Electron Commer Res (2013) 13:199–236 DOI 10.1007/s10660-013-9110-7

Factors affecting the adoption of B2B e-commerce technologies Ismail Sila

Published online: 30 March 2013 © Springer Science+Business Media New York 2013

Abstract The objective of this study is to analyze the factors affecting the adoption of Internet-enabled business-to-business electronic commerce (B2B EC) and test their applicability in different contexts. We used 275 responses from an online survey of North American firms and tested our hypotheses with Multiple Regression and Analysis of Variance (ANOVA). We found that scalability is the biggest contributor to B2B EC usage. We also compared each adoption factor across adopters and nonadopters of B2B EC. Six of the nine adoption factors tested distinguished adopters of B2B EC from nonadopters. Then we analyzed the effects of these factors on adoption using several contextual variables, including firm size, firm type, management level of respondents, and country of origin of firms. The results showed that all of the contextual variables, except country of origin, influenced some of the adoption factors. Managers can use the findings of this study to understand which factors will most likely facilitate the implementation of B2B EC and be prepared to manage the effects of these factors on their initiatives more effectively. Many of the studies in this area have not tested the effects of contextual variables on B2B EC adoption. Thus, we contribute to the limited literature on this issue. The study shows that the technology–organization–environment (TOE) framework provides a strong foundation for the study of B2B EC. It also provides evidence that this framework is strengthened further when contextual variables are integrated into the theoretical model. Keywords B2B electronic commerce · Supply chain management · Internet · Adoption factors · Interorganizational information systems · Information technology · Contextual variables I. Sila () Girne American University, P.O. Box 388, Kyrenia, Cyprus e-mail: [email protected]

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1 Introduction A major portion of online sales still takes place between firms. In 2009, the use of B2B EC by US manufacturers and merchant wholesalers was more widespread than retailers or selected service businesses and accounted for 91 % of all online transactions. B2B activities of manufacturers were the highest among all sectors (accounting for 42 % of total shipments or $1,862 billion), followed by merchant wholesalers (manufacturing sales branches and offices), whose e-commerce activities constituted 23.4 % ($1,211 billion) of total sales [103]. An analysis of wholesalers suggested that B2B 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-eight percent of manufacturing firms and 50 % of wholesale firms use the Internet to buy goods or services, followed by 46 % of retailers [92]. There is also a positive outlook on the future of B2B EC in countries such as China and India. In the first quarter of 2011, B2B EC sales increased by 7.7 % from 2.7 billion yuan in the previous quarter to 2.9 billion yuan ($444 million). Compared to one year earlier, the revenue was up by 40.9 % [112]. In India, even though firms have not yet taken full advantage of EC [104], B2B sales grew by 30–40 % in 2008 and were predicted 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 also attempt to determine whether various contextual variables play a significant role in making this decision. For the purposes of this study, we use the term B2B EC for all Internet-enabled B2B technologies that allow supply chain partners to buy and sell products and share information. We use this term interchangeably with phrases such as Internet-based interorganizational systems, e-business, e-commerce, and Web technologies, 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 existing literature by showing that various contextual variables play an important role in the adoption of B2B EC technologies. It also provides strong evidence for the efficacy of the TOE framework for analyzing the factors that influence the adoption of these technologies. The rest of the paper is organized as follows: the next section discusses related work in this area, followed by theoretical framework, methodology, and statistical analyses. The article concludes with a discussion of findings and their implications, as well as recommendations for future research. 2 Related work Adoption factors in the interorganizational information systems literature In the general IT literature, several studies have been conducted before to determine the factors 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

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richness theory, theory of communicative action, and structuation theory, innovation diffusion theory has received the most attention from researchers [56]. In fact, it has been widely used as a foundation in Electronic Data Interchange (EDI) research [14]. Innovation diffusion theory provides several perceived innovation characteristics, including relative advantage, complexity, compatibility, observability, and trialability, that may either encourage or inhibit innovation adoption. These characteristics have been 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 theory only dealt with technological factors (i.e., perceived characteristics of the technology) that affected adoption, most research on EDI adoption took an organizational approach, focusing on organizational and interorganizational factors in addition to technological factors. The identification of organizational factors that influenced EDI drew mainly from the organizational innovativeness perspective based on the works of researchers such as Damanpour [18] and Premkumar and Ramamurthy [70]. Teo et al. [97] posited that institutional pressures also affect the adoption of supply chain linkages. Drawing from institutional theory and using data from Singaporebased firms, the authors found that these institutional factors are strong predictors of financial EDI adoption. Institutional theory suggests that organizations face pressures to conform to practices and polices widely deemed to be legitimate in their institutional environments. Failure to do so may deny them the resources and social support needed to be competitive [21]. Although the institutional perspective has been used by previous studies (e.g., Havema [36]; Han [33]; Goodstein [28]) to analyze the influence of institutional environments on organizational structure and practices, Teo et al. [97] claim that they were the first to test the effects of institutional factors on the adoption of financial EDI. These institutional factors are based on DiMaggio and Powell’s [21] three types of isomorphic pressures—coercive, mimetic, and normative. Mimetic pressures may force organizations to adopt the practices or innovations of other organizations in their environments, whether they carry any technical value or not, to gain social legitimacy. Coercive pressures entail formal or informal pressures firms face from other organizations such as governmental regulatory bodies, parent corporations, or other organizations they are dependent on, which are more dominant in terms of the resources they own. These pressures force firms to adopt structures or practices that serve the interests of the organizations exerting the pressure. Finally, normative pressures are those that may be exerted by suppliers, customers, and business, trade, and professional organizations to adopt a certain innovation [21]. Some of the previous studies (e.g., Gibbs and Kraemer [27]; Soares-Aguiar and PalmaDos-Reis [86]) used institutional theory and the TOE framework together, because institutional factors supplement the environmental context of the TOE framework. Adoption factors in the B2B EC literature In the B2B EC literature, although various factors have been proposed and tested by researchers to determine what factors affect the adoption of B2B EC, these factors have not always been consistent across studies. Some of the previous studies that operationalized such factors are relevant to our analysis of the factors affecting the adoption of B2B e-commerce technologies and are discussed in this section. Ranganathan et al. [76] argued that the key drivers of the assimilation and diffusion of B2B EC consist of such organizational and external 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 structure. On the other hand, Barua et al. [10] used supplier and customer-related factors as the determinants of the implementation of B2B EC. These factors had direct or indirect effects and included supplier readiness, supplier process alignment, supplierside 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 innovation characteristics (relative advantage, compatibility, costs, and security concern) and four contextual variables (technology competence, organization size, competitive pressure, and partner readiness) drove e-business usage. Three of these factors (costs, security concern, organization size) had a negative effect on e-business usage, whereas the rest of the factors was positively correlated with this variable. In turn, e-business usage had a positive impact on e-business performance (a combined measure of upstream communication, internal operations, and downstream sales). Together with the contextual factors, the study points to the importance of taking into account economic and regulatory factors in predicting e-business usage. Cho [12], who studied the factors affecting the adoption of third-party B2B portals in the Hong Kong garment industry, find that the key factors affecting this decision were firm size, perceived benefits, perceived hindrances, and perceived external pressure. A study of small and medium-sized firms in Australia by Chong and Pervan [13] concludes that both internal and external environmental factors significantly affected the extent to which B2B EC were implemented. These factors consisted of perceived relative advantage, trialability, observability, variety of information sources, amount of communication with other firms, competitive pressure, and non-trading institutional 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 frequently used theories by these studies are innovation diffusion theory, the TOE framework, institutional theory, and the resource-based view (RBV), in descending order of frequency of use, as shown in Table 1. Fichman [23] also argues that innovation diffusion theory is the most often used theory by researchers in the IT adoption area. It includes several innovation characteristics such as relative advantage, complexity, compatibility, observability, and triability that may either promote or hamper the adoption of IT. According to Zhu et al. [120], Rogers’ [78] innovation diffusion theory is consistent with the TOE framework, since it includes three types of factors that predict innovation adoption in addition to these technological factors: leader characteristics (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 B2B EC adoption literature is the RBV. The RBV argues that firms have heterogeneous resources (valuable, rare, imperfectly imitable, and non-substitutable), which enable them to achieve competitive advantage and superior long-term performance (e.g.,

