenous “strategic groups” (Smith et al., 1997). As a result, prior work has focused on

awareness of rivals that are “local” or in dense (i.e. tightly-connected) clusters (Chen et al.,

40

2007; Ferrier et al., 1999). In contrast, we expand to those monitored competitors that are

“further afield,” e.g. on the periphery of the network or spanning structural holes. Further, we

find that firms facing the same competitive environment vary in their propensity to monitor these

“non-local” competitors, which creates variation in network positioning. As one industry

informant noted, there is never “definitive data” about competition, just “a lot of built-in

assumptions.” Highlighting the existence of these varied positions in rivalry networks is

therefore one of the core contributions of our work.

We further contribute by examining the implications of these varied network positions for

competitive actions. Prior research suggests multiple, contrasting influences of spanning

structural holes in competitive networks (Chen and Miller, 2012; Tsai et al., 2011). Tsai et al.

(2011) argue for a negative influence, as closed networks help the focal firm understand the

behaviors of its primary competitors better. In contrast, we find that spanning structural holes has

a positive influence on competitive action, by prompting the firm to respond to competition

through product introductions. An exemplar case is two similar security software firms in our

data: Axent Technologies and Cyberguard. Axent, which spanned structural holes, introduced

more new products and grew rapidly. In contrast, Cyberguard, which monitored a denser, closed

network, struggled to launch products and grew more slowly.

We also contribute by showing that not all positions in rivalry networks are beneficial; in

particular, monitoring peripheral firms can be harmful. In rivalry networks, the objectives of

monitored firms are to compete rather than collaborate, and the knowledge that is obtained in

particular from peripheral firms (that are not monitored by many other firms) is not “kept in

check” by other network members, and so is less likely to be thoroughly vetted. Perhaps for these

reasons, we find that monitoring of peripheral firms in rivalry networks is detrimental. Further,

we find that more competitively experienced firms can alleviate these negative effects, possibly

41

because they are better able to distinguish trustworthy knowledge or better able to connect the

dots in case of missing information. Overall, then, the competitive objectives of network

members introduce an additional constraint on positioning and explain why effective positioning

in rivalry networks differs from positioning in collaboration networks.

Finally, we contribute by specifically examining networks of monitoring relationships

that we label as rivalry networks, and by using fine-grained new measures of awareness. Prior

work on rivalry networks has typically conceptualized competitive relationships through product

or geographic overlap (Hsieh and Vermeulen, 2014; Tsai et al., 2011). From this perspective,

competition is a characteristic of the firm’s environment that is generally symmetric (i.e. mutual)

between firms. In contrast, we build a network in which “ties” represent unilateral monitoring

relationships, allowing for variation between firms facing the same environment (e.g. firms in the

same product markets) and asymmetry (monitoring is not always reciprocated), and in fact

highlight the intriguing pattern that a vast majority (67%) of competitive ties, at least in

enterprise software, are uni- rather than bi-directional. Analyzing a rivalry network thus allows

us richer insight into which firms are truly “peripheral” in the eyes of other firms within an

industry, as well as identify where a lack of monitoring between clusters of firms creates

opportunities for brokered information flows.

There are several items of future work. We examined unilateral monitoring through 10-

Ks and analyst calls. Future work could expand to bilateral types of monitoring such as board

interlocks or exchange relationships (Vissa, 2011), or expand to specifically examine cases

where competitor monitoring is symmetric (both parties monitor each other) vs not. Further,

while our study focused on the consequences of monitoring, future work could also investigate

what drives some firms to think more broadly about competition in the first place. Prior work on

the drivers of competitor monitoring has focused on firm characteristics, such as firm size or

42

geographic location (Chen et al., 2007). Future work could investigate, for instance, whether

variation in executive team or board characteristics is associated with differences in monitoring

(c.f., Connelly, Ferrier et al., 2017). Finally, we focused on when firms respond to competition

by building up their product offerings. Future work could examine when firms respond by

cutting their prices so they are the cheapest ones out there to buy from, or increase their spending

on sales and marketing to make sure that they are in front of the competition in the eyes of

customers. These are interesting avenues for future research.

CONCLUSION

While research has uncovered several “levers” that executives use to facilitate new

product introductions, the firm’s understanding of competition has received less attention than it

deserves. Our analysis suggests that executives can improve competitive actions by thinking

about positioning of their firm within the wider network of competitor monitoring relationships

within an industry. Competition is thus not merely an obstacle to overcome but rather a pathway

to strategic advantage. As one CEO put it, “Celebrate competition…It’s good to have a bad guy.”

43

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Figure 1. Model of Competitor Monitoring and New Product Introductions

Figure 2a. Redundant versus spanning ties in competitor monitoring

Redundant Spanning

Figure 2b. Monitoring of prominent versus peripheral competitors

Prominent Peripheral

Each node represents a potential competitor and directed ties between nodes indicate that the originating firm monitors the target firm. Inward ties to the focal firm (i.e. indegree centrality) are excluded for clarity.

