Technology Shocks, Technological Collaboration, and ......Innovation Outcomes Melissa A. Schilling Stern School of Business, New York University, New York, New York 10012,[email protected]

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Technology Shocks, Technological Collaboration, andInnovation OutcomesMelissa A. Schilling

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Technology Shocks, Technological Collaboration, andInnovation Outcomes

Melissa A. SchillingStern School of Business, New York University, New York, New York 10012, [email protected]

In the early to mid-1990s, technology alliances suddenly surged to unprecedented levels—roughly 300% growth peryear from 1990 to 1995—and then declined just as precipitously. This massive increase in alliance activity causedthe crystallization of a giant component in the global technology network that connected a large portion of the world’sfirms, government labs, universities, and other organizations. However, when alliance activity declined, the componentdisintegrated. What caused this spike in alliance activity? And did this large-but-transient change in collaboration activityleave any enduring effect? The data here suggest that a major technology shock may have provoked this alliance surge.A technology shock may simultaneously unleash significant innovation opportunities while creating great uncertainty in theeconomic environment. Though it is well known that firms often use alliances both to respond to uncertainty and facilitateinnovation, little is known about how technology shocks affect the collaboration behavior of firms and how these twofactors separately influence innovation outcomes. I integrate an inductive study of collaboration activity and a technologyshock with existing research on economics, alliances, and networks to build a set of arguments about how technologyshocks will influence alliance behavior, how changes in alliance behavior will influence the global technology collaborationnetwork, and about how each of these changes is likely to influence the innovative outcomes of firms. I then explore theseparate and joint effects of the technology shock and collaboration activity on innovation using a large sample panel studyof patenting by North American firms.

Keywords : technology shocks; alliances; networks; patents; innovationHistory : Published online in Articles in Advance.

IntroductionIn the early to mid-1990s, there was a significantsurge in alliance activity. This was no minor uptick—itwas a dramatic spike wherein the number of publiclyannounced technology alliances grew roughly 300% peryear from 1990 to 1995 and then subsequently declinedjust as sharply, stabilizing in 2002 at levels of allianceactivity close to those in 1990. The spike occurred simul-taneously in many industrial sectors and geographicalregions and involved substantial investment. It cannotbe explained away as an anomaly of one particu-lar data source—it is nearly identical across three ofthe most prominent multisector alliance databases in theworld, including Thomson’s SDC Platinum database, theMERIT-CATI database, and the CORE database of jointresearch ventures filed under the National CooperativeResearch Act (NCRA; which reports the population ofsuch filings and thus is not subject to the kinds of samplebiases we might suspect of other databases). Further-more, despite its size, the spike went largely unnoticed atthe time it was unfolding—probably because multisectoralliance databases were not yet in widespread use.

The transience of the alliance spike does not mean thatit was unimportant or without consequence. I provideevidence here that the alliance surge was a response to,and a signal of, a large-scale technology shock. It was,

in fact, a harbinger of a major technology and indus-try speciation event that, had we recognized it earlier,might have helped us to better anticipate the industrialand financial churn that was to follow. Furthermore, thealliance spike and its concomitant effect on the structureof the global technology collaboration network also leftenduring effects of their own on the world. Just as a goldrush leaves behind towns, railways, and other technolog-ical and social artifacts after the bulk of mining activityhas departed, an alliance spike creates new relation-ships between organizations, innovations, and a redistri-bution of information and other resources that endureafter alliance activities have diminished. I empiricallyexamine one of these outcomes here—innovations asembodied in patents—and show the importance of disen-tangling the direct and indirect effects of the technologyshock, alliances, and collaboration network structure.

This study has been a complex journey that began as aquest to solve what seemed to be a simple puzzle: Whydid alliance activity go up so dramatically? I interviewedexperts in academia and in industry1 and presented thedata at numerous workshops to get input. I read thedeal text from thousands of alliance agreements, andI graphed the data in dozens of different ways. Afterstrong suggestions that the spike was due to a technologyshock in information technologies, I examined dozens oftimelines and data series in information technology (IT).

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Schilling: Technology Shocks, Technological Collaboration, and Innovation Outcomes2 Organization Science, Articles in Advance, pp. 1–19, © 2015 INFORMS

At each step, however, new questions emerged: How didthe technology shock change the structure of collabora-tive networks? Did the alliance spike or changes in thestructure of collaboration networks have any effects overand above the effect of the initial technology shock; i.e.,did they matter?

There is significant research in economics on tech-nology shocks. In this literature, “technology shocks”are defined as technological changes that affect produc-tion outcomes through, for example, the invention ofnew production processes or the improvement of exist-ing ones (Alexopoulos 2011, Hansen and Prescott 1993).Although researchers in this area have extensively exam-ined the influence of technology shocks on aggregatelabor and manufacturing productivity (e.g., Basu et al.2004, Christiano and Eichenbaum 2003, Gali 1999), theyhave not examined how such shocks influence collab-oration activity or industry boundaries more generally.Similarly, although research in management on alliancesand networks has examined their effects on innovation, ithas not typically explored what would lead to large-scalechanges in alliance activity or networks. Without jointlyconsidering both technology shocks and their resul-tant collaboration activity, it is possible to significantlyoverestimate the effect of each on important outcomes.Innovation outcomes attributed to alliance patterns, forexample, may actually be due in part to an underlyingtechnology shock that influenced both alliance activityand innovation activities. This study integrates multiplestrands of research from economics, management, andinnovation, with archival data on alliances, patents, mul-tifactor productivity, and more to yield what turns out tobe a relatively simple but important explanation: a large-scale general-purpose technological innovation can cre-ate a shock that generates both great opportunity anduncertainty. One of the earliest ways that firms respondto such a shock is to forge collaboration agreementsbecause they are perceived as fast and reversible, andthey enable firms to pool scarce resources (Eisenhardtand Schoonhoven 1996, Kogut 1991, Schilling andSteensma 2001). Alliances are one of the mechanismsthat help firms to harvest the opportunities unleashed bythe technology shock; however, they also have effects oftheir own. Through their direct effects on the distribu-tion of information and other resources and their indirecteffects on the structure of collaboration networks, theyhave an additional effect on outcomes beyond just medi-ating the influence of the original shock.

This study also yields several other contributions.First, it is (to the best of my knowledge) the first studyto attempt to map the entire global technology collabo-ration network across sectors, nations, and organizationtypes, showing its form and its changes over time. Thecrystallization and subsequent fragmentation of the giantcomponent in the global technology collaboration net-work should be as interesting to scholars of industry

boundaries and evolution as it is to scholars of networks.The study shows, for example, how the boundaries oforganizational fields can become blurred and redefinedin the wake of a major technological change, thus con-tributing to the recently emerging research on indus-try preinception dynamics (e.g., Moeen and Agarwal2014). It also reveals that during periods of intensealliance activity, organizations across a very broad rangeof industries and nations can become connected into onegiant component, which has significant implications forhow network studies should be designed. Furthermore,the research here suggests that tracking alliance activityacross sectors and over time may give economists andinvestors an indicator of significant technological inno-vation that is observable well in advance of newspaperarticles, patent filings, or stock market indices. Alliancespikes are likely to also give us insight into other typesof disruptive events or opportunities, yet few harnessthis widely available, multisector data for its signalingcapabilities.

