On the performance of B2B e-markets: An analysis of organizational capabilities and market opportunities

Electronic Commerce Research and Applications 11 (2012) 59–74

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Electronic Commerce Research and Applications

journal homepage: www.elsevier .com/locate /ecra

On the performance of B2B e-markets: An analysis of organizational capabilitiesand market opportunities

Shan Wang a,⇑, Ji-Ye Mao a, Norm Archer b,1

a School of Business, Renmin University, 59 Zhong Guan Cun Avenue, Beijing 100872, PR Chinab DeGroote School of Business, McMaster University, 1280 Main Street West, Hamilton, Canada L8S 4M4

a r t i c l e i n f o

Article history:Received 30 April 2010Received in revised form 1 July 2011Accepted 1 July 2011Available online 14 July 2011

Keywords:B2B electronic marketplacesOrganizational capabilityMarket opportunityService capability

1567-4223/$ - see front matter � 2011 Elsevier B.V. Adoi:10.1016/j.elerap.2011.07.001

⇑ Corresponding author. Tel.: +86 82504648.E-mail addresses: [email protected] (S.

(J.-Y. Mao), [email protected] (N. Archer).1 Tel.: +1 905 525 9140x23944.

a b s t r a c t

This research is a study of factors leading to the success of business-to-business (B2B) electronic market-places (EMs). A model based on both organizational capability and market opportunity theories wasdeveloped to explain the performance of B2B EMs. Organizational capabilities included service provisioncapability and its enabling capabilities, entrepreneurial orientation and human resource capability,whereas market opportunity was modeled as market size and e-commerce awareness of the industry.Data were collected from 128 B2B EMs in China and analyzed using Partial Least Squares. Results suggestthat the research model explains the performance of B2B EMs well. More specifically, among the two ser-vice capabilities studied, service width contributes significantly to EM performance, while the effects ofservice depth are yet to be seen. Moreover, the enabling organizational capabilities and market opportu-nity factors affect EM performance both directly and indirectly through their enhancement of EM serviceprovision capability.

� 2011 Elsevier B.V. All rights reserved.

1. Introduction

This research is a study of the performance of business-to-business (B2B) electronic marketplaces. B2B e-markets are Inter-net-based business syste-markets that support transactions andother interactions between companies. E-markets aggregate manybuyers and sellers in one platform by facilitating product search,contract negotiation, and transaction fulfillment among participat-ing firms. Many e-markets also provide value added services suchas industry news and intelligence, supplier evaluation, feedbackmechanisms, and consultations (Bakos 1998, Choudhury andHartzel 1998, Christiaanse 2005, Grewal et al. 2010, Holzmullerand Schluchter 2002, Pavlou 2002). E-markets are normally classi-fied into three types: independent e-markets that are operated by athird party, consortium-based e-markets formed by groups ofindustry players, and private e-markets owned by single compa-nies in order to support their own transaction and supply chainmanagement (Dai and Kauffman 2002a,b). This research focuseson the first type: independent third party e-markets.

The performance of third party B2B e-markets is of great inter-est to both entrepreneurs and researchers due to the innovative-ness of their business models and the large potential of the B2Bmarket. However, despite the benefits that B2B e-markets can

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potentially bring to firms (Christiaanse 2005), many e-marketventures have failed. These failures have been attributed to lackof e-commerce awareness and participation (Rao et al. 2007,Truong 2008).

However, there is a paucity of research on third party B2Be-market performance, partly because of the limited number ofsuch e-markets (Laseter et al. 2001). Most previous research hasfocused on e-market governance (Grewal et al. 2010, Pavlou2002) and adoption (Cho 2006, Grewal et al. 2001, Hsiao 2003,Son and Benbasat 2007, Truong 2008). Furthermore, existingresearch about B2B e-market performance is primarily based oncase studies (Driedonks et al. 2005, Kambil and Heck 1998, Kumaret al. 1998, Montazemi et al. 2008). Research findings from thesestudies remain to be verified and generalized.

This research strives to fill the gap by building a synthesizedmodel based on organizational capability and market opportunitytheories, and empirically testing it. Both theories are importantin strategic management for explaining heterogeneity in firm per-formance. The former suggests that organizational capabilities thata company owns determine its success, and it helps to identifyimportant internal capabilities of B2B e-markets, which operatein a dynamic environment. The latter suggests that the exploitationof a large market leads to high firm performance (Venkataraman1997), providing insights into environmental factors. The twoperspectives can be integrated in the sense that organizationalcapabilities can be sources of business opportunity discovery andexploitation.

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60 S. Wang et al. / Electronic Commerce Research and Applications 11 (2012) 59–74

The necessity for examining market opportunities is highlightedby a comparison between different economic environments. Theactual market size for third party B2B e-markets in western coun-tries seem limited due to trading habits rooted in long-term rela-tionships, through which 70% of business purchases are made(Håkansson 1982, Hakansson and Snehota 1994). Potential pur-chasing companies are generally reluctant to give up existinglong-term business relationships with suppliers because of non-contractible factors (Mithas 2008). Western B2B e-markets surviveby conforming to the expectations of the institutional environmentand converting to private and consortia based ownership (Ganesh2004), suggesting that business opportunities for independente-markets are limited (Cousins and Robey 2005, Ganesh 2004,Holzmuller and Schluchter 2002, Lee et al. 2003). In contrast, thecurrent stage of economic development in China is characterizedby rapid growth, the existence of a large number of small andmedium-sized enterprises (SMEs), and prosperous export busi-nesses. The potential market size for e-markets is large becausecompanies are highly motivated to expand their market, launchnew products, and build company brands. This has led to the pro-liferation of B2B e-markets in China, which reached 4510 by theend of 2007 (Xu 2007), compared to 200–300 e-markets in the Uni-ted States (Laseter et al. 2001). China has the world’s biggest B2B e-market, Alibaba, and numerous small and medium-sized ones.Therefore, market opportunities as reflected in sizes seem to beimportant to the survival and growth of B2B e-markets.

The remainder of the paper is organized as follows. The existingliterature is reviewed, along with relevant theories in Section 2.Next, a research model and hypotheses are developed in Section3. The research methods are described in details in Sections 4and 5. Data analysis is presented in Section 6. The paper concludeswith a discussion of research contributions, managerial implica-tions, and future research opportunities.

2. Literature review and theoretical background

2.1. B2B e-markets

Two related streams of research are reviewed hereinafter, con-cerning the adoption and performance of B2B e-markets. Prior re-search has shown that the adoption of B2B e-markets is driven byefficiency and legitimacy motivations in order to save transactioncosts and achieve legitimacy within the industry (Choudhury andHartzel 1998, Grewal et al. 2001, Son and Benbasat 2007). Capabil-ities to adopt B2B e-markets, including learning capability, infor-mation technology (IT) capability, and e-readiness, are equallyimportant for both initial adoption and continued use (Grewalet al. 2001, Truong 2008). This stream of research implies thatthe ability of e-markets to attract participants depends on howwell their services can reduce transaction inefficiencies, and theability of participants to assimilate the services and technologiesprovided.

The performance of B2B e-markets has not received as muchattention as their adoption. Table 1 provides a summary of keyempirical studies, which identified a number of factors from a vari-ety of e-markets.

Although there is only limited research on e-market perfor-mance, certain patterns have emerged. First, e-markets sponsoredby incumbent companies stand a better chance of success (Clasenand Mueller 2006, Gosain and Palmer 2004, Laseter and Bodily2004, Ordanini 2006). Second, advanced features, such as elec-tronic data interchange (EDI) and collaborative planning, forecast-ing, and replenishment (CPFR), have not been found to correlatewith e-market success (Clasen and Mueller 2006, Gosain andPalmer 2004), mainly due to their requirements for internal IT in

participating companies. Moreover, the scope of e-marketofferings, in terms of both market scope (Clasen and Mueller2006, Gosain and Palmer 2004) and service scope (Clasen andMueller 2006, Laseter and Bodily 2004), correlates with moste-market success criteria. Last, the amount of time an e-markethas been in business is significantly related to its success (Clasenand Mueller 2006, Gosain and Palmer 2004). This is largely dueto network externalities and the importance of early mover advan-tage for e-markets.

