factors affecting the adoption of b2b e-commerce technologies
TRANSCRIPT
Electron Commer ResDOI 10.1007/s10660-013-9110-7
Factors affecting the adoption of B2B e-commercetechnologies
Ismail Sila
© Springer Science+Business Media New York 2013
Abstract The objective of this study is to analyze the factors affecting the adop-tion of Internet-enabled business-to-business electronic commerce (B2B EC) and testtheir applicability in different contexts. We used 275 responses from an online sur-vey of North American firms and tested our hypotheses with Multiple Regressionand Analysis of Variance (ANOVA). We found that scalability is the biggest con-tributor to B2B EC usage. We also compared each adoption factor across adoptersand nonadopters of B2B EC. Six of the nine adoption factors tested distinguishedadopters of B2B EC from nonadopters. Then we analyzed the effects of these fac-tors on adoption using several contextual variables, including firm size, firm type,management level of respondents, and country of origin of firms. The results showedthat all of the contextual variables, except country of origin, influenced some of theadoption factors. Managers can use the findings of this study to understand whichfactors will most likely facilitate the implementation of B2B EC and be prepared tomanage the effects of these factors on their initiatives more effectively. Many of thestudies in this area have not tested the effects of contextual variables on B2B ECadoption. Thus, we contribute to the limited literature on this issue. The study showsthat the technology–organization–environment (TOE) framework provides a strongfoundation for the study of B2B EC. It also provides evidence that this frameworkis strengthened further when contextual variables are integrated into the theoreticalmodel.
Keywords B2B electronic commerce · Supply chain management · Internet ·Adoption factors · Interorganizational information systems · Informationtechnology · Contextual variables
I. Sila (�)Girne American University, P.O. Box 388, Kyrenia, Cypruse-mail: [email protected]
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1 Introduction
A major portion of online sales still takes place between firms. In 2009, the use ofB2B EC by US manufacturers and merchant wholesalers was more widespread thanretailers or selected service businesses and accounted for 91 % of all online transac-tions. B2B activities of manufacturers were the highest among all sectors (account-ing for 42 % of total shipments or $1,862 billion), followed by merchant wholesalers(manufacturing sales branches and offices), whose e-commerce activities constituted23.4 % ($1,211 billion) of total sales [103]. An analysis of wholesalers suggested thatB2B EC still relies mainly on proprietary Electronic Data Interchange (EDI) systems[103]. In Canada, 61 % of the private sector sales are conducted by manufacturing,transportation and warehousing, wholesale trade, and retail trade sectors. Fifty-eightpercent of manufacturing firms and 50 % of wholesale firms use the Internet to buygoods or services, followed by 46 % of retailers [92].
There is also a positive outlook on the future of B2B EC in countries such as Chinaand India. In the first quarter of 2011, B2B EC sales increased by 7.7 % from 2.7 bil-lion yuan in the previous quarter to 2.9 billion yuan ($444 million). Compared to oneyear earlier, the revenue was up by 40.9 % [112]. In India, even though firms have notyet taken full advantage of EC [104], B2B sales grew by 30–40 % in 2008 and werepredicted to play a big role in multimedia, entertainment, and fashion industry [2].Overall, the global B2B EC transactions reached $12.4 trillion in 2012, compared to$3.4 trillion in 2005 [67].
In this study, one of our objectives is to identify the factors that determine firms’decision to adopt or not to adopt B2B EC to conduct B2B transactions. We alsoattempt to determine whether various contextual variables play a significant role inmaking this decision. For the purposes of this study, we use the term B2B EC forall Internet-enabled B2B technologies that allow supply chain partners to buy andsell products and share information. We use this term interchangeably with phrasessuch as Internet-based interorganizational systems, e-business, e-commerce, and Webtechnologies, provided that they involve transactions between firms.
Using survey data from North American firms and utilizing statistical techniques,including ANOVA and regression for analysis, the study contributes to the existingliterature by showing that various contextual variables play an important role in theadoption of B2B EC technologies. It also provides strong evidence for the efficacyof the TOE framework for analyzing the factors that influence the adoption of thesetechnologies. The rest of the paper is organized as follows: the next section discussesrelated work in this area, followed by theoretical framework, methodology, and sta-tistical analyses. The article concludes with a discussion of findings and their impli-cations, as well as recommendations for future research.
2 Related work
Adoption factors in the interorganizational information systems literature In thegeneral IT literature, several studies have been conducted before to determine thefactors that affect the adoption of IT (e.g., Premkumar et al. [71]; Chwelos et al. [14];Teo et al. [97]). Among competing theories of IT adoption, including information
Factors affecting the adoption of B2B e-commerce technologies
richness theory, theory of communicative action, and structuation theory, innovationdiffusion theory has received the most attention from researchers [56]. In fact, it hasbeen widely used as a foundation in Electronic Data Interchange (EDI) research [14].Innovation diffusion theory provides several perceived innovation characteristics, in-cluding relative advantage, complexity, compatibility, observability, and trialability,that may either encourage or inhibit innovation adoption. These characteristics havebeen used in EDI adoption research by researchers such as Premkumar et al. [71].
Chwelos et al. [14] stated that since research based on innovation diffusion theoryonly dealt with technological factors (i.e., perceived characteristics of the technol-ogy) that affected adoption, most research on EDI adoption took an organizationalapproach, focusing on organizational and interorganizational factors in addition totechnological factors. The identification of organizational factors that influenced EDIdrew mainly from the organizational innovativeness perspective based on the worksof researchers such as Damanpour [18] and Premkumar and Ramamurthy [70].
Teo et al. [97] posited that institutional pressures also affect the adoption of sup-ply chain linkages. Drawing from institutional theory and using data from Singapore-based firms, the authors found that these institutional factors are strong predictors offinancial EDI adoption. Institutional theory suggests that organizations face pressuresto conform to practices and polices widely deemed to be legitimate in their institu-tional environments. Failure to do so may deny them the resources and social supportneeded to be competitive [21]. Although the institutional perspective has been usedby previous studies (e.g., Havema [36]; Han [33]; Goodstein [28]) to analyze the in-fluence of institutional environments on organizational structure and practices, Teoet al. [97] claim that they were the first to test the effects of institutional factors onthe adoption of financial EDI. These institutional factors are based on DiMaggio andPowell’s [21] three types of isomorphic pressures—coercive, mimetic, and normative.Mimetic pressures may force organizations to adopt the practices or innovations ofother organizations in their environments, whether they carry any technical value ornot, to gain social legitimacy. Coercive pressures entail formal or informal pressuresfirms face from other organizations such as governmental regulatory bodies, parentcorporations, or other organizations they are dependent on, which are more dominantin terms of the resources they own. These pressures force firms to adopt structuresor practices that serve the interests of the organizations exerting the pressure. Finally,normative pressures are those that may be exerted by suppliers, customers, and busi-ness, trade, and professional organizations to adopt a certain innovation [21]. Someof the previous studies (e.g., Gibbs and Kraemer [27]; Soares-Aguiar and Palma-Dos-Reis [86]) used institutional theory and the TOE framework together, becauseinstitutional factors supplement the environmental context of the TOE framework.
Adoption factors in the B2B EC literature In the B2B EC literature, although vari-ous factors have been proposed and tested by researchers to determine what factorsaffect the adoption of B2B EC, these factors have not always been consistent acrossstudies. Some of the previous studies that operationalized such factors are relevant toour analysis of the factors affecting the adoption of B2B e-commerce technologiesand are discussed in this section. Ranganathan et al. [76] argued that the key driversof the assimilation and diffusion of B2B EC consist of such organizational and ex-ternal factors as supplier interdependence, competitive intensity, IT activity intensity,
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managerial IT knowledge, as well as centralization and formalization of IT unit struc-ture. On the other hand, Barua et al. [10] used supplier and customer-related factorsas the determinants of the implementation of B2B EC. These factors had direct orindirect effects and included supplier readiness, supplier process alignment, supplier-side online information capabilities, system integration, customer process alignment,customer-side online information capabilities, and customer readiness.
An empirical study of European firms by Zhu et al. [121] reports that four inno-vation characteristics (relative advantage, compatibility, costs, and security concern)and four contextual variables (technology competence, organization size, competi-tive pressure, and partner readiness) drove e-business usage. Three of these factors(costs, security concern, organization size) had a negative effect on e-business us-age, whereas the rest of the factors was positively correlated with this variable. Inturn, e-business usage had a positive impact on e-business performance (a combinedmeasure of upstream communication, internal operations, and downstream sales). To-gether with the contextual factors, the study points to the importance of taking intoaccount economic and regulatory factors in predicting e-business usage.
Cho [12], who studied the factors affecting the adoption of third-party B2B portalsin the Hong Kong garment industry, find that the key factors affecting this decisionwere firm size, perceived benefits, perceived hindrances, and perceived external pres-sure. A study of small and medium-sized firms in Australia by Chong and Pervan [13]concludes that both internal and external environmental factors significantly affectedthe extent to which B2B EC were implemented. These factors consisted of perceivedrelative advantage, trialability, observability, variety of information sources, amountof communication with other firms, competitive pressure, and non-trading institu-tional influences (e.g., from governments, banks, consulting firms etc.).
