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Manuscript Number: Title: AN EXPLORATORY STUDY OF FACTORS INFLUENCING INTERNET/E-BUSINESS TECHNOLOGIES ADOPTION BY SMES IN CANADA Article Type: Research Paper Keywords: Internet and e-business technologies; Innovation adoption; SMEs; Canada; Structural equation modeling. Corresponding Author: Dr. Princely Ifinedo, Corresponding Author's Institution: First Author: Princely Ifinedo Order of Authors: Princely Ifinedo Abstract: Small and medium enterprises (SMEs) around the world engage in e-commerce and ebusiness to support business operations, as well as enhance revenue generation from non-traditional sources. Internet and e-business technologies (IEBT) are the pillars of e-commerce and e-business. Despite the universal appeal of IEBT, it has been reported that the adoption of such technologies by SMEs is influenced by contextual imperatives. The objective of this research is to investigate factors impacting the adoption of IEBT in SMEs based in the Maritime region of Canada. A research model based on the Diffusion of Innovation (DIT) and the Technology-Organization-Environment (TOE) frameworks was used to guide this discourse. Such factors as relative advantage, compatibility, complexity, management support, organizational readiness, external pressure, and government support were used to develop relevant hypotheses. Questionnaires were mailed to key SMEs' informants. Data analysis was performed using the partial least squares (PLS) approach. Predictions related to relative advantage, management support, pressure from competition and government support (lack thereof) were confirmed. The study did not support compatibility and complexity of IEBT as well as customers' and business partners' pressures as being significant predictors of IEBT adoption by the SMEs. Suggested Reviewers: Ying Zhu [email protected] Has published on IEBT adoption in Canada Stephen Drew [email protected] Has published in the area of IEBT adoption in the Uk Boumediene Ramdani [email protected] Published in the area of IT adoption in SMEs Chad Lin

[email protected] Published in the area of IEBT adoption by SMEs

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International Journal of Information Technology & Decision Making  World Scientific Publishing Company

AN EXPLORATORY STUDY OF FACTORS INFLUENCING INTERNET/EBUSINESS TECHNOLOGIES ADOPTION BY SMES IN CANADA PRINCELY IFINEDO Cape Breton University, Shannon School of Business, 1250 Grand Lake Rd., Sydney, NS, Canada [email protected] http://www.cbu.ca Received (Day Month Year) Revised (Day Month Year) Communicated by (xxxxxxx) Small and medium enterprises (SMEs) around the world engage in e-commerce and e-business to support business operations, as well as enhance revenue generation from non-traditional sources. Internet and e-business technologies (IEBT) are the pillars of e-commerce and e-business. Despite the universal appeal of IEBT, it has been reported that the adoption of such technologies by SMEs is influenced by contextual imperatives. The objective of this research is to investigate factors impacting the adoption of IEBT in SMEs based in the Maritime region of Canada. A research model based on the Diffusion of Innovation (DIT) and the Technology–Organization–Environment (TOE) frameworks was used to guide this discourse. Such factors as relative advantage, compatibility, complexity, management support, organizational readiness, external pressure, and government support were used to develop relevant hypotheses. Questionnaires were mailed to key SMEs’ informants. Data analysis was performed using the partial least squares (PLS) approach. Predictions related to relative advantage, management support, pressure from competition and government support (lack thereof) were confirmed. The study did not support compatibility and complexity of IEBT as well as customers’ and business partners’ pressures as being significant predictors of IEBT adoption by the SMEs. Keywords: Internet and e-business technologies; Innovation adoption; SMEs; Canada; Structural equation modeling. 1991 Mathematics Subject Classification: 22E46, 53C35, 57S20

1. Introduction The Internet has given rise to the digital or network economy in which businesses around the world, including Canadian enterprises, utilize Internet and e-business technologies (IEBT¥) to support online or electronic commerce (e-commerce) and electronic business (e-business) activities (Riemenschneider & McKinney, 1999; Chang & Cheung, 2001; Zhu et al., 2009; Turban et al., 2010). For the purpose of this study, the application and technologies supporting e-commerce and e-business will hereafter be referred to as IEBT. The Internet, email, and secured online transactions, as well as web page ownership are some of examples. In fact, Sadowski et al. (2002, p. 76) noted that ―in establishing a new connection to the Internet, new users are required to adopt a series of related new technologies.‖ 1

According to recent industry reports, Canadian online businesses sold CAD$13.8 billion (USD$12.9 billion) in 2007 (Grau, 2008). The same source predicted that ecommerce sales in Canada will reach CAD$22.8 billion (USD$22.2 billion) in 2014 to show a compound annual growth rate (CAGR) of 10.6%. Similar trends have been reported in comparable countries around the world. In fact, Leadpile (2006) predicted that e-commerce sales around the world will likely surpass the $1 trillion mark by 2012. Given that IEBT have a global outlook and appeal, researchers across the world have investigated and continue to study their adoption and acceptance in differing contexts and locations (Farhoomand et al., 2000; Sadowski et al., 2002; Zhu et al., 2003; 2006; Tarafdara & Vaidya, 2006; Wymer & Regan, 2005; Al-Qirim, 2007; Kartiwi & MacGregor, 2007). It is critically important for attention to be paid to salient issues across contexts. In fact, information systems (IS) researchers including Gibbs and Kraemer (2004) and Lefebvrea et al. (2005) assert that the diffusion of IEBT in organizations and across countries is an uneven process. That is, issues considered vital in one setting may not be so in others. Furthermore, the propensity to adopt and accept IEBT varies by enterprise size and location (country) (Daniel & Grimshaw, 2002; Net Impact Study Canada, 2004; Gibbs & Kraemer, 2004; Wymer & Regan, 2005; Al-Qirim, 2007). In that regard, larger businesses tend to outperform small and medium sized enterprises (SMEs) with respect to the adoption and usage of IEBT for business operations and activities (Cragg & King, 1993; Thong et al., 1996; Daniel & Grimshaw, 2002; Gibbs & Kraemer, 2004; Net Impact Study Canada, 2002; 2004; Buonanno et al., 2005; Noce & Peters, 2006; AlQirim, 2007). As well, the diffusion of IEBT in organizations differs significantly between developing and developed countries (Farhoomand et al., 2000; Tan et al., 2007; Kartiwi & MacGregor, 2007). Even when studying the adoption of IEBT in developed parts of the world, some researchers, perhaps realizing the significance of contextual and regional imperatives, have clearly highlighted settings in which their studies were conducted (see for example, Premkumar & Roberts, 1999 (rural USA); Scupola, 2003 (Southern Italy); Drew, 2003 (East of England); Grandon & Pearson, 2004 (Mid West region of the US); Lawson et al. (2003) (SW Sydney and SE Melboune); Simpson & Doherty (2004) (the Yorkshire region)). This present study aims at presenting empirical insights related to the adoption of IEBT by SMEs based in a region of Canada: the Maritime Provinces. It is hoped that by focusing attention on this particular region, concerns specific to this context will be identified and discussed accordingly. The Maritime region of Canada was primarily chosen for illustrative purposes and for the fact that it apparently lags behind the rest of the country on a variety of issues, including the use of the Internet for business and commerce (Industry Canada, 2001; 2010; Statistics Canada, 2006). It is worth noting that the focus of this study is not on cross-country and cross-enterprise analyses, as others have already published in such areas (e.g. Farhoomand et al., 2000; Zhu et al., 2003; 2006; Gibbs & Kraemer, 2004; Daniel & Grimshaw, 2002; Wymer & Regan, 2005; Kartiwi & MacGregor, 2007). Furthermore, this research departs from other previous studies that were designed to compare adoption and non-adoption of such technologies in