48

Figure 3. Interaction between firm’s structural holes spanning and firm competitive motivation

(H2)

The x-axis displays the largest part of the range of the data (i.e. the 5th to 95th percentiles).

Figure 4. Interaction between firm’s monitoring of peripheral competitors and firm competitive

capability (H4)

Figure 5. Interaction between firm’s monitoring of structural holes and peripheral competitors

(H5)

0123456

-0.6 -0.4 -0.2

New

Pro

duct

s

Structural Holes Spanning

5th Percentile Firm Competitive Motivation

95th Percentile Firm Competitive Motivation

0123456

1 3 5 7

New

Pro

duct

s

Peripheral Competitors

5th Percentile Firm Competitive Capability

95th Percentile Firm Competitive Capability

0

1

2

3

4

1 3 5 7

New

Pro

duct

s

Peripheral Competitors

5th Percentile Structural Holes Spanning

95th Percentile Structural Holes Spanning

49

Table 1. Descriptive statistics and correlationsVariable Mean S.D. 1 2 3 4 5 6 7 8 9 10 11 12 13

1 Product introductions 2.95 3.372 Structural holes spanning1 -0.32 0.17 0.253 Peripheral competitors 2.22 1.91 0.12 0.494 Firm competitive motivation 0.04 0.06 0.41 0.26 0.155 Firm competitive capability2 1.73 0.85 0.15 0.07 -0.02 0.426 Firm scope 1.62 0.81 0.27 0.15 -0.07 0.37 0.387 Firm performance -0.39 2.72 0.05 0.12 0.03 0.08 0.05 0.058 Firm R&D intensity 0.42 3.28 -0.02 -0.05 -0.05 -0.04 -0.09 -0.04 0.319 Team turnover 0.36 0.31 -0.03 -0.04 -0.07 -0.15 -0.18 -0.13 -0.05 0.01

10 Competitor density 36.63 17.47 0.26 0.17 -0.08 0.06 -0.06 0.51 -0.01 0.03 0.0211 Developer tools 0.28 0.45 0.05 -0.06 -0.08 0.02 0.13 0.46 -0.02 -0.02 0.02 0.1312 Integration and middleware 0.32 0.47 -0.08 -0.04 -0.07 -0.02 0.03 0.43 -0.02 -0.03 -0.07 0.23 0.2713 Databases 0.25 0.43 0.15 0.12 0.01 0.18 0.27 0.50 0.05 -0.03 -0.09 0.12 0.20 0.0814 Security 0.25 0.43 0.15 0.14 0.14 0.35 0.13 0.01 0.02 -0.03 0.00 -0.19 -0.32 -0.36 -0.17

121 firms, 823 firm-yearsCorrelations above .07 are significant at p<.051 Burt's constraint measure, multiplied by -1 2 Logged

50

Table 2. Fixed effects Poisson models predicting number of product introductions

DV: Product introductions 1 2 3 4 5 6Random

EffectsStructural holes spanning 0.60 * 1.31 *** 1.50 *** 1.45 *** 2.24 *** 2.66 ***

(0.29) {0.04} (0.33) {0.000} (0.37) {0.000} (0.38) {0.000} (0.46) {0.000} (0.40) {0.000}

Firm competitive motivation -1.84 † -2.07 * -1.26 -0.37 0.69(1.00) {0.07} (1.03) {0.04} (1.07) {0.24} (1.11) {0.74} (0.94) {0.46}

Structural holes spanning x Firm competitive motivation 31.83 *** 33.52 *** 29.43 *** 22.18 ** 22.29 ***(7.09) {0.000} (7.30) {0.000} (7.39) {0.000} (7.72) {0.004} (6.79) {0.001}

Peripheral competitors -0.02 -0.05 † -0.14 *** -0.14 ***(0.02) {0.29} (0.02) {0.06} (0.04) {0.000} (0.03) {0.000}

Firm competitive capability 0.24 † 0.26 † 0.19 *(0.14) {0.09} (0.15) {0.07} (0.09) {0.03}

Peripheral competitors x Firm competitive capability 0.06 * 0.07 ** 0.06 **(0.02) {0.02} (0.02) {0.004} (0.02) {0.005}

Structural holes spanning x Peripheral competitors 0.59 *** 0.65 ***(0.19) {0.001} (0.17) {0.000}

ControlsFirm controlsFirm scope 0.68 *** 0.64 *** 0.46 * 0.47 * 0.49 ** 0.37 † 0.33 *

(0.18) {0.000} (0.18) {0.000} (0.19) {0.02} (0.19) {0.01} (0.19) {0.010} (0.20) {0.06} (0.14) {0.01}