3 Theoretical framework
Our review of 77 empirical studies in B2B EC adoption suggests that 47 of them(61 %) used at least one theory as a framework (see Table 1). The four most fre-quently used theories by these studies are innovation diffusion theory, the TOE frame-work, institutional theory, and the resource-based view (RBV), in descending orderof frequency of use, as shown in Table 1. Fichman [23] also argues that innovationdiffusion theory is the most often used theory by researchers in the IT adoption area.It includes several innovation characteristics such as relative advantage, complex-ity, compatibility, observability, and triability that may either promote or hamper theadoption of IT. According to Zhu et al. [120], Rogers’ [78] innovation diffusion the-ory is consistent with the TOE framework, since it includes three types of factors thatpredict innovation adoption in addition to these technological factors: leader charac-teristics (leader’s attitude toward change), internal characteristics of the organization(centralization, complexity, formalization, interconnectedness, organizational slack,and size), and external characteristics of the organization (system openness).
Another theory that has been widely used in the innovation adoption and the B2BEC adoption literature is the RBV. The RBV argues that firms have heterogeneousresources (valuable, rare, imperfectly imitable, and non-substitutable), which enablethem to achieve competitive advantage and superior long-term performance (e.g.,
Factors affecting the adoption of B2B e-commerce technologies
Table 1 Theories used in empirical research on factors that affect B2B EC adoption
Guiding theory Study
Innovation diffusion theory [13, 37, 44, 57, 72, 76, 109, 110, 114, 120, 123]
TOE framework [27, 57, 62, 83, 85, 98, 99, 119–121]
Institutional theory [8, 27, 38, 47, 58, 89, 111, 116]
Resource-based view [10, 43, 62, 91, 116, 119]
The technology acceptance model [2, 20, 59, 72, 114]
Transaction cost theory [43, 46, 89, 113]
The relational view [8, 16, 43]
Theory of planned behavior [1, 59, 72]
Socio-political theory [46, 47]
Interactionism [63, 96]
Theory of reasoned action [59, 72]
Dynamic capabilities framework [49]
Contingency theory [38]
Structuration theory [79]
Agency theory [84]
Systems engineering principles [32]
Governance theory [66]
Network effect theory [122]
The path dependency perspective [122]
Rational efficiency theory [25]
Bandwagon theory [25]
Organizational inertia theory [6]
The hierarchy of effects model [72]
Hofstede’s cultural dimensions theory [11]
Customer-supplier life cycle framework [48]
Wernerfelt [108]; Barney [9]). Based on various definitions of IT resources used inWade and Hulland [105], B2B EC can be categorized as “outside-in” resources (i.e.,those resources used to manage external relationships and related to market respon-siveness). The literature is in agreement that IT resources alone do not produce com-petitive advantage. Instead, they produce business value when they are combined andcoordinated with other organizational and environmental resources (Mata et al. [60];Wade and Hulland [105]). Daft [17] states that “. . .firm resources include all assets,capabilities, organizational processes, firm attributes, information, knowledge, etc;controlled by a firm that enable the firm to conceive of and implement strategies thatimprove its efficiency and effectiveness”.
Along with institutional theory, the TOE framework [102] is one of the mostfrequently used guiding theories in technology adoption research. It identifies threetypes of factors that affect technology innovation adoption: the technological context(e.g., availability, characteristics), organizational context (e.g., size, complexity ofmanagerial structures, communication processes, availability of slack resources), andenvironmental context (e.g., industry characteristics and market structure, IT infras-
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tructure, government regulation). In this study, we use the TOE framework becauseit is a well-defined framework. It is also consistent with Rogers’ innovation diffusiontheory [78].
3.1 TOE framework
Technological context The TOE framework can be used to study the diffusion ofvarious IS innovations, including technical tasks, those that support business admin-istration, and IS innovations that are integrated into the core business (e.g., B2B ECtechnologies) [94]. Internal technology resources such as infrastructure, technicalskills, as well as developer and user time are significant for successful IS adop-tion [51]. Teo et al. [98, 99] state that even though the TOE framework has beenwidely used by previous researchers, the specific factors within each characteris-tic (i.e., technological, organizational, and environmental characteristics) vary acrossdifferent studies. In our study, this context was operationalized by cost, complexity,network reliability, data security, and scalability.
Cost The interactive nature of the Internet gives firms access to global markets andenables them to reduce inventory, procurement, and coordination costs [120]. There-fore, the adoption of B2B EC along the supply chain can lead to big cost savings[95], encouraging firms to conduct electronic business. However, high costs associ-ated with the implementation of B2B EC can also be impediments to the adoption ofthese technologies [120].
Complexity Complexity is “the degree to which an innovation is perceived as rela-tively difficult to understand and use” [79]. Less complex innovations are more likelyto be adopted [79]. According to Lin [57], B2B EC is a complex innovation, be-cause it requires both technological adjustments such as combining the Internet plat-form with the existing IT infrastructure, as well as administrative adjustments suchas changes in organizational processes of supply chain partners. Other authors (e.g.,Gallear et al. [26]) contend that the implementation of B2B EC is less complex com-pared to EDI.
Network reliability Network reliability deals with the ability of a firm to success-fully transfer critical business applications to and from its supply chain partners overthe Internet [88]. Outdated web servers and applications can prevent firms from trans-ferring confidential and critical data reliably over the Internet [87].
Data security Data security refers to security issues associated with transactionsconducted over the Internet. B2B EC are based on open standards and are morevulnerable to security breaches compared to legacy systems such as EDI that arebased on VAN [87]. Therefore, some firms may find it too risky to use these systems[64, 90].
Scalability Scalability refers to the economies of scale and scope provided by theInternet. As a result of adopting B2B EC, firms can expand their market reach andcreate new markets for their products [53], as well as integrate with numerous entitiessuch as customers, suppliers, retailers etc. [45].
Factors affecting the adoption of B2B e-commerce technologies
Organizational context Previous studies in this area have tested various organiza-tional variables, including organizational readiness for EDI adoption (e.g., Iacovouet al. [39]), CEO characteristics, business size, and employee’s IS knowledge for ISadoption (e.g., Thong [100]), as well as internal need and top management supportfor EDI adoption (e.g., Premkumar and Ramamurthy [70]). In this study, we usethe following organizational factors for B2B EC adoption: top management support,firm size, firm type, and management level. Top management support is a subjectivemeasure that is posited to have a direct effect on adoption. On the other hand, thelast three organizational factors, which consist of objective demographic informationabout firms’ organizational characteristics, have been used as contextual variablesin our study in line with the general literature. Gibbs and Kramer [27] suggest thatthe TOE framework does not include interorganizational factors such as trust (e.g.,Hart and Saunders [35]), which some interorganizational systems researchers used.Therefore, we have added this factor to supplement the TOE framework.
Top management support A positive attitude on the part of managers toward changecreates an organizational environment that is receptive to innovation. Top man-agement commitment support for innovation is particularly important during theimplementation stage, when coordination across organizational units and conflictresolution are necessary [18]. Top management support is critical for the success-ful adoption of interorganizational systems [30]. Research on EDI adoption (e.g.,Finnegan et al. [24]; Premkumar and Ramaurthy [70]) also provides support for theimportance of this factor.
Trust Trust is a critical factor in most transactions between buyers and suppliers [4].This makes it important for supply chain partners to develop mutual trust before B2BEC technologies are adopted [90]. According to Hart and Saunders [35], mutual trustis important for EDI adoption, since it encourages firms to make the needed invest-ments for adoption and discourages them from engaging in opportunistic behavior.
Firm size Firm size is one of the frequently-cited factors in the literature for influ-encing IT adoption (e.g., Patterson et al. [69]; Wang et al. [107]). Large firms oftenpossess certain advantages that enable them to adopt B2B EC technologies more eas-ily than small firms, such as more slack resources, economies of scale, higher risktolerance, and more power over trading partners [120].
Firm type Evidence on the effect of firm type on B2B EC use has been largely mixedin the literature. For example, Rutner et al. [82] found that nonmanufacturing firmsutilized B2B EC more than manufacturing firms to sell to and communicate with theirtrading partners. On the other hand, studies by Meroño-Cerdan and Soto-Acosta [61]and Feng and Yuan [22] reported no such differences.
Management level Several types of resources—economic and financial bresources,human resources, business resources, and slack resources—are potential predictorsof B2B EC adoption. Several studies (e.g., Molla and Licker [63], Corsten and Ku-mar [16]) also looked at how various skills possessed by a firm’s human resources
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Fig. 1 General conceptual framework
affect adoption. In general, these studies indicated that the availability of strong hu-man resources is important in technology adoption. Wagner et al. [106] also reportedthat managers who have great skills in e-business are more likely to facilitate theimplementation of B2B EC.
Environmental context Some of the factors used to operationalize this context byprevious studies on EDI adoption include external pressure [39], perceived industrypressure and perceived government pressure [50], and competitive pressure and ex-ercised power [70]. In line with these studies, we use pressure from trading partnersand pressure from competitors to define this context. Firms may face pressure fromtheir environment to adopt B2B EC (e.g., pressure from suppliers, customers, com-petitors, consultants, and others). These pressures may come in the form of force,threats, persuasion, and invitations [90] and are consistent with the three types of in-stitutional pressures discussed earlier. Country of origin is another institutional factorin that firms’ management practices may converge or vary based on whether they op-erate in the same or different geographical region or country. For example, practicesacross firms in the same geographical region are often expected to converge due topressures to conform or imitate. This factor has also drawn a lot of attention in thegeneral management literature because of the potential effects of national culture onmanagement practices.