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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 in Wade 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 responsiveness). The literature is in agreement that IT resources alone do not produce competitive advantage. Instead, they produce business value when they are combined and coordinated 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 that improve its efficiency and effectiveness”. Along with institutional theory, the TOE framework [102] is one of the most frequently used guiding theories in technology adoption research. It identifies three types of factors that affect technology innovation adoption: the technological context (e.g., availability, characteristics), organizational context (e.g., size, complexity of managerial structures, communication processes, availability of slack resources), and environmental 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 because it is a well-defined framework. It is also consistent with Rogers’ innovation diffusion theory [78]. 3.1 TOE framework Technological context The TOE framework can be used to study the diffusion of various IS innovations, including technical tasks, those that support business administration, and IS innovations that are integrated into the core business (e.g., B2B EC technologies) [94]. Internal technology resources such as infrastructure, technical skills, as well as developer and user time are significant for successful IS adoption [51]. Teo et al. [98, 99] state that even though the TOE framework has been widely used by previous researchers, the specific factors within each characteristic (i.e., technological, organizational, and environmental characteristics) vary across different 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 and enables them to reduce inventory, procurement, and coordination costs [120]. Therefore, 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 associated with the implementation of B2B EC can also be impediments to the adoption of these technologies [120]. Complexity Complexity is “the degree to which an innovation is perceived as relatively difficult to understand and use” [79]. Less complex innovations are more likely to be adopted [79]. According to Lin [57], B2B EC is a complex innovation, because it requires both technological adjustments such as combining the Internet platform with the existing IT infrastructure, as well as administrative adjustments such as 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 compared to EDI. Network reliability Network reliability deals with the ability of a firm to successfully transfer critical business applications to and from its supply chain partners over the Internet [88]. Outdated web servers and applications can prevent firms from transferring confidential and critical data reliably over the Internet [87]. Data security Data security refers to security issues associated with transactions conducted over the Internet. B2B EC are based on open standards and are more vulnerable to security breaches compared to legacy systems such as EDI that are based 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 the Internet. As a result of adopting B2B EC, firms can expand their market reach and create new markets for their products [53], as well as integrate with numerous entities such as customers, suppliers, retailers etc. [45].

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Organizational context Previous studies in this area have tested various organizational variables, including organizational readiness for EDI adoption (e.g., Iacovou et al. [39]), CEO characteristics, business size, and employee’s IS knowledge for IS adoption (e.g., Thong [100]), as well as internal need and top management support for EDI adoption (e.g., Premkumar and Ramamurthy [70]). In this study, we use the following organizational factors for B2B EC adoption: top management support, firm size, firm type, and management level. Top management support is a subjective measure that is posited to have a direct effect on adoption. On the other hand, the last three organizational factors, which consist of objective demographic information about firms’ organizational characteristics, have been used as contextual variables in our study in line with the general literature. Gibbs and Kramer [27] suggest that the 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 change creates an organizational environment that is receptive to innovation. Top management commitment support for innovation is particularly important during the implementation stage, when coordination across organizational units and conflict resolution are necessary [18]. Top management support is critical for the successful adoption of interorganizational systems [30]. Research on EDI adoption (e.g., Finnegan et al. [24]; Premkumar and Ramaurthy [70]) also provides support for the importance 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 B2B EC technologies are adopted [90]. According to Hart and Saunders [35], mutual trust is important for EDI adoption, since it encourages firms to make the needed investments 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 influencing IT adoption (e.g., Patterson et al. [69]; Wang et al. [107]). Large firms often possess certain advantages that enable them to adopt B2B EC technologies more easily than small firms, such as more slack resources, economies of scale, higher risk tolerance, and more power over trading partners [120]. Firm type Evidence on the effect of firm type on B2B EC use has been largely mixed in the literature. For example, Rutner et al. [82] found that nonmanufacturing firms utilized B2B EC more than manufacturing firms to sell to and communicate with their trading 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 predictors of B2B EC adoption. Several studies (e.g., Molla and Licker [63], Corsten and Kumar [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 human resources is important in technology adoption. Wagner et al. [106] also reported that managers who have great skills in e-business are more likely to facilitate the implementation of B2B EC. Environmental context Some of the factors used to operationalize this context by previous studies on EDI adoption include external pressure [39], perceived industry pressure and perceived government pressure [50], and competitive pressure and exercised power [70]. In line with these studies, we use pressure from trading partners and pressure from competitors to define this context. Firms may face pressure from their environment to adopt B2B EC (e.g., pressure from suppliers, customers, competitors, 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 institutional pressures discussed earlier. Country of origin is another institutional factor in that firms’ management practices may converge or vary based on whether they operate in the same or different geographical region or country. For example, practices across firms in the same geographical region are often expected to converge due to pressures to conform or imitate. This factor has also drawn a lot of attention in the general management literature because of the potential effects of national culture on management practices. There is evidence in the literature that various technological, organizational, and environmental factors distinguish B2B EC adopters from nonadopters and influence the extent to which firms adopt these technologies. Some firms are not willing to commit any resources to participate in online markets, whereas others dedicate their resources to establish the required processes to engage in online business [29]. For example, although some firms may pursue little or no web-based integration, others only integrate their operations with either customers or suppliers, or with both customers and suppliers [25]. Sila and Dobni [85] found that e-leaders (firms with the highest level of B2B EC usage and integration) scored significantly higher on many of the adoption factors than e-laggards (firms that are not willing to adopt these technologies). Overall, the factors that influence the adoption of B2B EC are being analyzed under three categories in this study: technological factors, organizational and interorganizational factors, and environmental factors. We propose the conceptual model illustrated in Fig. 1 for the analysis of factors affecting B2B EC adoption. Figure 1 shows the three general categories of the TOE framework and Fig. 2 consists of a more detailed visual depiction of the factors that fall under each of these categories.