However, prior research has two weaknesses. First, these explo-rations of e-market performance have been mostly atheoretical inthe sense that a coherent theory about the performance of B2B e-markets has not been developed. Second, in some research, mea-surements of e-market performance have been based on websitetraffic and the number of links pointing to the website rather thanmore meaningful financial performance measures (Clasen andMueller 2006, Gosain and Palmer 2004). In studies that used sur-vey data (Laseter and Bodily 2004, Ordanini 2006), sample sizestended to be small, which limits the robustness of their findings.

2.2. Theory of organizational capabilities

The theory of organizational capabilities is an extension of theresource-based view (RBV), which argues that the performance offirms is explained by the rare, valuable, inimitable and non-substi-tutable resources that the firms own (Barney 1991, Mahoney andPandain 1992). However, resources are static, and the processthrough which particular resources provide competitive advantageis not clear (Priem and Butler 2001). Organizational capabilitiestheory was derived to address this deficiency. Researchers havesuggested that the combination of a set of resources and comple-mentary organizational components can form organizational capa-bilities, which empower a firm to gain competitive advantage(Russo and Fouts 1997). These organizational components, includ-ing organizational structure, processes, control systems, and cul-ture (Barney 1986), are conceptualized as implementation skills(Barney and Mackey 2005) that ensure resources are properly lev-eraged or managed.

Organizational capabilities can be classified into functionalcapabilities (Grant 1991, Pavlou and El Sawy 2006) and dynamiccapabilities (Teece et al. 1997). Functional capabilities, also calledoperational capabilities, represent a firm’s ability to perform‘‘repeatedly a productive task which relates either directly or indi-rectly to a firm’s capacity for creating value through affecting thetransformation of inputs into outputs’’ (Grant 1996, p. 377). Exam-ples include marketing capabilities (Morgan et al. 2009, Morganand Jenny 2008, Siu et al. 2004), new product development capa-bilities (Pavlou and El Sawy 2006), production capabilities (Zahraand Nielsen 2002), and e-commerce capabilities (Zhu 2004, Zhuangand Lederer 2006).

The term, dynamic capabilities, is defined as the ability of firms‘‘to integrate, build and reconfigure internal and external compe-tences to address rapidly changing environments’’ (Teece et al.1997, p. 516). From a dynamic view, organizational capabilitiescan be renewed so as to achieve congruence with a changing busi-ness environment (Teece et al. 1997). Knowledge, learning and so-cial relationships are enablers of organizational capabilitiesrenewal (Bhatt and Grover 2005, Kogut and Zander 1992, Pavlouand El Sawy 2006, Teece et al. 1997). The current literature sug-gests that dynamic capabilities are more important for organiza-tions operating in uncertain environments (Wiklund andShepherd 2003, Santos and Eisenhardt 2009), because they areenablers of the renewal of an organization’s functional capabilitiesand thus are conceptualized as higher-order capabilities (Bhatt andGrover 2005, Kogut and Zander 1992, Pavlou and El Sawy 2006,Teece et al. 1997).

Table 1Literature summary concerning e-market performance.

Independent variables (a) Dependent variables Sample (research method)

Laseter and Bodily(2004)

Industry concentration Revenue 61 e-markets from around the globe(survey)

Consortium ownership structure (+) Time to first financial milestoneInitial funding ratio (+)Strategic continuity (+)Service breath (�)Execution speed

Ordanini (2006) E-markets targeting big companies (+) Percentage of revenues from transaction fees 32 European operators (survey)Inclusion of established firms as shareholders(+)Offering e-auctions (+)

Clasen andMueller (2006)

Offering complementary services (+) Existence (a website must have undergone somechanges in 12 months)

177 e-markets from around the globe(website codingb)

Multiple languages (+)E-readinessCost of e-market usage (�) Number of hits received

Number of incoming linksOffering EDI Number of page views per visitor sessionStart-up firms or attached to a conventional‘brick-and-mortar’ firmAge (+)Trading agriculture machinery (+)

Gosain and Palmer(2004)

Informational benefits Exchange traffic 194 e-markets (website codingb)

Transactional benefits (+) Exchange prominence (websites links in)Relational benefits (�)Market focus (horizontal vs. vertical)(Horizontal +)Time online (+)Resource endowment (affiliation with existingindustry players) (+)IT vendor-operated e-market

a The signs in brackets indicate significant relationships, either positive or negative.b Website coding means that the values of variables were obtained by coding website information.

S. Wang et al. / Electronic Commerce Research and Applications 11 (2012) 59–74 61

B2B e-markets operate with great uncertainty, such as whatservices to offer and how to monetize their trading platforms,due to the emerging nature of the industry. The characteristics ofmost B2B e-markets as SMEs further complicate this problem be-cause they have a lower capacity to collect and analyze informa-tion (Caldeira and Ward 2003, DØving and Gooderham 2008,Wiklund and Shepherd 2003). Therefore, it is necessary to studythe effect of dynamic capabilities on e-market performance. In thisresearch, human resource capabilities (Chang and Chen 2002) andentrepreneurial orientation (Moreno and Casillas 2008, Wiklundand Shepherd 2003, 2005) are conceptualized as key elements ofdynamic capabilities because they are more pervasive in e-mar-kets, affecting the configuration and renewal of e-market func-tional capabilities.

Human resources are a firm’s human capital. A highly skilledand motivated workforce is a strategic asset that cannot be easilyimitated by other firms, and it can help a firm develop other criticalorganizational capabilities (Huselid 1995). For firms facing highuncertainty, such as entrepreneurial ventures in emerging markets(Santos and Eisenhardt 2009), the knowledge and skill of employ-ees become particularly important, due to the need for identifyingmarket opportunities and adapting to environmental changes(Pena 2002, Teece et al. 1997, Ucbasaran et al. 2008).

Entrepreneurial orientation, as proposed by Covin and Slevin(1991) and extended by Zahra (1993), is a firm’s strategic orienta-tion. It involves a firm’s willingness to innovate in order to rejuve-nate market offerings, to take risks in trying out new and uncertainproducts, services, and markets, and to be more proactive thancompetitors towards new marketplace opportunities. A numberof studies have found that businesses adopting a more entrepre-neurial orientation perform better (Wiklund and Shepherd 2003,

Wiklund and Shepherd 2005, Zahra and Covin 1995). Enterpre-neurial orientation is especially important for e-markets operatingin an uncertain market due to the need for ongoing new opportu-nity discovery and exploitation, and new capability building.

To date, little effort has been made to examine the organiza-tional capabilities of third party B2B e-markets. Weill and Vitale(2002) distinguished e-commerce capabilities among nine typesof business models, including intermediaries that aggregate buyersand sellers. They suggested that capabilities possessed by an inter-mediary should include application infrastructure, communication,database management, and IT management, and emphasized thatsuch a business model demands higher levels of IT infrastructure.In this research, we incorporate into our model two dynamic orga-nizational capabilities: human resource capabilities and entrepre-neurial orientation, and service provision capabilities that reflectthe characteristics of B2B e-markets as service providers.

2.3. Theory of market opportunity

The research domain of market opportunity makes entrepre-neurial research stand out as a separate field from other strategicmanagement research streams, because entrepreneurship is aboutthe discovery and exploitation of market opportunities (Shane andVenkataraman 2000, Short et al. 2010, Venkataraman 1997). Entre-preneurial ability to discover and exploit market opportunities ispositively related to the success of entrepreneurial firms (Gruberet al. 2008). Market opportunities are defined as those situationsin which new goods, services, raw materials, and organizing meth-ods can be introduced and sold at greater than their cost of produc-tion (Casson 1982). They include both the discovery of newproducts and services or new usage of products, and the feasibility


of starting new businesses (Gruber et al. 2008). This definition sug-gests that new products and services are the physical embodimentof business opportunities.

To date, the literature has concentrated on making sense of theopportunity construct (Buenstorf 2007, McMullen et al. 2007). Ithas focused on the type and nature of opportunities, by categoriz-ing opportunities into three types: (1) creation of new information,as with the invention of new technologies, (2) exploitation of mar-ket inefficiencies that result from information asymmetry acrosstime and geography, and (3) reaction to shifts in the relative costsand benefits of alternative uses for resources, such as political, reg-ulatory, or demographic changes (Drucker 1985, Eckhardt andShane 2003).

The nature of opportunity discovery and exploitation is also aninteresting topic. Two views emerge from this research stream.One implicitly treats opportunity discovery and exploitation asthe launch of a new business, which is a one-time event. The otherresearch stream, mainly evolutionary economics, suggests thatopportunity discovery and creation is a continuous social process,in which organizational development and evolution of industriescan be sources of opportunity discovery (Buenstorf 2007). In thisresearch, we adopt the latter view.