There is evidence in the literature that various technological, organizational, andenvironmental factors distinguish B2B EC adopters from nonadopters and influencethe extent to which firms adopt these technologies. Some firms are not willing tocommit any resources to participate in online markets, whereas others dedicate theirresources to establish the required processes to engage in online business [29]. Forexample, although some firms may pursue little or no web-based integration, oth-ers only integrate their operations with either customers or suppliers, or with bothcustomers and suppliers [25]. Sila and Dobni [85] found that e-leaders (firms withthe highest level of B2B EC usage and integration) scored significantly higher onmany of the adoption factors than e-laggards (firms that are not willing to adopt thesetechnologies).
Overall, the factors that influence the adoption of B2B EC are being analyzedunder three categories in this study: technological factors, organizational and interor-ganizational factors, and environmental factors. We propose the conceptual modelillustrated in Fig. 1 for the analysis of factors affecting B2B EC adoption. Figure 1shows the three general categories of the TOE framework and Fig. 2 consists of amore detailed visual depiction of the factors that fall under each of these categories.
Factors affecting the adoption of B2B e-commerce technologies
Fig. 2 B2B EC adoption factors and contextual variables
More specifically, the direct effects of the adoption factors and the indirect effects ofthe contextual variables (firm size, firm type, country of origin, management level)listed above are shown in Fig. 2 and are being tested in this study. Based on a literaturereview, we propose the following hypotheses:
H1: Several factors (technological, organizational, interorganizational, and environ-mental factors) will influence the adoption of B2B EC technologies.
H2: Various adoption factors will distinguish B2B EC adopters from nonadopters.H3: Context influences the adoption of B2B EC.H4: Different factors will be significant in the adoption of B2B EC in different con-
texts.
4 Methodology
To show how our study contributes to and advances knowledge in the field, we con-ducted an extensive review of the relevant empirical literature. We carefully screenedeach article to make sure that it only dealt with B2B EC technologies and that it wasempirical. Consequently, we identified 155 articles, which enabled us to get a good
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idea about the state of empirical research in this area. A careful examination of eacharticle yielded several distinct categories of objectives across the 155 articles: studiesin the first category mainly dealt with the analysis of various factors that affect B2BEC adoption (e.g., Ranganathan et al. [76]; Sila [84]). Studies included in the sec-ond category were similar to these except that they focused on specific factors thatmay influence adoption such as trust related factors (e.g., Ratnasingam [77]), supplierrelated factors (e.g., Deeter-Schmelz et al. [19]; Andreu et al. [3]), and capability re-lated factors (e.g., Koch [49]; Lee et al. [54]). Another group of studies (e.g., Zahayand Handfield [115]; Harrison and Waite [34]; Sila and Dawn [85]) analyzed B2BEC adoption through the lens of different types of adopters, including early adopters,early majority, late majority, and laggards. A number of studies tested the various per-formance effects of B2B EC adoption using several statistical methods (e.g., Baruaet al. [10]; Rosenzweig and Roth [81]), while others explored B2B EC diffusion andperformance relationships (e.g., Zhu et al. [120]; Rosenzweig [80]).
Research in another category of studies (e.g., Lefebvre et al. [55]; Zhu et al. [123];Wu and Chuang [110]) made a distinction between the adoption and diffusion phases(e.g., initiation, adoption, routinization) of B2B EC initiatives. Other studies (e.g.,Ranganathan et al. [76]; N’Da et al. [65]) incorporated the adoption and diffusionphases of B2B EC implementations, as well as the performance effects of these ini-tiatives.
Another distinct research category analyzed the level of firms’ participation in e-marketplaces (e.g., Grewal et al. [29]; Papastathopoulou and Avlonitis [68]) and theextent to which firms are integrated with their supply chain partners through Internet-based technologies (e.g., Thun [101]), while a related stream of research tested the ef-fects of the level of Internet-enabled supply chain integration on various performancemeasures such as operational performance (e.g., Frohlich and Westbrook [25]) andprocurement productivity (e.g., Rai et al. [74]).
Our study falls under the first category and aims to build on the previous stud-ies in this category. Although various adoption factors have been operationalized bythese studies, the context within which these factors are applicable have, with certainexceptions (e.g., Subramaniam and Shaw [93]; Corsten and Kumar [16]; Sila [84]),largely been ignored. Since contextual variables can be influential in creating suc-cessful knowledge management systems (Raisinghani and Meade [75]) and that theyare often overlooked by many studies in this area (Zhang et al. [117]; Bakker et al.[7]), we attempt to shed more light on these issues in this study.
We measured B2B EC adoption factors by nine variables, which were made upof 23 items. These items are listed in Table 2 and have been adopted from Solimanand Janz [87]. B2B EC adoption was measured by taking the average score on theextent of firms’ usage of seven B2B EC technologies (purchasing and procurementapplications, inventory management applications, transportation applications, orderprocessing applications, customer service applications, vendor relations applications,production scheduling applications) [52].
We used a 1–7 Likert scale for all the items and determined whether firms ac-tually adopted B2B EC technologies by asking the respondents a yes/no question.We operationalized the contextual variables by posing firms questions about theirfirm size, firm type, management level, and their country of origin in the demo-graphics section of the survey. Two common measures of firm size are employee
Factors affecting the adoption of B2B e-commerce technologies
Table 2 Items for B2B EC adoption factors
Pressure from trading partnerMy main trading partner usually sets the mode of communication (e.g., fax, e-mail, etc.)My main trading partner decides on pricing, delivery schedules, etc.My main trading partner decides on the rules and regulations for using an interorganizational system inorder processingMy main trading partner decides on what information systems applications are to be exchanged with myfirm
Pressure from competitionAn industry move to utilize the Internet for interorganizational communications would put pressure onmy firm to do the sameThere is a trend in my industry to more utilize the Internet more for business-related activities andbusiness communications
CostsEstablishing Internet-based business-to-business operations with my trading partners would be costeffectiveIt would be less expensive to conduct business with several trading partners utilizing the Internet thanusing Electronic Data Interchange (EDI)
Network reliabilityThe Internet is considered to be a reliable communication medium to conduct business with tradingpartners along the supply chainCurrent Internet communication speeds are sufficient to handle the data movement necessary for ourcompany to communicate with our trading partner
Data securityThe nature of the business data regularly exchanged between our firm and our trading partners requires asecured communication mediumInternet security is a major concern to our firm when deciding to adopt Internet-basedbusiness-to-business transactions
ScalabilityThe availability of the Internet as a business communication medium is likely to increase the number oftrading partners with whom we can do businessThe Internet is likely to facilitate linking several of our firm’s business units together (e.g., branch offices,remote sites, etc.)
ComplexityThe existence of several communication standards when using EDI makes it more difficult to establishlinks with several trading partnersThe Internet’s one common communication standard (TCP/IP) would make it easier to communicate withmultiple trading partnersInternet-based business-to-business communication would be considered less complex to implement thanalternative methods such as EDI
Top management supportOur top management is likely to invest funds in ITOur top management is willing to take risks involved in the adoption of the InternetOur top management is likely to be interested in adopting the Internet-based business-to-businesstransactions in order to gain competitive advantageOur top management is likely to consider the adoption of Internet-based business-to-business applicationsas strategically important
TrustHow would you characterize the degree of mutual trust between your firm and your trading partner?What is the degree of comfort about sharing sensitive information in your area with your trading partner?
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Table 3 Pearson’s correlation to measure the association between adoption factors and B2B EC adoption
Variables Coefficient
Costs 0.224∗Network reliability (NR) 0.237∗Data security (DS) 0.120∗∗Scalability (Scal) 0.310∗Complexity (Comp) −0.012
Top management support (TMS) 0.302∗Trust 0.125
Pressure from trading partner (PFTP) 0.197∗Pressure from competition (PFC) 0.268∗
∗Significant at p < 0.01∗∗Significant at p < 0.05
size and sales amount. Firm size has been defined differently in the business liter-ature. Therefore, we decided to test the effect of firm size using several firm sizeclassification approaches: firm size 1 (small: 0–20 employees, medium: 21–100 em-ployees, large: 100+ employees), firm size 2 (small: 0–100 employees, medium:101–500 employees, large: 500+ employees), firm size 3 (small: 0–20 employees,medium: 21–500 employees, large: 500+ employees), firm size 4 (SMEs: sales ofless than $50 million, large: sales greater than $50 million).
We randomly selected 3000 firms from the mailing list of the Council of SupplyChain Management Professionals and Industry Canada’s website [41]. Selected firmswere asked via email to respond to the Web-based survey. We sent two reminders overa two-week period. We received a total of 420 responses. Service firms that respondedwere excluded, since preliminary analyses showed that the survey instrument was notas applicable to them as it was to manufacturing and merchandising firms. In addition,some of the responses could not be used because of missing data. As a result, thenumber of usable responses was 275. We tested for non-response bias by splittingthe responses into early and late respondents and conducting t-tests on their meanresponses to 10 randomly selected survey questions [6]. The results suggested thatthere was no evidence of non-response bias.
5 Analyses
First, we conducted tests to see whether the basic assumptions of ANOVA such asnormality and homogeneity of variance were met. For example, a series of Levene’stests confirmed that the variances across samples were equal. All the analyses sug-gested that there were no significant violations of these assumptions.