Technology Shocks andCollaboration ActivityAs noted previously, multiple databases confirm thatthe early 1990s witnessed a dramatic surge in tech-nology alliance activity, with the number of alliancesreaching unprecedented highs in the 1994–1995 timeframe, followed by an equally sharp decline (seeFigure 1).2 To explore patterns within this alliancespike, I use announcements of technological collabo-ration agreements drawn from Securities Data Corpo-ration’s Joint Venture and Alliance database becauseit is the largest and most internationally compre-hensive of the publicly available multisector alliancedatabases.3 I included every publicly announced technol-ogy collaboration agreement (joint research and devel-opment (R&D) agreements, cross-licensing agreements,

Figure 1 Standardized Number of Alliances Reported in theSDC, CORE, and MERIT-CATI Databases

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Schilling: Technology Shocks, Technological Collaboration, and Innovation OutcomesOrganization Science, Articles in Advance, pp. 1–19, © 2015 INFORMS 3

Table 1 SDC “Completed” Technology Collaboration Agreements and Network Statistics

Largest connected component (LCC)Technology Organizations Avg. number of

Year agreements with agreements agreements per organization Organizations % in LCC Avg. degree

1990 425 683 1087 128 19 30171991 941 11375 1096 321 23 30461992 11480 11963 2016 906 46 30181993 11745 21505 2027 962 38 30621994 21251 31176 2040 11220 38 30911995 11878 21865 1091 799 28 20941996 670 11074 2001 200 19 50281997 903 11409 1068 222 16 30241998 584 11023 1043 79 8 20631999 356 629 1029 16 3 30002000 394 722 1049 73 10 20852001 311 567 1039 23 4 30302002 271 489 1018 12 2 10832003 257 476 1024 17 4 10882004 254 452 1019 13 3 10852005 583 11050 1020 15 1 1087Average 831 11279 1067 313 16 3000

and cross-technology transfer agreements) completedbetween any two or more organizations (including firms,nonprofits, government agencies, universities, etc.), fromanywhere in the world. Notably, this data source under-states the connectivity of the network that would existif I was able to incorporate informal collaboration rela-tionships. However, there is a strong correlation betweenthe pattern of formal and informal relationships becauseinformal arrangements often lead to the types of formalagreements I observe here (Rosenkopf et al. 2001). Thedata were gathered for the period 1990–2005 (inclusive).The resulting data set includes 13,304 total alliancesbetween 13,906 organizations from 105 nations.

As shown in Table 1, the number of new technol-ogy alliances (joint R&D agreements, cross-technologytransfers, and cross-licensing agreements) in Thomp-son’s SDC database rises from 425 in 1990 to 2,251 in1994, drops back down below 300 at the beginning ofthe next decade, and then recovers to 583 by 2005—a number that is less than one-third of the peak num-ber of alliance announcements. Table 1 also indicatesthat the number of organizations announcing technologyalliances hits a peak of 3,176 in 1994 and that organiza-tions announced more alliances on average (2.4 alliancesper firm, compared to an average of 1.67 for the studyperiod). The alliance surge was exhibited in a broadrange of sectors, though some sectors experienced thealliance spike much more strongly than others, a point Iwill return to shortly.

The Global NetworkAs firms forge these collaborative relationships, theyweave a network of paths between them that can act asconduits for information and other resources. These net-works may initially be sparse or fragmented, but eitheran increase in the number of alliances or an increase

in alliances between atypical partners can dramaticallyincrease the connectivity of such networks. As firstshown with random graphs, if one starts with a set ofdisconnected nodes and adds links one at a time betweenrandomly chosen pairs of nodes, initially most nodeswill be isolated and a few nodes will be connectedto one or two others. As the ratio of links to nodesincreases past 0.5, a single giant component connect-ing most of the nodes will suddenly crystalize (Erdősand Rényi 1959)—this is known as a phase transition(Kauffman 1993). Alliance networks are not randombecause firms are more likely to form relationships withother firms with which they share some type of proxim-ity or similarity, such as geography or technology (Baumet al. 2003, Rosenkopf and Almeida 2003), resulting ina highly clustered network. However, as shown by Wattsand Strogatz (1998), a relatively small portion of ran-dom or atypical links in a nonrandom graph can lead toa sharp decrease in the average path length between con-nected nodes, enabling even a highly clustered networkto have an average path length close to that of a ran-dom graph. The preceding suggests that an increase ineither the degree of alliance formation or the formationof alliances that forge “shortcuts” to formerly distantclusters of firms could greatly increase the likelihoodof a phase transition that causes a giant component ofconnected firms to emerge.

To empirically examine this possibility, I construct theglobal technology collaboration network based on thealliance announcements described previously. Alliancestypically last for more than one year, but alliance ter-mination dates are rarely reported. This requires theresearcher to make an assumption about alliance dura-tion. Previous research has typically used windows

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Figure 2 Global Technology Collaboration Network, 1990–2005 (One-Year Snapshots)

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ranging from one to five years (e.g., Gulati and Gargiulo1999, Stuart 2000). I have used one-year alliance win-dows for the graphical pictures of the alliance networksin Figure 2 because three-year windows obscure someof the temporal changes; however, three-year windowsare used for the panel analysis described later. Each net-work snapshot was constructed as an undirected binaryadjacency matrix (Wasserman and Faust 1994). Multi-ple alliances between the same pair of firms in a timewindow are treated as one link.

Ucinet 6.2 was used to obtain measures of the struc-tural properties of each of these networks (Borgatti et al.2002). NetDraw 2.24 was used to generate pictures ofthe networks (Borgatti et al. 2002). The “spring embed-ding” feature was used in NetDraw to better visualizehow close or far each organization is from the othersin the network. This algorithm locates nodes closer toeach other if there is a short path length between them.4

A “node repulsion” feature helps to reduce the likeli-hood of nodes being located on top of each other, andan “equal path length” feature helps to ensure that thedistances between adjacent nodes are commensurate. Ifa network has one large “component” (a group of nodesthat are all connected together) and many pairs or triplesof nodes that are not connected to this large component,the algorithm often (but not always) results in the pairsand triples being grouped into a single mass that is sep-arate from the large component. For example, in most ofthe network snapshots here, there is a single large com-ponent that wraps around the graph space, and the pairs

and triples that are not connected to this large compo-nent form a lima bean-shaped mass in the center of thegraph. When there are multiple large components, or asingle large component that has several distinct lobes,the pairs and triples not connected to these componentsmay be pushed out into a ring around the large compo-nent(s) or may not exhibit any discernible organization.

Table 1, mentioned previously, provides some statis-tics for the largest connected component of the globaltechnology collaboration network, and Figure 2 pro-vides graphical pictures of the network overall. Asindicated in Table 1, for several years a large per-centage of the organizations participating in technol-ogy collaboration agreements were connected into asingle large component, reaching highs of 38%–46%in the 1992–1994 snapshots. However, when the num-ber of alliances dropped precipitously toward the end ofthe decade, the network fragmented into many smallercomponents—in the years 2001–2005, less than 5% ofthe organizations reported to have technology allianceswere connected to the largest component based on yearlysnapshots. A similar pattern is observed if three-yearalliance windows are used to create the networks—inthis case, the percentage of organizations connected tothe largest component reaches 58% in the mid-1990s anddrops to an average of 9% for 2001–2004.