The value and significance of opportunities has been ignored inthe literature. The limited literature suggests that the value ofopportunity can be contingent on favorable industry and marketenvironment factors, the substance of which includes high con-sumer demand (Choi and Shepherd 2004), and a product marketin the growth stage rather than emergent and mature stage(Eisenhardt and Schoonhoven 1990). In this research, we focuson external market factors as determinants of opportunity value.

3. The success of B2B e-markets: a model and hypotheses

A research model based on both organizational capability andmarket opportunity perspectives was developed to explain thesuccess of B2B e-markets. (See Fig. 1.)

The two perspectives are not isolated from each other. First, theevolutionary perspective of market opportunities suggests that theactivities and development of existing organizations are the-marketselves sources of new market opportunity creation anddiscovery (Buenstorf 2007). Therefore, services developed by ane-market are a reflection of the continuous capture and exploita-tion of emergent opportunities. Second, a focus on organizationalcapability allows us to understand how opportunities are exploitedas service provision and its related driving forces.

Fig. 1. Research model for performance of B2B electronic marketplaces.

Next, we will define B2B e-market capabilities, followed bythe impact of market opportunity on e-market performance, andthe relationships between market opportunities and e-marketcapabilities.

3.1. Organizational capabilities

As discussed earlier, organizational capabilities are classified asfunctional and dynamic. Functional capabilities in this research aredefined as service provision capabilities, because e-markets aremainly service companies. Human resource capability andentrepreneurial orientation are studied as elements of dynamiccapabilities. They can enable the restructuring and adaptation offunctional capabilities to fit with new technologies and environ-mental changes. B2B e-markets in emerging markets operate in ahighly uncertain environment, which creates special requirementsfor dynamic capabilities that help them to adapt to market changes(Bhatt and Grover 2005, Teece et al. 1997).

3.1.1. E-market service provision capabilitiesService provision capabilities are defined as the ability of an

e-market to provide a variety of services. B2B e-markets on theInternet now offer a wide array of services, including consultingand other services that may be intelligence related, transactionrelated, or value added, in addition to IT services. Consulting andintelligence related services not only give buyers and sellers theability to list information online, but also extend to leveraginginformation about buyers and sellers that can be used to produceindustry intelligence reports and provide consulting servicesrelated to buying and selling. It has been suggested that servicesthat leverage industry knowledge contribute to the success of ane-market (Dai and Kauffman 2002a,b; Wise and Morrison 2000).

Transaction support services provide order placement, manage-ment, payment and fulfillment of online transactions. Value addedservices are not the main value proposition of e-markets, but theycan contribute significantly to e-market survival and performance(Cousins and Robey 2005). For example, e-markets can help evalu-ate products, or finance transactions. Some e-markets also offer ITservices to help SMEs build their own websites and internal ITinfrastructures.

The more services an e-market offers (its service width), themore potential sources of revenue become available to the e-mar-ket, and the higher its perceived value from customer perspectives.The service width of e-markets is therefore expected to contributepositively to the performance of e-markets.

Hypothesis 1 (The Service Width and e-Market PerformanceHypothesis). Service width is positively related to e-marketperformance

Service depth is another dimension of e-market service provi-sion capability. The use of in-depth service demands higherinvolvement from participant companies and this enhances ane-market’s ability to charge higher service fees. The evolution ofB2B e-markets mostly follows three stages: from simply support-ing aggregation, to supporting transactions, and then to supportingintegration and collaboration (Ganesh 2004), from the simplestservice to the most in-depth functions.

We propose that service depth contributes positively toe-market performance because it adds more sources of revenuefor e-markets. Moreover, the revenue from more in-depth serviceis likely to be higher than that from supporting simple aggregationand information provision for the following two reasons. First,customer transaction cost savings related to online payment,logistics, and supply chain management are higher than savingsfrom simple information search. Second, the more depth there is


to these services, the higher the commitment that is needed fromcustomers and the higher the probability that customers will belocked into the e-market platform. Based on the above argument,we propose that:

Hypothesis 2 (The Service Depth and Market PerformanceHypothesis). Service depth is positively related to e-marketperformance

3.1.2. Human resource capabilitiesHuman resources can contribute to e-market performance

either directly or indirectly by enabling service capability (Changand Chen 2002, Chang and Huang 2005, Wiklund and Shepherd2003). The reason is that employees may have both technical skillsthat qualify them for routine jobs, and entrepreneurial skills, whichenable them to discover more market opportunities and take stra-tegic initiatives (Huselid et al. 1997, Schuler and MacMillan 1984).Therefore, the quality of employees is positively related to thequality of both service delivery and service design of e-markets.

With respect to the direct effect of human resources, servicesoffered by e-markets need to be delivered by their employees, thusthe quality of employees affects the quality of services delivered tocustomers, whose satisfaction has a direct impact on e-market per-formance. Some e-market services need human intervention, suchas customized marketing packages and market intelligence reports,so high quality employee support is important for the delivery ofservice content. For services that are automated, the quality ofemployees can also be important, because when human interac-tion is less frequent, customers may demand even better experi-ence with each occasional employee interaction, resulting inuncertainty assurance. The better the services are implementedwithin the e-markets organization, the better the performance ofthe e-market. Hence, we propose:

Hypothesis 3 (The Human Resources Capabilities and e-MarketPerformance Hypothesis). Human resource capabilities is positivelyrelated to e-market performance

In terms of the indirect effect, high quality human capital isconducive to service design of e-markets. Talented and devotedemployees are able to discover and exploit more market opportu-nities than weaker ones (Sigal and Arie 2007, Ucbasaran et al. 2008,Wiklund and Shepherd 2003). They can do a better job of research-ing, collecting, and analyzing customer needs, and discover moreservices to eliminate inefficiencies in supply chains and meet theneeds of client firms. This enhances B2B e-markets’ service provi-sion capabilities. Therefore, we propose:

Hypothesis 4a (The Human Resources Capabilities and ServiceWidth Hypothesis). E-market human resource capabilities are pos-itively related to service width

Hypothesis 4b (The Human Resources Capabilities and ServiceDepth Hypothesis). E-market human resource capabilities are posi-tively related to service depth

3.1.3. Entrepreneurial orientationEntrepreneurial orientation is an important organizational

capability that affects e-markets performance. Innovation, risk tak-ing, and proactiveness have been identified as three dimensions ofentrepreneurial orientation (Covin and Slevin 1991). Entrepreneur-ial orientation contributes directly to the performance of e-mar-kets because e-markets the-marketselves are innovative businessmodels on the Internet. Since the environment in which they oper-ate is characterized by high velocity technology changes, services

or even business model innovations can occur that may rendertheir current offerings less valuable. Moreover, customer needsalso change when customers grow (Ganesh 2004). This requirese-markets to take risks, to continuously evolve their strategies,and to modify their business models. Therefore, we propose that:

Hypothesis 5 (The Entrepreneurial Orientation and e-MarketPerformance Hypothesis). Entrepreneurial orientation is positivelyrelated to e-market performance

Entrepreneurial orientation also contributes to the service de-sign of e-markets. An e-market with higher entrepreneurial orien-tation often encourages its employees to be innovative. Thiscreates a climate for employees to design or provide suggestionson service design. Service innovation is more likely to be favoredby the management in such e-markets. Because service innovationincreases both service depth and service width, we propose:

Hypothesis 6a (The e-Market Entrepreneurial Orientation andService Width Hypothesis). E-market entrepreneurial orientation ispositively related to its service width

Hypothesis 6b (The e-Market Entrepreneurial Orientation andService Depth Hypothesis). E-market entrepreneurial orientation ispositively related to its service depth

3.2. Market opportunity

B2B e-markets exploit market inefficiencies that result frominformation asymmetry and invisibility by leveraging Internettechnology. Market opportunities for B2B e-market entrepreneursarise from supporting aggregation and information search, transac-tion fulfillment, and supply chain activities. B2B e-market marketopportunities are hierarchical, in the sense that the above identi-fied opportunities serve as the business concepts of many B2B e-markets that can frame later opportunity searches. Subsequentopportunity discovery and exploitation are a social process involv-ing interactions among entrepreneurs, their employees, and themarket. In this research, our focus is not how entrepreneurs dis-cover opportunities in B2B e-markets, but the value of these mar-ket opportunities. That is, what determines the potential value ofthe market opportunity, as evaluated by e-market performance?