5.1 Testing the effects of several factors on the adoption of B2B EC technologies
We conducted Pearson’s correlation and multiple regression analyses to test H1. Pear-son’s correlation results (Table 3) show that all of the adoption factors except com-
Factors affecting the adoption of B2B e-commerce technologies
Table 4 Multiple regression results-estimates of effect of different factors on B2B EC adoption
Variables Coefficient
Model 1 Model 2 Model 3 Model 4
Constant 3.024∗ 3.009∗ 3.189∗ 2.470∗
Technological variables
Costs 0.144∗∗ 0.087
NR −0.011 −0.056
DS 0.029 0.022
Scal 0.266∗ 0.185∗∗Comp −0.071 −0.082
Organizational and interorganizational variables
TMS 0.290∗ 0.097
Trust 0.07 0.029
Environmental variables
PFTP 0.088 0.079
PFC 0.246∗ 0.095
R2 0.159 0.104 0.127 0.188
Adjusted R2 0.141 0.096 0.12 0.155
F 8.630∗ 13.387∗ 16.811∗ 5.758∗
∗Significant at p < 0.01∗∗Significant at p < 0.05
plexity and trust are significantly correlated with B2B EC adoption. Scalability andtop management support had the largest correlation with B2B EC adoption.
We also conducted multiple regression analysis to estimate simultaneous correla-tions among the nine predictor variables (i.e., the nine adoption factors) and a single,continuous response variable (i.e., B2B EC adoption). Various tests revealed that allof the assumptions of regression, including linearity, independence, constant vari-ance, and normality were met. To find the contribution of significant adoption factorsthat could explain variations in B2B EC, regression was carried out using four appro-priate combinations of variables (Table 4). Model 1 only included technological vari-ables, Model 2 only organizational and interorganizational variables, Model 3 onlyenvironmental variables, and Model 4 consisted of all the three categories of vari-ables. Model 4, with adjusted R2 = 0.155, and Model 2, with adjusted R2 = 0.141,had the highest explained variance. Scalability emerged as the single most importantfactor in Model 4 and was also one of the two significant factors in Model 1.
5.2 Comparing adoption factor across adopters and nonadopters
We tested whether each of the nine factors distinguished B2B EC adopters from non-adopters (H2) by conducting a series of one-way ANOVAs. Table 5 shows the de-scriptive statistics and Table 6 illustrates the results of ANOVA analyses. According
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Table 5 Summary descriptive statistics for adopters and nonadopters of B2B EC
Factors Groups n Sum Average Variance
PFTP Adopters 176 897.75 5.10 1.06
Nonadopters 99 504 5.09 1.04
PFC Adopters 176 936 5.32 1.68
Nonadopters 99 468.50 4.73 2.00
Costs Adopters 176 863.50 4.91 1.36
Nonadopters 99 470.85 4.76 0.98
NR Adopters 176 898 5.10 1.22
Nonadopters 99 450 4.55 1.44
DS Adopters 176 748 4.25 1.45
Nonadopters 99 393 3.97 1.53
Scal Adopters 176 948 5.39 1.58
Nonadopters 99 458 4.63 1.92
Comp Adopters 176 680.17 3.86 2.34
Nonadopters 99 378.67 3.82 1.94
TMS Adopters 176 972.42 5.53 1.18
Nonadopters 99 492.67 4.98 0.97
Trust Adopters 176 845.17 4.80 1.08
Nonadopters 99 438.50 4.43 1.54
to Table 6, five of the nine factors (Pressure from competitors (PFC), Network relia-bility (NR), Scalability (Scal), Top management support (TMS), Trust) distinguishedadopters from nonadopters at the 0.05 significance level. For example, there werestatistically significant differences between group means on PFC as determined bya one-way ANOVA (F(3.88) = 12.12, P = 0.00058). In each of the five cases, themean score on each factor for adopters was greater than that for nonadopters. Giventhat five of the nine factors distinguished adopters from nonadopters, H1 was partiallysupported. Thus, our findings conflict with those of Archer et al. [5] in terms of therelative number of differences found between adopters and nonadopters.
5.3 Comparing adopters and nonadopters within each contextual variable
We tested H3 by using three organizational factors as contextual variables: firm size,firm type, and management level. We could not perform country comparisons be-tween adopters and nonadopters, since we had small sample sizes for the Europe andUSA groups.
Firm size First, we classified small firms as those that have 0–20 employees,medium-sized firms as those with 21–100 employees, and large firms as those withmore than 100 employees. A series of one-way ANOVA analyses for each of the ninefactors yielded the results displayed in Table 7 (1a, 1b, and 1c). For example, smalladopters, medium nonadopters, and large nonadopters differed from each other onfour factors (Data security (DS), Scal, TMS, Trust). Tukey tests (Table 8) indicated
Factors affecting the adoption of B2B e-commerce technologies
Tabl
e6
One
-way
AN
OV
As:
com
pari
ngad
opte
rsan
dno
nado
pter
sof
B2B
EC
Sour
ceof
vari
atio
nSu
mof
squa
res
df
Mea
nsq
uare
FP
-val
ueF
crit
Con
clus
ion
R2
PFT
PB
etw
een
grou
ps0.
0062
642
10.
0062
642
0.00
5959
60.
9385
223
3.87
5746
5N
onsi
gnifi
cant
(NS)
0.00
0W
ithin
grou
ps28
6.95
419
273
1.05
1114
3To
tal
286.
9604
527
4
PFC
Bet
wee
ngr
oups
21.7
4707
11
21.7
4707
112
.120
223
0.00
0580
63.
8757
465
Sign
ifica
nt(S
)0.
043
With
ingr
oups
489.
8383
827
31.
7942
798
Tota
l51
1.58
545
274
Cos
tsB
etw
een
grou
ps1.
4292
023
11.
4292
023
1.16
8759
50.
2806
104
3.87
5746
5N
S0.
004
With
ingr
oups
333.
8344
927
31.
2228
37To
tal
335.
2636
927
4N
RB
etw
een
grou
ps19
.644
545
119
.644
545
15.0
7701
0.00
0129
53.
8757
465
S0.
052
With
ingr
oups
355.
7045
527
31.
3029
471
Tota
l37
5.34
909
274
DS
Bet
wee
ngr
oups
4.97
8181
81
4.97
8181
83.
3564
167
0.06
8032
93.
8757
465
NS
0.01
2W
ithin
grou
ps40
4.90
909
273
1.48
3183
5To
tal
409.
8872
727
4
Scal
Bet
wee
ngr
oups
36.6
0646
51
36.6
0646
521
.473
112
5.56
3E-0
63.
8757
465
S0.
073
With
ingr
oups
465.
3989
927
31.
7047
582
Tota
l50
2.00
545
274
Com
pB
etw
een
grou
ps0.
0996
977
10.
0996
977
0.04
5413
80.
8314
043.
8757
465
NS
0.00
0W
ithin
grou
ps59
9.32
111
273
2.19
5315
4To
tal
599.
4208
127
4
TM
SB
etw
een
grou
ps19
.073
381
19.0
7338
17.2
5408
24.
377E
-05
3.87
5746
5S
0.05
9W
ithin
grou
ps30
1.78
556
273
1.10
5441
6To
tal
320.
8589
427
4T
rust
Bet
wee
ngr
oups
8.80
6204
11
8.80
6204
17.
0714
634
0.00
8294
93.
8757
465
S0.
025
With
ingr
oups
339.
9711
727
31.
2453
157
Tota
l34
8.77
738
274
I. Sila
Tabl
e7
One
-way
AN
OV
As:
com
pari
ngad
opte
rsan
dno
nado
pter
sof
B2B
EC
usin
gco
ntex
tual
vari
able
s
Con
text
ualv
aria
bles
aPF
TP
PFC
Cos
tsN
RD
SSc
alC
omp
TM
ST
rust
Firm
size
(1a)
grou
psb:s
mal
lado
pter
s(n
=80
),m
ediu
mno
nado
pter
s(n
=30
),la
rge
nona
dopt
ers
(n=
11)
NS
NS
NS
NS
Sc(R
2=
0.08
)S
(R2
=0.
102)
NS
S(R
2=
0.09
3)S
(R2
=0.
050)
Firm
size
(1b)
grou
psb:m
ediu
mad
opte
rs(n
=40
),sm
alln
onad
opte
rs(n
=58
),la
rge
nona
dopt
ers
(n=
11)
NS
S(R
2=
0.12
0)N
SS
(R2
=0.
114)
NS
S(R
2=
0.14
8)N
SS
(R2
=0.
080)
S(R
2=
0.06
9)
Firm
size
(1c)
grou
psb:l
arge
adop
ters
(n=
55),
smal
lnon
adop
ters
(n=
58),
med
ium
nona
dopt
ers
(n=
30)
NS
NS
NS
S(R
2=
0.07
3)N
SN
SN
SN
SN
S
Firm
size
(2a)
grou
psd:s
mal
lado
pter
s(n
=80
),m
ediu
mno
nado
pter
s(n
=37
)N
SN
SN
SN
SS
(R2
=0.
043)
S(R
2=
0.05
5)N
SS
(R2
=0.
084)
NS
Firm
size
(2b)
grou
psd:m
ediu
mad
opte
rs(n
=63
),sm
alln
onad
opte
rs(n
=58
)
NS
S(R
2=
0.09
8)N
SS
(R2
=0.
114)
NS
S(R
2=
0.07
5)N
SS
(R2
=0.
057)
NS
Firm
size
(2c)
grou
psd:l
arge
adop
ters
(n=
32),
smal
lnon
adop
ters
(n=
58),
med
ium
nona
dopt
ers
(n=
37)
NS
NS
NS
S(R
2=
0.07
2)N
SN
SS
(R2
=0.
053)
NS
NS
Firm
size
(3a)
grou
pse :
SME
adop
ters
(n=
124)
,lar
geno
nado
pter
s(n
=10
)N
SN
SN
SN
SS
(R2
=0.
089)
S(R
2=
0.06
8)N
SN
SS
(R2
=0.
