interfirm collaboration and firm value in software...

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IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT 1

Interfirm Collaboration and Firm Value in SoftwareEcosystems: Evidence From Cloud Computing

Rahul C. Basole , Senior Member, IEEE, and Hyunwoo Park

Abstract—In a rapidly evolving cloud computing industry,legacy software vendors are racing to transform their existingbusiness models to compete against both established and emerg-ing cloud “native” software vendors. To cope with these chal-lenges, vendors are forming partnerships with other firms to accessand leverage new and complementary resources, capabilities, andknowledge. Building on prior work on two traditionally distinctliterature streams—interfirm networks and software ecosystems—we theorize that structural characteristics of a software vendor’spartner network can influence its firm value. Specifically, we argueand find empirical support that partner network size, intercon-nectedness, and diversity impact firm value, measured by mar-ket capitalization for publicly listed firms and total funding forprivately held firms, and that the association between structureand firm value differs by vendor type (legacy versus cloud-native).We explore these associations using a data-driven ecosystem anal-ysis of 5429 firms, apply text analytic methods to differentiatevendors by technology stack focus (infrastructure, platform, soft-ware), and complement our empirical analyses with visualizationsof the collaborative structure of the cloud computing ecosystem.We conclude with theoretical, methodological, and managerialimplications.

Index Terms—Cloud computing, ecosystem, firm value, inter-firm collaboration, visualization.

I. INTRODUCTION

THE EVOLUTION of software to a service-based busi-ness model, commonly referred to as cloud computing,

has fundamentally changed the way software is delivered, de-ployed, and used [1], [2]. Cloud computing enables ubiqui-tous, convenient, on-demand network access to a shared poolof configurable computing resources (e.g., networks, servers,storage, applications, and services) that can be be rapidly provi-sioned and released with minimal management effort or serviceprovider interaction [3]. The impact of this evolution has been

Manuscript received December 9, 2017; revised May 25, 2018; accepted July3, 2018. This work was supported in part by the Tennenbaum Institute and theInstitute for People and Technology at Georgia Tech. Review of the manuscriptwas arranged by Department Editor T. Daim. (Corresponding author: Rahul C.Basole.)

R. C. Basole is with the College of Computing and the Institute for People andTechnology, Georgia Institute of Technology, Atlanta GA 30332 USA (e-mail:,[email protected]).

H. Park is with the Fisher College of Business, Ohio State University, Colum-bus, OH 43210 USA (e-mail:,[email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TEM.2018.2855401

particularly significant for software vendors1 who must not onlyreconsider their existing software business models and revenuestrategies, but also adapt to an increasingly competitive market-place of emerging players. While in the past vendors focusedon the provision of fully integrated software solutions on theirown, vendors today are building, joining, and cultivating part-ner ecosystems in order to offer new services, scale operations,enter global markets, and accelerate innovation [4], [5].

Despite the widely acknowledged benefits of collaboration,our understanding of the nature of partnership strategies andresulting impact on firm value in a rapidly evolving softwareecosystem context such as cloud computing is still lagging.To explore this issue, we build on and extend prior researchon interfirm relationships and business ecosystems (e.g., [6]–[9]). Drawing on the ecosystem-as-structure line of work, weposit that a vendor’s structural partner network characteristics—partner network size, partner network interconnectedness, andpartner network diversity—influences its firm value. Moreover,we argue that the extent of this association is dependent on thevendor type. Incumbent legacy vendors have established inter-firm relationships that can constrain their ability to transition toan appropriate partner network needed for cloud-based serviceofferings, whereas cloud-native vendors can start their partner-ship strategy from “scratch.”

Our study uses a data-driven, multimethod ecosystem analy-sis approach. We first curate a dataset of 5429 companies frommultiple structured and unstructured data sources. Given theemerging nature of the cloud computing ecosystem, our ven-dor set contains both publicly listed and privately held firms.To appropriately assess firm value for these two categories offirms, our study uses two separate measures, namely marketcapitalization and total funding. We use text analytic methodsto categorize vendors by cloud service stack focus (software,platform, infrastructure). We conduct a split-sample, regressionanalysis to test our hypotheses. To support our sensemakingand discovery process, we develop network visualizations ofthe cloud partner ecosystem.

The contributions of our work are threefold. First, we ad-vance our understanding of a rapidly emerging software ecosys-tem context, namely cloud computing. Second, we extend ourknowledge of interfirm relationships and firm value in softwareecosystems by providing a more nuanced view of technological

1We use vendors as an umbrella term to refer to all software companies.As we will explain later, we distinguish between the following two types ofvendors: incumbent vendors and cloud “native” vendors.

0018-9391 © 2018 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See http://www.ieee.org/publications standards/publications/rights/index.html for more information.

https://orcid.org/0000-0002-7328-5276

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2 IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT

partnering considering four different types of vendors (incum-bent or cloud-native; public or private). Last, methodologicallywe demonstrate how the curation of large-scale datasets andthe fusion of regression analysis and visualization can help aug-ment the data-to-insight process. In doing so, we address the callfor more comprehensive mixed-method analyses of complexsystems.