3.2.1. Market sizeMarket size refers to the total transaction volume of the market

that a B2B e-market can potentially service. Both the new productdevelopment literature (Brown and Eisenhardt 1995) and entre-preneurial opportunity literature (Carroll and Delacroix 1982,Eisenhardt and Schoonhoven 1990) suggest that products or firmsentering a munificent market are more likely to succeed. A largermarket size implies that the services of an e-market can reachmore customers, and the revenue generation capabilities of e-mar-kets will be higher. Empirically, Gosain and Palmer (2004) foundthat horizontal e-markets (covering more than one industry) arebetter performers. Therefore, we propose:

Hypothesis 7 (The Market Size and e-Market PerformanceHypothesis). The size of the markets in which B2B e-markets operateis positively related to their performance

Larger markets also imply more abundant opportunities forfirms to discover. They enable firms to generate a larger opportu-nity set, and the firms can make better choices of which opportu-nities to pursue (Bingham et al. 2007). In this sense, marketopportunity can help enhance the service provisions of B2B e-mar-kets. In the process of catering to a larger customer population,


more service provision ideas can be generated and exploited by e-markets. This can increase both service width and depth. Thus wepropose:

Hypothesis 8a (The Market Size and e-Market Service WidthHypothesis). E-market market size is positively related to e-marketservice width

Hypothesis 8b (The Market Size and e-Market Service DepthHypothesis). E-market market size is positively related to e-marketservice depth

3.2.2. E-commerce awarenessE-commerce awareness pertains to a client company’s knowl-

edge of e-commerce and its applications. A company with highere-commerce awareness is more likely to use e-markets for pro-curement and marketing. B2B e-market services also mean busi-ness opportunities to reduce transaction costs and extendmarkets further afield. Firms with high e-commerce awarenessare more likely to recognize the value of B2B e-market services,to have developed a tendency to use e-commerce applications(i.e., technological opportunism) (Mishra and Agarwal 2010,Srinivasan et al. 2002), and to have higher acceptance of B2Be-market services (Truong 2008). By targeting such clients, B2Be-markets can attract more clients for their services. This maylower the cost of customer acquisition and enhance opportunityvalue appropriability due to having customers with a higherwillingness to pay. As a result, we propose:

Hypothesis 9 (The e-Commerce Awareness and e-MarketsPerformance Hypothesis). The e-commerce awareness of the indus-try is positively related to B2B e-market performance

Cooperation from high acceptance customers also enlarges thenetwork of B2B e-market operators that are available for newopportunity search, enabling them to develop more services(Choi and Shepherd 2004, Shepherd and DeTienne 2005). There-fore, we suggest that an industry with higher e-commerce aware-ness enables B2B e-markets to discover more services, which maycontribute to the service capability of e-markets. Based on thisargument, we propose:

Hypothesis 10a (The e-Commerce Awareness and Service WidthHypothesis). Industry e-commerce awareness is positively related tothe service width of an e-market

Hypothesis 10b (The e-Commerce Awareness and Service DepthHypothesis). Industry e-commerce awareness is negatively relatedto the service depth of an e-market

2 As shown in Fig. 2 in the data analysis section, only 9.8% of the companiessupported system integration among companies, and their websites revealed thatmost of them provided it as a software service, rather than as a hosting service.

4. Research methods

4.1. Measurement

The instruments used for data collection are described as fol-lows, and shown in the Appendix:

� Entrepreneurial orientation (EO). This was measured by fourite-markets indicating a firm’s innovativeness, willingness tocollaborate, and growth orientation. The original eight-iteminstrument was developed by Miller (1983), and validated byother researchers (Covin and Slevin 1989, Ferreira and Azevedo2007, Merz et al. 1994). We followed Ferreira and Azevedo

(2007) and used four of these ite-markets to measure this con-struct as a one-dimensional construct.� Human resource capability (HRC) was measured by four ite-mar-

kets indicating marketing knowledge, productivity, efficiency,and commitment to the company. The ite-markets wereadapted from Wiklund and Shepherd (2003).� Service depth (SD) was measured by an e-market’s capability to

support transactions. This treatment is empirically necessaryand sound. In China, very few e-markets have started to supportintegration and collaboration between companies, while somesupport transactions and almost all support aggregation.2

� If we used integration and collaboration supporting capabilityas surrogates for service depth, there would be almost no com-panies in the sample. Second, even in western countries, empir-ical research shows that offering sophisticated features such asCPFR and web-based EDI has not contributed to the perfor-mance of B2B e-markets (Clasen and Mueller 2006, Gosainand Palmer 2004). Therefore, using transaction support as a sur-rogate of service depth is less likely to invalidate our majorargument that service depth is positively related to e-marketperformance. Three ite-markets adapted from Zhuang andLederer (2006) were used to measure this capability.

These three constructs, service depth, human resource capabil-ity, and entrepreneurial orientation, were measured as reflectivescales in the seven point Likert style, following their originalsources:

� Service width (SW) was measured by the number of services thatan e-market provides. Nineteen services were identified fromprevious research (Dai and Kauffman 2002a,b; Hopkins andKehoe 2007) and from interviews with industry informants.Respondents were asked to indicate what services they offered,and the construct was measured by the sum of the number ofthese services.� Market size (Msize) was assessed by the annual total sales vol-

ume of the industry in which the e-market operated, segmentedinto intervals of seven levels ranging from RMB5 million (aboutUS$0.67 million) to RMB1 billion (about US$133 million) andabove.� E-commerce awareness (EA) was measured by a single item, an

e-market operator’s estimate of the extent to which companiesin the industry that it served were aware of e-commerce. It wasmeasured by a five-point Likert scale ranging from very low tovery high.

4.1.1. E-market performanceSpecifying a suitable measure for e-market performance (PER)

was a challenge because most e-markets were still in their earlylife cycles. They needed to strategically balance between extendingthe size of their operations while sacrificing profit, and pocketingprofit in their early life cycle while maintaining a restrained sizeof operation. Three types of measures were identified from the lit-erature: (1) the number of participating companies, (2) technicalmeasurements of e-market performance such as network traffic,the number of page views per visitor session, and the number ofin-links (the number of other Web pages that link to this particularwebsite) obtained from a search engine www.Alexa.com, and (3)financial indicators, such as break-even in net profit before tax, po-sitive operating cash flow on a quarterly basis, and revenue. Thesewere considered more direct and accurate measures of e-marketperformance. Among all the financial indicators, revenue was

http://www.Alexa.com


highly recommended since it measured how large the business en-tity had become. It gave a sense of how well the entity had sur-vived and its prospects for the future, and it was also more likelyto be revealed by respondents than more proprietary measures.Last, it is a measure gainfully used by other researchers (Laseterand Bodily 2004).

We used the number of companies participating in the e-mar-ket (including both registered members (PERM) and paying mem-bers (PERPM)), and financial indicators including profit (PERP) andrevenue (PERR) as performance measurements. Technical measure-ments were not considered due to their inaccuracy. Since the num-ber of members, revenue, and profit were different dimensions ofe-market performance, they were treated as formative indicators.

Table 2Profiles of responding e-markets.

Frequency Percent

Position of respondent Owner or CEO 36 28.13Senior manager 51 39.84Department manager 32 25.00Other employee 9 7.03Total 128 100.00

Industry Multiple industries 28 21.88Machinery 17 13.28Energy and electricity 10 7.81Others 73 57.03Total 128 100.00

Number of employees (size) 0–20 36 28.1321–50 31 24.2251–100 32 25.00101–500 29 22.66Total 128 100.00

Organization age (in years) 1–2 38 29.693–4 37 28.915–6 25 19.537–14 28 21.88Total 128 100

Revenue (PERR) (Unit: RMBa) 0–1 million 46 35.941–3 million 31 24.223–5 million 19 14.845–10 million 14 10.9410–30 million 10 7.8130–50 million 5 3.9150–100 million 3 2.34Total 128 100.00

Profit (PERP) (Unit: RMBa) 0–100,000 40 31.25110,000–500,000 36 28.13500,000–1 million 16 12.501–5 million 28 21.885–10 million 3 2.3410–50 million 5 3.91Total 128 100.00

Registered members (PERM) 300–3000 24 18.753001–10,000 19 14.8410,001–50,000 33 25.7850,001–100,000 13 10.16100,000 and above 39 30.47Total 128 100.00

Paid members (PERPM) 100–1000 80 62.501001–5000 30 23.445001–10,000 9 7.0310,001–30,000 4 3.1330,001–50,000 3 2.3450,000 and above 2 1.56Total 128 100.00

a Exchange rate between USD and RMB is approx. 1: 6.7.