062)
Firm
size
(3b)
grou
pse :
larg
ead
opte
rs(n
=52
)SM
Eno
nado
pter
s(n
=89
)N
SN
SN
SS
(R2
=0.
048)
NS
NS
NS
S(R
2=
0.04
2)N
S
Factors affecting the adoption of B2B e-commerce technologies
Tabl
e7
(Con
tinu
ed)
Con
text
ualv
aria
bles
aPF
TP
PFC
Cos
tsN
RD
SSc
alC
omp
TM
ST
rust
Firm
type
(1a)
grou
psf :
Man
ufac
ture
rad
opte
rs(n
=10
9),M
erch
andi
ser
nona
dopt
ers
(n=
30)
NS
S(R
2=
0.07
3)N
SS
(R2
=0.
071)
NS
S(R
2=
0.05
5)N
SS
(R2
=0.
090)
NS
Firm
type
(1b)
grou
psf :
Mer
chan
dise
rad
opte
rs(n
=57
),M
anuf
actu
rer
nona
dopt
ers
(n=
67)
NS
S(R
2=
0.08
0)S
(R2
=0.
032)
S(R
2=
0.05
1)S
(R2
=0.
060)
S(R
2=
0.09
8)N
SS
(R2
=0.
090)
NS
Man
agem
entl
evel
(1a)
grou
psg:C
EO
adop
ters
(n=
21),
Dir
ecto
rno
nado
pter
s(n
=9)
,Man
ager
nona
dopt
ers
(n=
14),
Pres
iden
tnon
adop
ters
(n=
29)
NS
S(R
2=
0.11
9)N
SN
SS
(R2
=0.
127)
NS
NS
NS
S(R
2=
0.18
8)
Man
agem
entl
evel
(1b)
grou
ps:
Cor
pora
teM
anag
erad
opte
rs(n
=9)
,C
EO
nona
dopt
ers
(n=
19),
Dir
ecto
rno
nado
pter
s(n
=9)
,Pre
side
ntno
nado
pter
s(n
=28
),M
anag
erno
nado
pter
s(n
=14
)
NS
NS
NS
NS
NS
NS
NS
NS
NS
Man
agem
entl
evel
(1c)
grou
ps:
Dir
ecto
rad
opte
rs(n
=20
),C
EO
nona
dopt
ers
(n=
19),
Man
ager
nona
dopt
ers
(n=
14),
Pres
iden
tno
nado
pter
s(n
=28
)
NS
NS
NS
NS
NS
NS
NS
NS
NS
I. Sila
Tabl
e7
(Con
tinu
ed)
Con
text
ualv
aria
bles
aPF
TP
PFC
Cos
tsN
RD
SSc
alC
omp
TM
ST
rust
Man
agem
entl
evel
(1d)
grou
ps:
Man
ager
adop
ters
(n=
36),
CE
Ono
nado
pter
s(n
=19
),D
irec
tor
nona
dopt
ers
(n=
9),P
resi
dent
nona
dopt
ers
(n=
28)
NS
NS
NS
NS
S(R
2=
0.09
8)N
SN
SN
SN
S
Man
agem
entl
evel
(1e)
grou
ps:
Pres
iden
tado
pter
s(n
=47
),C
EO
nona
dopt
ers
(n=
19),
Dir
ecto
rno
nado
pter
s(n
=9)
,Man
ager
nona
dopt
ers
(n=
14)
NS
NS
NS
S(R
2=
0.10
4)S
(R2
=0.
126)
S(R
2=
0.12
0)N
SN
SS
(R2
=0.
094)
Man
agem
entl
evel
(1f)
grou
ps:V
Pad
opte
rs(n
=11
),C
EO
nona
dopt
ers
(n=
19),
Dir
ecto
rno
nado
pter
s(n
=9)
,M
anag
erno
nado
pter
s(n
=14
),Pr
esid
entn
onad
opte
rs(n
=28
)
NS
NS
NS
NS
NS
NS
NS
NS
NS
a Cou
ntry
com
pari
sons
coul
dno
tbe
mad
ebe
twee
nad
opte
rsan
dno
nado
pter
sdu
eto
the
smal
lsa
mpl
esi
zes
ofth
eE
urop
ean
dU
SAgr
oups
.A
sw
ell,
the
anal
ysis
for
the
follo
win
gfir
msi
zegr
oupi
ngs
coul
dno
tbe
cond
ucte
ddu
eto
sam
ple
size
limita
tions
:Sm
all:
0–20
empl
oyee
s,m
ediu
m:2
1–10
0,la
rge:
100+
bSm
all:
0–20
empl
oyee
s,m
ediu
m:2
1–10
0,la
rge:
100+
c All
sign
ifica
ntat
p<
0.05
dSm
all:
0–20
empl
oyee
s,m
ediu
m:2
1–50
0,la
rge:
501+
(lar
geno
nado
pter
sw
ere
excl
uded
due
toa
very
smal
lsam
ple
size
)e S
ME
s:sa
les
less
than
$50
mill
ion,
larg
e:>
$50
mill
ion
f Mer
chan
dise
rsin
clud
ew
hole
sale
rs/d
istr
ibut
ors
and
reta
ilers
gSo
me
ofth
em
anag
emen
tlev
elno
nado
pter
grou
psha
dve
rysm
alls
ampl
esi
zes
and
wer
eth
eref
ore
omitt
ed
Factors affecting the adoption of B2B e-commerce technologies
Tabl
e8
Tuk
eyte
sts
for
sign
ifica
ntfa
ctor
sw
ithth
ree
orm
ore
grou
psin
adop
ter
and
nona
dopt
erfir
ms
Sign
ifica
ntad
optio
nfa
ctor
Con
text
ualv
aria
bles
Mea
ndi
ffer
ence
95%
Con
fiden
cein
terv
al
Low
erbo
und
Upp
erbo
und
Firm
Size
1agr
oups
DS
Lar
geno
nado
pter
sM
ediu
mno
nado
pter
s0.
735
−0.3
041.
774
Smal
lado
pter
s1.
189∗
0.24
22.
135
Med
ium
nona
dopt
ers
Smal
lado
pter
s0.
454
−0.1
761.
084
Scal
Lar
geno
nado
pter
sM
ediu
mno
nado
pter
s0.
714
−0.3
631.
79
Smal
lado
pter
s1.
339∗
0.35
82.
32
Med
ium
nona
dopt
ers
Smal
lado
pter
s0.
625
−0.0
271.
278
TM
SL
arge
nona
dopt
ers
Med
ium
nona
dopt
ers
−0.5
12−1
.389
0.36
5
Smal
lado
pter
s0.
268
−0.5
321.
068
Med
ium
nona
dopt
ers
Smal
lado
pter
s0.
780∗
0.24
81.
312
Tru
stL
arge
nona
dopt
ers
Med
ium
nona
dopt
ers
0.41
7−0
.509
1.34
3
Smal
lado
pter
s0.
790∗
∗−0
.054
1.63
4
Med
ium
nona
dopt
ers
Smal
lado
pter
s0.
373
−0.1
880.
935
Firm
Size
1bgr
oups
PFC
Lar
geno
nado
pter
sM
ediu
mad
opte
rs−0
.124
−1.2
090.
961
Smal
lnon
adop
ters
−1.0
76∗
−2.1
24−0
.028
Med
ium
adop
ters
Smal
lnon
adop
ters
−0.9
52∗
−1.6
07−0
.297
NR
Lar
geno
nado
pter
sM
ediu
mad
opte
rs0.
593
−0.3
181.
504
Smal
lnon
adop
ters
−0.2
59−1
.14
0.62
1
Med
ium
adop
ters
Smal
lnon
adop
ters
−0.8
53∗
−1.4
03−0
.303
I. Sila
Tabl
e8
(Con
tinu
ed)
Sign
ifica
ntad
optio
nfa
ctor
Con
text
ualv
aria
bles
Mea
ndi
ffer
ence
95%
Con
fiden
cein
terv
al
Low
erbo
und
Upp
erbo
und
Scal
Lar
geno
nado
pter
sM
ediu
mad
opte
rs1.
476∗
0.41
42.
538
Smal
lnon
adop
ters
0.46
7−0
.559
1.49
3
Med
ium
adop
ters
Smal
lnon
adop
ters
−1.0
09∗
−1.6
50−0
.368
TM
SL
arge
nona
dopt
ers
Med
ium
adop
ters
0.12
6−0
.673
0.92
6
Smal
lnon
adop
ters
−0.4
74−1
.246
0.29
8
Med
ium
adop
ters
Smal
lnon
adop
ters
−0.6
00∗
−1.0
83−0
.118
Tru
stL
arge
nona
dopt
ers
Med
ium
adop
ters
0.93
2∗0.
081
1.78
4
Smal
lnon
adop
ters
0.51
7−0
.306
1.34
0
Med
ium
adop
ters
Smal
lnon
adop
ters
−0.4
15−0
.930
0.09
9
Firm
Size
1cgr
oups
NR
Lar
gead
opte
rsM
ediu
mno
nado
pter
s−0
.430
−1.0
700.
210
Smal
lnon
adop
ters
−0.7
41∗
−1.2
72−0
.211
Med
ium
nona
dopt
ers
Smal
lnon
adop
ters
−0.3
11−0
.945
0.32
3
Firm
Size
2cgr
oups
NR
Lar
gead
opte
rsM
ediu
mno
nado
pter
s−0
.383
−1.0
750.
