1

Into the Drivers of Innovation Adoption:

What is the Impact of the Current Level of Adoption? Maryse J. Brand, Faculty of Economics and Business, University of Groningen, P.O. Box 800, 9700 AV Groningen, The Netherlands. tel. +31 50 363 7492/3453, fax: +31 50 363 7110, e-mail: m.j.brand@rug.nl Eelko K.R.E. Huizingh, Faculty of Economics and Business, University of Groningen, P.O. Box 800, 9700 AV Groningen, The Netherlands. e-mail: K.R.E.Huizingh@rug.nl

- IN PRESS: European Journal of Innovation Management, 2008, no. 1 - Abstract Purpose Complex innovations may be adopted at multiple levels. This raises the question whether the impact of factors that are supposed to influence innovation adoption vary with the current level at which an innovation has been adopted. The two main objectives of this study are (1) to systematically and formally test for differences in the impact of various determinants of e-commerce adoption depending upon the current level of e-commerce, and (2) to investigate the possible direct impact of the current level of adoption on the intention to further adopt. Design/methodology/approach. A conceptual framework is developed from the literature. The model is tested using survey data from 98 small and medium-sized enterprises in the Netherlands. Findings The results indicate significantly smaller effects of both knowledge and satisfaction for companies at the advanced level of e-commerce compared to companies at the basic level. The current adoption level has a highly significant positive direct effect on adoption intention. These findings imply that at the higher levels of adoption the classical adoption determinants have less effect and other, less explored factors are more important. Originality/value This study is one of the first empirical studies that deal with multiple levels of innovation adoption. We conclude that when innovations can be adopted at various levels, the determinants of innovation adoption vary between different levels of adoption. This finding deserves further attention of researchers, consultants and policy makers. Keywords E-commerce, internet, innovation, innovation adoption, small firms Paper type Research paper * We would like to thank G.J. Bronsema and A. Hunneman for their contributions to the data

collection and analyses of this study.

2

Introduction Many innovation researchers have focused on the determinants and stages of the adoption process of innovations. Recently, a few studies pointed at the incremental nature of this process (Daniel et al., 2002; Freel,2003; Waarts, van Everdingen and van Hilligersberg, 2002). Since the introduction of a disruptive technology is often followed by a series of incremental innovations, the adoption of such technologies is not a binary process, but one that involves multiple levels. For example, several scholars proposed that e-commerce can be adopted at multiple levels, with websites that offer information, interaction, or transactions (e.g., Bégin, Tchokogué and Boivert, 2001; Daniel, Wilson and Myers, 2002; Dholakia and Kshetri, 2004; Levy and Powell, 2003; Raymond, 2001; Sadowski, Maitland and van Dongen, 2002; Teo and Pian, 2004). Recent empirical research confirms that e-commerce is an innovation that can be adopted at multiple levels (Forman, 2005; Zhu and Kreamer, 2005). When we accept the notion that an innovation can be adopted at multiple levels, its adoption process actually consists of a series of adoption processes. This raises the question whether the impact of factors that are supposed to influence adoption vary with the current level at which the innovation has been adopted. A few recent studies have explored this issue by showing that the factors that influence further adoption differ between various levels of e-commerce (e.g., Ching and Ellis, 2004; Dholakia and Kshetri, 2004; Forman, 2005). We contribute to this emerging stream of research in three ways. First, in contrast to previous studies we systematically and formally test for differences in the impact of various determinants of innovation adoption depending upon the current adoption level. Second, we investigate the possible direct impact of the current level of adoption on the intention to further adopt, a relationship that (to our best knowledge) has not been studied before. Finally, previous studies have the methodological limitation that they tend to explain past adoption behaviour by present attributes, thus confusing pre-adoption attributes of the adoption with post-hoc opinions. To avoid this problem, we focus on the intention to adopt as the dependent variable (Kendall et al., 2001; Sathye and Beal, 2001).

Our field study involves small and medium-sized enterprises (SMEs). This is an interesting group of firms for an e-commerce adoption study, since the Internet is supposed to diminish the negative scale effects (such as transaction costs) SMEs are facing, enabling them to develop more efficient and effective business strategies (Fariselli, Oughton, Picory and Sugden, 1999; Santarelli and D’Altri, 2003; Walczuch, van Braven and Lundgren, 2000). Small firms in rural areas, for example, overcome geographical disadvantages by adopting the Internet (Mitchell and Clark, 1999; North and Smallbone, 2000). Also, a large scale international study by Zhu, Kraemer and Dedrick (2004) found a negative relation between firm size and e-business value. In the empirical part of our study we investigate several hypotheses using data from 98 SMEs in the Netherlands.

This paper is structured as follows. First, the literature section discusses the notion of e-commerce adoption as a multi-level innovation adoption, and reviews previous studies in this area. This section ends with the conceptual model containing the hypothesized relations. The next two sections contain the research design and results of the empirical part of our study. The paper ends with a discussion of the results and an overview of the main conclusions, limitations and implications for practitioners.

3

Literature review In our study we consider e-commerce as a multi-level innovation and e-commerce adoption as consisting of a series of adoption processes. As visualized in figure 1, an organization moves through the adoption process multiple times, each time to reach the next level of the innovation. E-commerce may first be used to present the company and its offerings (a ‘brochureware’ site), then the company may add some interactive features by which potential customers get access to information and services tailored to their needs, next the company may include a transaction function for standard products or services, and finally the site may be fully integrated with internal systems (cf. Teo and Pian, 2004). When a firm has implemented the lowest level of e-commerce, further expansions of e-commerce activities are thus considered as additional adoptions at subsequent levels of the e-commerce innovation. Firms do not need to go through all levels. They may very well decide not to adopt any additional e-commerce innovations at any of the adoption levels.