4.1.2. Control variablesWe included several variables used in prior research as control

variables:

� Organization size (Size) was measured by the number ofemployees.� Organization age (Age) (in years) captured learning effects and

the early mover advantage of e-markets.� Type of e-market (WT) measured whether an e-market was a

comprehensive or an industrial e-market (coded as 1 or 0,respectively). Organizational age, size and type (comprehensivee-markets) were expected to positively correlate withperformance.� Regional e-commerce maturity (REM), was assessed using the

regional net entrepreneur development index produced by Alib-aba (Aliresearch 2010). This comprehensive index included six-teen ite-markets covering many aspects of e-commercedevelopment in each province of China, including size, active-ness of online vendors (net entrepreneurs), and e-commerceservice and infrastructure development in each region.� Industry structure (IS) Industry structure measured whether the

industry targeted by the e-market was concentrated or frag-mented (coded as 1 or 0).� Targeted client size (Csize): The size of major targeted customers,

measured by a five-level interval scale of revenue. e-marketstargeting larger clients are likely to generate more revenue.

4.2. Instrument development and survey

An exploratory approach was used for instrument validation.Initial interviews were conducted to provide face and contentvalidity. Executives from five leading e-markets were interviewed.A semi-structured questionnaire that included both market oppor-tunity and organizational capability perspectives was developed toguide the interviews. Subjects were asked about the importanceand relevance of each perspective and the relevant constructsinfluencing the success of an e-market. The interviews also in-cluded open questions about what other factors might contributeto e-market success. Subjects were also asked how best to measuresuccess factors they identified in the e-market context. Interviewswere conducted by telephone, lasting between one and one and ahalf hours. Conversations were recorded and transcribed for fur-ther analysis. The purpose of the interviews was to validate the rel-evance of the instrument ite-markets to the proposed constructs,and the relevance and importance of the constructs to the researchquestion. This process enhanced the content validity of theresearch.

The preliminary measures were pre-tested with 56 B2B e-mar-kets, and validated using principal components analysis and confir-matory factor analysis, following published guidelines (Straubet al. 2004, Straub and Carlson 1989). The preliminary instruments

were then further adjusted. For example, one item, SD3, was de-leted from the service depth measure due to its low reliability.

The final questionnaire, as presented in the Appendix, wasadministered to a list of 524 Chinese B2B e-markets, provided bythe well-known Chinese e-commerce media company, Ebrun. Thequestionnaire was posted on a website, and emails sent to all ofthe contacts to invite their participation, requesting that the surveybe completed by a manager familiar with e-market operations.Only 36 companies completed the questionnaire within one weekas a result of the first email contact. Follow-up telephone callswere made twice to the rest of the e-markets to urge them to com-plete it. To motivate subjects, a ticket to a SME conference hostedby eBrun was awarded to those who completed the questionnaire.Altogether, 145 of the 524 B2B e-markets completed it. The web-sites of participating companies were then checked, and fifteencompanies that did not fit our specifications were deleted fromthe survey, for example, those that supported B2C transactions orwere an affiliate website of a major B2B company.


Outlier checks were further performed using Mahanalobis dis-tance (Tabachnik and Fidell 2001). Two cases had exceptionallyhigh Mahanalobis distance values, and were checked by callingthe company respondents and double-checking with other experts.The two cases were found to be abnormal and therefore removed.The final count was 128 valid questionnaires, for a response rate of24%.

To check non-response bias, t-tests were performed on the basicvariables listed in Table 2, to see whether there were significantdifferences between the initial 36 responses and the rest afterincentives were offered. There were no significant differences be-tween the two groups. Additionally, because not all companies inthe sample frame provided all the basic information required, addi-tional information was retrieved on two variables for each e-mar-ket from Alex.com: Alex website traffic ranks and number of in-links. Two t-tests were conducted to see whether the sampledcompanies differed from other companies in the sample frame.The test results showed that the two groups were not significantlydifferent.

5. Data analysis and results

5.1. Descriptive statistics

Table 2 also shows basic information about the responding e-markets. In the survey, 77% of the e-markets were small companieswith fewer than 100 employees, and 78% had existed less than se-ven years. Furthermore, 86% had annual revenue less than RMB10million (US$1.5 million). This threshold is called ‘‘the growth ceil-ing,’’ which was very hard to surpass, according to practitionerswhom we interviewed. Although Chinese B2B e-markets have largenumbers of registered users (more than 100,000), 86% of the sur-veyed e-markets had fewer than 5000 fee-paying members. Theypaid various fees, such as subscription and advertisement fees.

Fig. 2 shows four types of services provided by the e-marketssurveyed. Supplier directories and product catalogues, web storehosting, advertisements, and industry news were the most

System integration

Firm internal IT services

Building company database

Website building

Building forum and community

Financial services

Sales and procurement

Evaluation services

Auctions or tendering services

Logistics

Online payment

Online management

Commodity exchange

Advertising

Consulting services

Industry analysis report

Industry news

Web store hosting

Supplier directory and product catalogue

0.00%

00.01%

00.02%

Fig. 2. Services provided

frequently provided services, and can be considered the basic ser-vices for e-markets operating in China. Website building, purchas-ing and selling-related consulting services, and industry analysisreports were among the next tier of popular services. Only 17%of the e-markets support online payments, which is consideredto be a key and in-depth functionality in supporting online trans-actions. Table 3 shows the correlation coefficients among the vari-ables studied.

5.2. Measurement model

Three latent variables with reflective indicators: service depth(SD), human resource capability (HRC), and entrepreneurial orien-tation (EO), were validated in terms of convergent validity, dis-criminant validity, and reliability.

5.2.1. Convergent validityThis ensures that all ite-markets measure a single underlying

construct (Bagozzi and Fornell 1982). Two decision criteria wereproposed by Straub et al. (2004). First, in principal componentanalysis (PCA), the loading of each indicator should be at least0.4, and ite-markets that do not load properly should be droppedfrom the instrument. Table 4 shows the results of the PCA test.One item, EO4, due to its high loading on a fourth factor that washard to explain, was dropped.

Second, indicators should pass the confirmatory factor analysis(CFA) test used in structural equation modeling. Major fit indexesshould pass the respective criteria, with an item loading higherthan 0.707, so that half of the variance is captured by the latentconstruct. In this research we ran a CFA test with LISREL software(version 8.7). Tables 5 and 6 show the results of the CFA test afterEO4 was removed, and all indicators met the above-mentionedcriteria.

5.2.2. Discriminant validityThis was assessed in the following three ways: (1) As shown in

Table 4, no cross loadings in the PCA test exceeded the criteria of

00.03%

00.04%

00.05%

00.06%

00.07%

00.08%

00.09%

by B2B e-markets.

Table 3Correlations among variables with Pearson correlations.

Mean Std dev Size Age REM CSize PERPM PERM PERP PERR EA Msize SW SDb EOb HRCb

Size 68.84 240.18 1Age 4.52 2.94 .235** 1REM 1.18 0.41 0.014 �.257** 1Csize 1.64 0.97 0.125 .267** 0.125 1PERPM 1.64 1.08 .493** .382** 0.053 .192* 1PERM 3.19 1.48 .231** .416** 0.049 .206* .412** 1PERP 2.48 1.39 .301** .374** 0.098 .425** .551** .452** 1PERR 2.52 1.62 .314** .442** 0.027 .400** .600** .406** .828** 1EA 2.72 1.02 0.15 0.064 0.063 0.143 .351** 0.147 .221* .294** 1Msize 3.62 1.73 0.129 0.11 0.071 0.126 .274** .292** .396** .383** 0.163 1SW 7.08 3.05 .180* 0.002 0.119 0.065 0.124 .317** .303** .260** .198* .191* 1SD 5.19 1.33 0.116 0.124 0.054 0.17 0.104 0.058 0.17 .192* 0.141 0.137 .197* 1EO 6.01 1.15 0.024 �0.065 0 �0.096 �0.025 0.077 0.035 0.023 0.056 0.075 .229** .433** 1HRC 5.72 1.07 0.067 0.117 0.023 0.16 0.173 .250** .222* .214* .177* 0.08 .300** .269** .527** 1WTa 0.22 0.42 – – – – – – – – – – – – – –ISa 10.29 8.97 – – – – – – – – – – – – – –

* Significant at the .05 level.** significant at the .01 level.a They are categorical dummy variables, thus correlations are not provided.b Correlations of composite variables are calculated using the average value of ite-markets.