business organizations, among other issues (Iacovou et al., 1995; Premkumar & Roberts, 1999; Goode & Stevens, 2000; Mirchandani & Motwani, 2001). This current research is motivated by three considerations. First, it seeks to add to prior studies that investigated similar concepts in Canada. Past studies included Globerman’s (1975) examination of technology diffusion in Canadian industries. More recent studies (e.g., Iacovou et al., 1995; Raymond & Bergeron, 1996; Chwelos et al., 2001) focused on the adoption of interorganizational systems (IOS) (such technologies are now linked to the Internet) (Turban et al., 2010). Iacovou et al. (1995) looked at the influence of perceived benefits, organizational readiness, and external pressure on IOS in Canada’s SMEs. Their findings indicated that such factors were relevant to the successful adoption of these systems. Raymond and Bergeron (1996) concluded that organizational support is needed for SMEs to benefit from business technologies. Chwelos et al. (2001) examined the impact of perceived benefits, organizational readiness, and external pressure on IOS in SMEs located in western parts of the country. They found the three factors to be significant predictors of the intent to adopt such technologies; as well, their study showed that external pressure and organizational readiness were more important than perceived benefits. Other researchers have focused on IEBT specifically. For example, Raymond (2001) surveyed 54 Canadian businesses and found competitive pressure, influences from business partners, owner’s experience with IS, nature of the business, and the relative advantage of technology, positively influenced website implementations. Wade et al. (2004, p. 336) studied the net impact of e-commerce on SME performance and concluded ―that business value can be significantly enhanced by the adoption of internet business solutions.‖ Lefebvrea et al. (2005) and Raymond and Bergeron (2008) examined the dynamics of e-commerce adoption in manufacturing SMEs in Canada. They noted that the pattern of adoption was evolutionary in nature. De Guinea et al. (2005) showed that managerial support is crucial for IS effectiveness in Canadian’s SMEs. Noce and Peters (2006) studied the barriers to e-commerce adoption in Canadian SMEs, and concluded that policies aimed at encouraging e-commerce adoption should target firm size and industry sector. The barriers to IEBT acceptance identified in their study included lack of skills. Hadaya’s (2006) showed that a business’ (large or SME) past experience with ecommerce and its business relationships affect its future use of electronic engagements; he also showed that the complexity of IEBT is negatively related to the future use of such technologies. Martin and Milway (2007) researched the business value of ICT products in SMEs. Davis and Vladica (2006) reported on the distribution and use of IEBT in Maritime Canada. Zhu et al.’s (2009) research concluded the Canadian businesses in the winery sector have a long way to go before reaching full potential of integrating IEBT usage with business operations. The implied inhibitor in their study was a lack of sufficient IS skills in that sector. This present research intends to complement these earlier studies; at the same time, it seeks to present current information on IEBT adoption by SMEs in the country.

Second, it is illuminating when attention is paid to SMEs instead of lumping them with larger enterprises. SMEs tend not to possess the same resources as larger organizations, and this imbalance presents a challenge for SMEs when adopting business technologies (Barua et al. 2001; Buonanno et al., 2005; Noce & Peters, 2006; Thong et al., 1996; Ramdani et al., 2009). Statistics Canada (2006), generally defines SMEs as firms with fewer than 500 employees. Another reason to focus on SMEs is because of their crucial importance to economic development. The Conference Board of Canada (2009, p. 4) noted that ―In Canada, close to 97 per cent of our estimated 2.4 million registered businesses are SMEs (having fewer than 500 employees) employing over 55 per cent of the [labor] force.‖ In fact, about 60% of Canada’s economic output is produced by its SMEs (Net Impact Study Canada, 2002). Third, this study desires to contribute to the discussion as to what causes Canadian SMEs to be reticent about adopting IEBT in their operations. The gravity of this issue was poignantly underscored by the country’s Net Impact Study’s highlight: ―A lukewarm SME response to IBS adoption may weaken any national strategy to bolster Canada’s international competitiveness. The challenge for industry leaders and policy makers is to bring lagging SMEs online and deepen the capabilities of those already online. The cost of inaction is to have this vital sector of the economy stall at current levels of engagement while other nations catch up or increase their lead‖ (Net Impact Study Canada, 2004, p.1). The foregoing call is both pertinent and timely. However, this study is not commissioned to uncover all the reasons why Canadian’s SMEs tend to have a discouraging outlook towards IEBT adoption. It is also not claimed herein that this research has all the answers. In actual fact, this study aims at presenting empirical understanding that could benefit practitioners and policy makers as they develop guidelines on how to encourage and motivate widespread adoption of IEBT in Canada. Specifically, the main purpose of this research is to: use relevant theoretical frameworks to investigate factors impacting the adoption of IEBT by SMEs based in Maritime Canada determine the extent to which the factors influence the acceptance of IEBT in the region gain an understanding as to the relative importance of the selected factors in the research framework This research drew from the Diffusion of Innovation Theory (DIT) (Rogers, 1995; 2003) and the Technology-Organization-Environment (TOE) (Tornatzky & Fleischer, 1990), which several IS researchers have used to investigate comparable issues (e.g. Iacovou et al., 1995; Chwelos et al., 2001; Raymond, 2001; Scupola, 2003; Gibbs & Kraemer, 2004; Kuan & Chau, 2001; Al-Qirim, 2007; Li et al., 2010).

2. Literature Review and Hypotheses Development 2.1. Theoretical Underpinnings It has been suggested that the adoption of IEBT in SMEs can be viewed from the innovation perspective. According to Damanpour (1992), an innovation is described as something that is ―new to the adopting organization.‖ A wide range of theoretical models have been used to investigate the adoption of innovations, including IEBT in SMEs (see Wymer & Regan, 2005; Tan et al., 2007; Ramdani et al., 2009); however, the literature indicates that a good number of studies have used two related theoretical frameworks, i.e. Rogers’ (1995) DIT and Tornatzky and Fleischer’s (1990) TOE. The DIT theory posits that the diffusion and adoption of technological innovations rests on five general attributes: relative advantage, compatibility, complexity, trialability, and, observability. The extant IS literature also suggests that three of the technical attributes—relative advantage, compatibility, and complexity—have been persistently regarded as the more important for the adoption process (Tornatzky & Klein, 1992). Despite the popularity of the DIT model, some researchers (Brancheau & Wetherbe, 1990; Fichman, 1992; Fichman & Kemerer, 1997) have criticized it for its bias towards the technological component of the adoption process. The argument offered by these researchers is that other relevant, contingent factors, beyond the technical features of an innovation should be considered for deeper understanding to emerge. Also, calls have been made to incorporate influences arising from other independent and control variables (Fichman, 1992; Chau & Hui, 2001). Conversely, the TOE framework posits that the adoption of innovations depends on organizational, environmental, and technological factors. Fundamentally, the TOE model is an integrative schema incorporating characteristics of the technology, contingent organizational factors, and elements from the macro-environment (Tornatzky & Fleischer, 1990; Tornatzky & Klein, 1992; Li et al., 2010). Several studies that used the TOE framework for examining the impact of relevant organizational and environmental factors included such variables as management support, organizational readiness, pressures from partners, customers and competition, and government support. Researchers found almost all the aforementioned factors to be crucial in the acceptance of IEBT and related technologies by SMEs (e.g. (Iacovou et al., 1995; Thong et al., 1996; Chwelos et al., 2001; Scupola, 2003; Gibbs & Kraemer, 2004; Hadaya, 2006; Al-Qirim, 2007; Tan et al., 2007). This information provides ample reason for their inclusion in this research; however, they are offered as illustrative rather than exhaustive examples. In TOE and DIT models, the dependent variable can be adoption, acceptance, receptivity, business performance, business value, or a combination of other relevant variables (Kendall et al., 2001; Love & Irani, 2004; Wade et al., 2004; Van der Deen, 2005; Davis & Vladica, 2006). In this study, the dependent variable is adoption, which was operationalized with measures related to the frequency, extent of use, and criticality of the use of such technologies in business operations. Other prior studies (e.g. Mustaffa & Beaumont, 2005; Chong & Pervan, 2007) have used a similar conceptualization to facilitate the emergence of deeper insight.

2.2. Hypotheses Development The research framework and the formulated hypotheses are highlighted in Figure 1. Technological context

Relative advantage

Compatibility

Complexity Control variables

Firm size: Organizational context

Revenue

Management support

Firm size: Workforce IEBT adoption

Organizational readiness Industry sector

Firm age Environmental context

Competition’s 3. Major pressure Headings

Customer’s pressure

Partner’s pressure

Government support

Fig. 1. The research framework.