<< Take in FIGURE 1 >> The notion that innovation adoption can be described as multiple sequential adoption decision processes is not new. For example, in their extensive literature review of the IT adoption literature Forman and Goldfarb (2006) distinguish between four separate adoption decisions that may occur at separate time periods. The four decisions are (1) whether to adopt IT at all (inter-firm diffusion), (2) which capabilities of IT the firm intends to use, (3) the rate of diffusion within the firm (intra-firm diffusion), and (4) individual employees deciding upon how often to use the technology. In our study we focus on the second decision, when firms decide which e-commerce capabilities they intend to use, and we distinguish multiple sequential adoption processes within this stage. We investigate whether the current level at which an organization has adopted an innovation influences the impact of various factors on the likelihood of further innovation adoption. In other words: does the impact of factors that influence adoption intention vary depending upon the current level of e-commerce? There are a few studies that focus on related issues. Ching and Ellis (2004) and Huizingh and Brand (forthcoming) distinguish between different levels of e-commerce and find several differences in the determinants of innovation adoption between these levels. For example, Ching and Ellis find that the mean scores on ‘relative advantage of the innovation’ and ‘compatibility’ are much higher at the most advanced e-commerce level. However, both studies only compare differences in the mean scores on the determinants, and do not study differences in the impact of the determinants on innovation adoption. Such an analysis has been performed by Forman (2005), who studied the determinants of Internet adoption comparing the adopters of simple and complex Internet technologies. The former group of firms had Internet access, while the latter had also adopted Internet applications. Forman found several differences, including a higher impact of organization size on Internet applications than on Internet access. Dholakia and Kshetri (2004) studied the impact of various factors on the adoption of two levels of e-commerce, namely having an informational web site or a transactional web site. Of the twelve variables studied, three

4

demonstrated a significant impact at only one level. Firm size, privacy-security and environmental monitoring had an impact on the adoption of an informational web site, but not on the adoption of a transactional site. Although the latter two studies (Forman, 2005 and Dholakia and Kshetri, 2004) provide evidence that the impact of the determinants of innovation adoption may differ depending upon the current level of adoption, they do not systematically and formally test for these differences.

Our second research question is whether the current level at which an innovation has been adopted is directly related to the intention to further adopt this innovation. To our best knowledge no previous study explored this issue. However, some studies report findings suggesting that it is worthwhile to investigate this relationship. Forman (2005) found 14 significant predictors for Internet access, but only seven predictors were significant for Internet applications. Similarly, Dholakia and Kshetri (2004) concluded that the relative importance of several predictors decreased when the level of Internet adoption increased. Their study did not find any predictor that was significant for the more advanced transactional sites but not significant for informational sites. The reverse was true for three predictors. These findings suggest that our understanding of the factors that influence innovation adoption is better for initial adoptions than for adoptions at higher (more advanced) adoption levels.

Studies on IT-adoption by SMEs typically measure present attributes and opinions to explain past adoption behaviour (e.g., Dholakia and Kshetri, 2004; Lucchetti and Sterlacchine, 2004; Thongh, 1999). This is an important methodological flaw that can be prevented by doing longitudinal research (which is very difficult in an SME context), or by using intention models (cf. Kendall et al., 2001; Sathye and Beal, 2001).

In this study we investigate both the direct effect and the moderating effect of the current adoption level on the intention to further adopt e-commerce. We use the diffusion of innovations theory (often referred to as DoI theory) as a starting point. The DoI theory is well established and widely used in diffusion-related research (Mustonen-Ollila and Lyytinen, 2003). DoI theory describes the adoption of a new product (or process) as a decision process that moves through different stages over time. The decision process begins with becoming aware of the existence and learning about the innovation (knowledge), subsequently the potential adopters assess the utility of the innovation (persuasion). Then the decision is made whether or not to adopt the innovation. If the decision is in favour of adoption, the innovation is implemented (implementation). Finally, the experiences with the innovation are evaluated leading to either continuation of use or discontinuance (confirmation) (Rogers 1962, 1995). Since this adoption model includes both pre-adoption and post-adoption stages, it is an appropriate starting-point for modelling inter-adoption relations. Following the broad criticism on the supposed sequential nature of diffusion of technological innovation models, we consider the stages as activities that may occur in parallel (cf. Cooper and Zmud, 1990) (see figure 2). For each of them we discuss both its hypothesized impact on adoption intention as well as the moderating effect of the current level of adoption.

<< Take in FIGURE 2 >>

The firm’s knowledge level refers to the available stock of various types of explicit e-commerce knowledge such as the e-commerce opportunities, technological

5

know-how, and knowledge of customer e-commerce demands (e.g., Thong, 1999, Mehrtens, Cragg and Mills, 2001). The level of knowledge about an innovation has a positive relation with the intention to adopt the innovation (Rogers, 1995; Thong, 1999; Luchetti and Sterlacchine, 2004).

As firms proceed from basic to more advanced adoption levels, additional organizational learning takes place. Organizational learning in this context refers to the changes in knowledge, skills and abilities as a result of hands-on experience with the innovation (Debowski, 2005). The collection of e-commerce knowledge, skills and abilities determines to what extent an organization is capable of successfully performing e-commerce. Skills and abilities, which are related to tacit knowledge, will profit more from growing experience than explicit knowledge. Therefore we expect that at a more advanced adoption level, when the firm has more experience with the innovation, the impact of knowledge on intention is lower. Hypothesis 1a: The availability of more e-commerce related knowledge leads to a higher

intention to (further) adopt e-commerce. Hypothesis 1b: Knowledge has a weaker positive effect on adoption intention for firms at

the advanced level.