Table 4Properties of the PCA for reflective constructs.

Component

HRC EO SD 4

HRC1 0.80 0.21 0.01 0.15HRC2 0.88 0.29 0.12 0.10HRC3 0.88 0.10 0.12 �0.10HRC4 0.86 0.31 0.12 0.12EO1 0.30 0.89 0.15 �0.06EO2 0.20 0.82 0.21 0.24EO3 0.27 0.86 0.21 �0.06EO4 0.11 0.04 0.05 0.98SD1 0.08 0.21 0.94 0.02SD2 0.14 0.20 0.93 0.05

The numbers are made bold in table 4 to indicate that these numbers are higherthan the threshhold value 0.5, and the items corresponding to these numbersbelong to the same factor.

Table 5Properties of the CFA for Constructs.

Standardizedparameter estimate

t-value

HRCHRC1 0.81 10.03HRC2 1.06 13.99HRC3 1.05 10.61HRC4 1.19 13.29

EOEO1 1.17 13.76EO2 1 10.41EO3 1.13 12.51

SDSD1 1.25 10.87SD2 1.3 11.45

Table 6Goodness of fit indices for the revised measurement model.

Goodness of fit indices Revised model Criteria

v2 (d.f.) 40.93 Smallera

d.f. 24v2/d.f. 1.71 <3.0Standardized RMR 0.035 <0.05RMSEA 0.068 0.05–0.08NFI 0.97 >0.9CFI 0.99 >0.9GFI 0.94 >0.9AGFI 0.88 >0.8Number of latent variables 3Total number of ite-markets 9

a The smaller, the better.

Table 7Assessment of discriminant validity: confidence interval and AVE test.a,b

HRC EO SD

HRC 0.89EO 0.59 0.89

(0.47, 0.71)SD 0.31 0.44 0.96

(0.13, 0.49) (0.28, 0.6)

a The square root of the variance shared between a construct and its ite-marketsis in the diagonal of the matrix.

b The correlations and their confidence intervals are reported by LISREL. Thecorrelation numbers may not correspond to Table 3, which was calculated in SPSSusing the average value of ite-markets.

Table 8Internal consistency assessment of constructs.

Cronbach’s a Composite reliability AVE

HRC 0.91 0.94 0.80EO 0.90 0.92 0.79SD 0.93 0.96 0.93


0.4, thus the retained constructs meet the first criteria of discrim-inant validity. (2) As per Fornell and Larcker (1981), the correla-tions between ite-markets in any two constructs should be lowerthan the square root of the average variance shared by ite-marketswithin a construct. As shown in Table 7, Fornell and Larker’s crite-ria were met. (3) The confidence interval test to assess discrimi-nant validity between two factors involved calculating aconfidence interval of plus or minus two standard errors aroundthe correlation between the factors, and determining whether thisinterval included 1. If it does not include 1, discriminant validity is

demonstrated (Anderson and Gerbing 1988). Discriminant validityis acceptable since no range within any bracket includes the value1. (See Table 7.)

5.2.3. Internal consistencyInternal consistency was assessed by computing Cronbach’s ,

composite reliability, and the average variance extracted (AVE)


(see Table 8). All Cronbach a and composite reliabilities exceededNunnally’s criterion of 0.7 (Nunnally 1978) while the AVEs forthese constructs were all above the recommended threshold valueof 0.5 (Hair et al. 1998).

5.2.4. Common method biasThe extent of common method bias was assessed with the fol-

lowing tests. First, the correlation matrix of reflective constructsdid not indicate any highly correlated factors (the highest correla-tion being 0.527), whereas common method bias would have re-sulted in extremely high correlations (q > .90) (Pavlou et al.2007). Second, the dependent variables, the number of members,revenues, and profits, were measured using different response for-mats including both objective and subjective measures. This con-tributed to the attenuation of common method bias (Podsakoffet al. 2003). Third, the Herman one factor test was conducted byloading all the variables in this study into an exploratory factoranalysis (Podsakoff et al. 2003). The test result shows only a slightsyndrome of common method since the highest factor explained47% of the variance among all reflective measures.

Fourth, the highest factor from the principal component analy-sis was added to the PLS model as a control factor on the depen-dent variable (Dong et al. 2009, Pavlou et al. 2007). This controlfactor was not significant, and the addition of this factor in thePLS model did not change the results of any path coefficients. Final-ly, following Liang et al. (2007), we added a method variable to thePLS model. As such, the variance of a specific observed indicatorwas partitioned into three components: trait, method, and randomerror. Among the nine weights from the method factor to indica-tors, only two were significant at the 0.05 level, indicating the exis-tence of a very small common method bias. However, the additionof a method factor did not change the significance level of any pathcoefficients in the PLS model. These tests show a relatively minoreffect of common method bias, but controlling for the method fac-tor did not change PLS data analysis results.

5.2.5. Formative scale validationThe performance of e-markets (PER) was measured as a forma-

tive scale. The following tests were done to check the constructvalidity of PER. First, the PLS weight for each indicator was checkedto see whether it could explain the PER results with significance.The results suggest that one indicator profit (PERP) was not signif-icant (p-value of 0.55), and should be removed from the construct.Second, we followed the approach suggested by Loch et al. (2003)to calculate item-to-construct correlation. A composite score wascreated by multiplying values by their individual PLS weights,and summing them for each construct. An item would be consid-ered to have no convergent validity on its construct if it was notsignificantly correlated with the composite value. The correlationanalysis showed that they were all significantly correlated withPER, as the Pearson coefficients were 0.901, 0.826, 0.723, and0.755 for PERR, PERP, PERM and PERPM, respectively, all significantat the 0.05 level.

Third, we checked multi-collinearity among ite-markets by cal-culating the variance inflation factors (VIF), following the methodprescribed in the literature (Bollen and Lennox 1991, Tabachnikand Fidell 2001). The VIF indices for PERR, PERP, PERM and PERPMwere 3.509, 3.368, 1.320, and 1.658, respectively. The VIFs for PERRand PERP were higher than the recommended cut-off value of 3.3(Diamantopoulos and Siguaw 2006), but less than 10 (Tabachnikand Fidell 2001). This result indicates a moderate multi-collinear-ity between PERR and PERP. Since we separated PERP from PER inlater data analysis, we also examined the multi-collinearity ofPER measured, only by three ite-markets (PERR, PERM and PERPM).The VIF values for all three ite-markets were far below the thresh-old of 3.3, showing no multi-collinearity proble-markets. The

mixed evidence suggested that we should be careful in data anal-ysis and interpretation. Therefore, we present PLS results usingeither all four ite-markets or profit only to measure PER.

6. The structural model

PLS, as implemented in SMART PLS Version 2.0, was used pri-marily for the following two reasons: (1) Compared with structuralequation modeling as implemented in LISREL, whose primaryemphasis is on overall model fit, PLS is a more exploratory, data-driven, and prediction-oriented method. PLS fits the purpose of thisresearch better than LISREL since the goal of our research is topredict e-market performance. (2) PLS allows for both reflectiveand formative scales to be modeled.

We performed four PLS analyses. In Model 1, both independentand control variables were regressed on a composite performancemeasure of e-market performance (PER). In Model 2, we excludedthe control variables to see how much variation the main con-structs explained. Because profit did not load properly on PER asindicated by Model 1, and exhibited multicollinearity with itemPERR, we performed two separate tests, using profit (Model 4)and PER with the remaining three ite-markets (Model 3) as thedependent variables.

The results of the PLS analysis of Model 1 are shown in Fig. 3and Table 9. This model includes all predictors and explains 59%of the variance in e-market performance.

Within the two dimensions of service capability, service width(SW) has a significant effect on e-market performance. The coeffi-cient for SW is 0.15 and the t-value is 2.25 (p < 0.05). However, ser-vice depth (SD) is not significantly correlated with e-marketperformance. Its coefficient is negative at �0.04 but not significant.Therefore, the Service Width and e-Market Performance Hypothe-sis (H1) is supported, whereas the Service Depth and e-Market Per-formance Hypothesis (H2) is not.