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Fig. 2 B2B EC adoption factors and contextual variables

More specifically, the direct effects of the adoption factors and the indirect effects of the 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 literature review, we propose the following hypotheses: H1: Several factors (technological, organizational, interorganizational, and environmental 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 contexts.

4 Methodology To show how our study contributes to and advances knowledge in the field, we conducted an extensive review of the relevant empirical literature. We carefully screened each article to make sure that it only dealt with B2B EC technologies and that it was empirical. 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 each article yielded several distinct categories of objectives across the 155 articles: studies in the first category mainly dealt with the analysis of various factors that affect B2B EC adoption (e.g., Ranganathan et al. [76]; Sila [84]). Studies included in the second category were similar to these except that they focused on specific factors that may influence adoption such as trust related factors (e.g., Ratnasingam [77]), supplier related factors (e.g., Deeter-Schmelz et al. [19]; Andreu et al. [3]), and capability related factors (e.g., Koch [49]; Lee et al. [54]). Another group of studies (e.g., Zahay and Handfield [115]; Harrison and Waite [34]; Sila and Dawn [85]) analyzed B2B EC 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 performance effects of B2B EC adoption using several statistical methods (e.g., Barua et al. [10]; Rosenzweig and Roth [81]), while others explored B2B EC diffusion and performance 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 diffusion phases of B2B EC implementations, as well as the performance effects of these initiatives. Another distinct research category analyzed the level of firms’ participation in emarketplaces (e.g., Grewal et al. [29]; Papastathopoulou and Avlonitis [68]) and the extent to which firms are integrated with their supply chain partners through Internetbased technologies (e.g., Thun [101]), while a related stream of research tested the effects of the level of Internet-enabled supply chain integration on various performance measures such as operational performance (e.g., Frohlich and Westbrook [25]) and procurement productivity (e.g., Rai et al. [74]). Our study falls under the first category and aims to build on the previous studies in this category. Although various adoption factors have been operationalized by these studies, the context within which these factors are applicable have, with certain exceptions (e.g., Subramaniam and Shaw [93]; Corsten and Kumar [16]; Sila [84]), largely been ignored. Since contextual variables can be influential in creating successful knowledge management systems (Raisinghani and Meade [75]) and that they are 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 up of 23 items. These items are listed in Table 2 and have been adopted from Soliman and Janz [87]. B2B EC adoption was measured by taking the average score on the extent of firms’ usage of seven B2B EC technologies (purchasing and procurement applications, inventory management applications, transportation applications, order processing 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 actually adopted B2B EC technologies by asking the respondents a yes/no question. We operationalized the contextual variables by posing firms questions about their firm size, firm type, management level, and their country of origin in the demographics section of the survey. Two common measures of firm size are employee

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Table 2 Items for B2B EC adoption factors Pressure from trading partner My 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 in order processing My main trading partner decides on what information systems applications are to be exchanged with my firm Pressure from competition An industry move to utilize the Internet for interorganizational communications would put pressure on my firm to do the same There is a trend in my industry to more utilize the Internet more for business-related activities and business communications Costs Establishing Internet-based business-to-business operations with my trading partners would be cost effective It would be less expensive to conduct business with several trading partners utilizing the Internet than using Electronic Data Interchange (EDI) Network reliability The Internet is considered to be a reliable communication medium to conduct business with trading partners along the supply chain Current Internet communication speeds are sufficient to handle the data movement necessary for our company to communicate with our trading partner Data security The nature of the business data regularly exchanged between our firm and our trading partners requires a secured communication medium Internet security is a major concern to our firm when deciding to adopt Internet-based business-to-business transactions Scalability The availability of the Internet as a business communication medium is likely to increase the number of trading partners with whom we can do business The Internet is likely to facilitate linking several of our firm’s business units together (e.g., branch offices, remote sites, etc.) Complexity The existence of several communication standards when using EDI makes it more difficult to establish links with several trading partners The Internet’s one common communication standard (TCP/IP) would make it easier to communicate with multiple trading partners Internet-based business-to-business communication would be considered less complex to implement than alternative methods such as EDI Top management support Our top management is likely to invest funds in IT Our top management is willing to take risks involved in the adoption of the Internet Our top management is likely to be interested in adopting the Internet-based business-to-business transactions in order to gain competitive advantage Our top management is likely to consider the adoption of Internet-based business-to-business applications as strategically important Trust How 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) Complexity (Comp) Top management support (TMS)

0.310∗ −0.012 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 literature. Therefore, we decided to test the effect of firm size using several firm size classification approaches: firm size 1 (small: 0–20 employees, medium: 21–100 employees, 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 of less than $50 million, large: sales greater than $50 million). We randomly selected 3000 firms from the mailing list of the Council of Supply Chain Management Professionals and Industry Canada’s website [41]. Selected firms were asked via email to respond to the Web-based survey. We sent two reminders over a two-week period. We received a total of 420 responses. Service firms that responded were excluded, since preliminary analyses showed that the survey instrument was not as 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, the number of usable responses was 275. We tested for non-response bias by splitting the responses into early and late respondents and conducting t-tests on their mean responses to 10 randomly selected survey questions [6]. The results suggested that there was no evidence of non-response bias. 5 Analyses First, we conducted tests to see whether the basic assumptions of ANOVA such as normality and homogeneity of variance were met. For example, a series of Levene’s tests confirmed that the variances across samples were equal. All the analyses suggested 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. Pearson’s correlation results (Table 3) show that all of the adoption factors except com-

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Table 4 Multiple regression results-estimates of effect of different factors on B2B EC adoption Variables