E-commerce can lead to superior company performance (Saeed, Grover and Hwang, 2005). However, firms may vary in their assessment of the potential value of e-commerce (Chircu and Kauffman, 2000). This may depend upon the fit with the firm’s core competencies (Ravichandran and Lertwongsatien, 2005), or the alignment with the firm’s IT infrastructure (Zhu, 2004). Potential value includes the perceptions of both the potential benefits and costs of adopting the innovation, and consists of dimensions that are specific to a particular adoption (Rogers, 1995; Zhu and Kraemer 2005). As such, potential value is similar to concepts such as relative advantage (Rogers, 1995), perceived benefits (Iacovou, Bensabat and Dexter, 1995) and anticipated satisfaction (Riemenschneider, Harrison and Mykytyn Jr., 2003). Several studies found evidence of a positive influence of perceived potential value on e-commerce adoption by SMEs (Thongh, 1999; Raymond, 2001; Kendall, Tung, Chua, Ng and Tan, 2001; Mehrtens et al., 2001; Ching and Ellis, 2004).

The perceptions of firms at the advanced level are more realistic and specific (i.e. related to the firm’s own situation and applications) compared to firms at the basic level, leading to higher self-efficacy which has a positive relation to intentions (Bandura, 1977). This would imply that at the advanced level, the relation between value and adoption intention is stronger. Hypothesis 2a. The higher the perceived potential value of e-commerce, the higher the

intention to (further) adopt e-commerce. Hypothesis 2b. Perceived potential value has a stronger positive effect on adoption

intention for firms at the advanced level. All kinds of conversion contingencies may hinder or facilitate realizing the potential value during the implementation of the innovation (Davern and Kauffman, 2000). Implementation refers to the extent to which a firm is fully using the possibilities of the

6

available e-commerce hardware and software (Tornatsky and Fleisher, 1990). A variety of related concepts has been developed to grasp this element of the adoption process, such as infusion (Cooper and Zmud, 1990), extent of use (Thong, 1999, Ching and Ellis, 2004), and actual usage (Zhu and Kraemer, 2005). Rogers (1995) argues that if implementation problems arise, an innovation will not be used to the full and the interest in expanding into related activities will be low. The available evidence leads to a positive hypothesized relation between the degree of implementation and adoption intention. Since we have no theoretical arguments for specific interaction effects between implementation and adoption level we only explore this relation. Hypothesis 3a. The higher the current implementation level of available e-commerce

resources, the higher the intention to (further) adopt e-commerce. Hypothesis 3b. The current implementation level may have a different effect on adoption

intention for firms at the advanced level. The value that a company realizes after implementing the innovation leads to a certain level of satisfaction. Since we model innovation adoption as repeat behaviour, satisfaction with earlier decisions becomes relevant and we include satisfaction as our fourth variable. Satisfaction is often used in innovation adoption studies at the individual level (Wixom and Todd, 2005), but largely ignored at adoption studies at the firm level. Given the broad agreement on the relation between satisfaction and behavioural intention, we expect that positively perceived experiences with earlier e-commerce activities has a positive effect on adoption intention.

Organizational behaviour literature provides arguments for the moderating effect of adoption level on the impact of satisfaction on intention. We expect a different effect for satisfaction at the basic adoption level (at which firms have not yet invested much) and the advanced level (at which considerable resources have been allocated). At the basic level, we expect a positive relation between satisfaction and adoption intention. Companies satisfied with their basic e-commerce operations will see future rewards and are more likely to further adopt e-commerce, while unsatisfied companies will not be inclined to further adopt. At the advanced level, this process may work differently. Both the sunk cost effect (Åstebro, 2004) and the tendency to aim for strategic consistency motivate dissatisfied advanced users to stick to their chosen strategy and to further adopt e-commerce, in an effort to make up for disappointing experiences. Also, the repeated finding that small business owners tend to demonstrate satisfising behaviour as soon as the company’s viability and a comfortable life style have been achieved (Davidsson, 1991; Nooteboom, 1994), suggests a less positive effect of satisfaction on adoption intention at the advanced level. Hypothesis 4a. The higher the satisfaction with earlier e-commerce activities, the higher

the intention to (further) adopt e-commerce. Hypothesis 4b. Satisfaction has a weaker positive effect on adoption intention for firms at

the advanced level. The final relationship concerns the direct effect of adoption level on adoption intention. Since, to our best knowledge, no previous studies address this relationship, we formulate

7

an explorative hypothesis. Usually, actual behaviour is the best predictor of future behaviour. In our case, that would imply a positive relationship between adoption level and adoption intention. Several theories could substantiate this relation, for example Cooper and Zmud (1990) who find that organizational learning models are useful in explaining technology infusion, and Åstebro (2004) who discusses sunk costs as a determinant of depth of technology adoption. Hypothesis 5. Firms at the advanced level of e-commerce exhibit a higher intention to

(further) adopt e-commerce than firms at the basic level of e-commerce. The conceptual model presented in Figure 2 summarizes the discussion above. The dependent variable is the intention to (further) adopt e-commerce, which has proven to perform well in innovation studies, including the adoption of ICT by SMEs (Kendall et al., 2001, Sathye and Beal, 2001). The focal variable in our model is the current level of adoption. We expect this variable to have both a moderating effect as well as a direct effect on adoption intention. Research design Sample Data were collected using a postal survey among small and medium-sized firms with Internet access. The requirement of Internet access ensures that in these companies the IT infrastructure for e-commerce is available. In developing and executing the survey, we collaborated with Syntens, a Dutch chain of government subsidized regional innovation promoters targeting small and medium sized firms (cf. Nooteboom, Coehoorn and van der Zwaan, 1992). The sample consisted of 1600 companies, 600 firms from Syntens’ customer database, and 1000 firms from the major commercial database of MarktSelect. A total of 137 questionnaires were returned. From this initial data base we excluded companies without employees or Internet access, or with missing values on the variables relevant for this study. This left us with 98 usable responses. The gross response rate is 8.6%, which is somewhat lower than comparable studies (cf. Brandyberry, 2003, 12.5%; Daniel et al., 2002, 10.4%; Dholakia and Kshetri, 2004, 11.3%; Kendall et al., 2001, 14.5%; Thongh, 1999, 13.8%).