Relative advantage is defined by Rogers (2003, p. 229) as ―the degree to which an innovation is perceived as being better than the idea it supersedes‖. Thus, if the benefits of the technological innovation are perceived as having advantages over existing practices and systems, the adoption of such an innovation will be positively encouraged. Prior research (Cragg & King, 1993; Iacovou et al., 1995; Premkumar & Roberts, 1999; Kendall et al., 2001; Chwelos et al., 2001; Raymond, 2001; Mehrtens et al., 2001; Chau & Jim, 2002; Gunasekaran & Ngai, 2005; Al-Qirim, 2007; Chong & Pervan, 2007; Huang et al., 2008; Ramdani et al., 2009; Li et al., 2010) found this factor to be positively related to the adoption of IEBT in SMEs. Accordingly, it is predicted that: H1a: The greater the perceived relative advantage of IEBT, the greater the adoption of such technologies by SMEs. Compatibly is defined as ―the degree to which an innovation is perceived as consistent with the existing values, past experience, and the needs of potential adopters‖ (Rogers (2003, p. 240). It has been suggested that technological innovations diffuse more freely and easily where such applications appear to match the adopter’s processes. However, some IS researchers (e.g. Gibbs & Kraemer, 2004; Ramdani et al., 2009) did not find support for a positive relationship between this factor and the adoption of IS in organizations. Yet, others have confirmed the relationship; for example, Cragg and King (1992; 1993), Grover (1993), Raymond (2001), Kendall et al. (2001), and Li et at al. (2010) found compatibility to be a significant predictor of IS innovation adoption. It is therefore predicted that: H1b: The greater the compatibility of IEBT with the adopting SME’s operations, the greater the adoption of such technologies by SMEs. Complexity refers to ―the degree to which an innovation is perceived to be relatively difficult to understand and use‖ Rogers (2003, p. 257). Consistent with the DIT theory, the acceptance of a new innovation is inhibited or discouraged if it perceived by the adopter to be complex. While Grover (1993), Premkumar and Roberts (1999), Hadaya (2006), and Huang et al. (2008) found it to be an important predictor of IEBT acceptance in SMEs, Beatty et al. (2001) and Kendall et al. (2001) did not confirm such a relationship in their studies. In general, researchers (Mirchandani & Motwani, 2001; Daniel & Grimshaw, 2002; Grandon & Pearson, 2004) have suggested that easy to use business applications tend to be more readily adopted than complex ones. This is to say that technologies perceived to be less complex will garner greater acceptance among adopters than more complex systems. Thus, it is predicted that: H1c: The lower the perceived complexity of IEBT, the greater the adoption of such technologies by SMEs.

Management support refers to as the ―active engagement of top management with IS implementation.‖ Thong et al. (1996, p. 253). Jeyaraj et al. (2006) found top management support to be one of the best predictors of organizational adoption of IS innovations. Past research indicates that management support generally boded well for the acceptance of technological innovations in SMEs (e.g., Iacovou et al., 1995; Grover, 1993; Premkumar & Roberts, 1999; Beatty et al., 2001; Chwelos et al., 2001; Grandon and Pearson, 2004; Al-Qirim, 2007; Huang et al., 2008; Ramdani et al., 2009). This is because top managers act as change agents in the adoption process of technological innovations (Thong et al., 1996; Igbaria et al., 1997). When top managers in SMEs understand the importance of computer technology, they tend to play a crucial role in influencing other organizational members to accept it. Conversely, where management support is low or unavailable, technology acceptance and adoption tend to be placed on the back-burner in terms of organizational priorities (e.g. Igbaria et al., 1997). Thus, it is predicted that: H2a: The greater the management support for IEBT acceptance, the greater the adoption of such technologies by SMEs. Organizational readiness is defined by Iacovou et al. (1995, p. 467) ―as the availability of the needed organizational resources for adoption.‖ Organizational readiness of businesses is critically important for IEBT adoption and it encompasses not only physical assets, but also human knowledge of IS (Mehrtens et al., 2001; Zhu et al., 2003; 2006). Prior studies have discussed this factor using financial resources, and organizational IT sophistication and expertise (Iacovou et al., 1995; Al-Qirim, 2007; Ramdani et al., 2009). Financial resources will be excluded for simplicity purposes, and the research will instead described it in terms of available IT knowledge in the adopting organization, as was the case in Mehrtens et al. (2001). Jutla et al. (1999) commented that SMEs and micro businesses tend to not have sufficient knowledge of IT and e-business. Thong and Yap (1995) found a lack of computer literacy among SME owners and a lack of knowledge regarding the benefits of IS use as an inhibitor to IS adoption in small businesses. Similarly, Chircu and Kauffman (2000) found that inability to acquire skill and expertise in new technologies, and a lack of training and education form significant barriers to the adoption of EC systems. SMEs possessing relevant expertise are more likely to accept innovations as they have a better understanding of the benefits of such innovations than if such competences were lacking (Cragg & King, 1993; Chwelos et al., 2001; Caldeira & Ward, 2002; Pflughoeft et al., 2003). Thus, it is predicted that: H2b: The greater the organizational readiness for IEBT acceptance, the greater the adoption of such technologies by SMEs. A business organization may be pressurized by its customers, partners, and competition to adopt an IS (Poon & Swatman, 1999; Hart & Saunders, 1998; Raymond, 2001). Indeed, intense competition can cause a business to look at new ways of doing business, including utilizing technological innovations for survival (Globerman, 1975;

Gatignon & Robertson, 1989; Looi, 2005). Pressure from the competitor can lead to environmental uncertainty that could increase the rates of innovation adoption in an industry (Looi, 2005). Competitive pressure does impact the adoption of IS innovations and has been reported to be one of the better predictors of IS innovations in business (large and SMEs) (Gatignon & Robertson, 1989; Looi, 2005; Jeyaraj et al., 2006; Chong & Pervan, 2007; Huang et al., 2008). With respect to business partners’ pressure, Grover and Malhotra (1997), Raymond (2001), and Hadaya (2006) showed that the deployment of IEBT and related technologies improves commercial transactions and relationships between businesses and their partners. Hart and Saunders (1998), Chau and Jim (2002), and Mehrtens et al. (2001) found that business partner influence is a significant predictor of the acceptance of IS innovations. However, others did not confirm this relationship (e.g. Chau & Hui, 2001; Windrum & de Berranger, 2004). Regarding customers’ pressure, Carmichael et al. (2000) suggest that the key driver for SMEs to innovate is customer feedback and demand. Also, Kula et al. (2003) indicated that most SMEs innovate only when they come under pressure from their clients. The foregoing discussion permits the development of the next set of hypotheses: H3a: The greater the pressure from the competition to adopt IEBT, the greater the need to adopt such technologies by SMEs. H3b: The greater the pressure from partners to adopt IEBT, the greater the need to adopt such technologies by SMEs. H3c: The greater the pressure from customers to adopt IEBT, the greater the need to adopt such technologies by SMEs. Government support in this study refers to the assistance provided by the authority to encourage the spread of IS innovations in businesses. Gibbs and Kraemer (2004) commented that government support for IEBT adoption is particularly relevant in newly industrializing and developing countries such as Singapore and India. However, studies that have used this factor have produced mixed results. For example, some studies (Tan & Teo, 2000; Beatty et al., 2001; Chau & Jim, 2002; Scupola, 2003; Looi, 2005; Gunasekaran & Ngai, 2005; Thatcher et al., 2006) reported that government assistance is vitally important in the IS adoption process of SMEs, while others (Chau & Hui, 2001; Gibbs & Kraemer, 2004) did not find support for a significant relationship between government support and the likelihood that an SME will adopt IEBT or other technological innovations. In fact, the study by Gibbs and Kraemer (2004) that included businesses from both developed and developing countries noted ―Government promotion [support] through incentives and procurement requirements was also marginally significant, although it had less of an effect‖ (p. 134).