Constant

Coefficient Model 1

Model 2

Model 3

Model 4

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 R 2

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 and top management support had the largest correlation with B2B EC adoption. We also conducted multiple regression analysis to estimate simultaneous correlations 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 all of the assumptions of regression, including linearity, independence, constant variance, and normality were met. To find the contribution of significant adoption factors that could explain variations in B2B EC, regression was carried out using four appropriate combinations of variables (Table 4). Model 1 only included technological variables, Model 2 only organizational and interorganizational variables, Model 3 only environmental variables, and Model 4 consisted of all the three categories of variables. Model 4, with adjusted R 2 = 0.155, and Model 2, with adjusted R 2 = 0.141, had the highest explained variance. Scalability emerged as the single most important factor 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 nonadopters (H2) by conducting a series of one-way ANOVAs. Table 5 shows the descriptive 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

PFTP

Adopters

176

Nonadopters PFC

Adopters Nonadopters

Costs

Adopters

NR

Adopters

DS

Adopters

Scal

Adopters

Nonadopters Nonadopters Nonadopters Nonadopters Comp

Adopters

TMS

Adopters

Nonadopters Nonadopters Trust

Adopters Nonadopters

99 176

Sum

Average

Variance

897.75

5.10

1.06

504

5.09

1.04

936

5.32

1.68

99

468.50

4.73

2.00

176

863.50

4.91

1.36

99

470.85

4.76

0.98

176

898

5.10

1.22

99

450

4.55

1.44

176

748

4.25

1.45

99

393

3.97

1.53

176

948

5.39

1.58

99

458

4.63

1.92

680.17

3.86

2.34

176 99

378.67

3.82

1.94

176

972.42

5.53

1.18

99

492.67

4.98

0.97

176

845.17

4.80

1.08

99

438.50

4.43

1.54

to Table 6, five of the nine factors (Pressure from competitors (PFC), Network reliability (NR), Scalability (Scal), Top management support (TMS), Trust) distinguished adopters from nonadopters at the 0.05 significance level. For example, there were statistically significant differences between group means on PFC as determined by a one-way ANOVA (F (3.88) = 12.12, P = 0.00058). In each of the five cases, the mean score on each factor for adopters was greater than that for nonadopters. Given that five of the nine factors distinguished adopters from nonadopters, H1 was partially supported. Thus, our findings conflict with those of Archer et al. [5] in terms of the relative 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 between adopters and nonadopters, since we had small sample sizes for the Europe and USA 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 with more than 100 employees. A series of one-way ANOVA analyses for each of the nine factors yielded the results displayed in Table 7 (1a, 1b, and 1c). For example, small adopters, medium nonadopters, and large nonadopters differed from each other on four factors (Data security (DS), Scal, TMS, Trust). Tukey tests (Table 8) indicated

Between groups Within groups Total

Between groups Within groups Total Between groups Within groups Total

Between groups Within groups Total

Between groups Within groups Total

Between groups Within groups Total

Between groups Within groups Total Between groups Within groups Total

PFC

Costs

DS

Scal

Comp

TMS

Trust

NR

Between groups Within groups Total

PFTP

Source of variation

19.07338 301.78556 320.85894 8.8062041 339.97117 348.77738

0.0996977 599.32111 599.42081

36.606465 465.39899 502.00545

4.9781818 404.90909 409.88727

1.4292023 333.83449 335.26369 19.644545 355.70455 375.34909

21.747071 489.83838 511.58545

0.0062642 286.95419 286.96045

Sum of squares

1 273 274 1 273 274

1 273 274

1 273 274

1 273 274

1 273 274 1 273 274

1 273 274

1 273 274

df

8.8062041 1.2453157

19.07338 1.1054416

0.0996977 2.1953154

36.606465 1.7047582

4.9781818 1.4831835

19.644545 1.3029471

1.4292023 1.222837

21.747071 1.7942798

0.0062642 1.0511143

Mean square

Table 6 One-way ANOVAs: comparing adopters and nonadopters of B2B EC

7.0714634

17.254082

0.0454138

21.473112

3.3564167

15.07701

1.1687595

12.120223

0.0059596

F

0.0082949

4.377E-05

0.831404

5.563E-06

0.0680329

0.0001295

0.2806104

0.0005806

0.9385223

P -value

3.8757465

3.8757465

3.8757465

3.8757465

3.8757465

3.8757465

3.8757465

3.8757465

3.8757465

F crit

S

S

NS

S

NS

S

NS

Significant (S)

Nonsignificant (NS)

Conclusion

0.025

0.059

0.000

0.073

0.012

0.052

0.004

0.043

0.000

R2

Factors affecting the adoption of B2B e-commerce technologies 213

NS

NS

S (R 2 = 0.098) NS

NS

NS

Firm size (2a) groupsd : small adopters NS (n = 80), medium nonadopters (n = 37)

NS

NS

NS

NS

Firm size (1c) groupsb : large adopters (n = 55), small nonadopters (n = 58), medium nonadopters (n = 30)

Firm size (2b) groupsd : medium adopters (n = 63), small nonadopters (n = 58)

Firm size (2c) groupsd : large adopters (n = 32), small nonadopters (n = 58), medium nonadopters (n = 37)

Firm size (3a) groupse : SME adopters (n = 124), large nonadopters (n = 10)

Firm size (3b) groupse : large adopters (n = 52) SME nonadopters (n = 89)

NS

NS

NS

NS

NS

NS

NS

NS

S (R 2 = 0.120) NS

NS

Firm size (1b) groupsb : medium adopters (n = 40), small nonadopters (n = 58), large nonadopters (n = 11)

NS

NS

Firm size (1a) groupsb : small adopters (n = 80), medium nonadopters (n = 30), large nonadopters (n = 11)

NS

NS

S (R 2 = 0.148) NS

NS

S (R 2 = 0.048) NS

NS

NS

Trust

NS

S (R 2 = 0.062) S (R 2 = 0.042) NS

NS

NS

S (R 2 = 0.057) NS

S (R 2 = 0.084) NS

NS

S (R 2 = 0.080) S (R 2 = 0.069)

S (R 2 = 0.093) S (R 2 = 0.050)

TMS

S (R 2 = 0.053) NS

S (R 2 = 0.075) NS

S (R 2 = 0.089) S (R 2 = 0.068) NS

S (R 2 = 0.072) NS

S (R 2 = 0.114) NS

NS

Comp

S (R 2 = 0.043) S (R 2 = 0.055) NS

S (R 2 = 0.073) NS

NS

Scal

Sc (R 2 = 0.08) S (R 2 = 0.102) NS

DS

S (R 2 = 0.114) NS

NS

Costs NR

PFTP PFC

Contextual variablesa

Table 7 One-way ANOVAs: comparing adopters and nonadopters of B2B EC using contextual variables