Graphical pictures of the network snapshots providefurther insight. The graphs are color-coded by com-ponent so that the nodes connected to the largest compo-nent are discernible from the other nodes (the largest

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component is in red). These graphs provide a stark visu-alization of how the dramatic rise and fall of allianceactivity in the mid-1990s impacted the overall con-nectivity of the technology collaboration network. Asshown, in the snapshots leading up to the mid-1990s, themain component grows very large and dense. After thealliance spike, the main component begins to thin, andby 1998 the main component has fragmented into manysmaller components.5

What Caused the Surge in Alliance Activity?To gain insight into what could have provoked this sud-den increase in alliance activity, I examined the sec-toral patterns in alliance activity and the deal text ofthe alliance announcements. A sector breakdown of thealliance activity indicates that of the five sectors thatappear to contribute most significantly to the mid-1990speak, four are central to IT: electronics and electri-cal equipment, business services (which is dominatedby software), industrial machinery, and communicationsservices (a graph of the sectoral breakdown is pro-vided in the online supplement, available as supplemen-tal material at http://dx.doi.org/10.1287/orsc.2015.0970).A count of the alliances by primary activity (based onSDC’s classification of alliance activities into StandardIndustrial Classification codes) reinforces the promi-nence of IT during the alliance spike. The percent-age of alliances that were formed primarily for ITactivities (computer equipment 3571–3577; communica-tion equipment 3661–3669, semiconductors and relatedcomponents 3671–3679, communication services 4812–4899, and software 7371–7379) rose from 26% in 1990to a peak of 58% in 1995, then dropped sharply.

Although there were a number of exciting IT develop-ments in the early 1990s (e.g., increasing use of mobilephones, continued growth in personal computer sales),the deal text of the alliances highlights one category ofIT more frequently than the others: the Internet, or net-working more generally. Figure 3 shows the percentageof alliances where the deal text specifically mentions“cellular phone” or “mobile phone,” “personal com-puter” or “ PC,” “Internet,” or “network.” The differ-ences in these percentages are striking; a much largerpercentage of the deals cite “Internet” or “network” inthe deal text than they do other terms. Furthermore, thereis a clear spike in use of the terms “Internet” and “net-work” in the mid-1990s that corresponds to the spike inthe overall use of alliances.

I now provide a brief history of the Internet andwhy its effects on industry rapidly accelerated in theearly 1990s.

The Rise of the Internet and NetworkingIn 1969 the U.S. Department of Defense commissionedthe ARPANET (Advanced Research Projects AgencyNetwork) program to research computer networking and

Figure 3 Percentage of Alliances with Deal Text Including theTerms “Cellular Phone,” “Mobile Phone,” “PersonalComputer,” “PC,” “Internet,” or “Network”

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established the first four nodes of what would later betermed the “Internet.” Although there was considerablegrowth in the Internet throughout the late 1970s andthe 1980s, through most of this time frame it was stillprimarily a tool of science and education—its relianceon text-based programs limited its commercial appeal,and commercial use of the ARPANET backbone wasforbidden.

By the early 1990s, however, the network began togrow beyond the constraints of the original ARPANETbackbone; other government institutions and commercialproviders had built their own backbones, and regionalnetwork access points had become the primary intercon-nections between networks, ending any limitations oncommercial use. Suddenly the Internet began to attractthe widespread attention of business and media. Thenumber of Internet hosts and the number of peopleusing the Internet began to grow explosively. Helping tofuel this growth were dramatic increases in the perfor-mance of semiconductors. It was the dramatic increasein semiconductor density that enabled similarly dramaticincreases in the speed and capacity of the Internet whilesimultaneously reducing communications costs (Roberts2000). Then, in 1993, the launch of Mosaic broughta consumer-friendly graphical interface to the Internet,unleashing an even larger frenzy of business activitydirected at leveraging what was now called the WorldWide Web.

The early 1990s were pivotal turning points for theInternet. In 1993, Internet penetration of the U.S. marketsurpassed 3%, moving from the “innovator” segment ofthe market to the “early adopter” segment of the mar-ket (Rogers 1995), and the number of Internet hostswas rising exponentially (see Figure 4). The sea changethat was underway is illustrated by the stark contrastin the following quotes, only two years apart: “Let’s

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Figure 4 Percentage of U.S. Population Using the Internetand Number of Internet Hosts (in Thousands)

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face it. Not many members of the public—even thecomputer literate public—are on the Internet” (Goodwin1993, §2.1) and “Businesses and entrepreneurs are rush-ing into cyberspace like forty-niners driven mad by goldfever” (Sussman and Pollack 1995, p. 72).

The rise of the Internet coincided with rapid inno-vation in related networking equipment and software,and precipitous drops in prices of IT products suchas semiconductors and telecommunications equipment(Roberts 2000). These interdependent processes collec-tively caused a major shock to the economy (Jorgenson2001). The epicenter of this shock was in the IT indus-tries, but its reverberations were felt in many indus-tries and in many layers of the economy. The Internetwas a “general-purpose technology” with the potential totransform information dissemination on a massive scale(Mowery and Simcoe 2002), creating tremendous uncer-tainty for organizations.

Responding to Technology ShocksNumerous studies have identified the rapid growth ofthe Internet in the early 1990s as a technology shock(e.g., Alexopoulos 2011, Arnold 2003, Lyytinen andRose 2003). Normally, reference is made to positive (i.e.,productivity enhancing) technological change, althoughtechnology shocks can also be contractionary (Basu et al.2004). The term “shock” connotes the fact that tech-nological progress is not always a gradual process—sometimes there are large-scale discontinuous changesthat significantly alter production methods and outputsin an industry or in the economy as a whole. Sucha technology shock can occur in many different ways.For example, it may be the result of advances in sci-ence that enable new trajectories of innovation (Dosi1982). It may also result when an existing technolog-ical alternative improves to a point that it overtakes

the dominant design (Tushman and Anderson 1986) oris transplanted to a new domain (Levinthal 1998). Itcan also occur as the result of a shock in another sys-tem, such as when a change in input prices dramaticallychanges the price/performance relationship for a tech-nology (Ehrnberg 1995) or when a change in the regu-latory environment significantly alters the technologiespermitted (or demanded) in the market. There is a richhistory of research in economics on technology shocksthat has shown that they can have a significant effecton investment, economic growth, and labor productiv-ity (e.g., Alexopoulos 2011, Christiano and Eichenbaum2003, Gali 1999).

The most visible technology shocks are those in“general-purpose technologies,” i.e., technologies thathave relevance well beyond a single industry such asthose in engines, telephony, the transistor, etc. General-purpose technology shocks can create significant uncer-tainty and economic churn in the form of new firmentries, exits, and mergers as capital is reallocated acrossorganizations with different capabilities (Jovanovic andRousseau 2005).6 As Levinthal (1998) notes, a majortechnology shock creates new selection pressures in theenvironment and thus can induce major shifts in in-dustrial configurations, much the same as punctuatedequilibrium models explain eras of major change inbiological species.

One of the ways firms respond to uncertainty and op-portunity is through the formation of alliances (Anandet al. 2010, Rosenkopf and Padula 2008). Alliancesserve at least four main functions in helping firms torespond to a turbulent environment. First, under con-ditions of great uncertainty, firms may use alliancesas a sensemaking activity that enables them to probethe knowledge of other firms and develop shared inter-pretations of the changes unfolding in their environ-ment (Hoffman 2007, Mitchell and Singh 1992, Pfefferand Salancik 1978).7 Second, alliances enable firms tomore quickly or effectively respond to a changing envi-ronment by accessing the resources and capabilities ofothers. Through collaborative agreements, organizationscan pool complementary skills and assets, exchange andjointly create new knowledge, and share risk (Gulati1998, Kogut 1991, Powell et al. 2005, Rothaermel 2001).By providing firms access to a wider range of resources,alliances enable firms to achieve much more than theycould achieve individually (Grant and Baden-Fuller2004, Powell et al. 2005, Rosenkopf and Almeida 2003).Third, firms can use alliances to create, and build sup-port for, shared standards (Schilling 2002), thus activelyinfluencing the outcome of the uncertain phase. Byagreeing to particular standards, allied firms work toresolve some of the uncertainty in the technologicaltrajectory and improve the likelihood of the industryselecting a dominant design that best leverages theirown technological positioning. Fourth, alliances are an

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Figure 5 Growth in Internet Hosts and SemiconductorMultifactor Productivity vs. IT Alliances

Note. Semicon MFP, semiconductor multifactor productivity.

important mechanism for building legitimacy at both theindividual firm level (Stuart 2000) and the collectivestandard level (Schilling 2002).