Firm performance 0.15 * 0.16 * 0.18 ** 0.18 ** 0.16 * 0.17 ** 0.11 *(0.06) {0.02} (0.06) {0.01} (0.06) {0.004} (0.06) {0.004} (0.06) {0.01} (0.06) {0.009} (0.05) {0.04}

Firm R&D intensity 0.59 * 0.63 * 0.70 ** 0.70 ** 0.64 * 0.65 ** 0.29(0.25) {0.02} (0.25) {0.01} (0.25) {0.004} (0.25) {0.005} (0.25) {0.01} (0.25) {0.010} (0.21) {0.16}

Team turnover 0.06 0.08 0.09 0.09 0.08 0.08 0.000(0.11) {0.60} (0.11) {0.46} (0.11) {0.41} (0.11) {0.43} (0.11) {0.45} (0.11) {0.47} (0.11) {1.00}

Market controlsCompetitor density -0.001 -0.001 -0.003 -0.004 -0.01 -0.01 -0.01

(0.01) {0.89} (0.01) {0.87} (0.01) {0.51} (0.01) {0.45} (0.01) {0.21} (0.01) {0.23} (0.004) {0.18}

Developer tools -1.25 *** -1.21 *** -0.82 * -0.80 * -0.79 * -0.70 * -0.09(0.31) {0.000} (0.31) {0.000} (0.32) {0.01} (0.33) {0.01} (0.32) {0.02} (0.32) {0.03} (0.15) {0.57}

Integration and middleware -0.62 † -0.55 -0.29 -0.33 -0.36 -0.23 -0.19(0.37) {0.09} (0.37) {0.14} (0.38) {0.45} (0.38) {0.39} (0.38) {0.35} (0.38) {0.55} (0.15) {0.22}

Databases -0.89 *** -0.85 *** -0.64 * -0.62 * -0.60 * -0.45 † -0.32 *(0.26) {0.000} (0.26) {0.000} (0.27) {0.02} (0.27) {0.02} (0.27) {0.02} (0.27) {0.10} (0.16) {0.04}

Security -0.24 -0.20 -0.02 -0.04 -0.07 -0.04 -0.01(0.23) {0.30} (0.23) {0.39} (0.23) {0.92} (0.23) {0.87} (0.23) {0.75} (0.23) {0.86} (0.15) {0.96}

Firm fixed effects Y Y Y Y Y Y Y

Year fixed effects Y Y Y Y Y Y Y

Chi-Squared 184.2 187.9 206.9 207.4 218.8 226.5 254.5

Standard errors in parentheses, p-values in brackets. Two-tailed significance tests: † p < .10 *p < .05 **p<.01 ***p<.001.

121 firms, 823 firm-years. All models include firm and year effects and two-year lagged dependent variable

51

Appendix

Comparisons are for the three years before and after the Microsoft trial.

Table A3. Two-stage instrumental variables models predicting number of product introductions

Table A1. Average number of listed competitors pre- and post-Microsoft trial

Treated (S&P index members) Control (Not S&P Members)Pre-trial 6.1 6.9Post-trial 7.2 6.6

Table A2. Average number of product introductions pre- and post-Microsoft trial

Treated (S&P index members) Control (Not S&P Members)Pre-trial 6.0 3.0Post-trial 4.1 2.4

Table A3. Two-stage instrumental variables models predicting number of product introductions1st Stage OLS 2nd Stage Poisson

Structural holes spanning

Number of product introductions

1 2

InstrumentsPost-Microsoft ruling -0.06 ***

(0.02) {0.001}

Member of S&P 1500 -0.01(0.02) {0.60}

Post-Microsoft ruling x Member of S&P 1500 0.08 ***(0.03) {0.001}

Instrumented explanatory variableStructural holes spanning 6.73 ***

(1.69) {0.000}

ControlsFirm controlsFirm scope 0.01 0.15

(0.01) {0.33} (0.11) {0.18}

Firm performance 0.01 *** -0.02(0.003) {0.001} (0.03) {0.44}

Firm R&D intensity -0.005 *** 0.01(0.001) {0.000} (0.01) {0.28}

Team turnover -0.01 0.07(0.02) {0.59} (0.16) {0.65}

Market controlsCompetitor density 0.001 * 0.004

(0.000) {0.02} (0.005) {0.42}

Developer tools -0.03 † 0.37 *(0.02) {0.08} (0.16) {0.02}

Integration and middleware -0.01 -0.27 *(0.02) {0.57} (0.13) {0.03}

Databases 0.04 ** -0.23(0.02) {0.003} (0.16) {0.15}

Security 0.06 *** -0.06(0.02) {0.000} (0.18) {0.71}

Standard errors in parentheses, p-values in brackets. Two-tailed significance tests: † p < .10 *p < .05 **p<.01 ***p<.001. 121 firms, 823 firm-years. All models include year effects.