214 I. Sila

NS

Firm type (1b) groupsf : Merchandiser adopters (n = 57), Manufacturer nonadopters (n = 67)

Costs

DS

S (R 2 = 0.071) NS

NR

NS

NS

NS

NS

Management level (1c) groups: Director adopters (n = 20), CEO nonadopters (n = 19), Manager nonadopters (n = 14), President nonadopters (n = 28)

NS

NS

S (R 2 = 0.119) NS

NS

NS

NS

NS

NS

NS

NS

S (R 2 = 0.127) NS

NS

NS

NS

Trust

NS

NS

NS

NS

NS

S (R 2 = 0.188)

S (R 2 = 0.090) NS

S (R 2 = 0.090) NS

Comp TMS

S (R 2 = 0.055) NS

Scal

S (R 2 = 0.080) S (R 2 = 0.032) S (R 2 = 0.051) S (R 2 = 0.060) S (R 2 = 0.098) NS

S (R 2 = 0.073) NS

Management level (1b) groups: Corporate Manager adopters (n = 9), CEO nonadopters (n = 19), Director nonadopters (n = 9), President nonadopters (n = 28), Manager nonadopters (n = 14)

Management level (1a) groupsg : CEO NS adopters (n = 21), Director nonadopters (n = 9) , Manager nonadopters (n = 14), President nonadopters (n = 29)

NS

PFTP PFC

Firm type (1a) groupsf : Manufacturer adopters (n = 109), Merchandiser nonadopters (n = 30)

Contextual variablesa

Table 7 (Continued)

Factors affecting the adoption of B2B e-commerce technologies 215

NS

NS

NS

NS

NS

NS

Management level (1d) groups: Manager adopters (n = 36), CEO nonadopters (n = 19), Director nonadopters (n = 9), President nonadopters (n = 28)

Management level (1e) groups: President adopters (n = 47), CEO nonadopters (n = 19), Director nonadopters (n = 9), Manager nonadopters (n = 14)

Management level (1f) groups: VP adopters (n = 11), CEO nonadopters (n = 19), Director nonadopters (n = 9), Manager nonadopters (n = 14), President nonadopters (n = 28)

NS

NS

NS

Costs

NS

S (R 2 = 0.126)

S (R 2 = 0.104)

NS

NS

S (R 2 = 0.098)

NS

NS

S (R 2 = 0.120)

Scal

DS

NR

NS

NS

NS

Comp

NS

NS

NS

TMS

NS

S (R 2 = 0.094)

NS

Trust

f Merchandisers include wholesalers/distributors and retailers g Some of the management level nonadopter groups had very small sample sizes and were therefore omitted

d Small: 0–20 employees, medium: 21–500, large: 501+ (large nonadopters were excluded due to a very small sample size) e SMEs: sales less than $50 million, large: >$50 million

b Small: 0–20 employees, medium: 21–100, large: 100+ c All significant at p < 0.05

a Country comparisons could not be made between adopters and nonadopters due to the small sample sizes of the Europe and USA groups. As well, the analysis for the following firm size groupings could not be conducted due to sample size limitations: Small: 0–20 employees, medium: 21–100, large: 100+

PFC

PFTP

Contextual variablesa

Table 7 (Continued)

216 I. Sila

NR

PFC

Trust

TMS

Scal

DS

Significant adoption factor

Medium adopters

Large nonadopters

Medium adopters

Large nonadopters

Firm Size 1b groups

Medium nonadopters

Large nonadopters

Medium nonadopters

Large nonadopters

Medium nonadopters

Large nonadopters

Medium nonadopters

Large nonadopters

Firm Size 1a groups

Contextual variables

1.084 1.79

−0.363

1.339∗

Small adopters

0.935

1.504 −1.14 −1.403

−0.259 −0.853∗

Small nonadopters Small nonadopters

−0.303

0.621

−0.297 −0.318

Small nonadopters

−1.607

−0.952∗

−0.028 0.593

−1.076∗

Small nonadopters Medium adopters

−1.209 −2.124

−0.124

Medium adopters

0.961

1.634

0.373

−0.188

Small adopters

−0.054

1.343

−0.509

0.417 0.790∗∗

1.068 1.312

0.248

0.780∗

Medium nonadopters

0.365 −0.532

0.268

1.278

−0.512

−1.389

2.32

0.358 −0.027

0.625

Small adopters

Small adopters

Small adopters

Medium nonadopters

Small adopters

0.454 0.714

Small adopters Medium nonadopters

2.135

0.242

Small adopters −0.176

−0.304

1.774

Upper bound

1.189∗

Lower bound

95 % Confidence interval

0.735

Mean difference

Medium nonadopters

Table 8 Tukey tests for significant factors with three or more groups in adopter and nonadopter firms

Factors affecting the adoption of B2B e-commerce technologies 217

Comp

NR

NR

Trust

TMS

Large nonadopters

Scal

Medium nonadopters

Large adopters

Medium nonadopters

Large adopters

Firm Size 2c groups

Medium nonadopters

Large adopters

Firm Size 1c groups

Medium adopters

Large nonadopters

Medium adopters

Large nonadopters

Medium adopters

Contextual variables

Significant adoption factor

Table 8 (Continued)

0.926

1.784 1.340

−0.673 −1.246 −1.083 0.081 −0.306 −0.930

−0.474 −0.600∗ 0.932∗ 0.517 −0.415

Small nonadopters Medium adopters Small nonadopters

−1.272 −0.945

−0.741∗ −0.311

Small nonadopters

1.538 0.878

0.072 −0.523

0.805∗

Small nonadopters Small nonadopters

0.177

0.175 1.431

−1.032

Small nonadopters

−0.176

−0.429

0.309 −0.180 0.627

−0.812∗

Small nonadopters

0.323

−0.211

0.210

0.099

−0.118

Medium nonadopters

−1.075 −1.444

−0.383

Medium nonadopters

Small nonadopters

−1.070

−0.430

Medium nonadopters

Small nonadopters

Small nonadopters

0.298

−0.368

−1.650

0.126

−1.009∗

Small nonadopters Medium adopters

2.538 1.493

0.414 −0.559

0.467

Small nonadopters

Upper bound

1.476∗

Lower bound

95 % Confidence interval

Medium adopters

Mean difference

218 I. Sila

Trust

DS∗∗∗

PFC

Significant adoption factor

Table 8 (Continued)