309
Smal
lnon
adop
ters
−0.8
12∗
−1.4
44−0
.180
Med
ium
nona
dopt
ers
Smal
lnon
adop
ters
−0.4
29−1
.032
0.17
5
Com
pL
arge
adop
ters
Med
ium
nona
dopt
ers
0.62
7−0
.176
1.43
1
Smal
lnon
adop
ters
0.80
5∗0.
072
1.53
8
Med
ium
nona
dopt
ers
Smal
lnon
adop
ters
0.17
7−0
.523
0.87
8
Factors affecting the adoption of B2B e-commerce technologies
Tabl
e8
(Con
tinu
ed)
Sign
ifica
ntad
optio
nfa
ctor
Con
text
ualv
aria
bles
Mea
ndi
ffer
ence
95%
Con
fiden
cein
terv
al
Low
erbo
und
Upp
erbo
und
Man
agem
entl
evel
1agr
oups
PFC
CE
Oad
opte
rsD
irec
tor
nona
dopt
ers
−0.3
10−1
.745
1.12
6
Man
ager
nona
dopt
ers
−0.5
00−1
.743
0.74
3
Pres
iden
tnon
adop
ters
−1.1
61∗
−2.2
01−0
.121
Dir
ecto
rno
nado
pter
sM
anag
erno
nado
pter
s−0
.190
−1.7
301.
349
Pres
iden
tnon
adop
ters
−0.8
51−2
.231
0.52
9
Man
ager
nona
dopt
ers
Pres
iden
tnon
adop
ters
−0.6
61−1
.840
0.51
8
DS∗
∗∗C
EO
adop
ters
Dir
ecto
rno
nado
pter
s−1
.294
−2.5
970.
010
Man
ager
nona
dopt
ers
−0.7
50−1
.879
0.37
9
Pres
iden
tnon
adop
ters
−0.0
54−0
.998
0.89
1
Dir
ecto
rno
nado
pter
sM
anag
erno
nado
pter
s0.
544
−0.8
541.
942
Pres
iden
tnon
adop
ters
1.24
0−0
.014
2.49
4
Man
ager
nona
dopt
ers
Pres
iden
tnon
adop
ters
0.69
6−0
.375
1.76
8
Tru
stC
EO
adop
ters
Dir
ecto
rno
nado
pter
s−1
.302
∗−2
.306
−0.2
97
Man
ager
nona
dopt
ers
−0.9
05∗
−1.7
75−0
.035
Pres
iden
tnon
adop
ters
−0.2
80−1
.008
0.44
8
Dir
ecto
rno
nado
pter
sM
anag
erno
nado
pter
s0.
397
−0.6
811.
474
Pres
iden
tnon
adop
ters
1.02
2∗0.
055
1.98
8
Man
ager
nona
dopt
ers
Pres
iden
tnon
adop
ters
0.62
5−0
.201
1.45
1
I. Sila
Tabl
e8
(Con
tinu
ed)
Sign
ifica
ntad
optio
nfa
ctor
Con
text
ualv
aria
bles
Mea
ndi
ffer
ence
95%
Con
fiden
cein
terv
al
Low
erbo
und
Upp
erbo
und
Man
agem
entl
evel
1dgr
oups
DS
CE
Oad
opte
rsD
irec
tor
nona
dopt
ers
−0.8
54−2
.096
0.38
8
Man
ager
nona
dopt
ers
−0.2
84−1
.155
0.58
6
Pres
iden
tnon
adop
ters
0.38
6−0
.526
1.29
9
Dir
ecto
rno
nado
pter
sM
anag
erno
nado
pter
s0.
569
−0.5
741.
713
Pres
iden
tnon
adop
ters
1.24
0∗0.
064
2.41
6
Man
ager
nona
dopt
ers
Pres
iden
tnon
adop
ters
0.67
1−0
.103
1.44
4
Man
agem
entl
evel
1egr
oups
NR
∗∗∗
CE
Ono
nado
pter
sD
irec
tor
nona
dopt
ers
0.10
8−1
.122
1.33
8
Man
ager
nona
dopt
ers
−0.1
97−1
.268
0.87
3
Pres
iden
tado
pter
s0.
712
−0.1
141.
538
Dir
ecto
rno
nado
pter
sM
anag
erno
nado
pter
s−0
.306
−1.6
040.
993
Pres
iden
tado
pter
s0.
604
−0.5
021.
710
Man
ager
nona
dopt
ers
Pres
iden
tado
pter
s0.
910
−0.0
161.
835
Factors affecting the adoption of B2B e-commerce technologies
Tabl
e8
(Con
tinu
ed)
Sign
ifica
ntad
optio
nfa
ctor
Con
text
ualv
aria
bles
Mea
ndi
ffer
ence
95%
Con
fiden
cein
terv
al
Low
erbo
und
Upp
erbo
und
DS
CE
Ono
nado
pter
sD
irec
tor
nona
dopt
ers
−0.8
54−2
.100
0.39
2
Man
ager
nona
dopt
ers
−0.3
10−1
.395
0.77
4
Pres
iden
tado
pter
s0.
464
−0.3
731.
301
Dir
ecto
rno
nado
pter
sM
anag
erno
nado
pter
s0.
544
−0.7
721.
859
Pres
iden
tado
pter
s1.
318∗
0.19
82.
438
Man
ager
nona
dopt
ers
Pres
iden
tado
pter
s0.
774
−0.1
631.
712
Scal
∗∗∗
CE
Ono
nado
pter
sD
irec
tor
nona
dopt
ers
−0.2
08−1
.567
1.15
2
Man
ager
nona
dopt
ers
−0.1
20−1
.304
1.06
3
Pres
iden
tado
pter
s0.
833
−0.0
811.
746
Dir
ecto
rno
nado
pter
sM
anag
erno
nado
pter
s0.
087
−1.3
481.
523
Pres
iden
tado
pter
s1.
040
−0.1
822.
263
Man
ager
nona
dopt
ers
Pres
iden
tado
pter
s0.
953
−0.0
701.
976
Tru
st∗∗
∗C
EO
nona
dopt
ers
Dir
ecto
rno
nado
pter
s−0
.506
−1.7
240.
713
Man
ager
nona
dopt
ers
−0.1
09−1
.170
0.95
2
Pres
iden
tado
pter
s0.
529
−0.2
901.
348
Dir
ecto
rno
nado
pter
sM
anag
erno
nado
pter
s0.
397
−0.8
901.
684
Pres
iden
tado
pter
s1.
035
−0.0
612.
131
Man
ager
nona
dopt
ers
Pres
iden
tado
pter
s0.
638
−0.2
791.
555
∗ Sig
nific
anta
tp<
0.05
∗∗Fi
sher
’sL
east
Sign
ifica
ntD
iffe
renc
e(L
SD)
test
sugg
ests
the
diff
eren
ceis
sign
ifica
nt,a
lthou
ghth
eom
nibu
sA
NO
VA
test
issi
gnifi
cant
atp
<0.
05an
dth
eT
ukey
test
sfin
dno
diff
eren
ces
betw
een
grou
ps(L
SD-t
ype
test
sar
ere
com
men
ded
byC
ohen
[15]
inca
ses
like
this
whe
nth
ree
grou
psex
ist)
∗∗∗ A
lthou
ghth
eom
nibu
sA
NO
VA
test
issi
gnifi
cant
atp
<0.
05,T
ukey
test
sfin
dno
diff
eren
ces
betw
een
grou
ps(w
ithm
ore
than
thre
egr
oups
,Coh
en[1
5]ar
gues
that
the
Tuk
eyre
sults
can
beus
ed,r
egar
dles
sof
the
resu
ltsof
the
omni
bus
AN
OV
A)
I. Sila
that the average score on these factors was significantly higher in small adopters thanin large nonadopters. When only small adopters and small nonadopters are compared(to save space, we did not report the details of these comparisons), a somewhat dif-ferent set of factors (PFC, NR, Scal, TMS) are significantly different across the twogroups, although Scal and TMS are common factors. This shows that firm size doesaffect the significance of these factors, providing support for Hadaya’s [31] findings.
Similarly, medium adopters, small nonadopters, and large nonadopters (1b) dif-fered from each other on five factors (PFC, NR, Scal, TMS, Trust). Again, whenmedium adopters are compared only with medium nonadopters, the set of significantfactors (PFC, DS, Scal, TMS) is slightly different, although PFC, Scal, and TMS arecommon. This indicates that firm size contributes to these differences. According toTukey tests conducted for 1b, the average score on PFC was lower in small non-adopters than in both medium adopters (these had the highest average) and large non-adopters. For NR and TMS, only the mean scores of medium adopters and small non-adopters differed, where the former had significantly higher scores. For Scal, mediumadopters had significantly higher mean scores than both small and large nonadopters.Medium adopters also scored significantly higher than large nonadopters on the Trustfactor. In 1c, we see that large adopters had a higher mean score on NR than smallnonadopters, but there were no other significant differences between large adoptersand small and medium nonadopters.
We also found differences on some of the adoption factors, ranging from two tofour factors out of the nine tested, when we slightly changed firm size classifications(2a, 2b, 2c, 3a, and 3b) as shown in Table 7. In all cases of significant differences,the mean scores for adopters were greater than those for nonadopters, except for thedifference in mean score on complexity, which was smaller in large adopters than insmall nonadopters. Interestingly, when large adopters were compared with small andmedium nonadopters, there were a small number of significant differences (only oneor two factors). The amount of explained variance (R2) for significant differencesranged from 0.042 to 0.148.