The responding firms are from a broad range of industries, including professional services (29%), contracting (24%), wholesale trade (20%), manufacturing (17%), and other (10%). It is likely that self-selection has taken place, leading to an overrepresentation of small firms that are already involved in e-commerce. Given the goal of our study this bias may enhance the testability of our conceptual model.

The large majority of respondents are CEOs (82%) who own the firm (81% of the responding CEOs). The average firm size is 23 employees (37% less than 10 employees, 29% between 10 and 20, and 32% between 20 and 100). Average turnover is 4.4 million Euros. Measurement and analyses We follow Rossiter (2002) who emphasizes the importance of content validity of constructs. Adoption variables are specific to and should be specified for particular

8

adoptions in particular contexts (e.g., Rogers, 1995; Zhu and Kraemer, 2005). Therefore experts of Syntens were involved in the scale development process. We used both multi-item constructs and single item measurements. For concrete characteristics, single item measures are preferred to prevent redundancy (Rossiter, 2002). In other cases, we used formative multi-item scales. Formative constructs consist of items that induce the construct, like an index (Diamantopoulos and Winklhofer, 2001). For such constructs statistical tests (including factor or reliability analysis) are not suitable, and researchers should focus on content validity by using expert interviews or other methods. The measurement scales are listed in the appendix.

To measure the dependent variable intention we make a distinction between adoptions of e-commerce in several fields of application. Following Waarts et al. (2002) and Kendall et al. (2001) we include three core processes: purchasing, selling, and customer service. For each process, respondents were asked whether they were planning to considerably expand their e-commerce activities within 2 years (this being an appropriate time horizon for analysing small firm strategic decision making, cf. Waarts et al., 2002). The variable intention reflects the number of processes for which a firm is planning to expand its e-commerce activities. The binary variable adoption level is measured in a similar way (cf. Forman, 2005). If the company is not using e-commerce currently for at least one of the three processes, it is classified at the basic level of e-commerce otherwise it is classified at the advanced level.

In order to measure knowledge we used a literature review and expert interviews to identify 11 types of information relevant in e-commerce adoption decisions (of SMEs). For each item respondents indicated on a 5-point Likert scale the availability of this information. The variable knowledge reflects the average of these scores. Also based on the literature review and expert interviews we developed a list of 18 items reflecting beliefs about the perceived potential value of e-commerce for SMEs. The items were measured on a 5-point Likert scale and included items such as improved sales, cost savings, and customer need. Potential value is the mean score on these 18 beliefs. Implementation is measured as the extent to which a company is currently using the available e-commerce applications (cf. Tornatzky and Fleisher, 1990). This is a single item measure on a 1-10 scale (from no use to complete use). Satisfaction reflects the respondent’s overall attitude based on previous experiences (Garbarino and Johnson, 1999). To measure this variable we used a three step procedure, based on Ajzen and Fishbein (1980), in which importance ratings are applied to select a limited number of salient beliefs. Satisfaction is measured as the weighted mean score of the respondent’s satisfaction regarding the most important potential value beliefs. The three control variables are size, industry and market share position (cf. Dholakia and Kshetri, 2004; Forman, 2005; Thongh, 1999; Zhu and Kraemer, 2005). Organizational size is measured by the number of employees (full time equivalents). Following Shalit and Sankar (1975) we take the natural logarithm of this variable to reduce the skewness of its distribution. Since information intensity is the major determinant of e-commerce use in a specific industry (Thongh, 1999), we distinguish between high information intensive industries (i.e., wholesale trade, transportation, professional services, and other services), and low information intensive industries (i.e., manufacturing, contracting and other production activities). Market share position

9

reflects on a 1-4 scale whether a company is one of the smaller players or the market leader in its main market (Van Campen, Huizingh, Oude Ophuis and Wierenga, 1994). Results To test the relationships hypothesized in our conceptual model we use moderated regression. We present the analyses in two steps. The first model does not include the effect of the current level of adoption, while the second model does. This procedure enables us to determine the added value of including the adoption level. Since adoption level is a binary variable (0/1), the regression coefficients of the interaction effects can be interpreted as the additional effect of an independent variable on intention at the advanced level of adoption. For example, if the regression coefficient of knowledge is significant, then the level of knowledge influences the intention to adopt e-commerce. If the regression coefficient of the interaction term of knowledge and adoption level is significant, then the impact of knowledge varies between companies at the basic and at the advanced level of e-commerce. If the coefficient’s sign is positive, the impact of knowledge on intention to adopt is greater at the advanced level than at the basic level. If the sign is negative, then knowledge has a greater impact on the intention to adopt for companies that are currently at the basic level of e-commerce.

<< Take in TABLE I >> Table I contains the results of both regressions. In model I two variables are significant, potential value and implementation. The effect of potential value is positive, implying that companies with a higher score on potential value are more likely to (further) adopt e-commerce. The effect of implementation is negative, implying that if a company is not making full use of the currently available e-commerce applications, it is more inclined to further adopt e-commerce. None of the control variables (size, industry and market share) are significant.

Model II shows the results of the regression where both adoption level and the interactions between adoption level and each of the four factors are added to the model as independent variables. In this case the direct effects of both knowledge and potential value are significant and positive (confirming hypotheses 1a and 2a). The effect of implementation remains negative, but has become insignificant, just like the direct effect of satisfaction (no support for hypotheses 3a and 4a).