Manager nonadopters

Director nonadopters

CEO adopters

Manager nonadopters

Director nonadopters

CEO adopters

Manager nonadopters

Director nonadopters

CEO adopters

Management level 1a groups

Contextual variables

−1.743 −2.201 −1.730 −2.231 −1.840

−0.500 −1.161∗ −0.190 −0.851 −0.661

President nonadopters Manager nonadopters President nonadopters

1.942 2.494 1.768

−0.014 −0.375 −2.306 −1.775

0.696 −1.302∗ −0.905∗ −0.280

Manager nonadopters President nonadopters

1.451

−0.201 0.625

1.474 1.988

0.055

1.022∗

President nonadopters President nonadopters

0.448 −0.681 0.397

Manager nonadopters

−0.035

−0.297 −1.008

President nonadopters Director nonadopters

1.240

President nonadopters

0.891 −0.854

0.544

−0.998

−0.054

President nonadopters Manager nonadopters

−1.879

−0.750

Manager nonadopters

0.379

−2.597

−1.294

0.010

0.518

0.529

1.349

−0.121

0.743

1.126

Upper bound

Director nonadopters

President nonadopters

−1.745

−0.310

Director nonadopters

Lower bound

95 % Confidence interval

Manager nonadopters

Mean difference

Factors affecting the adoption of B2B e-commerce technologies 219

NR∗∗∗

DS

Significant adoption factor

Table 8 (Continued)

Manager nonadopters

Director nonadopters

CEO nonadopters

Management level 1e groups

Manager nonadopters

Director nonadopters

CEO adopters

Management level 1d groups

Contextual variables

President adopters

President adopters

Manager nonadopters

President adopters

1.538 0.993 1.710 1.835

−1.604 −0.502 −0.016

−0.306 0.910

0.604

0.873 −0.114 0.712

Manager nonadopters

−1.268

−1.122 0.108

−0.197

Director nonadopters

1.338

1.444

−0.103

0.671

1.713 2.416

0.064

1.240∗

President nonadopters President nonadopters

1.299 −0.574

0.569

0.386

President nonadopters Manager nonadopters

0.586

−1.155

−0.284

−0.526

−2.096

−0.854

0.388

Upper bound

Manager nonadopters

Lower bound

95 % Confidence interval

Director nonadopters

Mean difference

220 I. Sila

Manager nonadopters

Manager nonadopters

Director nonadopters

CEO nonadopters

Manager nonadopters

Director nonadopters

CEO nonadopters

1.712

1.746 1.523 2.263 1.976

−1.348 −0.182 −0.070

0.952 1.348 1.684 2.131 1.555

−1.170 −0.290 −0.890 −0.061 −0.279 0.638

1.035

President adopters President adopters

0.397

Manager nonadopters

0.529

−0.109

Manager nonadopters President adopters

−1.724

−0.506

Director nonadopters

0.953

1.040

President adopters President adopters

0.087

Manager nonadopters

0.713

1.063

−1.304 −0.081

0.833

−0.120

President adopters

Manager nonadopters

1.152

−0.163 −1.567

0.774 −0.208

Director nonadopters

President adopters

0.198

1.318∗

1.859 2.438

−0.772

0.544

0.774 1.301

−0.373

President adopters

0.464

0.392

Manager nonadopters

President adopters

−0.310

Upper bound

−1.395

−2.100

−0.854

Manager nonadopters

Lower bound

95 % Confidence interval

Director nonadopters

Mean difference

∗∗∗ Although the omnibus ANOVA test is significant at p < 0.05, Tukey tests find no differences between groups (with more than three groups, Cohen [15] argues that the Tukey results can be used, regardless of the results of the omnibus ANOVA)

no differences between groups (LSD-type tests are recommended by Cohen [15] in cases like this when three groups exist)

∗ Significant at p < 0.05 ∗∗ Fisher’s Least Significant Difference (LSD) test suggests the difference is significant, although the omnibus ANOVA test is significant at p < 0.05 and the Tukey tests find

Trust∗∗∗

Scal∗∗∗

CEO nonadopters

DS

Director nonadopters

Contextual variables

Significant adoption factor

Table 8 (Continued)

Factors affecting the adoption of B2B e-commerce technologies 221

222

I. Sila

that the average score on these factors was significantly higher in small adopters than in 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 different set of factors (PFC, NR, Scal, TMS) are significantly different across the two groups, although Scal and TMS are common factors. This shows that firm size does affect the significance of these factors, providing support for Hadaya’s [31] findings. Similarly, medium adopters, small nonadopters, and large nonadopters (1b) differed from each other on five factors (PFC, NR, Scal, TMS, Trust). Again, when medium adopters are compared only with medium nonadopters, the set of significant factors (PFC, DS, Scal, TMS) is slightly different, although PFC, Scal, and TMS are common. This indicates that firm size contributes to these differences. According to Tukey tests conducted for 1b, the average score on PFC was lower in small nonadopters than in both medium adopters (these had the highest average) and large nonadopters. For NR and TMS, only the mean scores of medium adopters and small nonadopters differed, where the former had significantly higher scores. For Scal, medium adopters had significantly higher mean scores than both small and large nonadopters. Medium adopters also scored significantly higher than large nonadopters on the Trust factor. In 1c, we see that large adopters had a higher mean score on NR than small nonadopters, but there were no other significant differences between large adopters and small and medium nonadopters. We also found differences on some of the adoption factors, ranging from two to four 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 the difference in mean score on complexity, which was smaller in large adopters than in small nonadopters. Interestingly, when large adopters were compared with small and medium nonadopters, there were a small number of significant differences (only one or two factors). The amount of explained variance (R 2 ) for significant differences ranged from 0.042 to 0.148. Firm type As Table 7 indicates, a comparison of manufacturing adopters and merchandising nonadopters, as well as merchandising adopters and manufacturing nonadopters, displays differences on four and six factors, respectively. Four of those factors were common across the two comparisons and included PFC, NR, Scal, and TMS. In all cases of significant differences, the mean scores for adopters were greater than those for nonadopters. Management level All combinations of comparisons among the different management 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). The factors that displayed differences across management levels include PFC, DS, and Trust. DS was a common significant factor across 1d and 1e. The Tukey tests for 1a (Table 8) suggest that the CEOs in adopter firms had a higher mean score than Presidents in nonadopter firms on the PFC factor and a higher mean score than both