For these reasons, I argue here that alliances may beone of the first ways that firms respond to a technol-ogy shock. Consistent with this, Figure 5 indicates thatthe explosive growth in Internet hosts and the multi-factor productivity growth in semiconductor manufac-turing (whose advances were primarily responsible forthe rise of the Internet, networking equipment, and per-sonal computers generally) shows a remarkable corre-spondence with the number of IT alliances formed.8

The correlation between the number of IT alliancesand growth in Internet hosts is 0.70 (p < 0�01), andthe correlation between the number of IT alliances andsemiconductor multifactor productivity growth is 0.72(p < 0�01). The standardized items collectively achievea Cronbach’s alpha of 0.82, suggesting that the threeseries could be considered multiple measures of thesame underlying construct.

As noted previously, although the IT industries dis-proportionately account for the spike in alliance activity,other industries also exhibit a surge in alliance activityduring this time period. This should not be surprising—the technological shock of the Internet created a wave ofuncertainty and opportunity that rippled through manyindustries. There was significant uncertainty in how theInternet and networking technologies would transforman industry’s business models. It would disintermediatesome value chains while creating new intermediaries inothers. It would enable major changes in how organiza-tions communicated both internally and externally withcustomers and suppliers. The net result would be sig-nificant industry churn that could cause once dominantorganizations to be displaced by competitors that madebetter gambles about how to exploit the new technolo-gies. To deal with all of this uncertainty, firms not onlyformed more alliances but also reached out beyond their

typical alliance partners. In particular, many organiza-tions sought to form relationships with IT firms in orderto access information and capabilities that would helpthem respond to—and benefit from—the rapid advancesin networking technologies.

The decline of alliance activity after the initial spikeis not surprising; there are at least three major reasonswe would expect a sudden increase in alliance activityafter a technological shock to be followed by an equallydramatic decline. First, the response of firms to a techno-logical shock is much like that of miners to a gold rush.Initial excitement may drive a frenzied initial responsethat is unsustainable, and only a portion of those whorush in will harvest significant value.9 It is costly toforge and sustain alliances. Such agreements can alsoput firms at risk of having their proprietary technologiesexpropriated by others. This puts significant constraintson the number of collaboration agreements that firms cansustain. As a result, a rapid increase in alliance activ-ity is, in general, likely to be followed by a subsequentdecrease in new alliance formations that returns overallalliance activity to an equilibrium level that is lower thanthe peak (though it need not match the level prior tothe sudden increase because some types of shocks createlasting changes in the cost and opportunity structure ofalliance activity).

Second, as noted previously, alliances are often usedto probe the environment and enable information shar-ing and sensemaking—all of these activities decrease inimportance as the environment becomes more certain. Inthe case of a technological shock, much of the uncer-tainty will be resolved over time as standards emerge andfirms gain a sense of how they should (or should not)respond. This is likely to be the case with the Internetshock. For example, by 1995 HyperText Markup Lan-guage had emerged as the standard markup languageto create Web pages. At the same time, Microsoft’srelease of Windows 95 (which incorporated the Inter-net Explorer browser by 1996) dealt a death blow toIBM’s operating system, OS/2, and sealed the fate ofNetscape’s Internet browser, Netscape Navigator.

Third, we would also expect to see the alliancesdecline over time because firms are able to replace orsupplement their alliance activity with other responses totechnological change, such as redirecting their in-houseR&D portfolios, hiring and developing employees withneeded capabilities, or merging with other firms. Theseactivities take longer to deploy but yield more controlover the information and capabilities that are acquired.Thus a decline in alliances need not be an indicatorthat firms consider the alliances failed; rather, they mayhave successfully laid the groundwork for more signifi-cant investments in the new technological era. This argu-ment is supported by examining Figure 6, which showsa graph of the IT alliance data shown previously alongwith other indicators of activity in the IT sector: the

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Figure 6 The Internet Technology Shock Rippling Through Layers of the Economic System

–1.5

–1.0

–0.5

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

IT alliances WSJ articles with Internet,,

Number of public computer HW&SW firms Market cap high computer HW&SW firmsR&D by public computer HW&SW firms Patents with Internet

,, in abstract

Sales by public computer HW&SW firms

,,

,,

Note. HW, hardware; SW, software; WSJ, Wall Street Journal.

number of public computer hardware and software firms,acquisitions of U.S. IT firms, R&D spend and sales bypublicly held computer hardware and software firms, themarket capitalization of computer hardware and softwarefirms, patents with “Internet” in the abstract (by applica-tion date), and Wall Street Journal articles that includethe word “Internet.” All of the series are standardizedusing z-scores to permit showing them on the samegraph. Notably, the alliance data show the first spike inactivity, hitting their peak in 1995. The next series to hita peak is the number of publicly held computer hardwareand software firms, which peaks (and then declines) in1998 and 1999, followed by patent applications with theterm “Internet” in the abstract, and then acquisitions of

Figure 7 Technology Shocks, Technological Collaboration, and Innovation Outcomes

U.S. IT firms, consistent with the argument that overtime firms replaced many of their Internet alliances withinternal activities.

The number of Internet patent applications, the marketcapitalization of computer hardware and software firms,acquisitions of U.S. IT firms, and the number of WallStreet Journal articles that mention the “Internet” areall extremely highly correlated and hit a sharp peak in2000—this is the IT “bubble” that the general publicwould have been aware of. By contrast, the data on R&Dspend and sales march upward more gradually and arealso highly correlated.

Similar to most gold rushes, the number of people andfirms that would ultimately strike it rich was a small

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portion of those who flooded into the market. By late2000, a large number of dot-com companies had burntthrough their capital and began to fail. IT stock indiceswent into sharp decline. Most had stabilized by 2004 ata level far lower than the 2000 peak. The world had,however, entered a new era of information access anddistribution.

Overall, the data suggest that rapid innovation inthe Internet and related information technologies dur-ing the early 1990s may have dramatically influencedalliance behavior and, as a result, had equally powerfuleffects on the overall collaboration network. The ques-tion that now looms large is, did any of this matter?I now turn to disentangling the effects of the technologyshock, the alliances, and changes in the global collabo-ration network on innovation outcomes.

Innovation OutcomesA major technology shock can lead to a surge in inno-vation and patenting by creating new innovation oppor-tunities and strategic imperatives (Kortum and Lerner1998)—this point is already well made in the eco-nomics and management literatures. However, a tech-nology shock may also indirectly influence innovativeoutcomes through its influence on alliance formationand the structure of the global collaboration network.There is already significant evidence that alliances arerelated to innovation outcomes. Large-sample studieshave found that alliance relationships facilitate knowl-edge flows between partners (Gomes-Casseres et al.2006, Mowery et al. 1996) and enhance the innova-tive performance of firms (e.g., Deeds and Hill 1996,Stuart 2000). Alliances may thus both directly influ-ence innovation and be an indicator that firms anticipateinnovation opportunities. Furthermore, because alliancesincrease the flow of information and resources betweenfirms generally (i.e., not just information and resourcesrelated directly to the technology shock), includinginformation about related opportunities, other potentialpartners, and more, if a technology shock induces greateralliance formation, those alliances may influence inno-vative outcomes even in ways that are not due to thetechnology shock itself.