Canadian businesses’ perception of government role towards IS adoption has not been encouraging. Feedback from this research’s participants indicated that much needs to be done by the provinces [regional government] with respect to promoting and stimulating IS adoption among local SMEs. The same observation is true at the national level. For instance, Jutla et al. (2002) noted that the Canadian government was slow to respond to its citizens’ and businesses’ needs with regard e-commerce initiatives in the 1990s. Also, one of the highlights offered by a report presented by the Information Technology Association of Canada stated that ―Canada lags competitor countries in the provision of incentives for activities that assimilate technology adoption at the firm level‖ (Warda, 2005, p. 3). (Please see also Sharpe & Arsenault, 2008). In view of this reality, it is predicted that: H3d: Government support will not be positively related to the adoption of IEBT by SMEs. 3.1. The Control Variables The control variables used for this study are as follows: firm size, industry sector, and firm age. Firm size has been found to positively predict the adoption of IS (Jeyaraj et al., 2006; Al-Qirim, 2007; Teo, 2007; Huang et al., 2008; Li et at al. 2010); at the same time, others did not confirm this relationship for IS adoption (e.g., Goode & Stevens, 2000; Gibbs & Kraemer, 2004). The industry sector in which a business operates may influence its ability to adopt IS innovations (Drew, 2003; Levenburg et al., 2006; Jeyaraj et al., 2006; Li et at al. 2010); the study by Chatterjee et al. (2002) and Teo (2007) did not affirm this view. It has been suggested that service businesses are more predisposed towards using the Internet for business activities than manufacturing enterprises (Drew, 2003; Goode & Stevens, 2000). Lai (2004) found that firm age was significantly associated with success of computer use and adoption. Other studies (e.g. Chatterjee et al., 2002; Li et al. 2010) found firm age to be insignificant in the assimilation of IEBT. 4. Research Methodology 4.1. Data Collection The data for this study was collected in the four provinces, i.e. Nova Scotia, Newfoundland and Labrador, Prince Edward Island, and New Brunswick, comprising Maritime Canada. A stratified random sampling approach was used in selecting the participating SMEs from telephone directories (Yellow Pages) in the four provinces. Other studies investigating comparable issues have used such an approach (e.g. Love & Iran, 2004; Mustaffa & Beaumont, 2005). A wide range of industries were considered for inclusion. As the unit of analysis of this study was at the organization level, it was ensured that key organizational informants including senior executives and owners of SMEs were contacted. Each received a packet containing a cover letter, a questionnaire, and a self-addressed, stamped envelope. To ensure content validity, eighteen (18) knowledgeable individuals (4 faculty, 4 local SMEs executives, and 10 college students)

participated in a pilot test with an initial draft of the questionnaire. Feedback from this test helped to improve the quality of the final questionnaire. In all, 2200 questionnaires were mailed out. The sample population was determined by the understanding that a sampling frame that is properly selected and large enough will increase the response rate (Iacobucci & Churchill, 2009). Other similar studies (e.g. Gibbs & Kraemer, 2004) have sampled a similar number of SMEs. Data collection took place between November 2007 and March 2008. Participation in the study was voluntary. Respondents were assured that their individual responses would be treated as confidential. The majority of the measures used in the study were taken from previously validated sources (e.g. Iacovou et al., 1995; Thong et al., 1996; Premkumar & Roberts, 1999; Tan & Teo, 2000; Grandon & Pearson, 2004; Chong & Pervan, 2007). The measurement items were anchored on a 7-point Likert scale ranging from ―strongly disagree‖ (1) to ―strongly agree‖ (7) in which participants were asked to indicate an appropriate response. Table 1 highlights the construct’s sources and their descriptive statistics. A full list of the measures used and their reliability scores is provided in the Appendix 1. The Cronbach alpha for each dimension exceeds the 0.7 limit, recommended by Nunnally (1978), to indicate a reasonably high reliability of the research measures and constructs. The control variables were assessed as follows: Firm size was measured by two subitems, i.e. annual sales revenue and workforce. Firm age was assessed by the length of time the SME had been in existence. Industry sector was delineated as manufacturing, services, and others (e.g. not-for-profit). Table 1 (Continued). The demographics of the respondents.

Profile Gender Male Female Missing Age Less than 20 years 21-30 31-40 41-50 51-60 60 years and above Education Primary education Secondary education College/Bachelor’s education Post-graduate degree Other Job title

Frequency

Percentage (%)

125 85 4

58.4 39.7 1.9

4

1.9

26

12.1

30

14.0

78

36.4

57

26.6

19

8.9

7

3.3

40

18.7

115

53.7

44

20.6

8

3.7

Owner/Proprietor VP, Director Business Manager, Accountant Other

84 41 67 22

39.3 19.2 31.3 10.2

4.2. Survey Result A total of 192 questionnaires were undelivered and 237 responses were received, of which, 214 were considered valid. The unusable 23 responses included questionnaires with a high percentage of missing entries and those indicating non-adoption of any IEBT. Thus, the effective response rate for the survey is 11.8%., which is considered good for an exploratory study such as this one. Table 2 shows the participants’ demographics. The participants’ average work experience was 13.4 years (s.d. = 11.01). The workforce ranged from 1 to 500 employees, with a median of 6 employees. The other profiles of the responding SMEs are highlighted in Table 3 and the distribution of the types of IEBT in use in the sampled SMEs is shown in Figure 2. Table 2. The profile of the participating SMEs. Type Business type Adverting, Marketing Manufacturing Retail, Wholesale Auto Dealership, Auto repairs Construction Design outfit, Decorator Education, Driving School Hotel, Hospitality Insurance, Accounting firms Real estate, Legal firm Education, Driving School Other Annual sales revenues Canadian (C$)

Frequency

Percentage (%)

19 41 35 14 6 8 5 10 21 12 4 39

8.9 19.2 16.4 6.5 2.8 3.7 2.3 4.7 9.8 5.6 1.9 18.2

Less C$500,000 C$500,000 - C$ 1.0 million C$ 1.1 - C$5.0 million C$ 5.1 - C$ 10.0 million C$ 10.1 - C$ 20.0 million C$ 20.1 - C$50.0 million Workforce Less 50 employees 51 - 99 employees 100 - 500 employees Missing data Firm age Less than 10 years 11 - 20 years 21 – 50 years 50 years and above Missing data C$ = Canadian dollar

102 48 38 9 11 6

47.7 22.4 17.8 4.2 5.1 2.8

175 23 11 5

81.8 10.7 5.1 2.3

75 36 74 25 4

35 16.8 34.6 11.7 1.9

Table 3. The research variables and their sources. Construct

No. of measures

Mean

S.d

Adapted from

Relative advantage

5

4.33

1.61

Compatibility

4

3.95

1.65

Tan & Teo (2000); Premkumar & Roberts (1999) Tan & Teo Premkumar & (1999)

(2000); Roberts

(2000); Roberts

Complexity

3

2.83

1.42

Tan & Teo Premkumar & (1999)

Management support

4

4.23

1.65

Thong et al. (1996) ; Igbaria et al. (1997)

Organizational readiness

4

4.29

1.52

Iacovou et al. (1995) ; Mirchandani & Motwani (2001) ; Kuan & Chau, (2001)

Competition’s pressure

3

3.83

1.49

Iacovou et al. (1995) ; Premkumar & Roberts (1999); Grandon & Pearson (2004); Looi (2005)

Customer’s pressure

3

3.43

1.55

Premkumar & Roberts (1999); Grandon & Pearson (2004)

Partner’s presuure

3

3.57

1.50

Iacovou et al. (1995) ; Grandon & Pearson (2004); Gatignon & Robertson, 1989

Government support

3

2.29

1.46

4

4.66

1.38

IEBT adoption

Tan & Grandon (2004)

Teo &

(2000); Pearson

Chong & Pervan (2007); self-developed

Fig. 2. The distribution of IEBT in use in the sampled SMEs

A test for non-response bias was done by comparing early and late respondents (Iacobucci & Churchill, 2009). Chi-square (χ2) test was used to compare the sampled firm size, annual revenue, and industry type. The results of the Chi-square tests (significant at p < 0.05) showed there were no significant differences along these key characteristics. It is worth noting that the problem of common method bias exists for studies that used single informants. The procedural remedies for controlling common method biases were followed. First, to increase the study’s validity, clear and concise questions were used in the questionnaire. Second, to reduce apprehension, respondents’ anonymity was assured. Third, a statistical procedure, i.e. the Harmon one-factor test, was used to assess if such biases were a problem in our sample (Podsakoff et al., 2003). The test results showed that several factors with eigenvalues greater than one are present in our data. As well, the most covariance explained by one factor in our data is 38.7% indicating that common method variance is not a problem for the data. 5. Data Analysis and Results The PLS (Partial Least Squares) technique was used for data analysis. The PLS approach is suitable for validating predictive models and theory building (Chin, 1998; Gefen & Straub, 2005). It provides information about the measurement and structural models. The specific tool used was SmartPLS 2.0, which was created by Ringle et al. (2005).