Factors affecting the adoption of B2B e-commerce technologies

223

Directors and Managers in nonadopter firms on the Trust factor. Presidents in nonadopter firms also scored higher than Directors in nonadopter firms on Trust. The Tukey tests for 1d and 1e, which had DS as the only significant factor, showed that the only differences were between Presidents in both adopter and nonadopter firms and Directors in nonadopter firms. Presidents had significantly higher mean scores than Directors. The R 2 values for the significant factors were generally larger than those for the other contextual variables and ranged from 0.098 to 0.188. Given the importance of top management support for technology adoption, we also conducted separate one-way ANOVAs (details not reported here) to compare CEOs in adopter firms with those in nonadopter firms for each of the nine factors. The results indicated that the two groups only differed on the top management support and trust dimensions, where the mean score on both factors were greater for CEOs in adopter firms. 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, firm size, firm type, management level. Country of origin Table 9 shows that there are no differences in adoption factors across three country of origin categories—Canada, USA, and Europe. Firm size There were some significant differences on one or two factors across all four approaches of firm size comparisons (see Table 9). These differences were fewer than those found for comparisons between adopter and nonadopter firms of different sizes (see results of H3). In fact, only DS and/or Complexity (Comp) showed significant differences across all four firm size comparisons (1a–1d). Tukey tests (see Table 10) show that large firms mainly differed from firms with sizes of 0–100 employees (defined as small or medium-sized firms, depending on the ranges used) in terms 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 from both small and medium-sized firms in that the mean score on Comp was smaller for large firms. Firm type Only three (PFC, Costs, DS) of the nine factors differed across manufacturing adopters and merchandising adopters. The mean scores on these factors were higher 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 adoption factors—DS, Scal, and Trust. Tukey tests (Table 10) have shown that, for DS, the only difference in perception was between Presidents and Managers, where Presidents had larger average scores on this factor. Managers’ opinions about Scal also differed from those of Presidents and Directors. Managers had lower mean scores on this factor than both Presidents and Directors. As for Trust, CEOs had a significantly larger average score on this factor than Corporate Managers. Based on these results, H4 is partially supported.

NS

NS

NS

NS

NS

NS

NS

Country groups: Canada (n = 120), Europe (n = 19), USA (n = 34)

Firm size (1a) groupsa : small (n = 80), medium (n = 40), large (n = 55)

Firm size (1b) groupsc : small (n = 121), medium (n = 23), large (n = 32)

Firm size (1c) groupsd : small (n = 80), medium (n = 63), large (n = 32)

Firm size (1d) groupse : SME (n = 124), large (n = 52)

Firm type groups: Manufacturer (n = 57), Merchandiser (n = 109)

Management level: CEO (n = 21), Corporate Manager (n = 9), Director (n = 20), Manager (n = 36), President (n = 47), VP (n = 11)

d Small: 0–20 employees, medium: 21–500, large: 500+ e SMEs: sales less than $50 million, large: >$50 million

b All significant at p < 0.05 c Small: 0–100 employees, medium: 101–500, large: 500+

NS

S (R 2 = 0.024)

S (R 2 = 0.034)

NS

NS

NS

NS

NS

NS

Costs

NS

NS

NS

NS

NS

PFC

a Small: 0–20 employees, medium: 21–100, large: 100+

PFTP

Contextual variables

NS

NS

NS

NS

NS

NS

NS

NR

NS S (R 2 = 0.090)

S (R 2 = 0.097)

NS

NS

NS

NS

NS

Scal

S (R 2 = 0.032)

S (R 2 = 0.029)

S (R 2 = 0.036)

S (R 2 = 0.047)

Sb (R 2 = 0.045)

NS

DS

Table 9 One-way ANOVAs: comparing only adopters of B2B EC using contextual variables

NS

NS

NS

S (R 2 = 0.071)

S (R 2 = 0.066)

NS

NS

Comp

NS

NS

NS

NS

NS

NS

NS

TMS

S (R 2 = 0.096)

NS

NS

NS

NS

NS

NS

Trust

224 I. Sila

Comp

DS

Comp

DS

DS

Significant adoption factor

Medium

Large

Medium

Large

Firm Size 1c groups

Medium

Large

Medium

Large

Firm Size 1b groups

Medium

Large

Firm Size 1a groups

Contextual variables

0.252 0.247 −1.065

0.944∗ −0.267

Small

0.408 0.116 −0.909

0.846∗ −0.322

Small Small

0.266

1.576

1.927

0.502

−0.443 0.029

Small

1.168∗

0.018

Medium

1.192

−0.035 0.576 0.605∗

Small

1.186

0.531

1.642

Medium

Small

2.17

1.01

−0.262

0.374

Small 1.211∗

0.096

Small Medium

1.208

−0.487

0.278 0.652∗

Medium

1.042

0.399

0.978

−0.683

−0.142

Small

1.212

0.049 −0.001

0.489

Upper bound

Small

Lower bound

95 % Confidence interval

0.631∗

Mean difference

Medium

Table 10 Tukey tests for significant factors with three or more groups in adopter firms

Factors affecting the adoption of B2B e-commerce technologies 225

DS

Significant adoption factor

Table 10 (Continued)

President

Manager

Director

Corporate Manager

CEO

Management level

Contextual variables

0.159 0.868

−1.151 −1.607 −0.820

−0.146 −0.724 0.024 −0.844

Manager President

1.827 1.157 1.877 0.319 1.029

−1.24 −0.463 −1.607 −1.475 −0.688 −1.905 0.036 −1.228 −1.945

−0.042 0.707 −0.162 −0.578 0.171 −0.698 0.749∗ −0.120 −0.868

Manager President Manager President

VP

VP

President

VP

VP

0.208

0.988

1.461

0.509

1.284

0.353 −0.755

0.536

Director

VP

−2.041

0.858

−1.964

−0.683

0.598

Upper bound

Director

Lower bound

95 % Confidence interval

Corporate Manager

Mean difference

226 I. Sila

CEO

Scal

President

Manager

Director

Corporate Manager

Contextual variables

Significant adoption factor

Table 10 (Continued)

−1.860 −1.012

−0.875 −0.071 −0.439

Manager President VP

1.671

−1.919 0.010 −0.800 −1.570

−0.573 0.804∗ 0.436 −0.368

VP President VP VP

1.599

−1.162

−0.204

President

0.833

0.774

0.753

−0.008

−1.44 −2.009

0.172 −1.008∗

Manager

1.784

VP

1.073

0.54

President

1.845

−0.264

Manager

−0.765

2.184

−0.695

0.744

Director

−1.601

0.896

−1.774

0.871

0.110

0.818 1.254

−2.040 −0.987

Upper bound

0.133

−0.611

Lower bound

95 % Confidence interval

Director

Corporate Manager

Mean difference

Factors affecting the adoption of B2B e-commerce technologies 227

∗ Significant at p < 0.05

CEO

Trust

President

Manager

Director

Corporate Manager

Contextual variables

Significant adoption factor

Table 10 (Continued)