Two interaction effects are significant. The first concerns the interaction between knowledge and adoption level. As expected this interaction effect is negative, implying that for companies at the advanced adoption level, the availability of explicit knowledge about e-commerce is less important in explaining the intention to adopt, compared to companies at the basic adoption level (confirming hypothesis 1b). The second significant interaction effect concerns satisfaction. Although the direct effect of satisfaction is not significant (p>.10), the interaction effect of satisfaction and adoption level is highly significant and negative. This implies that companies at the advanced level of e-commerce that are less satisfied with their current e-commerce activities, show a greater impetus to further adopt e-commerce then satisfied advanced users. This finding confirms the hypothesized effect (hypothesis 4b). The other two interaction effects, regarding

10

potential value and implementation, are both negative but (highly) insignificant. So, we do not find support for our hypotheses 2b and 3b. Finally, adoption level has a significant and positive direct effect, confirming hypothesis 5.

When comparing both models, the results clearly indicate that the model with the interaction effects outperforms the model without interaction effects (the adjusted R square increases almost threefold). This implies that not only our understanding of how various factors influence the intention to adopt an innovation is enhanced by including the adoption level, but also that the predictions the model can make about the intention to adopt improve significantly by explicitly modelling the moderating impact of the level of adoption. Discussion In this study we explored whether the effect of four well-known factors (knowledge, potential value, implementation, and satisfaction) on the intention to adopt is moderated by the current level of adoption. In other words, we investigate whether the effect of a factor is different for companies at the basic level of e-commerce compared to companies at the advanced level of e-commerce. For two of the four factors we find that the effect is indeed different, namely for knowledge and satisfaction. These findings confirm hypotheses 1b and 4b. However, the hypothesized interaction effects regarding potential value and implementation are not significant (hypotheses 2b and 3b). We discuss these findings in greater detail.

<< Take in FIGURE 3 >>

By taking into account both the signs and magnitudes of the direct and interaction effects of knowledge we get a deeper understanding of when and how this factor influences the intention to adopt. Figure 3a illustrates the results. For companies at the basic level of e-commerce, knowledge is a significant predictor of the intention to adopt. The more knowledge these companies have about e-commerce, the more likely it is that they intend to adopt e-commerce. For companies at the advanced level of e-commerce, the situation is different. The direct effect of knowledge is (more than) cancelled out by the negative interaction effect, resulting in a slightly negative relation between knowledge and intention. While for companies at the basic level of e-commerce the lack of explicit e-commerce knowledge is a barrier to adoption, this is not the case for companies at the advanced level. A possible explanation is that at the advanced level, when companies have acquired hands-on experience with e-commerce, explicit knowledge is not that important anymore for making adoption decisions. The acquired experience may have increased the level of tacit knowledge, which replaces the effect of explicit knowledge. For example, at the basic level explicit knowledge about the possible cost and benefits of e-commerce may be relevant, while at the advanced level e-commerce skills and abilities may be more important.

The second significant interaction effect concerns satisfaction. In contrast to knowledge, the direct effect of satisfaction is not significant, but the interaction effect is highly significant and negative. Figure 3b illustrates these results. For companies at the basic level of e-commerce satisfaction with the available rudimentary e-commerce

11

applications is not a good predictor of the intention to further adopt e-commerce (a slightly positive but insignificant effect). However, for companies at the advanced level of e-commerce less satisfaction is related to greater intention to further adopt e-commerce. Companies at the advanced level that are satisfied, do not seem to feel a strong need to further expand their e-commerce activities in the next few years. We see a complex of behavioural mechanisms which may explain these findings. Companies at the advanced level will be aware of the potential beneficial impact of e-commerce, and sunk costs and the urge to make up for earlier mistakes will press less satisfied companies at the advanced level to invest further in e-commerce. Another complementary explanation points at the fact that owners of small and medium-sized enterprises (SMEs) tend to be satisfiers instead of optimisers, and do not strive for maximum growth or constant innovation.

The other two hypothesized interaction effects, regarding potential value and implementation, are not significant. Potential value reflects the perceived utility of an innovation. The direct effect of this factor is positive and significant, confirming the well-known relationship between potential value and intention. However, contrary to our expectations, the magnitude of this relationship is similar for companies at the basic and advanced level of e-commerce. We expected that the hands-on experience of companies at the advanced level would somehow have increased the impact of utility perceptions on intention, but the data do not reflect this. Potential value is an equal good predictor of intention to adopt for both groups of companies.

Also, the results provide no evidence for the hypothesized positive interaction effect of implementation. Both the direct effect and the interaction effect of implementation are not significant. The direct effect is significant in the model without interactions (model I), but contrary to our hypothesis this effect is negative. A possible explanation is related to the multiple causes of a low level of implementation. Following Rogers (1995:173) we expected that when problems arise, an innovation will not be used to the full. However, an alternative reasoning may apply. Since the implementation of e-commerce is a time-consuming process, companies may just have lacked the time to fully implement the available applications. Additional data analyses provide some support for this explanation since they show that companies with higher scores on implementation on average acquired e-commerce technology earlier than other companies. Since implementation has received much less attention in innovation studies than the other three factors, this factor and its relationship with adoption intention deserve more attention in further research.

It is striking that all four interaction effects are negative (although two of them are insignificant) while adoption level has a highly significant positive direct effect on intention. These findings indicate that for companies at the advanced level other factors determine the intention to further adopt. There are many possible explanations for this conclusion. They could be related to having first-hand experience with the day-to-day operation of the innovation, or with having the innovation embedded within the established organizational processes. Also, strategic consistency could play a role, implying that once a firm has committed itself to an e-commerce strategy, it is inclined to continue doing so. Since this topic has not received much interest of researchers yet, further research is needed to increase our understanding of these differential effects.