5.1. The Measurement Model The psychometric properties of the measurement model were assessed by the reliability, convergent, and discriminant validities. Internal consistency of the data measures is assured when the reliability of each measure in a scale is above 0.7 (Nunnally, 1978). As indicated above, the Cronbach alphas were also above the 0.7 threshold (Nunnally, 1978; Hair et al., 1998). Convergent validity is assured when each item should have item loading greater than 0.6 on their respective constructs, and should not load highly on any other construct(s) (Hair et al., 1998; Chin, 1998). The confirmatory factor analysis of the measures in Appendix 2 shows that the requirements have been met. Fornell and Larcker (1981) recommended that the following conditions be met for adequate discriminant validity to be assured: a) the square root of the average variance extracted (AVE) of all constructs should be larger than all other cross-correlations; b) the value of the AVE should be of the threshold value 0.50 (Fornell & Larcker, 1981). Table 4 shows that the AVE ranged from 0.65 to 0.86, and in no case was any correlation between the constructs greater than the squared root of AVE (the principal diagonal element). Thus, the measurement items used for this study demonstrate good convergent and discriminant validities. Table 4. Inter-construct correlations, AVE, and the square root of AVE. Dimension

AVE

1

2

1:Relative advantage 2:Compatibility

0.779 0.837

0.883 0.652

0.915

3:Complexity

0.669

0.010

-0.31

0.818

4:Management support 5:Organizational readiness 6:Competition’s pressure

0.859

0.589

0.392

-0.15

0.927

0.784

0.602

0.579

-0.22

0.708

0.885

0.845

0.414

0.469

-0.06

0.392

0.438

0.919

7:Partner’s pressure

0.639

0.443

0.477

-0.09

0.558

0.585

0.605

0.799

8:Customer’s pressure

0.859

0.402

0.514

-0.13

0.464

0.459

0.664

0.723

0.923

9:Government support

0.861

0.145

0.190

0.088

0.165

0.087

0.190

0.449

0.410

0.928

10:IEBT adoption

0.615

0.566

0.532

-0.13

0.517

0.469

0.534

0.478

0.491

0.159

Note:

3

4

5

6

7

a) The bold fonts in the leading diagonals are the square root of AVEs, b) off-diagonal elements are correlations among constructs

5.2. The Structural Model The structural model in PLS presents information about the path coefficients (β) and the squared R (R2). The strength of the relationship is indicated by the β. The R 2 highlights the percentage of variance in the model and gives indication of its predictive power. The path significance levels (t-values) are estimated by the bootstrap method. The SmartPLS 2.0 results for the βs and the R2 are shown in Figure 3.

8

9

10

0.784

Technological context

Relative advantage

0.29*

* Compatibility

0.10

Complexity -0.04

Control variables

Firm size: Revenue Organizational context

‡ 2

R = 0.49

Management support

0.19

*

‡

Firm size: Workforce

IEBT adoption

Organizational readiness 0.06

‡

Industry sector

‡

Firm age

Environmental context

Competition’s pressure 0.27** Customer’s pressure

Partner’s pressure

Government support

0.06

0.11

-0.09µ

Fig. 2. The PLS result for the data

* = significant at p < 0.05; ** = significant at p < 0.01 ‡ = not significant µ = confirmed as hypothesized

Four out of the nine hypotheses were supported; hypothesis (H1a) was confirmed to show that relative advantage of IEBT will lead to greater adoption of such technologies. The data found support for hypothesis (H2a) to support the notion that management support is crucial in encouraging IEBT acceptance in SMEs. The result also demonstrated significant, statistical support for hypothesis (H3a), which predicted that competitive pressure will enhance IEBT adoption. Additionally, the data analysis provided support for hypothesis (H3d) suggesting that government support will not influence the adoption of IEBT for the sampled SMEs. The data did not provide support for H1b, H1c, H2b, H3b, and H3c. Contrary to the prediction made in hypothesis (H1b), compatibility did not lead to greater adoption. Hypothesis (H1c) which predicted that lower perceived complexity of IEBT will result in greater adoption of such technologies by SMEs was unconfirmed. Similarly, predictions regarding the relevance of customers’ pressure and partners’ pressure were unsupported by the data. All the variables together explain 48.5% of the variance in the dependent construct. This indicates that the proposed research conceptualization possess adequate predictive power and is successful in explaining the acceptance of IEBT among the sampled SMEs. A summary of the results is presented in Table 5. Further discussion on the results is presented in the next section. Table 4. Variable

Inter-construct correlations, AVE, and the square root of AVE. Model with controls (β)

Relative advantage Compatibility Complexity Management support Organizational readiness Competition’s pressure Partner’s pressure Customer’s pressure Government support IEBT adoption Firm size (annual sale) Firm size (workforce) Industry sector Firm age

* significant at p< 0.05,

R2 = 0.078 0.264** -0.032 0.062 -0.005

Model without controls (β) 0.288 0.094 -0.050 0.206 -0.088 0.274 0.065 0.082 -0.087

Model with factors (β) 0.287** 0.097 -0.040 0.193* -0.059 0.270** 0.106 0.058 -0.094

R2 = 0.477

R2=0.485 0.051 -0.005 -0.021 -0.035

all

Result Supported Not supported Not supported Supported Not supported Supported Not supported Not supported Supported (as hypothesized)

** significant at p< 0.001

6. Discussion, Implications, and Future Research Drawing from the DIT and TOE models, this study posits that relative advantage, compatibility, complexity, management support, organizational readiness, pressures from customers, partners and competition, and government support will influence the adoption of IEBT in SMEs based in the Maritime region of Canada. Subsequent data analysis confirmed the significance of relative advantage in the adoption of IEBT in SMEs. This result shows that SMEs in the region are generally positively encouraged to adopt IEBT upon realizing the perceived benefits of such technologies over existing practices. This finding supports previous studies that have signified the critical importance of relative advantage in the adoption processes of technological innovations, including IEBT in SMEs (e.g. Iacovou et al, 1995; Chwelos et al., 2001; Mehrtens. et al., 2001; Looi, 2005).

In relation to other selected factors of this study, relative advantage was seen to be the most important predictor of IEBT adoption in the sampled SMEs. Prior studies have also noted the paramount importance of this particular factor in the adoption of IS products in SMEs (e.g. Premkumar & Roberts, 1999; Mehrtens et al., 2001; Kuan & Chau, 2001; Gibbs and Kraemer, 2004; Looi, 2005). To the extent that management support is considered crucial for the successful adoption and use of innovations in SMEs, this study’s finding provide empirical support for such a claim. SMEs in the Maritimes indicated that the levels of IEBT adoption were higher where management support and commitment were available. This finding lends credence to the body of work indicating that management support is positively associated with the successful acceptance of IEBT and related technologies in small businesses (Thong et al., 1996; Igbaria et al., 1997; Teo et al., 1997; Premkumar & Roberts, 1999). Pressure from the competition (competitive pressure) was also found to be an important factor that positively influences the adoption of IEBT. In this study, competitive pressure was found to be the second most important predictor of IEBT adoption. The likelihood of SMEs adopting emerging technologies is greater where the competition is believed to derive some advantages from such applications. Others (e.g. Cragg and King, 1993; Raymond, 2001; Beatty et al., 2001; Chang & Cheung, 2001) have also highlighted the significance of competitive pressure in the adoption of IEBT in SMEs. The perception of government support (or the lack thereof) for IEBT use and acceptance ranked low compared to the other independent variables. In fact, this factor had the least mean score of 2.29 (Table 3) compared to the other independent variables. The perceptions of the sampled SMEs seem to suggest that government needs to offer more encouragement for SMEs to accept IEBT in their operations. Feedback from one proprietor of a retail outfit notes: ―As far as I’m concerned, the province [provincial government] ought to be promoting the use of these products [IEBT] more strongly.‖ To some extent, the foregoing insight seems to mirror the conclusions made by others including the Information Technology Association of Canada (Warda, 2005). Government roles and guidelines have been reported to augur well for the adoption of IEBT in small business in some parts of the world (e.g. Teo & Tan, 2000; Scupola, 2003; Thatcher et al., 2006). Yet, other researchers have also shown that government support is least effective in enhancing IEBT adoption (Sadowski et al., 2002; Gibbs & Kraemer, 2004; Looi, 2005). Unfortunately, the perception of the sampled SMEs is that the lack of such support from the authority may be inhibiting their capability to adopt IEBT. The data showed that the technological attributes of compatibility and complexity are insignificant in the adoption of IEBT for the sampled SMEs. With respect to compatibility, the results are perhaps indicating that the SMEs do not perceived IEBT to be consistent with their existing values and practices, and may hold a belief that such technologies do not meet their needs. A similar finding has been reported elsewhere. For instance, Davis (2009, p. 9) citing IDC (2006) asserted that: ―Canadian SMEs prefer to invest in ICT in order to solve specific business problems or when the firms can reasonably anticipate growth [in their businesses]. SMEs say they are aware of the benefits of IT and hold positive views about the value of investing in ICT [as confirmed by the significance of relative advantage], but they consider that absence of visible quantifiable benefits is a barrier to adoption.‖ Similarly, a comparative study of broadband adoption in the Maritimes and Manitoba (another Canadian province) noted that businesses regarded broadband as an asset, ―but