0.655

1.049 −1.315

−0.330

VP

1.017 −0.978 0.035

VP

0.570 −0.286

−1.638

−0.534

VP

0.366

−0.204

President

0.251 0.581

−1.390 −0.989

−0.569

Manager President

2.047 1.968

−0.093

0.646

−0.675

0.977

President VP

2.361 1.707

0.000

0.495

0.503

−0.485

−1.694

−0.600

VP

0.173

0.611

−1.041

−0.269

President

0.853

1.181

−1.442

−0.635

Manager

−0.074

Director

−0.984

−0.065

Director

Upper bound

Manager

−2.418

−1.246∗

Lower bound

95 % Confidence interval

Corporate Manager

Mean difference

228 I. Sila

Factors affecting the adoption of B2B e-commerce technologies

229

6 Discussion of findings and implications Findings from Pearson’s correlation analysis show that each adoption factor except complexity and trust is significantly associated with the extent of B2B EC usage. According to multiple regression results, scalability is the biggest contributor to B2B EC usage, suggesting that firms place great value on the capability provided by the Internet to reach or create new markets and link with their supply chain partners. When contextual variables are not accounted for, the findings show that pressure from competitors, network reliability, scalability, top management support, and trust play 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 a significant contextual variable. Therefore, managers of firms with different countries of 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 were actually located in different countries and included firms from countries with very different cultures. Larger group sample sizes would also be more effective in identifying any such differences. Results suggest that firm size is an important contextual variable in B2B EC adoption. Based on comparisons of adopter and nonadopter firms of different sizes, we see that there are certain unique adoption factors that spur firms of different sizes to adopt B2B EC. This is more evident in SMEs than large firms. This may be the case because SMEs face more constraints when implementing these technologies. Some studies take the view that large firms are better positioned to adopt B2B EC because these 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 trading partners to adopt the same technologies [120], and have more champions than small firms to facilitate the adoption of innovative technologies [42]. Findings also show that, for firms that actually adopted B2B EC, only data security and complexity are affected by firm size. There is evidence that data security is less of a concern for large firms and that large firms find it less complex than SMEs to implement B2B EC, possibly due to greater previous experience in implementing these technologies. Although the managers of SMEs do not have as much leverage as those of large firms when it comes to resource issues or economies of scale, they can focus on factors they have more control over. For example, they can focus on building closer ties and trust with their trading partners and hire knowledgeable IT personnel to better deal with the complexities and security issues involved in the implementation of B2B EC. We also observe that firm type influences the factors that shape firms’ adoption decisions. The key factors that were affected by firm type include pressure from competitors, costs, network reliability, data security, scalability, and top management support. On the other hand, when we compare firms of different types that have already adopted B2B EC (i.e., manufacturer adopters and merchandising adopters), we see that only three of these factors (pressure from competitors, costs, data security) are different across the two groups. Findings reveal that these three factors were slightly more significant in motivating managers in manufacturing firms to adopt B2B EC.

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This may in part be the reason why manufacturing shipments exceed those of other sectors, representing 56 % of B2B EC transactions. Wholesale trade comes in second with 37 % of these transactions [40]. Thus, manufacturing managers may have to implement B2B EC at higher levels than their merchandising counterparts to handle the 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’ intention to adopt B2B EC. Pressure from competitors, data security, and trust are the three factors that showed differences across management levels. Higher-ranked management in adopter firms feels stronger about the importance of these factors than lowerranked management in nonadopter firms. Separate analyses for CEOs in adopter and nonadopter firms show that the two factors that distinguish the two groups are top management support and trust. The results provide evidence that these two factors play a key role in the CEOs’ decisions to adopt B2B EC. When we compare management levels only in adopter firms, we find that data security, trust, and scalability are significant factors. Findings suggest that Presidents are more concerned about data security than Managers, and Managers are less likely to believe in the importance of scalability than Presidents and Directors in implementing B2B EC. Results also show that CEOs rated their trading partners higher for trust than Corporate Managers. Once again, this provides support for the above argument that firms are more likely to adopt B2B EC when higher-ranked management has more trust in trading partners. It appears that higher-ranked managers have a broader perspective on and better awareness of some of the key adoption factors. This makes 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 with those of some of the previous studies that analyzed the effects of contextual variables. For example, Frohlich and Westbrook [25] reported that firm type (manufacturing versus service firms) influenced the effect of Internet-enabled supply chain integration on operational performance. The significant effects of industry and firm size on the relationship between e-commerce and firm performance have also been evidenced by the findings of Zhu and Kraemer [118]. In addition, studies such as Sila [84] and Sila [85] reported that some organizational and environmental factors played a significant role in the adoption of B2B EC, as well as in the relationship between B2B EC adoption and performance. Even though the adoption factors and contextual variables used varied across these different studies, we can still infer in general terms that context 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 the TOE framework provides a strong foundation for the study of B2B EC. Second, it provides evidence that this framework is strengthened further when contextual variables are integrated into the theoretical model. This contextual nature of IT adoption is often mentioned but rarely explored empirically in the literature. We attempted to fill this gap with this research. However, the need for more research remains. In the

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following paragraphs, we offer recommendations for further development and testing of the model presented in this study, which we believe will contribute to current theory and practice in the implementation of B2B EC. One of the limitations of many studies in this area, including the current study, is that only the effects of a limited number of adoption factors are empirically tested in each study. In reality, the number of potential factors and contextual variables is much larger. The current study has shown that context does indeed play a role in B2B EC adoption. This contradicts with some of the studies conducted in this area. For example, Rahim et al. [73] found that industry type was not an important contextual factor in determining how inter-organisational systems are implemented. Therefore, future studies should test the effects of a wider range of factors and contextual variables on B2B EC adoption. In addition, the fact that many previous studies did not account for differences in industry or organizational characteristics and utilized different adoption factors may have produced inconsistent finding across these studies. Thus, it is important that future studies attempt to resolve such inconsistent findings by utilizing appropriate methodologies and research designs. Another opportunity for future research lies in the area of stage-based implementation 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 EC implementation. Even though stage-based models have been studied frequently in the general innovation adoption and information systems literature, they have been rarely analyzed in the implementation of B2B EC. Although researchers have studied various B2B EC adoption factors over the past decade, there are still lingering questions regarding the significance of some of these factors. This is because there are inconsistencies across these studies, partially due to a lack of common set of factors being tested and the scarcity of contextual variables being used. This empirical study provides a groundwork for future studies in this area.

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