II. BACKGROUND AND RELATED WORK

Prior work on software ecosystems and interfirm collabora-tion is extensive. Research on software ecosystems is primarilyrooted in software engineering and computer science. Studieson interfirm collaboration are predominantly centered in themanagement, strategy, and social sciences. At a high level, bothstreams of research are concerned about the study of complexsystems. Software ecosystem research explores the design andcomposition of socio-technical systems, such as programminglanguages, software libraries, or other technological artifacts.Interfirm network research, on the other hand, studies the de-sign, management and governance of socio-economic systems,including enterprises, organizations, and supply chains. As thenature of software development changes, technological systemsbecome more intertwined with economic systems, thus blurringthe boundaries between the two traditionally distinct domains.In this section, we review related work and highlight their rele-vance in the study of cloud computing.

A. Interfirm Networks

In dynamic and competitive business environments, firms facea challenge to compete on their own [10]. The resources, knowl-edge, and capabilities necessary to innovate and survive are oftendistributed among many other firms. It is this reality that forcesfirms, irrespective of size and industry, to engage in diversetypes of relationships with others within and across industries,often creating complex interfirm networks [11], [12].

Given the ubiquity of this novel form of organizing, it is notsurprising that we have seen a continuous growth in the studyof interfirm networks across many industry domains [13]–[15].Three common themes permeate this body of literature: networkformation, relational and structural configuration of networks,and network governance and management. Studies related tonetwork formation have found that firms engage in interfirmrelationships to obtain access to new markets and technolo-gies [16], speed products to market [17], pool complementaryskills [18], share risks [19], and grasp opportunities connectedto knowledge exploration and exploitation [20]. Studies relatedto the relational and structural configuration of networks seekto understand the topological nature of interfirm networks [21].Prior studies have examined different types of links (e.g., al-liances, partnerships, coopetitive agreements) and levels of in-terconnectedness ranging from dense [22] to dispersed [23] andthe resulting impact of these network structures on knowledgeexploration and exploitation and competitive performance [24].Studies related to network governance and management exam-ine the types of incentives and social mechanisms that must existfor networks to perform well [25].

B. Software Ecosystems

The development of a software product was commonly doneby a single software vendor [26]. Modern software products,on the other hand, have shifted from creating “monolothic”products and rely on many different technologies provided bydifferent vendors, creating complex evolving digital infrastruc-tures [4], [9], [27]. Similar to business ecosystems in gen-eral [28], [29], the success of a software vendor is thus code-pendent on the relationships it maintains with other players inthe ecosystem. Since firms can operate in one or more layers ofthe software stack, it is not unusual for two software vendors tocollaborate on one activity level and compete in another. Thereare many definitions of what constitutes a software ecosystem.Bosch [30], for instance, defines it as a set of software solutionsthat enable, support, and automate activities and transactionsby actors in a business ecosystem and in organizations thatprovide these solutions. Messerschmidt and Szypersrki [31],on the other hand, define a software ecosystem as a collectionof software products that have some given degree of symbi-otic relationships. Several elements are common across thesediverse definitions. First, the notion of software is central tosoftware ecosystems. Software can refer to a technological plat-form, a software package/solution, or a standard. Second, valueexchange between actors is a foundational aspect of softwareecosystems. This exchange can come in many forms, includ-ing information, financial, and knowledge. Last, all softwareecosystems are characterized by relationships between actors.

C. Relevance of Interfirm Networks and Software Ecosystemsto Cloud Computing

It is evident from recent work that interfirm networks playan important role in software ecosystems. Huang et al. [32], forinstance, examine vendor participation in platform ecosystems.Chellapa et al. [33] investigate the financial performance im-pact of alliances between software providers in the enterprisesystem market. Rather than focusing on focal vendors, Kudeet al. [34] examine the role of complementors. The integrationof the two streams of research is particularly important for thestudy of cloud computing industry for several reasons. First,cloud computing is a rapidly evolving software industry. Col-laboration and partnerships are pervasive, yet the nature andextent of these relationships is not well understood. Second,cloud computing is characterized by several service models cor-responding to different parts of the software stack. In the past,the entire software stack was deployed on-premise and man-aged by the customer. In cloud computing, some or all of thesoftware stack is managed by the vendor. Rather than consider-ing a one-strategy-fits-all model, collaboration and partnershipapproaches can potentially vary across different service models.

It should be acknowledged that cloud computing is not a newtopic in the information systems (IS) literature. Wang et al. [35]and Yang and Tate [36] provide excellent reviews of cloud com-puting research to date. Their review however highlights thatthe majority of existing work has focused primarily on the cus-tomer side of cloud computing, including studies examining thecapabilities and value of clouds [37]–[39], the strategic, opera-


BASOLE AND PARK: INTERFIRM COLLABORATION AND FIRM VALUE IN SOFTWARE ECOSYSTEMS 3

Fig. 1. Research model

tional, and economic importance of the cloud [2], [40], [41], thetransformative impact on organizations [42], [43], how, when,what, and why organizations adopt cloud computing solutions[44], [45], and governance and control of cloud computing [46].The vendor side of cloud computing, on the other hand, is notwell understood. Following evidence of interfirm relationshipsin other software contexts, it is reasonable to assume that part-nerships would also matter in a cloud computing context. Typ-ically, cloud services are not provided by a single vendor, butrather a wide spectrum of stakeholders, ranging from technol-ogy partners, channel partners, original equipment manufac-turer (OEMs), and system integrators. These partners performa range of important functions, including consulting, customdevelopment, installation, managed hosting, enterprise, perfor-mance tuning, and training. An examination of the structure ofcloud ecosystem partnerships is thus crucial to a more completeunderstanding of the nature of this industry.