they were hard pressed to attach specific performance measures to [them]‖ (Annis et al. 2005, p. 32). As well, researchers elsewhere have also shown that this variable is insignificant in the adoption of IEBT in organizations, including SMEs (Grandon & Pearson, 2004; Huang et al., 2008; Ramdani et al., 2009). Indeed, Karahanna et al. (2006) called on IS researchers to reconceptualize compatibility beliefs in technology acceptance studies. Although the nature of the relationship between the complexity variable and the dependent variable is consistent with prediction, the hypothesis was unsupported by the data. This result suggests that executives in the sampled SMEs may be finding IEBT and similar technologies relatively difficult to use and understand. As a consequence, the acceptance levels of such technologies are not being positively encouraged. This observation is consistent with findings in Peters (2006) and Zhu et al. (2009). A closer look at the distribution of IEBT in use in the study (Figure 3) shows that relatively easy to use technologies such as email were more widely accepted than such complex systems as e-CRM and e-ERP. Nonetheless, this particular finding is consistent with observations and results in Premkumar and Roberts (1999), Beatty et al. (2001) and Kendal et al. (2001), indicating that complexity is unimportant in the adoption of IS innovations (see also Grover, 1993; Hadaya, 2006; Huang et al., 2008; Ramdani et al., 2009). Organization readiness was found to be an insignificant predictor of IEBT in the SMEs. To some extent, this result corroborates observations related to the poor showing of organizational IS sophistication and computer knowledge in small businesses in the country (Noce & Peters, 2006; Martin & Milway; 2007; Zhu et al., 2009). For example, Martin and Milway (2007) noted that one of the biggest problems facing the growth of business technologies use among SMEs is a lack of organizational readiness, in particular the poor awareness levels of IS issues that are projected by owners and employees of small businesses. This observation does not depart from the views espoused by others (Annis et al. 2005; Sharpe & Davis, 2009; Zhu et al., 2009) underscoring the gravity of lack of IS knowledge in businesses (large and SMEs) in the country. Nonetheless, the studies of IOS that were conducted by Chwelos et al. (2001) found organizational readiness to be positively associated with the intent to adopt such systems. Their finding, which was in the context of small businesses having better financial resources, is different from this study’s focus. However, a closer look at the distribution of IEBT in use in the study (Figure 3) shows that relatively easy to use technologies such as email were more widely accepted than such complex systems as e-CRM and e-ERP. This information indirectly supports the view indicating that the SMEs do not possess a high level of organizational IS readiness. Further to this, feedback from some of this study’s participants indicated that online business transaction is unsuitable for their business activities; other respondents commented that they simply do not posses skills to implement top end IEBT products in their businesses. Pressures from both customers and business partners did not significantly predict the adoption of IEBT for the SMEs. A plausible explanation for the lack of support for the two factors might be due to this research’s focus as well as the type of technologies considered. This study did not investigate the influence of customers’ and partners’ pressures on innovation adoption in the context of dyadic technologies such as electronic data interchange (EDI) as was the case in Iacovou et al.’s (1995) and Chwelos’ et al. (2001) studies. For studies focusing on interorganizational linkages supported by EDI, the coercive and normative influences of dominant entities have been known to impact the acceptance of such technologies by other less powerful parties (Hart & Saunders,

1998; Raymond, 2001; Son et al., 2008). Moreover, Mehrtens et al. (2001) asserted that significant differences exist in EDI and IEBT adoption processes. Of note is the fact that none of the control variables, i.e. firm size, industry sector, and firm age were found to have a significant relationship with the dependent construct. Their effects were marginally low to underscore their insignificance in this research conceptualization. Researchers such as Huang et al., (2008) and Li et al. (2010) have indicated that some of the control factors may, in fact, have no pertinence in the adoption of IS innovations. However, the tested model with the control variables only indicated that SMEs with better financial resources are able to adopt IEBT more than counterparts with less resource. Others have provided a similar observation in their studies (Cragg & King, 1993; Beatty et al, 2001; Daniel & Grimshaw, 2002; Buonanno et al., 2005; Li et al., 2010). 6.1. Implications for Research This research has implications for future study. First, the incorporation of both the DIT and TOE frameworks for this current study has engendered deeper insight regarding factors that influence the adoption of IEBT. Rogers (1995; 2003) indicate that the five technical attributes explain 49% to 87% of the variance in the rate of adoption. The study by Kendall et al. (2001) showed that 36% of the willingness to adopt IEBT in Singaporean SMEs is explained by the technical attributes alone. In parts, the study’s results support the amount of variance explained by the variables used in innovation adoption studies and also suggest that greater variance in IEBT adoption can be explained by the inclusion of other relevant contingent factors (Tornatzky & Fleischer, 1990). Comparisons of this study’s results with those obtained in Kendall et al. (2001) suggest that such methodological refinement (i.e. incorporating other relevant factors) may be worthwhile and fruitful. It is argued that the literature of innovation adoption, in general, benefits from such enhancements, and future research may be enticed to continue using such an approach in related works. As well, the salient pertinence of relative advantage in innovation adoption is affirmed with data from this study. Second, the dependent variable, i.e. adoption used in this study departs from prior research efforts that tend to operationalize such constructs with a single item of Use (Usage) or Intention to use. The utilization of such singular items may obfuscate reality and has, in fact, been criticized for limiting insight (Legris et al., 2003). In that respect, the measures used to operationalize adoption in this study may be beneficial to others wishing to investigate comparable issues. Third, this research isolated the impact of control variables, which permits more useful conclusions to be made. This exercise has supported the interpretation that the uncovered findings were not due to the influences of other independent factors. Other researchers may consider doing likewise in comparable studies. Fourth, this research generally lends credence to findings and observations regarding the factors that influence the adoption and acceptance of IEBT in SMEs (these have already been noted above). With respect to critical importance of competitive pressure in the adoption of innovations in organizations, which others (Gatignon & Robertson, 1989; Jeyaraj et al., 2006; Chong & Pervan, 2007; Huang et al., 2008) have also signified as being important, this present study adds to the knowledge that competitive pressure in the business environment generally increases overall rates of innovation adoption (Globerman, 1975; Looi, 2005). By the same token, this study is suggesting that the adoption of IEBT by SMEs may not depend on pressures from customers and businesses partners especially if a dyadic interorganizational relationship is not in place.