A rapidly growing body of recent research also sug-gests that the size and structure of technology collab-oration networks can significantly influence importantoutcomes such as knowledge spillovers, innovation rates,initial public offering success, the diffusion of gover-nance practices, and others (e.g., Ahuja 2000, Gilsinget al. 2008, Gulati and Higgins 2003, Robinson andStuart 2007, Rosenkopf and Almeida 2003, Schilling andPhelps 2007). This suggests that if a technology shocksignificantly influences the size or density of a tech-nology collaboration network, it can set up an endoge-nous cycle of innovation: a technology shock increases

the degree and/or diversity of alliance formation, whichcreates a larger or more robust collaboration networkthat creates paths for information and other resources toflow between organizations that would not normally beconnected, which in turn results in greater rates and/orgreater diversity of subsequent innovation.

These arguments warrant a moment to consider whyinformation would be likely to travel along the pathsof an alliance network beyond any individual alliance.Individuals and firms go to great lengths to protect theirproprietary information from being transmitted withinor beyond a particular collaboration, suggesting thatthe appropriate level of analysis is the dyad and thelarger network ought not matter very much. It is impor-tant to note, however, that much of the informationexchanged between individuals and firms is considerednonproprietary and thus is not deliberately protectedfrom diffusion. For example, firms engaged in tech-nological collaboration might freely exchange infor-mation about their suppliers, potential directions forfuture innovation, scientific advances in other fields,etc. There is considerable evidence, for example, thata firm’s alliance partners are a key source of referralsto other potential partners that possess needed technolo-gies, are trustworthy, or possess other desirable qualities(Gulati 1995). Other information exchanged betweenfirms is considered proprietary but is imperfectly pro-tected from diffusion. Even when collaboration agree-ments have extensive contractual clauses designed toprotect the proprietary knowledge possessed by eachpartner or developed through the collaboration, it is stillvery difficult to prevent that knowledge from ultimatelybenefiting other organizations. Secrecy clauses are verydifficult to enforce when knowledge is dispersed over alarge number of employees or embedded in visible arti-facts. Even patenting provides only limited protectionfor knowledge embedded in technological innovations.In many industries it is relatively simple for competitorsto “invent around” the patent (Levin et al. 1987). A richhistory of economic research provides evidence of tech-nological spillovers created by an organization’s researchand development efforts (Jaffe et al. 2000), suggestingthat information diffuses between organizations whetherintended or not, fueling innovation in the broader com-munity. Consistent with this, research has shown thatthe extent to which a firm is indirectly connected toothers in an alliance network enhances its innovativeness(Ahuja 2000, Owen-Smith and Powell 2004, Schillingand Phelps 2007, Soh 2003).

The preceding suggests that there may be (at least)three potential paths by which a technology shock mightfoster innovation. First, the technological shock mayhave a direct effect because of the inherent technolog-ical opportunities unleashed by the shock. Second, thealliances formed in response to the shock may leadto innovation as a result of the increased pooling and

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cross-fertilization of the knowledge and resources ofpartner firms, partially mediating the effect of the tech-nology shock and also potentially leading to innova-tion outcomes that are not due to the underlying shock.Third, the larger or denser overall collaboration net-work may facilitate the flow of a greater amount anddiversity of information between connected firms, par-tially mediating the direct effect of alliances and alsoenabling innovation that is not due to the underlyingtechnology shock (see Figure 7). If each of these pathsbetween a technology shock, alliances, network struc-ture, and innovative outcomes is important, then studiesthat focus on one or the other could potentially sufferfrom an important omitted variable bias. Understandinghow these variables influence each other and innovativeoutcomes could bring much greater clarity to our under-standing of major technological change. I thus test thefollowing hypotheses.

Hypothesis 1. A technology shock will have a directpositive effect on innovative outcomes.

Hypothesis 2. Alliance formation will have a directpositive effect on innovative outcomes and partiallymediate the positive effect of the technology shock.

Hypothesis 3. A larger or denser overall collabora-tion network will have a direct positive effect on inno-vation outcomes and partially mediate the positive effectof alliance formation.

MethodsIn this section, I attempt to disentangle the effects ofthe technology shock, firm-level alliances, and firms’network reach on subsequent innovation using a large-sample panel study of firm-level patenting rates. Patentsprovide a measure of novel invention that is exter-nally validated through the patent examination pro-cess (Griliches 1990, Griliches et al. 1988). Patentcounts have been shown to correlate well with newproduct introductions and invention counts (Basberg1987). Trajtenberg (1987) concluded that patents arevalid and robust indicators of knowledge creation andinnovation.10

Since patenting norms and systems vary across re-gions, I utilize data only on North American firms forthis portion of the study. From the set of 13,906 orga-nizations that participated in the global technology col-laboration network between 1990 and 2005, I identifiedall North American firms that were publicly held for atleast three years of the study period and that appliedfor at least one subsequently granted patent during thestudy period. This yielded 449 firms. I then added to thisset 86 North American firms that met the same criteria(publicly held for at least three years of the study periodand that applied for at least one subsequently grantedpatent during the study period) but that did not appear in

the global technology collaboration network (i.e., did nothave any technology alliances listed in the SDC databasefrom 1990 to 2005). This yielded a total of 535 firms.

Dependent Variable: PatentsI measure the dependent variable, ln(Patentsit), as thenumber of successful patent applications for firm i inyear t. I used the Delphion database to collect yearlypatent counts for each of the firms, aggregating sub-sidiary patents up to the ultimate parent level. Patentswere counted in their year of application. Yearly patentcounts were created for each firm for the period of1990–2005, enabling different lag specifications betweenthe independent variables and patent output and the cre-ation of a patent stock variable (discussed in the controlssection).11 The minimum patent lag is one year; thus thepatent counts do not overlap with any of the indepen-dent or control variables. Because this measure has highvariability across firms and is highly skewed, I use a logtransformation of the counts.12

One of the challenges with using patents to measureinnovation is that the propensity to patent may vary withindustry, firm size, or other factors, resulting in a poten-tial source of bias (Levin et al. 1987). I addressed thispotential bias in two ways. First, to control for sectoraldifferences in propensity to patent, I use dummy vari-ables 40115 for each of the eight major sectors repre-sented in the database (the eighth sector is the omitteddummy variable and is an aggregate of the service indus-tries not captured by the other categories—wholesaleand retail trade, lodging and entertainment, etc.). Sec-ond, to control for unobserved factors that influencefirm-level propensity to patent, I include a firm-levelpatent stock variable as described in the controls section.

Independent Variables

Technology Shock Composite Measure. To measurethe technological shock, I use Technology Shock, a com-posite measure of the growth rate of the Internet andtechnological change in semiconductors (which directlycontributed to the advances in networking equipmentused to exploit the Internet). The former is measured asthe yearly percentage increase in Internet hosts. The lat-ter is measured as the yearly change in semiconductortotal factor productivity. As noted previously, these mea-sures are highly correlated, consistent with my presump-tion here that they are closely interdependent, and serveas multiple measures of the technological shock in IT.I thus standardized each measure using z-scores andadded them together to create a single yearly index of thegrowth rate of the Internet and networking technologies.