12

Although in this discussion we focused on the moderating and direct effects of adoption level, it is also interesting to review the direct effects of the four factors. The four factors are partly derived from Rogers’ (1995) stages model. We find that only the two factors preceding the decision stage, i.e. knowledge and potential value, have a direct impact on intention. We postulated that two factors related to stages following the decision stage, i.e. implementation and satisfaction, would have a direct effect on the intention to further adopt. However, we do not find evidence for these effects, although the interaction effect of satisfaction and adoption level is significant. Conclusion and implications This study focused on the direct and moderating impact of the current level of adoption on the intention to (further) adopt an innovation. The two main objectives are to systematically and formally test for differences in the impact of various determinants of innovation adoption depending upon the current level of adoption, and to investigate the possible direct impact of the current level of adoption on the intention to further adopt. The four determinants investigated are knowledge, potential value, implementation and satisfaction.

Our empirical findings clearly indicate an important role for the current adoption level. For two of the four determinants (knowledge and satisfaction) we found that their effect was significantly smaller for companies at the basic level compared to companies at the advanced level of e-commerce. The remaining two interaction effects, regarding potential value and implementation, were also negative but insignificant. The current adoption level has a highly significant positive direct effect on adoption intention. These findings lead us to the conclusion that in cases where an innovation can be adopted at various levels, the determinants of innovation adoption are different for companies at the basic level compared to companies at a more advanced level. We recommend that in future research on the adoption of innovations with a multi-level character, the current adoption level is explicitly taken into account. This conclusion is further supported by the strong and positive direct impact of the current adoption level on intention. Although there are theoretical reasons why this factor may have a direct effect, it could also be the case that this variable incorporates the impact of other factors not included in the model. This topic deserves further attention of researchers.

Our findings confirm previous studies by corroborating the importance of knowledge and potential value for innovation adoption. However, we did not find evidence that the degree of implementation or satisfaction with the current adoption level impacts the intention to further adopt. In model I, which did not include the current adoption level, implementation had a significant negative effect on intention. This finding seems to contradict the classical notion of implementation, where incomplete implementation is linked to problematic adoption of an innovation (e.g., Rogers, 1995). An alternative explanation is that companies just lacked the time to fully implement an innovation. This would explain why companies with an incomplete implementation can still have a positive intention to (further) adopt an innovation. Further research is needed to unravel the reasons of incomplete implementation and how these affect the intention to further adopt.

13

There are some limitations to our study that should be acknowledged. First, we only included two adoption levels while in fact there could exist more. Several e-commerce field studies provide evidence for groups of companies with varying levels of e-commerce sophistication (e.g., Bégin et al., 2001; Dholakia and Kshetri, 2004; Levy and Powell, 2003; Raymond, 2001; Teo and Pian, 2004). It would be interesting to see how the impacts of various determinants change when companies reach higher levels of an innovation. Second, due to our sampling method it is likely that companies active in e-commerce and interested in the topic are overrepresented. This may limit the generalisability of our findings to other groups of small and medium-sized enterprises. Third, we rely on the perceptions of one key informant, mostly the owner-manager or CEO, which may imply cognitive biases. However, earlier empirical research has demonstrated this type of methodology to be valid (James and Hatten, 1995). Moreover, in many small firms there often is only one suitable informant on strategic issues such as the adoption of e-commerce. A final limitation is related to the finding of previous studies that many small firms have fears that hinder the adoption of innovations (Sathye and Beal, 2001; Walczuch et al., 2000; Kendall et al., 2001). These fears may concern security problems or failing payment procedures. It would be interesting to model perceived value as a compound variable with perceived advantages and perceived disadvantages as antecedents and then study how the effect of these antecedents change over time when companies reach higher levels of e-commerce.

We conclude with some implications for practitioners. First, we found that the role of explicit knowledge differs according to the current level of adoption. When the innovation is relatively new to the company, explicit knowledge plays a much larger role than when the company has already acquired hands-on experience with the innovation. We suggested that tacit knowledge may have taken the role of explicit knowledge. However, there is a pitfall as well. With the rapid development of new technologies, customers getting more and more used at applying the Internet in their purchase processes, and outside competitors entering existing markets, companies should not neglect updating their explicit e-commerce knowledge on a regular basis. Secondly, our findings indicate that e-commerce adoption is to a large extent determined by earlier adoption behaviour. Although we acknowledge the importance of strategic consistency, managers should be aware of the risk of ineffective replication behaviour. This is in line with the conclusion of Kim, Umanath and Kim (2005-6) that concerning e-commerce ‘more is not always better’. Regular evaluation of the appropriateness of a strategy is needed.

Both consultants and institutions promoting the adoption of innovations by small and medium-sized enterprises (SMEs) should realize that the current adoption level is a major determinant of firm behaviour. Third parties that strive for increased used of e-commerce by SMEs should target their services at different segments of SMEs, based on their current e-commerce level. Focusing on novice users is probably most effective and perhaps even sufficient. Our findings indicate that once the first level of adoption has been reached, further adoption is likely to follow, based on the initial experiences. These findings also underscore the importance of distinguishing between different adoption levels where possible. If third parties are able to show SMEs that a new innovation can be adopted in smaller steps, this may enhance its adoption by SMEs. Especially if the innovation’s initial step involves only limited resources and risks. A final implication of

14

our study is that third parties should be aware that if SMEs are not fully using their e-commerce resources, this does not necessarily determine the SMEs’ willingness to further expand e-commerce activities. Their willingness probably depends upon the reasons for incomplete implementation, which can be either positive or negative.