The hypothesized direct effect of human resource capability(HRC) (the Human Resources Capabilities and e-Market Perfor-mance Hypothesis, H3) on e-market performance is supported.HRC is a significant predictor of e-market performance (p < 0.05).However, entrepreneurial orientation (EO) is negatively related toe-market performance, but the coefficient is not significant. There-fore, the Entrepreneurial Orientation and e-Market Performance(H5) is not supported.

The hypothesized effects of HRC (the Human Resources Capabil-ities and Service Width/Depth Hypotheses, H4a/H4b) and EO (thee-Market Entrepreneurial Orientation and Service Width/DepthHypotheses, H6a/H6b) on service capability received mixed evi-dence. First, both HRC and EO contribute significantly to servicewidth. The coefficients for HRC and EO are 0.21, and 0.1, and thet-values of these coefficients are 1.93 (very close to the critical va-lue of 1.96 for p < 0.05), and 3.79 (p < 0.01). Second, HRC is not asignificant explanatory factor of SD, while EO is. The coefficientsfor HRC and EO are 0.03 and 0.4, and the t-values of these coeffi-cients are 0.49 and 3.98 (p < 0.01).

The hypothesized direct effects of market opportunity one-market performance are supported. The size of potential B2Bservice market (Msize) and e-commerce awareness (EA) are signif-icant predictors of e-market performance, as hypothesized in theMarket Size and e-Market Performance Hypothesis (H7) andthe e-Commerce Awareness and e-Market Performance Hypothesis(H9). The coefficients for Msize and EA are 0.26 and 0.12, and thet-values of these coefficients are 6.4 (p < 0.01) and 2.21 (p < 0.05).

The Market Size and e-Market Service Width/Depth Hypotheses(H8a/H8b) and the e-Commerce Awareness e and e-Market ServiceWidth/Depth Hypotheses (H10a/H10b) predict positive relation-ships among Msize, EA and service provision capabilities. The

Fig. 3. Results of PLS analysis (Model 1).

Table 9Path Coefficients of PLS Results.

Model 1 Model 2 Model 3 Model 4

Weights t-value Weights t-value Weights t-value Weights t-value

dep. var: PER (4 ite-markets) dep. var.: PER dep. Var.: PER (3 ite-markets) dep. var.: Profit

EO �0.01 0.22 �0.19*** 3.18 �0.01 0.24 �0.02 0.35HRC 0.10** 2.53 0.24*** 4.4 0.10** 2.49 0.07 1.47SW 0.15** 2.25 0.19** 2.26 0.14** 2.18 0.18** 2.85SD �0.04 1.02 0.08 1.28 �0.04 0.95 �0.02 0.37Msize 0.26*** 6.4 0.35*** 6.22 0.25*** 6.00 0.27*** 4.82EA 0.12** 2.21 0.15** 2.03 0.13** 2.55 0.04 0.6REM 0.10** 2.41 0.11** 2.72 �0.01 0.48WT 0.12** 2.62 0.13** 2.68 0.05 0.72Csize 0.23** 3.19 0.21*** 3.38 0.36*** 6.61IS �0.08 1.34 �0.07 1.26 �0.12** 2.31Size 0.17 1.52 0.17 1.62 0.12 1.3Age 0.42*** 7.7 0.43*** 8.39 0.24*** 3.82R2 0.59 0.32 0.58 0.44

Sub-DV: SD Sub-DV: SD Sub-DV: SD Sub-DV: SD

EA 0.1 1.64 0.1 1.58 0.1 1.62 0.1 1.55EO 0.40*** 3.98 0.40*** 3.72 0.40*** 3.75 0.40*** 4.07HRC 0.03 0.49 0.03 0.46 0.03 0.49 0.03 0.5Msize 0.09 1.5 0.09 1.42 0.09 1.43 0.09 1.41R2 0.21 0.21 0.21 0.21

Sub-DV: SW Sub-DV: SW Sub-DV: SW Sub-DV: SW

EA 0.13 1.95 0.13** 2.01 0.13** 2.13 0.13** 2.08EO 0.10 1.93 0.10 1.95 0.10 1.95 0.10 1.91HRC 0.21*** 3.79 0.21*** 3.72 0.21*** 3.73 0.21*** 3.74Msize 0.15** 2.59 0.15** 2.59 0.15** 2.70 0.15** 2.44R2 0.14 0.14 0.14 0.14

** Significant at the .05 level.*** significant at the .01 level.


results indicate that EA is positively related to SW but not SD. Thecoefficients of SW and SD are 0.13 and 0.1, respectively, and the t-values of these coefficients are 1.95 (very close to the critical valueof 1.96 for p < 0.05) and 1.64. The predicted relationship betweenMsize and SW (the Market Size and e-Market Service WidthHypothesis, H8a) is supported (coefficient is 0.15, p < 0.05), while

the predicted relationship between Msize and SD (the The MarketSize and e-Market Service Depth Hypothesis, H8b) is not supported(coefficient and t-value of 0.09 and 1.5, respectively).

In sum, among the six variables (Msize, EA, HRC, EO, SD, and SW)that were hypothesized to explain e-market performance directly,four are supported and two (SD and EO) are not. Among all the


indirect effects, all factors accounted for SW to a certain degree,and only EO significantly explained service depth.

The control variables, organization age, regional e-commercematurity, and the size of clients, are positively and significantly re-lated to e-market revenue. Organizational age turns out to be veryimportant (with a coefficient of 0.42 and a t-value of 7.7), suggest-ing a first-mover advantage in achieving higher performance.Organizational type is also significantly related to e-market perfor-mance, indicating that comprehensive e-markets have better per-formance than single industry e-markets. Organization size andmarket structure show no significant relationships with e-marketperformance.

Model 2, as shown in Table 9, which includes only the mainconstructs, explains 32% of e-market performance variance. Noneof the previous conclusions changed except for EO. EO is still neg-atively correlated with e-market performance, but the coefficientbecomes significant. This seems to imply that EO can contributeto e-market performance through improved service capability,but on the other hand, its risk effects may be detrimental to e-mar-ket performance. For example, Laseter and Bodily (2004) foundthat e-markets without strategic continuity are unlikely to havehigh performance.

Model 3 is a check of the robustness of the results of Model 1. Itindicates that the results of Model 1 do not change significantlyafter removing PERP from the composite dependent variable. Infact, the relationships among e-commerce awareness, entrepre-neurial orientation and SW become stronger.

In Model 4, we included only profit as the dependent variable.Compared to Model 1, HRC becomes less significant. Among allthe control variables, Csize becomes the most significant variable,and market structure becomes a significant predictor of profit.However, WT becomes insignificant.

7. Conclusion

7.1. Key findings

The integrated model based on both the organizational capabil-ity and market opportunity perspectives accounted for a large por-tion of the variance in B2B e-market performance. Moreimportantly, consistent with the market opportunity perspective,it was found that both market size and e-commerce awarenesshave a significant direct effect on e-market performance and anindirect effect through service width. From an organizational capa-bility perspective, we identified significant enabling factors of ser-vice capability, including entrepreneurial orientation and humanresource capability. The latter contributes to the performance ofe-markets directly, and indirectly through service width. Table 10

Table 10Summary of hypothesis testing results.

# Hypotheses

1 Service width is positively related to e-market performance2 Service depth is positively related to e-market performance3 Human resource capability is positively related to e-market4a E-market human resource capability is positively related to4b E-market human resource capability is positively related to5 E-market Entrepreneurial orientation is positively related to6a E-market entrepreneurial orientation is positively related to6b E-market entrepreneurial orientation is positively related to7 The size of the markets in which B2B e-markets operate is p8a E-market market size is positively related to e-market servic8b E-market market size is positively related to e-market servic9 Industry e-commerce awareness is positively related to B2B10a Industry e-commerce awareness is positively related to the10b Industry e-commerce awareness is positively related to the

summarizes the results of hypothesis testing based on Models 1and 3.

Within the two service capabilities, service width is positivelyrelated to e-market performance, whereas service depth showsno significant effect on e-market performance. This finding is dif-ferent from Clasen and Mueller (2006) that service breath de-creases e-market performance, but agrees with Ordanini (2006)that offering complementary services enhances firm performance.This is probably because the Chinese market is still not generallyready to accept online transactions, and this market opportunityis ambiguous, compared with a growing and mature market. Anambiguous market is less able to yield significant revenues andprofits (Eisenhardt and Schoonhoven 1990).