III. HYPOTHESIS DEVELOPMENT

One of the most prominent strategies technology-based firmsuse for innovation and growth is to leverage interfirm relation-ships [13]. From a resource-based view, establishing relation-ships with external partners provide focal firms with resourcesand capabilities that they are missing, are complementary, and/orare difficult or time-consuming to recreate internally [47]. Firmsthus seek partners with resources and capabilities that they canleverage and integrate to create synergy [48]. From an institu-tional perspective, firms enter relationships either for reasons ofsocial justification or obligation, seeking legitimacy by tappinginto their partners’ reputation, or enhancing their own status[49]. We build on and extend these views to develop our hy-potheses. Fig. 1 summarizes the research model.

A. Partner Network Size and Firm Value

Research on interfirm networks has long stressed the impor-tance of relationships for accessing resources, capabilities, andcomplementary assets, facilitating external legitimacy, and cre-ating competitive advantage [50]. The importance of interfirmrelationships is particularly amplified in dynamic, high-velocityindustry environments, where changes occur frequently and theneed to innovate is high [21]. The cloud computing ecosys-tem is one such context. By forming partnerships with otherfirms, cloud vendors can access important technical, social, andcommercial competitive resources that normally require yearsof operating experience to acquire [13]. Prior work has shownthat the larger the network of a firm, the more the firm is inthe “thick of things,” reflecting visibility, information access,

and communication activity in an ecosystem [51]. Moreover, acloud vendor’s number of partnerships is also an indication of itsinfluence in the ecosystem. The larger the partner network size,the greater the extent to which the firm can influence other firmswith their operations and decisions. From a resource-based per-spective, cloud vendors will collaborate with external partnersto augment, enhance, and complete their internal offerings [52].From an institutional perspective, cloud vendors will partnerwith others to gain both legitimacy and differentiation. Priorwork has shown that a firm’s performance increases with thesize of its network [53]. A large network helps the focal cloudvendor overcome the resource/capability barrier in two signif-icant ways. First, by having more partners, a focal firm enjoysa higher status in the ecosystem [54], which can increase itsvisibility and reputation as a partner. In turn, the focal cloudvendor can attract other resource-rich partners. The ability toform new relationships is also critical because changes in thebusiness environment often require new capabilities, a condi-tion particularly true for cloud computing. Second, followinga resource-dependence lens, firms with higher prominence aremore likely to receive opportunities not available to others [55].For instance, a lower status firm is more likely to offer a promi-nent vendor preferential access or preview of their current andfuture technological capabilities before others, in order to getconnected to it. Partnerships in cloud computing are also im-portant from a technology stack perspective as cloud vendorsfrequently focus on specific layers. By partnering with otherfirms, cloud vendors are able to leverage capabilities at differ-ent levels of the stack, thereby enhancing and complementingtheir offering. A large network helps the cloud firm in copingwith value rigidity, providing exposure and access to new valueofferings, facilitating new value experimentations, and creatingnew channels and opportunities.

Hypothesis 1: Cloud partner network size is positively associatedwith firm value.

B. Cloud Partner Network Interconnectedness and Firm Value

The second relational aspect of a cloud vendor’s partner net-work structure pertains to the interconnectedness of partners.Prior work has shown that there are significant advantages stem-ming from an absence of connections between a firm’s partners.Firms that act as brokers between otherwise disconnected part-ners enhance their access to capabilities and resources as wellas their control over their relationships with those partners [23].Even if a focal firm’s partners are aware of each other, the lackof relationship between them can serve as a buffer that allowsthe focal firm (broker) to present different identities to eachpartner [56]. Furthermore, the lack of connections between afirm’s partners can lead to competition for the focal firm’s atten-tion and resources, which in turn enhances the firm’s leverageon its partners. This perspective is in contrast to the positiveeffect of highly interconnected networks. Prior work has high-lighted the positive effect of connections among partners, ornetwork closure, on the creation of social norms that facilitateproductive, cooperative exchanges [57]. In densely connectedpartner networks, firms can trust each other to meet obliga-tions, thereby lowering the risk of opportunistic behavior and



creating a better environment for their exchanges [58]. More-over, firms in highly interconnected networks forgo individualshort-term interests and develop joint problem-solving arrange-ments [59]. The presence of relationships between partners thuscan facilitate voluntary agreements on information sharing andtechnical standards, which are critical in high-technological en-vironments, such as cloud computing. Highly interconnectednetworks thus also serve as social control mechanisms, gov-erning partnership behaviors. A more interconnected networknot only makes it easier for partners to cooperate, but alsomakes firms less likely to depart the partnership amplifying thelikelihood of joint initiatives. These two perspectives provideopposite perspectives of interconnectedness. However, Rowleyet al. [60] showed that for firms operating in highly dynamic andrelatively uncertain environments, access to novel informationand opportunities to explore are more desirable. Dense connec-tions thus would be constraining, as it limits a firm’s access todivergent perspectives. Indeed, a firm gains unique informationand perspectives from each of its partners when occupying astructural hole [23].

Hypothesis 2: Cloud partner network interconnectedness is nega-tively associated with firm value.