15

References Ajzen, I. and Fishbein, M. (1980), Understanding Attitudes and Predicting Social Behavior, Prentice Hall,. Englewood Cliffs, NJ. Åstebro, T. (2004), “Sunk Costs and the Depth and Probability of Technology Adoption”, The Journal of Industrial Economics, Vol. LII, No. 3: pp. 381-399. Bandura, A. (1977), “Self-efficacy: Toward a Unifying Theory of Behavior Change”, Psychological Review, Vol. 84, No. 2, pp. 191-215. Bégin, L., Tchokogué, A. and Boisvert, H. (2001), Strategic Deployment of e-Commerce, Isabelle Quentin, Canada. Brandberry, A.B. (2003), “Determinants of adoption for Organisational Innovations Approaching Saturation”, European Journal of Innovation Management, Vol. 6, No. 3, pp. 150-158. Ching, H.L. and Ellis, P. (2004), “Marketing in Cyberspace: What Factors Drive E-Commerce Adoption?”, Journal of Marketing Management, Vol. 20, No. 3-4, pp. 409-429. Chircu, A.M. and Kauffman, R.J. (2000), “Limits to Value in electronic Commerce-Related IT Investment”, Journal of Management Information Systems, Vol. 17, No. 2, Fall, pp. 59-80. Cooper, R.B. and Zmud, R.W. (1990), “Information Technology Implementation Research: A Technological Diffusion Approach”, Management Science, Vol. 36, No. 2, pp. 123-139. Daniel, E., Wilson, H. and Myers, A. (2002) “Adoption of E-Commerce by SMEs in the UK: Towards a Stage Model”, International Small Business Journal, Vol. 20, No. 3, pp. 253-270. Davern, M. and Kauffman, R.J. (2000), “Discovering Potential and Realizing Value from Information Technology Investments”, Journal of Management Information Systems, Vol. 16, No. 4, pp. 121-143. Davidsson, P. (1991), “Continued Entrepreneurship: Ability, Need and Opportunity as Determinants of Small Firm Growth”, Journal of Business Venturing, Vol. 6, No. 6, pp. 405-429. Debowski, S. (2005), Knowledge Management a Strategic Management Perspective, Wiley, New York. Dholakia, R.R. and Kshetri, N. (2004), “Factors Impacting the Adoption of the Internet among SMEs”, Small Business Economics, Vol. 23, No. 4, pp. 311-322.

16

Diamantopoulos, A. and Winklhofer, H.M. (2001), “Index Construction with Formative Indicators: An Alternative to Scale Developments”, Journal of Marketing Research, Vol. 38, No. 2, pp. 269-277. Fariselli, P., Oughton, C., Picory, C. and Sugden, R. (1999), “Electronic Commerce and the Future for SMEs in a Global Market-Place: Networking and Public Policies”, Small Business Economics, Vol. 12, No. 3, pp. 261-275. Forman, C. (2005), “The Corporate Digital Divide: Determinants of Internet Adoption”, Management Science, Vol. 51, No. 4, pp. 641-654. Forman, C. and Goldfarb, A. (2006), “Diffusion of Information and Communication Technologies in Business”, in: T. Hendershott (ed.), Handbook of Economics and Information Systems, Elsevier, forthcoming. Freel, M.S. (2003), “Sectoral Patterns of Small Firm Innovation, Networking and Proximity”, Research Policy, Vol. 32, pp. 751-770. Garbarino, E. and Johnson, M.S. (1999), “The Different Roles of Satisfaction, Trust and Commitment in Customer Relationships”, Journal of Marketing, Vol. 63, April, pp. 70-87. Huizingh, K.R.E., and Brand, M.J. (forthcoming), “Stepwise Innovation Adoption: A Neglected Concept in Innovation Research”, International Journal of Technology Management. Iacovou, C.L., Bensabat, I. and Dexter, A.S. (1995), "Electronic Data Interchange and Small Organizations: Adoption and Impact of Technology", MIS Quarterly, Vol. 19, No. 4, pp. 465-485. James, W.L. and Hatten, K.J. (1995), “Further Evidence on the Validity of the Self Typing Approach: Miles and Snow Strategic Archetypes in Banking”, Strategic Management Journal, Vol. 16, pp. 161-168. Kendall, J.D., Tung, L.L., Chua, K.H., Ng, C.H.D. and Tan, S.M. (2001), “Receptivity of Singapore's SMEs to electronic commerce adoption”, Strategic Information Systems, Vol. 10, No. 3, pp. 223-242. Kim, K.K., Umanath, N.S. and Kim, B.H. (2005-6), “An Assessment of Electronic Information Transfer in B2B Supply-Channel Relationships”, Journal of Management Information Systems, Vol. 22, No. 3, pp. 294-320. Levy, M. and Powel, P. (2003), “Exploring SME Internet Adoption: Towards a Contingent Model”, Electronic Markets, Vol. 13, No. 2, pp. 173-181.

17

Lucchetti, R. and Sterlacchini, A. (2004), “The Adoption of ICT among SMEs: Evidence from an Italian Survey”, Small Business Economics, Vol. 23, No. 2, pp. 151-168. Mehrtens, J., Cragg, P.B. and Mills, A.M. (2001), “A Model of Internet Adoption by SMEs”, Information and Management, Vol. 39, No. 3, pp. 165-176. Mitchell, S. and Clark, D. (1999), “Business Adoption of Information and Communications Technologies in the Two-tier Rural Economy: Some Evidence from the South Midlands”, Journal of Rural Studies, Vol. 15, pp. 447-455. Mustonen-Ollila, E. and Lyytinen, K. (2003), “Why Organizations adopt Information System Process Innovations: a Longitudinal Study Using Diffusion of Innovation Theory”, Information Systems Journal, Vol. 13, No. 3, pp. 275-97. Nooteboom, B. (1994), “Innovation and Diffusion in Small Firms: Theory and Evidence”, Small Business Economics, Vol. 6, No. 5, pp. 327-347. Nooteboom, B., Coehoorn, C. and van der Zwaan, A. (1992), “The Purpose and Effectiveness of Technology Transfer to Small Businesses by Government-Sponsored Innovation Centers”, Technology Analysis and Strategic Management, Vol. 4, No. 2, pp. 149-166. North, D. and Smallbone, D. (2000), “The Innovativeness and Growth of Rural SMEs During the 1990s”, Regional Studies, Vol. 34, No. 2, pp. 145-157. Ravichandran, T. and Lertwongsatien, C. (2005), “Effect of Information Systems Resources and Capabilities on Firm Performance: A Resource-Based Perspective”, Journal of Management Information Systems, Vol. 21, No. 4, pp. 237-276. Raymond, L. (2001), “Determinants of Web site Implementation in Small Businesses”, Internet Research: Electronic Networking Applications and Policy, Vol. 11, No. 5, pp. 411-422. Riemenschneider, C.K., Harrison, D.A. and Mykytyn Jr, P.P. (2003), “Understanding IT Adoption Decisions in Small Business: Integrating Current Theories”, Information and Management, Vol. 40, No. 4, pp. 269-285. Rogers, E.M. (1962, 1995), Diffusion of Innovations, 1st and 4th edition, Free Press, New York. Rossiter, J.R. (2002), “The C-OAR-SE Procedure for Scale Development in Marketing”, International Journal of Research in Marketing, Vol. 19, No. 4, pp. 305-335. Sadowski, B.M., Maitland, C. and van Dongen, J. (2002), “Strategic Use of the Internet by Small- and Medium-Sized Companies: an Exploratory Study”, Information Economics and Policy, Vol. 14, No. 1, pp. 75-93.