With respect to the literature of IEBT adoption in specific locations or regions of developed countries around the world, this study concurs with Premkumar and Roberts (1999) that relative advantage, management support, and competitive pressure are predictors of IEBT adoption in SMEs. This study’s results support Drew (2003) and Simpson and Doherty (2004) with information indicating that the industry sector in which an SME belongs is irrelevant to how it adopts IEBT. Like this study, Drew highlighted the importance of relative advantage of IEBT in the adoption process of SMEs. Also, Simpson and Doherty (2004) noted the absence of adequate and relevant support from government agencies as a problem. Lawson et al. (2003) highlighted organizational readiness (i.e. lack of internal IT expertise and knowledge) as a major barrier to the adoption of IEBT by SMEs in the study’s context. Unlike what was reported in Scupola (2003), government role and support was not found to be a significant predictor of IEBT adoption for SMEs in Maritime Canada. However, this study like observation in Scupola (2003) supported that competitive pressure is an important predictor of IEBT adoption in SMEs. Support is provided to the finding in Grandon & Pearson’s (2004) study indicating that external pressure (competitive pressure, in this instance) and perceived usefulness (relative advantage) influence the adoption of EC in SMEs; the result here did not uphold the relevance of organizational readiness as a predictor. Fifth, with respect to prior studies conducted in Canada (Iacovou et al., 1995; Chwelos et al., 2001; Raymond, 2001; Raymond & Bergeron, 1996; de Guinea et al., 2005; Davis & Vladica, 2006; Hadaya, 2006; Zhu et al., 2009), this research affirms the views indicating that perceived benefits (relative advantage), competitive pressure, and management support are important predictors of innovations adoption in the country’s small businesses. This study’s finding with respect to management support concurs with results in Raymond and Bergeron (1996) and de Guinea et al. (2005) to signify its importance for achieving IS success in Canadian’s SMEs. The inadequate levels of IT expertise, and overall lack of awareness of IS products/issues (Noce & Peters, 2006; Zhu et al., 2009), as well as perceived government’s apathy towards IS/IT use as reported elsewhere (Warda, 2005), is supported by this study’s findings. Additionally, the trend of IEBT in use by SMEs in the Maritimes compares to findings in Davis and Vladica (2006). With respect to government support as an enabler of IEBT adoption for SMEs, it will be interesting to know what the data analysis results will look like for a more favorable perception of that factor. PLS analysis suggests a higher R 2 value is possible with a higher level of an independent construct. Overall, this research has consolidated other prior research efforts in the area; at the same time, providing current understandings regarding the importance of selected factors. 6.2. Implications for Practice and Policy Making By focusing on a less endowed part of Canada, this research has focused attention on possible factors or issues that could serve to inhibit/enable the adoption of IEBT in SMEs. Policy makers, industry leaders, and SMEs’ executives wishing to understand some of the reasons why certain SMEs in the country lag in the adoption of IEBT and related technologies can benefit from the information provided in this study. Given that executives in SMEs indicated a poor understanding of the compatibility of IEBT and related technologies in business, this study recommends that more concerted efforts be made toward sensitizing SMEs’ owners and their employees in the area about the pertinence of such innovations for enhanced business operations. For the same reasoning,

the need for e-business mentoring, coaching, and training (Simpson & Doherty, 2004) becomes more cogent. Given that relative advantage was shown to be an important factor in the adoption of IEBT for SMEs. It is reasonable to suggest that by increasing user awareness of the benefits of IEBT, the adoption of such technologies would be positively encouraged. Thus, awareness campaigns tailored for SMEs’ owners would be useful in increasing their knowledge of how an IS can be used in business operations. Incentives can be provided by the authority to local IS vendors to assist SMEs’ executives and employees acquire needed insight. By the same token, relevant government agencies and other local sources of expertise can be marshaled towards providing such training and coaching to SMEs in need of such services. With these kind of supports on board it is to be expected that more and more SMEs will acquire the knowledge to try out IEBT and related technologies in their businesses. When knowledge of the relevance of technological innovations in business activities becomes deep-rooted and ubiquitous, critical mass in the acceptance of IEBT will ensue. Subsequently, SMEs, their partners and customers will collectively be pushing and pulling themselves towards accepting IEBT use in their engagements. Ultimately, the Canadian business climate and economy stand to profit from the widespread use of ICT in business operations (Wade et al., 2004; Warda, 2005; Statistics Canada, 2006; Sharpe & Arsenault, 2008). It is worth noting that an initiative similar to what is being discussed has surfaced in the period following this study. For instance, the government of Canada launched an initiative called the Small Business Internship Program (SBIP) for 2010-11. Industry Canada (2010) noted that ―the SBIP is a collaborative effort of Canadian small businesses, post-secondary institutions and non-government organizations‖ to help SMEs acquire e-commerce expertise and coaching from the aforementioned. This idea is not at all novel. Premkumar and Roberts (1999, p. 483) commented that ―in some [US] communities, nonprofit agencies and community groups have taken the lead in providing such [training and awareness] for SMEs.‖ Similarly, Mehrtens et al. (2001, p.) writes about the emergence of eClusters, which is basically an intermediary group or groups of firms providing computing and IS services to small businesses. Such groups allow SMEs to focus on their core competence while tackling issues related to IS planning, selection, and implementation, on their behalf. This model can be copied and applied in the context of Canadian SMEs. The clear advantage it offers is that the burden of ―acquiring and executing‖ IT skills needed to deploy IEBT products is taken off the shoulders of SMEs’ executive operators, without diminishing the capability to benefit from applying such technologies in their operations. Industry leaders and policy makers may take a look at this model and see how to apply it in the context of Canada. 6.3. Limitations and Avenues for Future Research First, despite the fact that common method bias was not seen to pose a problem for this study’s data, it is still possible that respondents may be subject to a halo effect. Asking only one respondent to present a view on behalf of their organization may be problematic. Second, the study included a variety of IEBT, the possible levels of complexity in the use of such technologies were not controlled in this study; this may be limiting. It is possible that perceptions for email and e-ERP acceptance in the sampled SMEs may not be similar. Thus, the inclusion of both types of technologies might negative influence the result. Third, findings from this study cannot be generalized for the whole of Canada. Data from other parts of the country may be different from what is

reported and discussed in this article. Caution should be taken in interpreting the results presented herein. This study opens up avenues for future research. Whenever possible some of the aforementioned limitations could be addressed in subsequent studies. This research effort can be replicated in other regions of Canada to reify or debunk claims presented in this study. The data used in this study is cross-sectional in nature; future efforts could consider using longitudinal data to facilitate more insight. It is possible that other factors not included in this study could be identified to enhance insight. The research framework could be used to study the impacts of similar factors in larger businesses in the region and across the country. Information from such an exercise could benefit research and policy making. Industry specific studies could also be commissioned to further enhance insight (Noce & Peters, 2006). Further, the effects of IS vendor support and other community agencies’ support including financial institutions, could be investigated. The research framework and concept could be further reinforced with the identification of relevant variables that could moderate the relationship between inhibiting and enabling factors and SMEs’ acceptance behaviors or patterns. Future research using meta-analytic approaches could examine the enablers and inhibitors of IEBT adoption in SMEs in comparable parts of the developed world. Knowledge from such efforts stands to consolidate theories related to the acceptance of IEBT and related technologies in SMEs. 7. Conclusion This research drew from the DIT and TOE frameworks in investigating the adoption of IEBT in SMEs based in the Maritime region of Canada. The research showed that relative advantage of such technologies, competitive pressure, and management support are significant predictors of the acceptance of IEBT in the region. The factors of organizational readiness, complexity, and compatibility of the technology, as well as customers’ and business partners’ pressures were found to be insignificant for IEBT adoption in the SMEs. The lack of government support is a point of contention. Overall, the study’ findings enrich the discourse related to the reticence of Canadian SMEs in adopting IS products for business operations. It is hoped that the discussions and conclusions made in this study benefits practitioners and policy makers in the country and elsewhere. As well, this effort contributes to the growing body of works researching the factors influencing IEBT adoption in SMEs across regional contexts, and it seeks to complement past research efforts in Canada. More work is expected.

Note (¥): IEBT as used in this study is synonymous with usage elsewhere in the literature. For example, Net Impact Study Canada (2002; 2004), Tarafdara and Vaidya (2006), Wymer and Regan (2005), Al-Qirim (2007), Riemenschneider and McKinney (1999), and Chang and Cheung (2001) used ―Internet business solutions‖ (IBS), ―e-ecommerce technologies‖ (ECT), ―e-commerce/e-business Internet technologies‖ (EEIT), ecommerce communications and applications technologies, web-based technologies, internet/WWW technologies, respectively. Acknowledgments This study was funded by grants #X (Omitted at this stage in the review process) .