C. Cloud Partner Network Diversity and Firm Value

Recent research has shown that firms increasingly use inter-firm portfolio strategies that augment not just the size but alsothe diversity of actors with which they interact. Partner diver-sity thus refers to to the distribution of differences in relation toa particular firm-level attribute [61], such as size, age, partnertype, geographic location, or industry segment (e.g., [62], [63]).The role of partner diversity and its implications on firm perfor-mance has received particular attention in the alliance literature(e.g., [53], [64]–[69]). It has been shown that cooperation witha diverse set of partners can lead to performance benefits, asdifferent types of organizations can provide access to more di-verse information and resources as well as learning opportunities(e.g., [70], [71]).

Following Hypothesis 1 cloud vendors will collaborate withexternal partners to complete their internal offerings (e.g., [52]).Since resources and capabilities will likely vary between part-ners, different relationships can lead to diverse and nonredun-dant resources and information [72]. Relationships with well-established partners provides legitimacy. Relationships withunique partners may also provide differentiation. In additionto providing access to unique nonredundant skills, a diverse setof partners may help a cloud vendor achieve a better overallrelationship balance, reducing risks, such as technological ob-solescence, or capability appropriation [73]. From a learningperspective, cloud vendors with a diverse set of partners arealso exposed to new routines, resources, and capabilities, whichcould potentially lead to better strategies because of cross fer-tilization of different approaches. We thus posit that higher firmvalue can be achieved by combining offerings of cloud partners,thereby exploiting synergies and complementarities.

Hypothesis 3: Cloud partner network diversity is positively associ-ated with firm value.

D. Partner Network of Cloud-Native Vendors and Firm Value

The emergence of the cloud computing paradigm has funda-mentally transformed the software industry. Existing “legacy”software vendors are racing to adapt to the new model of devel-oping and delivering software. Legacy solutions, even if they arenot that old, can constrain the ability to seamlessly embrace newparadigms. It is often those firms that are starting with a “blank”slate that can take rapid advantage of new standards and solu-tions; however, the cost of starting fresh can be substantial forlegacy vendors. Rather than replacing existing solutions, theymost often incrementally migrate their offerings using legacymodernizations strategies. Legacy vendors often find it diffi-cult to change their existing technologies and systems. Theyare frequently locked into existing architectures and in order tomove to the cloud must fundamentally transform. This processof transformation can be timely and costly if all done in-house.Leveraging external relationships with other cloud vendors canhelp with this transformation. On the other hand, cloud-nativevendors, defined as those firms whose software applications,services, and business models are built specifically for a cloud-connected environment, have the luxury of leapfrogging legacysolutions. As cloud-native vendors are not locked into any spe-cific technological approaches, they can strategically design andengineer their offerings to meet best-of-practice solutions andstandards. While partner networks are still important to cloud-native vendors for value complementarity and differentiation,the dependency on partners is less pronounced than that forlegacy vendors and their partner choices are most likely muchmore focused. Given this difference in starting points and re-duced constraints to providing cloud offerings, we argue that thevalue of cloud-native vendors will be less reliant on its partnerscompared to legacy vendors.

Hypothesis 4: The association of cloud partner network size, collab-oration intensity, and partner diversity with firm value will be lesspositive for cloud-native vendors.

IV. METHODOLOGY

A. Data Sources and Sampling

1) Identifying Vendors: The identification of cloud ecosys-tem vendors is not a trivial task. It can be argued that, in re-sponse to market dynamics, almost all software vendors havetransitioned some, if not all, of their offerings to the cloud. How-ever, considering all software firms (for instance those listed inSIC 7372/NAICS 511210) as cloud ecosystem vendors wouldbe misleading. Instead, we chose to seed our sample with ven-dors listed in two categories: cloud-native vendors and legacysoftware vendors. As we describe below, these vendors collec-tively represent more than $250 billion in market value and 95%market share in the cloud computing space.

a) Cloud-native vendors: We use two data sources to iden-tify cloud-native vendors. Curated by Forbes, Bessemer Ven-tures, and Salesforce Ventures, the Cloud1002 identifies the mostpromising and fastest-growing privately held CVs. Vendors onthe Cloud100 list are industry stand-outs that have demonstrated

2https://www.forbes.com/cloud100/list/



TABLE IFIRM TYPE BREAKDOWN AND DATA SOURCES

the power of the cloud and have a significant financial healthscore, computed by revenue growth, financing status and othermetrics ranging from traction to market share. The BVP CloudIndex3 is a comprehensive and dynamic market index that tracksthe leading publicly listed CVs. The index only includes com-panies that offer a multitenant, subscription business models,although some hybrid model companies are included as well.

b) Legacy software vendors: We define legacy softwarevendors as those companies whose primary source of revenuecomes from on-premise software. Specifically, we considered allcompanies listed on the 2016 PwC Top 1004 software list (andnot included in the Cloud100 or BVP Cloud Index) with at least10% of their revenue coming from cloud computing as legacySVs.

In total, we identified 223 core focal vendors (both publiclyheld and privately listed) across the two vendor categories. Af-ter dropping vendors with missing dependent variable values,our final sample reduced to 195 vendors. Table I provides asummary.

2) Identifying Partners: We identified a vendor’s partnernetwork using multiple data sources [74]. First, we leveraged thepartner information listed on each vendor’s corporate website.This data can either be found on a dedicated partner or affil-iate program page, in news/press release sections (keywords:partner, partnership, collaborate, collaboration), or on corporateblogs. In instances where partner information was not availableor the list was simply a directory of companies without anydifferentiation in relationship type, alternate sources were used.One alternative source we used was Spiderbook,5 one of themost comprehensive lead-generation data sources, which pro-vides limited public data access to partners, partner types, anddate of collaboration formation for more than 100 000 compa-nies. We extracted all of the available partner data fields for allof our focal vendors and corroborated it with data we extractedfrom the corporate websites; in the few instances where wefound inconsistencies, the research team investigated further,discussing the conflict, and resolving it by consensus.