Firm type As Table 7 indicates, a comparison of manufacturing adopters and mer-chandising nonadopters, as well as merchandising adopters and manufacturing non-adopters, displays differences on four and six factors, respectively. Four of those fac-tors were common across the two comparisons and included PFC, NR, Scal, andTMS. In all cases of significant differences, the mean scores for adopters were greaterthan those for nonadopters.
Management level All combinations of comparisons among the different manage-ment levels of respondents in adopter and nonadopter firms are shown in Table 7.The results show that, of the six sets of comparisons, only three of the comparisons(1a, 1d, 1e) yielded differences. Most of the differences were among CEO adopters,Director nonadopters, Manager nonadopters, and President nonadopters (1a). Thefactors that displayed differences across management levels include PFC, DS, andTrust. DS was a common significant factor across 1d and 1e. The Tukey tests for1a (Table 8) suggest that the CEOs in adopter firms had a higher mean score thanPresidents in nonadopter firms on the PFC factor and a higher mean score than both
Factors affecting the adoption of B2B e-commerce technologies
Directors and Managers in nonadopter firms on the Trust factor. Presidents in non-adopter firms also scored higher than Directors in nonadopter firms on Trust. TheTukey tests for 1d and 1e, which had DS as the only significant factor, showed thatthe only differences were between Presidents in both adopter and nonadopter firmsand Directors in nonadopter firms. Presidents had significantly higher mean scoresthan Directors. The R2 values for the significant factors were generally larger thanthose for the other contextual variables and ranged from 0.098 to 0.188.
Given the importance of top management support for technology adoption, we alsoconducted separate one-way ANOVAs (details not reported here) to compare CEOs inadopter firms with those in nonadopter firms for each of the nine factors. The resultsindicated that the two groups only differed on the top management support and trustdimensions, where the mean score on both factors were greater for CEOs in adopterfirms. Given these results, H3 is supported.
5.4 Comparing adopters within each contextual variable
We tested H4 by utilizing the following contextual variables: country of origin, firmsize, firm type, management level.
Country of origin Table 9 shows that there are no differences in adoption factorsacross three country of origin categories—Canada, USA, and Europe.
Firm size There were some significant differences on one or two factors across allfour approaches of firm size comparisons (see Table 9). These differences were fewerthan those found for comparisons between adopter and nonadopter firms of differentsizes (see results of H3). In fact, only DS and/or Complexity (Comp) showed sig-nificant differences across all four firm size comparisons (1a–1d). Tukey tests (seeTable 10) show that large firms mainly differed from firms with sizes of 0–100 em-ployees (defined as small or medium-sized firms, depending on the ranges used) interms of DS. In all cases (1a–1d), the mean score on DS was smaller for large firms.In two of the cases where Comp was significant (1b and 1c), large firms differed fromboth small and medium-sized firms in that the mean score on Comp was smaller forlarge firms.
Firm type Only three (PFC, Costs, DS) of the nine factors differed across manufac-turing adopters and merchandising adopters. The mean scores on these factors werehigher for manufacturing firms.
Management level CEOs, Corporate Managers, Directors, Managers, Presidents,and VPs of adopter firms had different perceptions on only three of the nine adoptionfactors—DS, Scal, and Trust. Tukey tests (Table 10) have shown that, for DS, the onlydifference in perception was between Presidents and Managers, where Presidents hadlarger average scores on this factor. Managers’ opinions about Scal also differed fromthose of Presidents and Directors. Managers had lower mean scores on this factor thanboth Presidents and Directors. As for Trust, CEOs had a significantly larger averagescore on this factor than Corporate Managers. Based on these results, H4 is partiallysupported.
I. Sila
Tabl
e9
One
-way
AN
OV
As:
com
pari
ngon
lyad
opte
rsof
B2B
EC
usin
gco
ntex
tual
vari
able
s
Con
text
ualv
aria
bles
PFT
PPF
CC
osts
NR
DS
Scal
Com
pT
MS
Tru
st
Cou
ntry
grou
ps:C
anad
a(n
=12
0),
Eur
ope
(n=
19),
USA
(n=
34)
NS
NS
NS
NS
NS
NS
NS
NS
NS
Firm
size
(1a)
grou
psa :
smal
l(n
=80
),m
ediu
m(n
=40
),la
rge
(n=
55)
NS
NS
NS
NS
Sb(R
2=
0.04
5)N
SN
SN
SN
S
Firm
size
(1b)
grou
psc :
smal
l(n
=12
1),m
ediu
m(n
=23
),la
rge
(n=
32)
NS
NS
NS
NS
S(R
2=
0.04
7)N
SS
(R2
=0.
066)
NS
NS
Firm
size
(1c)
grou
psd:s
mal
l(n
=80
),m
ediu
m(n
=63
),la
rge
(n=
32)
NS
NS
NS
NS
S(R
2=
0.03
6)N
SS
(R2
=0.
071)
NS
NS
Firm
size
(1d)
grou
pse :
SME
(n=
124)
,lar
ge(n
=52
)N
SN
SN
SN
SS
(R2
=0.
029)
NS
NS
NS
NS
Firm
type
grou
ps:M
anuf
actu
rer
(n=
57),
Mer
chan
dise
r(n
=10
9)N
SS
(R2
=0.
034)
S(R
2=
0.02
4)N
SS
(R2
=0.
032)
NS
NS
NS
NS
Man
agem
entl
evel
:CE
O(n
=21
),C
orpo
rate
Man
ager
(n=
9),
Dir
ecto
r(n
=20
),M
anag
er(n
=36
),Pr
esid
ent(
n=
47),
VP
(n=
11)
NS
NS
NS
NS
S(R
2=
0.09
7)S
(R2
=0.
090)
NS
NS
S(R
2=
0.09
6)
a Sm
all:
0–20
empl
oyee
s,m
ediu
m:2
1–10
0,la
rge:
100+
bA
llsi
gnifi
cant
atp
<0.
05c S
mal
l:0–
100
empl
oyee
s,m
ediu
m:1
01–5
00,l
arge
:500
+dSm
all:
0–20
empl
oyee
s,m
ediu
m:2
1–50
0,la
rge:
500+
e SM
Es:
sale
sle
ssth
an$5
0m
illio
n,la
rge:
>$5
0m
illio
n
Factors affecting the adoption of B2B e-commerce technologies
Tabl
e10
Tuk
eyte
sts
for
sign
ifica
ntfa
ctor
sw
ithth
ree
orm
ore
grou
psin
adop
ter
firm
s
Sign
ifica
ntad
optio
nfa
ctor
Con
text
ualv
aria
bles
Mea
ndi
ffer
ence
95%
Con
fiden
cein
terv
al
Low
erbo
und
Upp
erbo
und
Firm
Size
1agr
oups
DS
Lar
geM
ediu
m0.
631∗
0.04
91.
212
Smal
l0.
489
−0.0
010.
978
Med
ium
Smal
l−0
.142
−0.6
830.
399
Firm
Size
1bgr
oups
DS
Lar
geM
ediu
m0.
278
−0.4
871.
042
Smal
l0.
652∗
0.09
61.
208
Med
ium
Smal
l0.
374
−0.2
621.
01
Com
pL
arge
Med
ium
1.21
1∗0.
252
2.17
Smal
l0.
944∗
0.24
71.
642
Med
ium
Smal
l−0
.267
−1.0
650.
531
Firm
Size
1cgr
oups
DS
Lar
geM
ediu
m0.
576
−0.0
351.
186
Smal
l0.
605∗
0.01
81.
192
Med
ium
Smal
l0.
029
−0.4
430.
502
Com
pL
arge
Med
ium
1.16
8∗0.
408
1.92
7
Smal
l0.
846∗
0.11
61.
576
Med
ium
Smal
l−0
.322
−0.9
090.
266
I. Sila
Tabl
e10
(Con
tinu
ed)
Sign
ifica
ntad
optio
nfa
ctor
Con
text
ualv
aria
bles
Mea
ndi
ffer
ence
95%
Con
fiden
cein
terv
al
Low
erbo
und
Upp
erbo
und
Man
agem
entl
evel
DS
CE
OC
orpo
rate
Man
ager
−0.6
83−1
.964
0.59
8
Dir
ecto
r−0
.146
−1.1
510.
858
Man
ager
−0.7
24−1
.607
0.15
9
Pres
iden
t0.
024
−0.8
200.
868
VP
−0.8
44−2
.041
0.35
3
Cor
pora
teM
anag
erD
irec
tor
0.53
6−0
.755
1.82
7
Man
ager
−0.0
42−1
.24
1.15
7
Pres
iden
t0.
707
−0.4
631.
877
VP
−0.1
62−1
.607
1.28
4
Dir
ecto
rM
anag
er−0
.578
−1.4
750.
319
Pres
iden
t0.
171
−0.6
881.
029
VP
−0.6
98−1
.905
0.50
9
Man
ager
Pres
iden
t0.
749∗
0.03
61.
461
VP
−0.1
20−1
.228
0.98
8
Pres
iden
tV
P−0
.868
−1.9
450.
208
Factors affecting the adoption of B2B e-commerce technologies
Tabl
e10
(Con
tinu
ed)
Sign
ifica
ntad
optio
nfa
ctor
Con
text
ualv
aria
bles
Mea
ndi
ffer
ence
95%
Con
fiden
cein
terv
al
Low
erbo
und
Upp
erbo
und
Scal
CE
OC
orpo
rate
Man
ager
−0.6
11−2
.040
0.81
8
Dir
ecto
r0.
133
−0.9
871.
254
Man
ager
−0.8
75−1
.860
0.11
0
Pres
iden
t−0
.071
−1.0
120.