Firm-Level Alliance Activityti. To capture the effectthat a firm’s direct alliances have on its subse-quent innovation, I use a measure, Firm-Level Alliance

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Activityti, which includes each firm’s number of tech-nology alliances formed for three-year windows lead-ing up to and including the observation year (e.g., thusthe observation for 1992 includes alliances formed in1990–1992). These data were collected from SDC, asdescribed earlier in the paper, and log-transformed toimprove their normality.

Firm-Level Network Reachti. To assess the networkeffect on innovation, I use Firm-Level Network Reachti,a measure that captures both the size of the networkcomponent within which a firm is embedded and theaverage path length of the firm to each other member inits component (which is affected by both the structure ofthe component and the centrality of the firm in question).The more firms that can be reached by any path from agiven firm, the more knowledge that firm can potentiallyaccess. However, the likelihood, speed, and integrityof knowledge transfer between two firms are directlyrelated to the path length separating those two firms.Networks with short average path lengths should enableinformation and knowledge to diffuse more rapidly andwith more integrity (Schilling and Phelps 2007). A firmthat is connected to a large number of firms by a shortaverage path can thus reach more information and cando so quicker and with less risk of information distortionthan a firm that is connected to fewer firms or by longerpaths. To capture this, I use distance-weighted reach.

Distance-Weighted Reach is the sum of the reciprocaldistances to every organization that is reachable from agiven firm; i.e.,

∑

j 1/dij , where dij is defined as the min-imum distance (geodesic) d from a focal firm i to part-ner j , where i 6= j . For example, a firm that is directlyconnected to two other organizations (that are not con-nected to each other) will have a distance-weighted reachof 2. A firm that is directly connected to one other orga-nization that, in turn, is directly connected to one otherorganization will have a distance weighted reach of 1.5.Other things being equal, a firm’s network reach willincrease with the size of the component within whichit is embedded, the shortness of path lengths in thatnetwork (which decreases with the density of the net-work or with centralization of the network, as whenmany organizations are all connected to the same central“hub”), and the firm’s centrality within that component.

The preceding reveals one of the key benefits of usingdistance-weighted reach: it provides a meaningful mea-sure of the size and connectivity of firms’ reachablenetworks, even when the overall network has multi-ple components and/or component structure is changingover time. It avoids the infinite path length problem typ-ically associated with disconnected networks by mea-suring only the path length between connected pairsof nodes and provides a more meaningful measurethan the simple average path length between connectedpairs by factoring in the size of connected compo-nents. The distance-weighted reach for each firm was

obtained from the network analysis conducted at thebeginning of the paper. Thus it is based on the firm’sposition in the global technology collaboration networkand includes any type of other reachable organization.Because distance-weighted reach (such as firm-levelalliance counts) is positively skewed, the measure waslog-transformed to improve its normality.

ControlsTo control for firm size, I include yearly sales data,log-normalized (Salesti). To control for differences infirm-level emphasis on innovation, I include a firm’s log-normalized yearly R&D investment (R&Dti). Note thatI used absolute R&D figures for firms rather than R&Dintensity since a firm’s total number of patents producedis much more closely related to its total R&D expendi-tures than to its R&D normalized by sales. To control forunobserved heterogeneity in firm patenting, I calculatethe variable Patent Stockti for each firm as the sum ofsuccessful patents applied for in the three years leadingup to and including the observation year. This variable isthen natural log-transformed. Since the dependent vari-able is always lagged by at least one year, the patentscounted for the dependent variable do not overlap withthe patent stock variable.

Model SpecificationTo analyze the data, I used STATA’s random effectsregression with robust clustered errors. Though the orig-inal patent data are in the form of counts, the counts arequite high, averaging 88 and ranging from 0 to 4,390.The Poisson specification is unreliable for counts thislarge because the probability mass function will be closeto zero for most of the range of the data (and poten-tially be incalculable, depending on the program used),which can lead to erroneous results. A range of robust-ness tests and examination of error graphs indicatedthat the results were more reliable when the dependentwas log-transformed and then utilized in a linear paneldata model.

A further consideration was whether to use fixed orrandom effects. In the choice to use fixed versus ran-dom effects, one must trade off the loss of efficiencyfor fixed effects (and the inability to include time invari-ant variables) versus the potential for bias of using ran-dom effects when the unit effect may be correlated withone or more covariates. In this data set, there are rel-atively few observations per firm (average of 6.9; min-imum of 3), and the covariates that are most likely tobe correlated with a firm-unit effect (e.g., sales, R&D,patent stock) exhibit relatively little change over time(i.e., they are “sluggish”) in comparison to the changesin the dependent variable. This can destabilize the resultsin a fixed effects estimation and lead to estimates ofthe coefficients for the covariates that are quite differentfrom the true (Clark and Linzer 2015). This is known

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as the high variance problem in fixed effects. Randomeffects models reduce the variability in estimates of bypartially pooling information across units (Gelman andHill 2007). Furthermore, there are two other practicalconsiderations in the choice between fixed effects andrandom effects here. First, in this study it makes senseto take advantage of between-unit variance (differencesin alliance rates across firms, for example) rather thanonly within-unit variance (i.e., over time) since we canexpect firms to vary significantly in their alliance behav-ior. Second, the data here include firms from a wide vari-ety of industries, some of which would be much moredirectly influenced by the Internet technology shock thanothers. It is thus very desirable to include industry con-trols. However, because industry membership is timeinvariant, this could not be included in a fixed effectspecification. Although the decision between using fixedeffects or random effects is often made strictly on thebasis of a Hausman test, Clark and Linzer (2015) showthat the Hausman test is neither a necessary nor suf-ficient means by which to choose between these mod-els; the researcher must consider the number of groupsand observations, the degree of variance in the covari-ates, the degree of correlation between the covariates andthe unit effects, the loss of fidelity as a result of eitherchanges in efficiency or bias, and practical considera-tions about the theory and model. For the models here,a Hausman test cannot be used without omitting theindustry controls, which would distort our interpretationof the results. These considerations collectively suggestthat a random effects specification is most appropri-ate. However, to control for unobserved heterogeneity(i.e., the possibility that unmeasured differences amongobservationally equivalent firms affects their patenting),I employ the patent stock variable discussed previously.When the dependent variable of a model is patentingoutcomes, a patent stock variable that captures priorpatenting behavior offers a strong firm-level control thatshould pick up much of the potential firm-specific time-invariant effects on patenting.

A third estimation issue concerns the appropriate lagstructure of the independent variables. Griffin’s (2002)study of new product development found that it takes

Table 2 Descriptive Statistics and Correlations

Variable Mean SD 1 2 3 4 5 6 7 8 9 10

1. ln(Patentst+1) 2.11 1.91 1.002. ln(Patentst+2) 2.14 1.94 0.94 1.003. ln(Patentst+3) 2.13 1.97 0.92 0.95 1.004. ln(Patentst+4) 2.08 2.00 0.89 0.92 0.95 1.005. ln(Patentst+5) 2.03 2.02 0.86 0.90 0.93 0.96 1.006. ln (Sales) 5.10 2.77 0.61 0.60 0.59 0.59 0.57 1.007. ln(R&D) 3.26 1.87 0.71 0.70 0.68 0.67 0.65 0.80 1.008. ln(Patent Stock) 2.80 2.10 0.92 0.89 0.86 0.83 0.81 0.62 0.72 1.009. Technology Shock 1.08 0.91 0.41 0.41 0.41 0.40 0.40 0.26 0.48 0.37 1.00

10. ln(Firm-Level Alliance Activity) 0.96 0.92 0.14 0.16 0.16 0.16 0.15 0.01 0.19 0.09 0.67 1.00

an average of 53 months for firms to develop prod-ucts that are new to the world, suggesting a four-and-a-half year lag. Similarly, Gomes-Casseres et al. (2006)find that when firms cite prior alliance partners in theirpatents, they are most likely to cite partners they wereallied with three to five years prior to the granting ofthe patent. Other research, however, has typically usedshorter lags (e.g., Ahuja 2000, Schilling and Phelps2007, Stuart 2000). I thus estimate models with lagsranging from one to five years to explore the influenceof lag structure.