Human resource capability was found to be an importantenabling capability for service width. This result contradicts theview that e-markets are impersonal, since operating e-marketsdoes require close interactions with customers. As one of the B2Be-market leaders and participant in our pilot interviews explained:‘‘We know each of our customers well. We are located next door toour customers. I know not only their boss’s name, but also hiswife’s and child’s names. We assign each of our staff members tokeep track of several customers. We know all that informationonce their cargo ship leaves the harbor.’’ Therefore, we argue thatclose interaction with customers requires high caliber employeesto build a quality customer interface. It is up to these employeesto detect industry subtleties, and to leverage information theygather in the field to design diversified services (Wiklund andShepherd 2003).

7.2. Theoretical contributions

This research contributes to the literature by building a theoret-ical model to explain the performance of B2B e-markets. It identi-fies important capabilities for B2B e-markets, including servicecapability and its enabling capabilities. This approach contrastswith prior research that emphasizes the importance of IT capabil-ities (Weill and Vitale 2002). The difference between our researchand that of Weill and Vitale (2002) can be attributed to the currentstage of Chinese B2B e-markets, which still focus on informationand complementary services. These services are labor-intensiveand relationship-based, which require high caliber human re-sources. When the B2B e-markets evolve into supporting transac-tions and supply chain infrastructure with sophisticated businessprocesses, the importance of IT capability may become moreprominent.

One of the key novel features of this research is the incorpora-tion of market opportunities to explain firm performance. Prior re-search already suggests that the market opportunity that a firm

Results

SupportedNot supported

performance Supportedservice width Supportedservice depth Not supportedits performance Not supportedits service width Marginally supportedits service depth Supportedositively related to their performance Supportede width Supportede depth Not supportede-market performance Supported

service width of an e-market Supportedservice depth of an e-market Not supported


exploits during its founding could result in a prolonged effect on itsperformance (Eisenhardt and Schoonhoven 1990). Linking marketopportunity to firm performance also provides guidance on identi-fication of attractive business opportunities, evaluated according totheir performance criteria, rather than the likelihood of a newstart-up, a criterion used in the current literature (Baron and Ens-ley 2006, Venkataraman 1997).

The integration of both market opportunity and organizationalcapability perspectives has proven to be helpful in explaining e-market performance. In an attempt to synthesize market opportu-nities theory and organizational capabilities theory, Foss et al.(2008, p. 26) commented that ‘‘there is limited systematic researchin management on the processes by which new resource servicesare discovered or created, and on how organizational structure,incentives, and governance mechanisms affect the creation anduse of valued attributes.’’ This research has addressed this issueby showing that firm capabilities, such as human resource capabil-ity and entrepreneurial orientation, can enable market opportunityrecognition and exploitation via B2B e-market service capabilitybuilding.

7.3. Managerial implications

This research has several managerial implications for B2Be-market operators. First, targeting a market with a large sizeand high e-commerce awareness could result in improvede-market performance. Second, in light of the importance of servicewidth, B2B e-markets should focus on providing more informationand complementary services to participating firms, before movingto the next stage of supporting transactions and supply chain man-agement. More importantly, e-markets should focus on recruitinghigh quality employees or providing advanced training to existingstaff. Cultivating an entrepreneurial orientation culture is alsoimportant for developing service depth.

7.4. Limitations and directions for future research

There are several limitations to this research. First, we mainlyused revenue and profit as performance measures. Financial mea-sures like these may not offer an accurate assessment of firm per-formance, given that they measure short-term performance only.Second, this research has a relatively narrow scope, focusing onthird party B2B e-markets only. As a result, what explains the per-formance of consortia and private e-markets remains unknown.Third and lastly, the research model was validated in China.Although our results might be generalized to third party e-marketsthat mainly support aggregation, as well as transactions to a cer-tain degree, the differences between Chinese and western e-mar-kets were not addressed.

These limitations also suggest future research opportunities.First, more research is needed to develop measures that capturelong-term performance of B2B e-markets, such as customer feed-back and satisfaction. Second, a comparison of organizationalcapabilities across different types of e-markets can also provideuseful guidance. For private and consortium e-markets, IT infra-structure may be more important for supporting complicatedtransactions and supply chain management. Therefore, differenttypes of e-markets will need somewhat different sets of re-sources and capabilities. Third, a comparison of cultural differ-ences between e-markets in western countries and in China orAsian countries can be an interesting further direction. In partic-ular, understanding cross-cultural differences can provide guid-ance for internationalization strategies of e-markets, whichcould be beneficial due to the importance of market size to e-market development. For example, our results recommend the

provision of more services, but the content and weight of theseservices can differ.

In sum, this research has shown that the incorporation of bothorganizational capability and market opportunity perspectives ispromising for explaining the performance of business-to-businesselectronic marketplaces, based on a large-scale survey in a keyemerging economy. In addition to filling some voids in the extantliterature, this research has identified a potentially fruitful pathfor studying electronic marketplaces in other contexts. The pro-posed future directions for research could lead to a more compre-hensive and refined understanding of B2B e-markets.

Acknowledgements

This research was supported partially by the National NaturalScience Foundation of China under Grants 70902078 and70888001, a Young Faculty fund from the Ministry of Educationof China (20090004120001), and Renmin University SurveyResearch Foundation.

Appendix A. Appendix: Measurement item for key researchvariables

A.1. Market Size (Msize)

By your estimation, what is the size of the industry that yourwebsite is in (seven levels of measurement):

1. 5 million-10 million2. 10.01 million -30 million3. 30.01million-50 million4. 50.01million-100 million5. 500.01million-1 billion6. 100.01million-500 million7. 1 billion and above

A.2. E-Commerce Awareness (EA)

Company e-commerce awareness in this industry is (a five pointscale ranging from very low to very high)

A.3. Entrepreneurial Orientation (EO)

To which degree you agree with the following statements (a se-ven point scale ranging from strongly disagree to strongly agree):

� EO1: We focus on product and service innovation� EO2: We are willing to collaborate with competitors� EO3: We have a strong incentive to grow� EO4: We always follow our competitors

A.4. HR Capability (HRC)

Compared to other companies in your industry, does your com-pany have a weak or strong position in terms of (a seven point scaleranging from extremely weak to extremely strong)

� HRC1: Staff with a positive commitment to the company’sdevelopment� HRC2: Staff willing to contribute with ideas for new products

and services� HRC3: Special expertise in marketing� HRC4: Highly productive staff


A.5. Service Depth (SD): Transaction Capability

(a seven point scale ranging from strongly disagree to stronglyagree)

� SD1: Our e-commerce website allows customers to completetheir orders online easily� SD2: Our e-commerce website allows customers to complete

their orders online securely� SD3⁄:Our e-commerce website allow users to track their orders

online

⁄ This item was dropped from the measurement in the finalsurvey.

A.6. Service Provision (SW)

The services your websites provide currently (yes/no)

� Consulting and intelligence related services:� Supplier directory and product catalogue� Web store for members� Industry dynamics and news� Industry analysis report� Industry procurement, sales, or technology consulting services� Advertisement

Transaction related services:

� Commodity exchange� Order administration and analysis� Online payment� Product delivery and logistics� Auction or tendering services

Value added services:

� Product evaluation service� Sales and procurement delegation� Financing service� Business forum and communities

Informatization services:

� Website building� Building company database� Company internal IT service (such as ERP, MRP and accounting

book system)� System integration between trading partners

A.7. Performance of e-markets (PER)

PEFR: The revenue of your company in 2008 was________(RMB) .

0–1 million, 1–3 million, 3–5 million, 5–10 million, 10–30 mil-lion, 30–50 million, 50–100 million, 100 million and above

PEFP: the profit of your company in 2008 was ________ (RMB)0–100,000, 100,000–500,000, 500,000–1million, 1–5 million,

5–10 million, 10–50 million, 50 million and abovePEFM: the number of registered members in your website:

_____________PEFPM: the number of fee paying members in your

website:________________Control Variable:Organizational size (SIZE)

The number of employees in your company: _____________Organization age (AGE)In which year was your organization launched: ___________The major targeted customers of your Website are (CSize):

� Small companies with less than RMB 10million in sales revenue.� Small companies with sales revenue between 10 and 30 million� Medium sized companies with sales revenue between 30 and

100million� Medium sized companies with sales revenue between 100 and

300million� Large companies with sales revenue above 300million

The market structure can be described as (IS):coded as 0 (frag-mented) and 1 (dominated)

� Fragmented on both sides� Dominated on one side and fragmented on the other side.

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