Appendix 1 (Continued). The items used in the questionnaires and their reliability scores Construct and measuring items

Item loading

Cronbach alpha

Composite reliability

0.942

0.955

0.934

0.953

0.862

0.853

0.945

0.961

0.904

0.935

0.900

0.942

Relative advantage Internet/e-business technologies allow our firm to manage its operations efficiently. Internet/e-business technologies improve the quality of our operations. Internet/e-business technologies enhance the effectiveness of our firm’s operations. Internet/e-business technologies enable us to perform our operations more quickly. Internet/e-business technologies give us a greater control over our operations. Compatibility Use of internet/e-business technologies is compatible with all aspects of our business operations. Use of internet/e-business technologies fit well with the way we operate. Use internet/e-business technologies fit into our working style. Use of internet/e-business technologies is completely compatible with our current business operations. Complexity Using internet/e-business technologies require a lot of mental effort Using internet/e-business technologies is frustrating Using internet/e-business technologies is too complex for our business operations Management Support Management is interested in the use of internet/e-business technologies in our operations. Management is supportive of the use of internet/ebusiness technologies in our operations. Our business has a clear vision regarding the use of internet/e-business technologies. Management communicates the need for internet/ebusiness technologies usage in the firm. Organizational Readiness Our firm knows how information technology (IT) can be used to support our operations. Our firm has a good understanding of how internet/ebusiness technologies can be used in our business. We have the necessary technical, managerial and other skills to implement IEBT. Our business values and norms would not prevent us from adopting IEBT in our operations.

0.864 0.908 0.930 0.888 0.872

0.867 0.936 0.943 0.912

0.601 0.831 0.975

0.939 0.934 0.902 0.933

0.907 0.950 0.876 0.804

Competition’s pressure Our firm is under pressure from competitors to adopt internet/e-business technologies.

0.860

Some of our competitors have already started using

0.899

internet/e-business technologies. Our competitors know the importance of IEBT and are using them for operations.

0.994

Customer’s pressure We know our customers are ready to do business over the Internet.

0.913

Our firm is under pressure from customers to adopt internet/e-business technologies. Our customers are demanding the use of IEBT in doing business with them. Partner’s pressure Our partners are demanding the use of IEBT in doing business with them. We know our suppliers and partners are ready to do business over the Internet. Our firm is under pressure from suppliers and partners to adopt IEBT. Government support Our firm is under pressure from some government agencies to adopt internet/e-business technology The government is providing us with incentives to adopt internet/e-business technologies. The government is active in setting up the facilities to enable internet commerce. Adoption of IEBT Our company makes use of IEBT, very often. Our company uses IEB e-commerce/e-payment, at all times, for its transactions. Our company uses IEB its critical operations. The number of business operations and activities in my company that requires IEBT is high.

0.868

0.914

0.948

0.905

0.941

0.913

0.948

0.815

0.864

0.994

0.850 0.882 0.644

0.879 0.905 0.995

0.790 0.818 0.717 0.814

International Journal of Information Technology & Decision Making  World Scientific Publishing Company

Appendix 2.

The confirmatory factor analysis and cross loadings Compatibility

Compt1 Compt2 Compt3 Compt4 Comx1 Comx2

Competition’s pressure 0.395119 0.425456 0.476138 0.415343 0.011232 -0.023333

0.866617 0.935902 0.942674 0.911928 -0.075172 -0.210003

Customer’s pressure 0.386228 0.482114 0.514836 0.492299 0.054010 -0.068303

Comx3

-0.063913

-0.305650

-0.124879

IEBT1

0.347345

0.403029

IEBT2

0.359511

IEBT3 IEBT4 ME1comp1 ME1comp2 ME1comp3 MEgov1 MEgov2 MEgov3 MgtSup1 MgtSup2 MgtSup3 MgtSup4

0.363061 0.554168 0.859538 0.898841 0.993657 0.311757 0.385292 0.371625 0.339294 0.304100 0.426111 0.385535

Item

Relative advantage 0.580175 0.640739 0.620745 0.541399 0.112467 0.073402

Complexity -0.192983 -0.266030 -0.274701 -0.401349 0.605196 0.831494

Govt. support 0.137473 0.196444 0.207750 0.148814 0.105223 0.093904

0.008833

0.974674

0.085040

0.338783

Partner’s pressure 0.348044 0.450450 0.493334 0.445105 0.089403 0.027152 0.080635 0.303731

0.341482

-0.162317

0.359642

0.350605

0.261500

0.367363

-0.121695

0.424764 0.457922 0.303269 0.379527 0.390477 0.092946 0.206845 0.152171 0.938748 0.933675 0.902290 0.933046

0.312014 0.443624 0.316972 0.443205 0.433571 0.026884 0.131054 0.077397 0.641584 0.672156 0.653126 0.661167

0.464972 0.439432 0.542603 0.533785 0.592767 0.330936 0.464315 0.441820 0.487928 0.484272 0.552087 0.546286

0.407516 0.597766 0.352106 0.373728 0.413222 0.080501 0.183399 0.133328 0.565763 0.547098 0.534126 0.537089

-0.082546 -0.048904 -0.016650 -0.095737 -0.047506 0.058971 0.085485 0.097095 -0.151384 -0.160966 -0.097903 -0.135311

0.020941 0.029908 0.284909 0.215059 0.412004 0.281535 0.383934 0.878885 0.905686 0.994807 0.124741 0.137361 0.164456 0.187722

Management support 0.457535 0.504590 0.559463 0.472060 0.026652 -0.097533

Organizational readiness 0.472978 0.526667 0.577378 0.535056 -0.046532 -0.178237

-0.135642

-0.199673

0.314556

IEBT adoption 0.451352 0.498788 0.529192 0.463870 0.021718 0.061152 0.126136 0.790092

0.357527

0.401578

0.306320

0.817873

0.398090 0.455359 0.392357 0.435595 0.461326 0.139377 0.196156 0.189242 0.542874 0.511896 0.462531 0.507624

0.427498 0.459592 0.593918 0.588260 0.650144 0.317955 0.409825 0.404281 0.412286 0.398401 0.459850 0.451726

0.711692 0.813824 0.416723 0.519063 0.525161 0.125441 0.150257 0.162770 0.509936 0.452979 0.468270 0.481950

1

ME1cust1 ME1cust2 ME1cust3 MEpart1 MEpart2 MEpart3 OCR1 OCR2 OCR3 OCR4 RA1 RA2 RA3 RA4 RA5

0.576896 0.624391 0.652342 0.570198 0.556098 0.319429 0.335391 0.418700 0.385633 0.411325 0.301937 0.360607 0.384426 0.417740 0.353231

0.520362 0.394469 0.503598 0.277621 0.345792 0.494120 0.498530 0.569961 0.521327 0.451828 0.516691 0.592607 0.615590 0.552428 0.639224

0.913823 0.867709 0.995052 0.644899 0.665039 0.411169 0.385668 0.390132 0.459778 0.390848 0.290159 0.332941 0.356089 0.380825 0.377985

0.478460 0.384701 0.491947 0.321569 0.414037 0.387647 0.396802 0.450721 0.429451 0.378992 0.460387 0.478284 0.541930 0.544220 0.513943

0.486556 0.328715 0.459667 0.297280 0.407253 0.597648 0.663529 0.684035 0.635315 0.516019 0.422882 0.481182 0.550948 0.573770 0.579285

0.471394 0.331217 0.459261 0.245409 0.302854 0.812878 0.906774 0.949473 0.876169 0.803722 0.474171 0.496715 0.573917 0.590908 0.551222

0.540706 0.639713 0.628375 0.850415 0.882181 0.644409 0.505258 0.493749 0.629090 0.413617 0.330140 0.369203 0.404744 0.442586 0.390994

0.412006 0.301389 0.394743 0.249455 0.303212 0.484073 0.540452 0.604163 0.502328 0.478796 0.863536 0.908430 0.929783 0.888067 0.871912

-0.183470 -0.030539 -0.134259 0.072103 -0.014871 -0.240443 -0.153454 -0.184865 -0.260537 -0.170338 0.054658 0.098284 0.045858 -0.039125 -0.087709

0.277252 0.478527 0.405975 0.483075 0.468501 0.127391 0.080099 0.102415 0.115903 0.001501 0.131136 0.121582 0.164931 0.112844 0.087787

International Journal of Information Technology & Decision Making  World Scientific Publishing Company

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