3) Identifying Cloud Vendor Type: To differentiate our re-sults by the different cloud service models, we identified andextracted vendor company descriptions and category tags fromCrunchbase.6 Using a modified text-analytic approach, we clas-sified a company to be an IaaS, PaaS, or SaaS provider if thecorresponding keywords appeared in their company descrip-

3https://www.bvp.com/strategy/cloud-computing/index4https://www.pwc.com/gx/en/industries/technology/publications/global-

100-software-leaders.html5http://spiderbook.com6https://www.crunchbase.com

tion. For vendors, where none of the keywords matched, weused their category tags. Any inconsistencies were resolved bya discussion of the research team.

B. Variables Construction

1) Dependent Variables: Since our sample contained bothpublicly listed and privately held companies, we used a differ-entiated approach to assess firm value. For public companies, wemeasured firm value by their market capitalization. In instanceswhere the local currency was not in U.S. dollars, we convertedthe market capitalization using the daily spot exchange rate pub-lished by the U.S. Federal Reserve Bank. Data on stock priceand the number of outstanding common stocks were extractedfrom Compustat North America and Compustat Global. In con-trast to publicly held companies, it is harder to measure the firmvalue of privately held companies because of the lack of pub-lic reporting and disclosure requirements. In order to overcomethis issue, we matched our privately held vendors with Crunch-base data, which contains detailed, timestamped information onfunding events. For each privately held vendor, we computedthe total funding amount it received. We acknowledge that thefunding amount does not equate to valuation of a privately heldfirm, but believe it represents a reasonable proxy.

2) Independent Variables: Our independent variables de-scribe the structural characteristics of a vendor’s partner net-work. Nodes represent firms (both vendors and their partners);edges represent a partner relationship between two firms. Edgesare undirected as we assume bilateral relationships. Two firmscan be connected by multiple partnership types (e.g., technol-ogy, strategy). We weigh each partnership type equally. For eachvendor i, let Pi be the set of partners directly collaborating witha focal vendor i. Ei is the set of relationships among partners aswell as the focal vendor i. For any given partner p ∈ Pi , ξ(p)represents the list of industries that p belongs to.

We measure partner network size by a vendor’s total numberof direct partners.

Partner network sizei = |Pi |. (1)

We measure partner network interconnectedness by a ven-dor’s ego-network density

Partner network densityi =2|Ei |

|Pi |(|Pi | − 1). (2)

We measure partner network diversity using the inverse ofpartner industry concentration represented by the Hirfindahl–Hirschman Index (HHI). The higher the HHI is, the more con-centrated and thus less diverse the partner network is. Formally,let Si be the set of unique industry sectors represented by part-



TABLE IISUMMARY STATISTICS

ners of a focal vendor i, i.e., Si = {ξ(p) for p ∈ Pi}. We thuscompute partner industry diversity as

Partner network diversityi =

1 −∑

s∈Si

( |{p for p ∈ Pi and ξ(p) = s}||Pi |

)2

. (3)

Finally, we include a dummy variable to capture whether avendor is a cloud-native or legacy software vendor.

3) Control Variables: We include several control variablesin our analysis. First, we compute firm age as the durationbetween the current and founding date. Second, to account forthe overall prominence of a vendor in an ecosystem, we computeeach vendor’s closeness centrality in the global network [51].Third, firm value can be heavily predicated by where it is located.U.S.-based firms are likely to have higher market capitalizationand funding performance. While our sample includes severalgeographical regions, we only include a U.S. dummy controlvariable. Last, vendors can operate across the entire technologystack. We include three dummy variables denoting whether afocal vendor offers IaaS, PaaS, and SaaS cloud services. Notethat these dummy variables are not mutually exclusive as avendor may offer multiple types of cloud services.

Table II shows summary statistics and correlations. As ex-pected, the overall funding for privately listed companies ismuch smaller than the market capitalization of publicly heldcompanies. In total, 67% of the sample are cloud native, while48% are publicly held companies. On an average, a vendor has37 partners and is 22 years old. More than 90% of the vendorsoffer SaaS cloud services, while those offering IaaS and PaaSservices are about 23% and 26%, respectively. In total, 89% ofthe vendors are U.S. based.

C. Estimation

Since we have two measures for firm value, we run a split-sample analysis with the following equation for publicly listed

and privately held company samples, respectively:

Valuei = α + β1PNSizei + β2PNSizei × Cloudi

+ β3PNDensityi + β4PNDensityi × Cloudi

+ β5PNDiversityi + β6PNDiversityi × Cloudi

+ β7Cloudi + γXi + εi (4)

where PN stands for “Partner Network,” i is a focal vendor,Xi is the matrix of control variables, and εi is the error term.Valuei is market capitalization if i is a public company and thetotal amount of funding raised if i is a private company. Asa robustness check, we also run the analysis with private andpublic companies together in the sample. In this case, we runthree-way interaction regressions with the “Public” dummy in-teracted with all main right-hand terms in (4). We do not presentthe full regression equation with the three-way interaction termfor brevity. As another robustness check, we perform the sameanalysis with a subsample of vendors that have at least onepartner.