To assess the degree to which alliances mediate theeffect of the technology shock on firm innovation ratesand whether network reach in turn mediates the effectof alliances on firm innovation rates, I use STATA’sstructural equation modeling (SEM) routine with robustclustered errors (clustered on firm ID). Clustered errorspermit SEM models to be used in a multilevel specifi-cation such as that in panel data. I used estat teffects toget the direct and indirect effects. I ran the routine firstwith the technology shock index as the exogenous vari-able and alliances as the endogenous variables, includingall covariates and repeated for each lag structure. I thenran the routine with alliances as the exogenous variable,network reach as the endogenous variable, and the tech-nology shock index was included as one of the covari-ates and repeated across all of the lag structures. It isthen straightforward to calculate the mediation effects:the proportion of total effect xi that is mediated is equalto the indirect effect of xi divided by the total effect xi.

ResultsDescriptive statistics and correlations are provided inTable 2. A sectoral breakdown of the number of firmsand their patents is provided in the online supplement.The results of the regression models are shown inTable 3, and the complete direct and indirect effects areshown in structural equation models in the online sup-plement. Models were run for lags ranging from one tofive years; the first model in each set includes the con-trol variables and the technology shock index, the sec-ond model adds the firm-level alliances, and the third

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Table

3Firm-Lev

elPaten

ting,Ran

dom

Effec

tsReg

ress

ionwithRobu

stClustered

Errors

Patents t

+1

Patents t

+2

Patents t

+3

Patents t

+4

Patents t

+5

12

31

23

12

31

23

12

3

Sectors

Tran

sportatio

neq

uipm

ent,

0018

0017

0017

0048

∗0045

∗0043

∗0088

∗∗

0083

∗∗

0081

∗∗

1034

∗∗

1027

∗∗

1034

∗∗

1072

∗∗

1064

∗∗

1062

∗∗

airan

dsp

ace

400135

400135

400135

400205

400195

400195

400285

400265

400265

400355

400335

400325

400415

400395

400385

Con

structionan

dmaterials

0008

0014

0013

0014

0022

∗0022

0025

0035

0034

0040

0050

†0048

0055

0064

†0063

†

400135

400135

400135

400185

400195

400185

400255

400255

400245

400305

400305

400305

400365

400365

400355

Food

andtextile

s−0011

−0005

−0005

−0004

0005

0006

−0002

0010

0011

−0008

0004

0005

−0001

0010

0010

400195

400195

400195

400255

400255

400265

400285

400295

400305

400325

400335

400335

400375

400385

400385

Pharma,

biotec

h,an

dmed

ical

0010

0007

0008

0023

†0018

0020

0040

∗0033

∗0035

∗0054

∗∗

0046

∗0048

∗∗

0062

∗∗

0055

∗∗

0056

∗∗

400105

400105

400105

400135

400145

400135

400165

400165

400165

400185

400185

400185

400205

400205

400205

IT0016

†0012

0011

0028

∗0020

0018

0043

∗∗

0033

∗0031

∗0058

∗∗

0047

∗∗

0045

∗0072

∗∗

0061

∗∗

0059

∗∗

400095

400105

400095

400125

400135

400125

400155

400165

400155

400185

400185

400175

400205

400205

400205

Mac

hine

san

dinstrumen

ts0019

†0020

∗0020

∗0035

∗∗

0037

∗∗

0036

∗∗

0055

∗∗

0056

∗∗

0056

∗∗

0077

∗∗

0078

∗∗

0077

∗∗

0096

∗∗

0096

∗∗

0095

∗∗

400105

400105

400105

400135

400145

400135

400175

400175

400175

400205

400205

400205

400245

400235

400235

Che

micals,

plas

tics,

andoil

0020

†0022

†0022

†0039

∗0042

∗∗

0041

∗∗

0066

∗∗

0069

∗∗

0067

∗∗

0095

∗∗

0096

∗∗

0094

∗∗

1021

∗∗

1021

∗∗

1019

∗∗

400115

400115

400115

400155

400165

400165

400205

400215

400205

400255

400265

400255

400315

400325

400315

Mea

sures

ln(Sales

) ti0020

†0002

∗0003

∗0005

∗∗

0005

∗∗

0006

∗∗

0008

∗∗

0008

∗∗

0008

∗∗

0009

∗∗

0009

∗∗

0010

∗∗

0008

∗∗

0008

∗∗

0008

∗∗

400015

400015

400015

400025

400025

400025

400025

400025

400025

400025

400025

400025

400025

400025

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model in each set includes the firm-level network reachvariable and is the full model. As shown, when onlythe technology shock variable is entered, it is positiveand significant with every lag used (though exhibitinga larger effect with shorter lags), providing support forHypothesis 1. Because the dependent variable is log-transformed, we can calculate the effect size of a one-unit increase in the technology index (which is slightlylarger than one standard deviation) by exponentiating thecoefficient. Thus the effect size of the technology shockwith a one-year lag is exp(0.12), or 1.127, indicatinga unit increase in the technology shock was associatedwith a 13% increase in patents, on average, in the sub-sequent year. When the alliances variable is added, it,too, is positive and significant for every lag. Its largesteffect is obtained with a four-year lag (0.24); becauseboth the alliances variable and the dependent variableare log-transformed, this coefficient indicates that eachdoubling of the number of alliances was associated withan increase of 18% (20024) in patents. Furthermore, itsinclusion causes a large decrease in the effect of thetechnology shock variable—even rendering it insignifi-cant in the model with a five-year lag. The SEM mod-els indicated that the technological shock and allianceshave significant direct and indirect effects on patent-ing output, consistent with the arguments made here.They also indicate that inclusion of the alliances vari-able mediates the technology shock effect by 27%–40%,providing support for Hypothesis 2. Similarly, the net-work reach variable has a significant and positive rela-tionship with subsequent firm patenting with every lag(its largest effect is obtained with a three-year lag), andSEM models indicate that it mediates 21%–38% of theeffect of alliances on subsequent patenting, supportingHypothesis 3. Its coefficient with a three-year lag indi-cates that each doubling of network reach is associatedwith a 9.4% increase in patents.

Although we can only make conclusions about asso-ciations given the data here, we can take some stepsto explore the direction of causality between collabora-tion activity and patenting. Firms that are quite innova-tive, for example, may attract more alliances. To evaluatethe degree to which reverse causality drives the resultshere (i.e., innovation causes collaboration), I regressedln(Firm-Level Alliance Activityti) on the ln(Patentsti) andthe control variables for sales, R&D, and the sectoraldummies. I examined one- to five-year lags betweenthe alliances variable and the patents and control vari-ables. The only variables that had a significant and posi-tive relationship with subsequent collaboration behaviorwere the R&D variable, the technology shock variable,and some sectoral dummies, suggesting that reversecausality is not driving the results.

DiscussionThis paper was motivated by several fundamental ques-tions about technology shocks, collaboration, and inno-vation: Did a technology shock cause the alliance spikeobserved in the early to mid-1990s? If so, what tech-nologies were central to that shock? How did changesin alliance behavior affect the overall collaboration net-work? And more importantly, how do shocks, alliances,and the global collaboration network jointly influenceinnovation outcomes?