871
VP
−0.4
39−1
.774
0.89
6
Cor
pora
teM
anag
erD
irec
tor
0.74
4−0
.695
2.18
4
Man
ager
−0.2
64−1
.601
1.07
3
Pres
iden
t0.
54−0
.765
1.84
5
VP
0.17
2−1
.44
1.78
4
Dir
ecto
rM
anag
er−1
.008
∗−2
.009
−0.0
08
Pres
iden
t−0
.204
−1.1
620.
753
VP
−0.5
73−1
.919
0.77
4
Man
ager
Pres
iden
t0.
804∗
0.01
01.
599
VP
0.43
6−0
.800
1.67
1
Pres
iden
tV
P−0
.368
−1.5
700.
833
I. Sila
Tabl
e10
(Con
tinu
ed)
Sign
ifica
ntad
optio
nfa
ctor
Con
text
ualv
aria
bles
Mea
ndi
ffer
ence
95%
Con
fiden
cein
terv
al
Low
erbo
und
Upp
erbo
und
Tru
stC
EO
Cor
pora
teM
anag
er−1
.246
∗−2
.418
−0.0
74
Dir
ecto
r−0
.065
−0.9
840.
853
Man
ager
−0.6
35−1
.442
0.17
3
Pres
iden
t−0
.269
−1.0
410.
503
VP
−0.6
00−1
.694
0.49
5
Cor
pora
teM
anag
erD
irec
tor
1.18
10.
000
2.36
1
Man
ager
0.61
1−0
.485
1.70
7
Pres
iden
t0.
977
−0.0
932.
047
VP
0.64
6−0
.675
1.96
8
Dir
ecto
rM
anag
er−0
.569
−1.3
900.
251
Pres
iden
t−0
.204
−0.9
890.
581
VP
−0.5
34−1
.638
0.57
0
Man
ager
Pres
iden
t0.
366
−0.2
861.
017
VP
0.03
5−0
.978
1.04
9
Pres
iden
tV
P−0
.330
−1.3
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Factors affecting the adoption of B2B e-commerce technologies
6 Discussion of findings and implications
Findings from Pearson’s correlation analysis show that each adoption factor exceptcomplexity and trust is significantly associated with the extent of B2B EC usage.According to multiple regression results, scalability is the biggest contributor to B2BEC usage, suggesting that firms place great value on the capability provided by theInternet to reach or create new markets and link with their supply chain partners.
When contextual variables are not accounted for, the findings show that pressurefrom competitors, network reliability, scalability, top management support, and trustplay a significant role in contributing to firms’ decision to adopt B2B EC. However,pressure from trading partners, costs, data security, and complexity do not.
When contextual variables are considered, we find that country of origin is not asignificant contextual variable. Therefore, managers of firms with different countriesof origin basically deal with similar factors when implementing B2B EC. However,we should keep in mind that the results could be different if the sampled firms wereactually located in different countries and included firms from countries with verydifferent cultures. Larger group sample sizes would also be more effective in identi-fying any such differences.
Results suggest that firm size is an important contextual variable in B2B EC adop-tion. Based on comparisons of adopter and nonadopter firms of different sizes, wesee that there are certain unique adoption factors that spur firms of different sizes toadopt B2B EC. This is more evident in SMEs than large firms. This may be the casebecause SMEs face more constraints when implementing these technologies. Somestudies take the view that large firms are better positioned to adopt B2B EC becausethese firms have more slack resources, can achieve economies of scale more easily,are more able to handle the risk of investment, can exert more pressure on their trad-ing partners to adopt the same technologies [120], and have more champions thansmall firms to facilitate the adoption of innovative technologies [42].
Findings also show that, for firms that actually adopted B2B EC, only data securityand complexity are affected by firm size. There is evidence that data security is lessof a concern for large firms and that large firms find it less complex than SMEs toimplement B2B EC, possibly due to greater previous experience in implementingthese technologies. Although the managers of SMEs do not have as much leverageas those of large firms when it comes to resource issues or economies of scale, theycan focus on factors they have more control over. For example, they can focus onbuilding closer ties and trust with their trading partners and hire knowledgeable ITpersonnel to better deal with the complexities and security issues involved in theimplementation of B2B EC.
We also observe that firm type influences the factors that shape firms’ adoptiondecisions. The key factors that were affected by firm type include pressure from com-petitors, costs, network reliability, data security, scalability, and top management sup-port. On the other hand, when we compare firms of different types that have alreadyadopted B2B EC (i.e., manufacturer adopters and merchandising adopters), we seethat only three of these factors (pressure from competitors, costs, data security) aredifferent across the two groups. Findings reveal that these three factors were slightlymore significant in motivating managers in manufacturing firms to adopt B2B EC.
I. Sila
This may in part be the reason why manufacturing shipments exceed those of othersectors, representing 56 % of B2B EC transactions. Wholesale trade comes in secondwith 37 % of these transactions [40]. Thus, manufacturing managers may have toimplement B2B EC at higher levels than their merchandising counterparts to handlethe mimetic pressures exerted by their competitors and deal with the cost pressures,while maintaining proprietary data secure.
Management level is also a significant contextual variable and affects firms’ inten-tion to adopt B2B EC. Pressure from competitors, data security, and trust are the threefactors that showed differences across management levels. Higher-ranked manage-ment in adopter firms feels stronger about the importance of these factors than lower-ranked management in nonadopter firms. Separate analyses for CEOs in adopter andnonadopter firms show that the two factors that distinguish the two groups are topmanagement support and trust. The results provide evidence that these two factorsplay a key role in the CEOs’ decisions to adopt B2B EC.
When we compare management levels only in adopter firms, we find that data se-curity, trust, and scalability are significant factors. Findings suggest that Presidentsare more concerned about data security than Managers, and Managers are less likelyto believe in the importance of scalability than Presidents and Directors in imple-menting B2B EC. Results also show that CEOs rated their trading partners higherfor trust than Corporate Managers. Once again, this provides support for the aboveargument that firms are more likely to adopt B2B EC when higher-ranked manage-ment has more trust in trading partners. It appears that higher-ranked managers have abroader perspective on and better awareness of some of the key adoption factors. Thismakes it even more critical for them to champion the implementation of B2B EC.
Overall, one of this study’s findings that context is relevant are in agreement withthose of some of the previous studies that analyzed the effects of contextual variables.For example, Frohlich and Westbrook [25] reported that firm type (manufacturingversus service firms) influenced the effect of Internet-enabled supply chain integra-tion on operational performance. The significant effects of industry and firm size onthe relationship between e-commerce and firm performance have also been evidencedby the findings of Zhu and Kraemer [118]. In addition, studies such as Sila [84] andSila [85] reported that some organizational and environmental factors played a sig-nificant role in the adoption of B2B EC, as well as in the relationship between B2BEC adoption and performance. Even though the adoption factors and contextual vari-ables used varied across these different studies, we can still infer in general terms thatcontext plays a role in the antecedents and consequences of B2B EC.
7 Recommendations for future research
This study contributes to supply chain theory in several ways. First, it shows that theTOE framework provides a strong foundation for the study of B2B EC. Second, itprovides evidence that this framework is strengthened further when contextual vari-ables are integrated into the theoretical model. This contextual nature of IT adoptionis often mentioned but rarely explored empirically in the literature. We attempted tofill this gap with this research. However, the need for more research remains. In the
Factors affecting the adoption of B2B e-commerce technologies
following paragraphs, we offer recommendations for further development and test-ing of the model presented in this study, which we believe will contribute to currenttheory and practice in the implementation of B2B EC.
One of the limitations of many studies in this area, including the current study, isthat only the effects of a limited number of adoption factors are empirically tested ineach study. In reality, the number of potential factors and contextual variables is muchlarger. The current study has shown that context does indeed play a role in B2B ECadoption. This contradicts with some of the studies conducted in this area. For exam-ple, Rahim et al. [73] found that industry type was not an important contextual factorin determining how inter-organisational systems are implemented. Therefore, futurestudies should test the effects of a wider range of factors and contextual variables onB2B EC adoption.
In addition, the fact that many previous studies did not account for differences inindustry or organizational characteristics and utilized different adoption factors mayhave produced inconsistent finding across these studies. Thus, it is important thatfuture studies attempt to resolve such inconsistent findings by utilizing appropriatemethodologies and research designs.
Another opportunity for future research lies in the area of stage-based implemen-tation of B2B EC in that the significance of these factors may vary at different stages(e.g., adoption, internal diffusion, external diffusion, routinization etc.) of B2B ECimplementation. Even though stage-based models have been studied frequently inthe general innovation adoption and information systems literature, they have beenrarely analyzed in the implementation of B2B EC.
Although researchers have studied various B2B EC adoption factors over the pastdecade, there are still lingering questions regarding the significance of some of thesefactors. This is because there are inconsistencies across these studies, partially due toa lack of common set of factors being tested and the scarcity of contextual variablesbeing used. This empirical study provides a groundwork for future studies in this area.
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Ismail Sila is a Professor of Operations Management and the Deanof the Faculty of Business and Economics at Girne American Univer-sity in Girne (Kyrenia), Cyprus. His research focuses on supply chainmanagement, e-commerce, quality management, and information tech-nology. Dr. Sila has published his research in journals such as the Jour-nal of Operations Management, the European Journal of InformationSystems, Supply Chain Management: An International Journal, the In-ternational Journal of Production Research, Industrial Management &Data Systems, and the International Journal of Operations & Produc-tion Management.