D. Visualization

Visual data representations are a fundamental component ofhuman learning and understanding [75]. They enable decisionmakers to see patterns, spot trends, and identify outliers in com-plex datasets, and thereby improves comprehension, memory,and decision making [76]. Visualization can make data moreaccessible and provide a method for improved communication.It has also been shown that well-designed visualizations can im-prove comprehension, memory, and decision making, critical inthe exploration, discovery, and analysis of complex business de-cision contexts. Given that we are interested in the collaborativestructure and dynamics of the cloud partner ecosystem, visualrepresentations that can depict interconnectivity, relationships,and change are particularly suitable [77]. The most appropriatetechnique for our context is thus a graph representation, withnodes representing entities of interest (i.e., firms, value seg-ments) and edges representing relationships (i.e., partnerships).



TABLE IIIRESULTS WITH PUBLICLY−LISTED COMPANIES

Note: Robust standard errors are in parentheses. †, *, ** denote statistical significanceat 10%, 5%, and 1%, respectively.

We use Gephi 0.91 to depict the partner ecosystems and Net-workX for the computation of network metrics.

V. RESULTS

A. Regression Analysis

Table III shows the results of our analysis focused on pub-licly listed vendors. Columns 1–4 show the estimation resultsof models with individual independent variables, while Column5 shows the estimation results of (4). The upper part of the ta-ble provides the estimates of our control variables. Column 1strongly confirms the positive association between partner net-work size and firm value (i.e., market capitalization for publiccompanies), supporting Hypothesis 1. Column 2 shows a neg-ative association between partner network interconnectednessand firm value, which corresponds with Hypothesis 2. How-ever, the effect is not statistically significant. Column 3 shows anegative coefficient at a 10% significance level between partnernetwork diversity and firm value, supporting Hypothesis 3. Col-umn 4 provides results for our model with all three independentvariables combined. The coefficient on partner network inter-connectedness is flipped and significant at a 10% level, whilethe effect and statistical significance of the other two variablesremain largely the same. Column 5 includes the full model,including the interaction terms with the “cloud” dummy vari-able. The results show that across all three structural dimensionsthere are not just significant but opposite differences between

TABLE IVRESULTS WITH PRIVATELY HELD COMPANIES

Note: Robust standard errors are in parentheses. †, *, ** denote statistical significanceat 10%, 5%, and 1%, respectively.

cloud-native and legacy software vendors. For instance, beinga cloud-native vendor not only reduces the positive associationbetween partner network size and firm value, but also flips thedirection. The same holds true for the other two independentvariables. According to Column 5, which captures the mostcomprehensive and inclusive model, we find strong support forHypotheses 1 and 3, but we find opposite evidence for Hypoth-esis 2 for our analysis of publicly listed vendors. R2 increasedfrom Columns 4 and 5, indicating a better model fit stemmingfrom the inclusion of all independent variables and interactionterms. There are also some noteworthy observations related toour control variables. First, firm age is not significant acrossall models. Global centrality appears to compete with partnernetwork size in terms of absorbing the variation in firm value.While IaaS and SaaS do not make any substantive impact onfirm value, vendors offering PaaS appear to consistently com-mand a significantly higher firm value by at least $40B U.S.dollars. As expected, a U.S.-based location is weakly associatedwith a higher firm value.

Table IV presents the estimation results for our analysis ofprivately held vendors. Similar to our prior analysis, (4) is es-timated. The results reveal that individual models (shown inColumns 1–4) all fit relatively poorly, leaving the coefficients



TABLE VRESULTS WITH BOTH PUBLICLY LISTED AND PRIVATELY HELD COMPANIES

Note: Columns 1 and 2 combined show a single regression with three-way interactions.Robust standard errors are in parentheses. †, *, ** denote statistical significance at 10%,5%, and 1%, respectively.

of our key independent variables largely insignificant. How-ever, the full model (in Column 5) fits much better, yet all ofthe coefficients turn out to be exactly opposite in sign relativeto our analysis of publicly held vendors. Hypothesis 2 is sup-ported, but we find statistically significant but opposite in signassociations for Hypotheses 1 and 3. With regards to Hypothe-sis 4, the effect of being a cloud-native vendor starkly contrastswith respect to legacy software vendors. Most control variablesare insignificant with a few exceptions. Offering IaaS cloud ser-vices is negatively associated with firm value (total amount offunding raised for privately held companies). U.S.-based pri-vately held vendors raised about $150M U.S. dollars more thannon-U.S.-based companies, significant at 10% level.

Before discussing these potentially contradicting sets of re-sults, we sought to confirm whether these findings were robust tomodel specification variations. First, we combined the two splitsamples into a single sample by merging the two types of firmvalue into a single variable. We then expanded our specificationto include three-way interaction of One of Three IndependentVariables × Cloud × Public. Table V shows the result of thisanalysis. It confirms the estimated signs of the coefficients foundin Tables III and IV. The coefficients of the terms interactingwith the Public dummy variable are in the opposite direction of

TABLE VIRESULTS WITH SUBSAMPLE WITH PARTNER NETWORK SIZE > 0

Note: Robust standard errors are in parentheses. †, *, ** de-note statistical significance at 10%, 5%, and 1%, respectively.

those without the interaction. The statistical significance seemsto be largely driven by publicly held vendors, as the coefficientsfor privately listed vendors are largely insignificant. The secondrobustness analysis included an examination of vendors withoutany partners. Table VI shows the results. The coefficients andsignificance levels of our key variables remained unchangedwith the exception of partner network diversity for publiclyheld vendors, which lost its significance found earlier. Last,we combined these two robustness checks by merging the twodependent variables and running a regression with three-wayinteraction terms. Table VII shows qualitatively similar resultsfound in Table V, except for the dropped significance for thepartner network diversity coefficient, possibly due to reducedstatistical power from the smaller sample size.