The first part of this study was an inductive explo-ration of the patterns in both the numbers and the text ofalliance announcements. There was significant evidencein the alliance data, suggesting the prominent role ofthe Internet in motivating the alliances. This conclusionwas strengthened by triangulating these data with dataon Internet host growth and multifactor productivity insemiconductors. Furthermore, the study showed that thechange in alliance activity led to the crystallization of agiant connected component—the world became far moreconnected. After the spike in alliance activity, however,alliance formation declined just as sharply, causing thegiant component to disintegrate. These dynamics thenraise a set of equally if not more important questions:How did the shock, and the resultant collaboration activ-ity, affect innovation outcomes? Can we disentangle theeffect of collaboration on innovation from the effect ofthe underlying technology shock itself?

To explore this, I conducted a large sample panel anal-ysis of patenting by North American firms. The resultsindicated that the technology shock, a firm’s allianceactivities, and a firm’s network reach (an outcome ofthe size and density of the connected component withinwhich a firm is embedded and the firm’s location withinthat component) each have significant and positive rela-tionships with subsequent patenting output, even whencontrolling for each other and controlling for firm size,prior patenting output, and other industry-level and firm-level factors. The results also suggest that alliances par-tially mediate the effect of the technology shock, andnetwork reach partially mediates the effect of alliances.

It is important to note that these results can onlybe considered exploratory; a more definitive test willrequire matching multiple technology shocks (from othertime periods) to collaboration and patenting data. Thiswas not possible in the current study because thealliance data are only reliably available from SDC after1990, and the period from 1990 to 2011 is overwhelm-ingly dominated by the Internet shock. However, in thefuture, new techniques for capturing collaboration activ-ity could permit us to assess whether other major tech-nology shocks had a similar impact on collaboration andpatenting.

It is also important to note that we cannot defini-tively conclude that the relationships between the majorvariables here reflect the causal relationships posited by

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the hypotheses; we can only draw tentative conclusionsbased on the significance of the associations, the lagstructure, and logic. However, despite these limitations,the results suggest that a significant portion of a technol-ogy shock’s effect on subsequent innovation may be dueto the collaboration behavior it induces and that collab-oration has its own effect on innovation separate fromthe shock. This result has interesting managerial impli-cations, because although we may not have control overthe timing or magnitude of major technology shocks,managers do have control over their alliance activity.Traditional wisdom suggests that firms should choosepartners based on resource fit (e.g., complementary orpooling benefits) and strategic fit (congruence of goalsand styles). However, the research here suggests thatmanagers should also consider how their partner choiceinfluences the overall collaboration network and theirposition in it. Linking to firms outside the focal firm’sindustry and linking to highly central firms are bothstrategies that can offer big potential payoffs in termsof information access. Related to this, the quality ofcollaboration networks is somewhat amenable to strate-gic and political intervention. For example, the Euro-pean Union’s EUREKA R&D program plays a large rolein organizing the collaborative R&D activities amongEuropean companies. MITI performs a similar functionin Japan. Research consortia also play a powerful rolein structuring relationships among consortia members.All of these organizations are in an excellent position toactively influence the structure of their respective col-laborative networks.

It is also worth noting that although the giant com-ponent that emerged subsequently disintegrated, its tran-sience does not imply that it left no enduring effect. Inaddition to the innovations that it may have spawned bybringing a broader range of information and resourcesto connected firms, it created pathways between indi-viduals and firms that did not previously exist. Just asgold rushes leave behind roads, railways, and towns thatthrive long after the gold frenzy has dissipated, it isprobable that many communication paths that existed inthe giant network remained long after the giant com-ponent disintegrated. First, we do not know how longmany of the alliances lasted—some may be long-lived.Second, even in the absence of alliances, communica-tion pathways may exist in the form of personal rela-tionships and referral networks. When individuals worktogether, they form a transactive memory system aboutwho possesses what kinds of knowledge (Wegner 1987);this transactive memory system does not immediatelyexpire after alliances terminate.

This research offers a number of important contribu-tions to the management and economics research. First,the data indicate that a shock in technological oppor-tunity can have a profound effect on both the rate ofalliance formation and the types of partners with whom

organizations choose to forge alliances. This contributesto both the economics research on technology shocksand the management research on alliances and networks.Previous economics research has typically only exam-ined the effect of technology shocks on aggregate pro-ductivity or investment outcomes. By connecting theshock to collaboration activity, this research helps toelucidate one of the mechanisms by which technologyshocks influence firm- and industry-level outcomes. Sim-ilarly, though research in management on alliances andnetworks has examined how collaboration affects inno-vation, it has typically not controlled for the underlyinglandscape of technological opportunity or uncertainty.This research reveals that doing so is important—in fact,it strengthens previous research on alliances and net-works by providing evidence that collaboration can havebeneficial effects over and above the underlying techno-logical opportunity.

Second, this research also makes an important con-tribution to the research on industry life cycles and theevolution of organizational fields. A major technolog-ical shock can cause the boundaries of organizationalfields to shift and blur, exposing firms to new compet-itive and institutional pressures. For example, the dataindicated that the rise of the Internet influenced allianceactivity not only in the IT industries; its effects werefelt through a wide range of industries. Many non-ITfirms formed alliances with IT firms or for IT specificprojects. During periods of stable competition, firms arelikely to form alliances with suppliers, complementors,and would-be competitors because these are the orga-nizations that are most likely to possess the relevantinformation needed by the firm or be the source of themost important interdependencies the firm needs to man-age. Consistent with this, previous work has emphasizedthe self-reinforcing nature of alliance networks due tothe benefits of learning through repeated partnershipsand referrals from common third parties (Anand andKhanna 2000, Goerzen 2007, Gulati and Gargiulo 1999,Uzzi 1997), leading organizational fields to become welldefined. However, when solutions are needed to a funda-mentally new kind of problem, the repository of knowl-edge within the existing network about both solutionsand prospective partners may be inadequate, leadingfirms to seek out new kinds of partners (Madhavan et al.1998, Rosenkopf and Padula 2008). Firms might seekrelationships with firms that appear more central to (orknowledgeable about) the new technology, blurring theboundaries of traditional organizational fields or creatingentirely new field boundaries.

The previous suggests that a technology shock canbe a speciation event for industries that results in newindustry structures and roles and that one of the earliestobservable stages of this event may be dramatic changesin partnering behavior that alter the flow of informa-tion and resources between firms. Consistent with this, a

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number of studies have identified an “incubation” periodbetween a technological breakthrough and new industryemergence (e.g., Agarwal and Bayus 2002, Golder et al.2009) during which firms vigorously engage in precom-mercialization activities. In a study of the emergence ofagricultural biotechnology (which they characterize asa disruptive technological shock), Moeen and Agarwal(2014) noted that alliance activity was extremely highduring the 10-year period prior to product commercial-ization. They argue that alliances are one of the activi-ties through which firms assemble necessary capabilities,seek legitimization, and reduce uncertainty. Collectively,and as Figure 6 suggests, these observations suggest thatspikes in alliance activity might give us an early signalof significant technological and industrial reconfigura-tion that might otherwise go unnoticed.

Related to the point above, a third contribution ofthe research here is that the data indicate that patternsin alliance activity may provide an early signal of atechnology shock—an index that could be important toinvestors and researchers. As shown in Figure 6, thealliance spike was much more proximate to th

Documents

Technology Shocks, Technological Collaboration, and ......Innovation Outcomes Melissa A. Schilling Stern School of Business, New York University, New York, New York 10012,[email protected]