B. Visualization

Fig. 2 shows a force-directed network visualization of col-laboration in the cloud computing ecosystem. The idea of aforce-directed network layout is to position prominent nodes



TABLE VIIPUBLIC–PRIVATE COMBINED RESULTS WITH SUBSAMPLE WITH PARTNER

NETWORK SIZE > 0

Note: Columns 1 and 2 combined show a single regression with three-way interactions.Robust standard errors are in parentheses. †, *, ** denote statistical significance at 10%,5%, and 1%, respectively.

at the center and less prominent nodes on the periphery. Nodeproximity indicates closer relatedness. We use OpenORD, amodified force-directed network layout algorithm, to empha-size clusters within the ecosystem. Red-colored nodes depictour core vendors; gray-colored nodes depict partner companies.Nodes are proportionally sized to the number of partners. Thethickness of edges represent the number of unique partnershipsbetween two firms.

Several interesting observations can be made. The mostprominent firms in the cloud ecosystem, based on the numberof partners, are legacy software vendors. Prominent examplesinclude IBM, Microsoft, and Cisco. Cloud-native vendors ingeneral appear to have much smaller partner networks. In fact,many privately held cloud natives operate more at the periph-ery of the ecosystem while publicly listed cloud vendors (CVs)are centrally located. This naturally opens the question whetherthere are significant differences between vendor types, specifi-cally whether legacy vendors manage larger networks than theircloud-native counterparts.

Fig. 3 shows a small-multiple visual representation of eightvendors across different vendor types and firm value level. Eachvisualization represents the direct partner network of a focalvendor as well as the relationships among its partners. Similarto the global ecosystem visualization, we use a force-directed

network layout. The visualizations provide several insights thatconfirm and complement our empirical results. First, the visu-alizations reveal that vendors maintain a wide range of partnernetworks, ranging from small (e.g., Blackboard) to medium(e.g., Atlassian) to very large (e.g., VMware) in size. Second,these visuals confirm our hypotheses at a descriptive level. Forlegacy software vendors, for instance, high-performing firmshave starkly more partners and extensive partner networks com-pared to low-performing firms. This difference is rather mutedfor cloud-native vendors. We also notice that all vendors have amix of cloud-native vendor partners and others. The balance ofpartner types varies from even distribution (e.g., SugarCRM),predominantly cloud-native (e.g., Blackboard) to others (e.g.,Atlassian). In some instances, a large set of these other part-ners are exclusive to the focal vendor (e.g., Citrix) while insome cases many partners are shared with other cloud ven-dors (e.g., Cloudera). What is particularly striking is the extentto which cloud-native vendors appear to collaborate with eachother, except for low-performing legacy vendors (e.g., Splunk,Blackboard).

VI. DISCUSSION

A. Designing and Managing Cloud Partner Networks

The results of our study indicate and suggest different typesof ecosystem challenges and opportunities across vendor types.It is well understood that legacy vendors face the challengeof transitioning their existing business to one that accommo-dates new cloud service offerings. Legacy vendors often inherita large base of ecosystem partners, which in turn can enablethem to attract more partners, but at the same time can actas inertia to transitioning. Legacy vendors thus need to care-fully balance their ecosystem partner portfolio, reconsideringexisting partners and establishing new ones needed to createvalue in the cloud ecosystem. For cloud native companies, ourresults reveal nuanced differences between privately held andpublicly listed firms. Similar to other industries, startups havethe challenge of establishing themselves in an already compet-itive space. Without a pre-existing ecosystem, these companiesare not constrained in pursuing their own or joining an existingecosystem. In part, given the importance of receiving venturefunding, startups may pursue clarity for their cloud offerings.Such an endeavor can drive firms to have partners representinghighly focused industries, which in turn can undermine theirgrowth prospect. On the other hand, publicly held cloud vendorsare more mature organizations. They have already cultivated alarge partner ecosystem. A relatively static partner network basecan thus act as a bottleneck to outperform their competitors.Common to all vendors is the necessity to continuously adaptand adjust to the changing conditions. Not all cloud ecosys-tems are created equal. Choosing the right set of partners andcultivating the ecosystem at the right speed will thus be criticalto the survival of any vendor.

B. Legacy Versus Cloud Natives Vendors

A central argument of our study was that there are differencesin partner network strategies between legacy and cloud native



Fig. 2. Visualization of the collaborative structure of the cloud computing ecosystem.

vendors. Indeed, our analysis shows that there are differences.Legacy vendors tend to maintain larger partner networks, partlyinherited from their previous activities and partly designed tohelp them with the orchestrating role they try to perform well insoftware ecosystems. Large partner networks, however, can beconstraining and act as barriers to successful transition to newbusiness models.

Cloud-native vendors, on the other hand, have the luxury ofstarting from blank slate, carefully selecting their partners andarchitecting the partner network. This path-dependency, andlack thereof, provides an interesting lens to why and how cloudvendors may succeed in this industry. Despite the fact the cloudnatives may have a competitive advantage, our results show thatlegacy vendors can achieve high value, further stressing thatpartner networks matter in this dynamic environment.

C. Uncovering the Duality of Cloud Partner Networks andFirm Value

One of the key observations from our analysis is the con-tradictory findings of the associations between structural char-

acteristics and firm value based on cloud vendor type. Almostacross the entire board we find that there are fundamental dif-ferences between vendor types. On the one hand, one may findthese findings contradictory to our theory. On the other hand, webelieve that these findings underline that there are several cloudvendor types and that each of them deserves closer investigation.

We argued that a larger partner network size is beneficial toa firm value due to the ability to gain access to resources andcapabilities; however, we find that while this argument holds forpublic companies it does not for private. There are several po-tential reasons for that. Entering into interfirm relationships hascosts, risks, and benefits. Costs include both financial resourcesand time. The marginal benefits of entering into additional part-nerships can decline while costs increase. An opposite resultcan also be observed for partner interconnectedness. For pub-lic companies we find that interconnectedness does improvefirm value, due to the affordances control and power providedby occupying structural holes. Yet, theories of network closurehave shown that focal firms reap benefits from tightly intercon-nected networks due to to the increase in trust and social norms.For firms in vulnerable and potentially less powerful positions,



Fig. 3. Comparison of the collaborative structure of select vendor partner networks.

these issues may be relevant. Instead of seeking positions ofstructural power, it would be advocated to help facilitate col-laboration among their partners. While innovativeness and newideas may decline, shared vision and knowledge may help am-plify firm value. Partner network diversity can have a similarsplit effect. While we argued that diversity increases firm value,we find that this result does not hold for private companies. Part-ners from different industries may have very different routinesand processes that can make collaboration difficult. Thus, whilegreater partner industry diversity may provide learning and re-source access benefits, vendors will have to first overcome twomain hurdles. First, different partners often bring myriad Type IIdiversities that can impede partner value creation (e.g., conflictswith competitors, lack of synergy with partners in unrelatedindustries). Second, increased diversity increases partner man-agement complexity.

Cumulatively, our results point to the need of a more nuancedexploration of vendor types. The duality of legacy versus cloudnative and public versus private vendors suggests that vendorcharacteristics and contexts ultimately matter for determiningfirm value. We acknowledge that further analysis beyond thescope of this paper is needed.

D. Closing the Data to Insight Loop

A key contribution of our study is demonstrating the pro-cess of going from data-to-insight using empirical analysis andvisualization of heterogeneous datasets. Traditionally, the twomethods are used separately. Studies create data-driven visual-izations, but then fail to develop and empirically test theory andhypothesis based on the findings; others develop theory and thenconduct empirical analyses. We are not suggesting that either ofthese methodological approaches is superior or more valuable,

but rather argue for more multimethodological data-driven stud-ies, which we believe are of particular value in many emergingIS domains.

VII. CONCLUDING REMARKS

In dynamic business environments, partnerships play a crucialrole to firm survival. The cloud ecosystem is unquestionably oneof the most rapidly growing and evolving software industries.Vendors of all sizes must adapt to these changes. Our study pro-vides important insights into partner network strategies vendorsemploy to achieve higher firm value. Our results demonstratethat pending vendor type strategies can vary significantly, if nothaving the opposite effect. Cloud native vendors have the luxuryto start from a clean slate and can be more judicious in selectingtheir partners. A more focused industry segment strategy allowscloud natives to provide specialized offerings. Legacy vendorson the other hand are somewhat constrained by the solutions andinfrastructure they may have offered with existing partners. It isthus not surprising to see that legacy vendors benefit from bothlarge and diverse networks. The implications of our work aremultifold. Theoretically, our study reveals that grouping firmsunder one umbrella would not reveal the nuanced network strate-gies that exist. Not all cloud vendors are made equal and startdifferences exist. Firms must balance their network strategiescorresponding to the type of firm they are and what role theyoccupy in the ecosystem. Managerially, our study highlights theimportance of partnerships, yet also underlines that managersmust be cognizant of the strategies their competitors employ. Inorder to achieve superior firm value, managers must architecttheir partner networks in such a way that it fits their vendortype. Irrespective of perspective, our study reveals the complexstructure of technological partnering in a business ecosystem.



Our approach can serve as a foundation for future strategic andcompetitive intelligence.

While every precaution was taken to ensure a rigorous datacuration and analysis process, our study has some limitations.First, our study focuses on the ecosystem of core cloud compa-nies derived from well-established industry lists. Future workmay want to expand this seed list to ensure broader coverage.Second, assessing the firm value of private firms is a difficult en-deavour due to limited data sources; new proxies beyond venturecapital funding need to be developed. Third, our study focusedprimarily on the association of network structure and firm value.However, temporal effects most likely exist. Fourth, our analysisdid not examine the demand side of the cloud ecosystem. Cus-tomers play an influential role in value cocreation and, pendingpower, may even actively shape the nature and types of ecosys-tem partnerships. Fifth, while we examine the nature of partnernetworks, we did not examine strategies that partners may useto align or attach to vendors. Last, we did not consider mergerand acquisition strategies vendors may pursue. Each of theselimitations presents exciting future research.

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