18

Saeed, K.A., Grover, V. and Hwang, Y. (2005), “The Relationship of E-Commerce Competence to Customer Value and Firm Performance: An Empirical Investigation”, Journal of Management Information Systems, Vol. 22, No. 1, pp. 223-256. Santarelli, E. and D'Altri, S. (2003), “The Diffusion of E-Commerce among SMEs: Theoretical Implications and Empirical Evidence”, Small Business Economics, Vol. 21, No. 3, pp. 273-283. Sathye, M. and Beal, D. (2001), “Adoption of e-commerce by SMEs: Australian Evidence”, Journal of E-business, Vol. 1, No. 1. Shalit, S.S. and Sankar, U. (1975), “The Measurement of Firm Size”, The Review of Economics and Statistics, Vol. 59, pp. 290-298. Teo, T.S.H. and Pian, Y. (2004), “A Model for Web Adoption”, Information and Management, Vol. 41, No. 4, pp. 457-468. Thong, J.Y.L. (1999), “An Integrated Model of Information Systems Adoption in Small Businesses”, Journal of Management Information Systems, Vol. 15, No. 4, pp. 187-214. Tornatzky L.G. and Fleisher, M. (1990), The Processes of Technological Innovation, Lexington Books, Lexington, MA. Van Campen, P.A.F.M., Huizingh, K.R.E., Oude Ophuis, P.A.M. and Wierenga, B. (1991), Marketing Decision Support Systems in Dutch Companies, Eburon, Delft. Waarts, E., van Everdingen, Y.M. and van Hilligersberg, J. (2002), “The Dynamics of Factors Affecting the Adoption of Innovations”, The Journal of Product Innovation Management, Vol. 19, No. 6, pp. 412-423. Walczuch, R., van Braven, G. and Lundgren, H. (2000), “Internet Adoption Barriers for Small Firms in the Netherlands”, European Management Journal, Vol. 18, No. 5, pp. 561-572. Wixom, B.H. and Todd, P.A. (2005), “A Theoretical Integration of User Satisfaction and Technology Acceptance”, Information Systems Research, Vol. 16, No. 1, March, pp. 85-102. Zhu, K. (2004), “The Complementarity of Information Technology Infrastructure and E-Commerce Capability: A Resource-Based Assessment of Their Business Value”, Journal of Management Information Systems, Vol. 21, No. 1, pp. 167-202. Zhu, K. and Kraemer, K.L. (2005), “Post-adoption Variations in Usage and Value of –Business by Organizations: Cross-Country Evidence from the Retail Industry”, Information Systems Research, Vol. 16, No. 1, pp. 61-84.

19

Zhu, K., Kraemer, K.L. and Dedrick, J. (2004), “Information Technology Payoff in E-Business Environments: An International Perspective on Value Creation of E-Business in the Financial Services Industry”, Journal of Management Information Systems, Vol. 21, No. 1, pp. 17-54.

20

Appendix: Measurement Scales

Intention Intention to expand e-commerce use for selling, purchasing and/or customer service within the next 2 years

Scale 0-3

Adoption level Currently using e-commerce for selling, purchasing or customer service

Dichotomous yes/no

Knowledge (availability of information on specific topics): Customer need Supplier need Opportunities Financial benefits Costs Technical specifications Experiences of peers Necessary skills Needed resources Organizational impact Competitors’ activities

5-point Likert scale

Potential Value (beliefs on relevance of specific factors for firm’s major e-commerce investment) Effect on firm’s image Customer need Supplier need Time savings Cost savings Improved sales Improved profitability Improved (brand) awareness Improved transfer of product information Being industry innovator Need to keep up in industry Improved product development New customer contacts International selling Learning about new medium Improved customer service Improved customer relations Decreasing stocks

5-point Likert scale

Implementation Usage level of all available e-commerce applications within the firms

10-point scale ranging from nil to fully

Satisfaction Satisfaction with results of e-commerce pertaining to the 18 items of Potential Value

5-point Likert scale

Control variables Firm size (full time equivalents) Industry (information intensity) Market position

Numerical Dichotomous low/high 4-point scale

21

Table I Regression without (Model I) and with (Model II) the direct and

moderating effects of the current adoption level (n = 98). Model I Model II

Unstand.

Beta t-value 1-tailed p-value

Unstand. Beta t-value

1-tailed p-value

Constant .51 .53 .299 -3.20 -2.66 .005 Knowledge .21 1.13 .131 .69 2.70 .004** Potential value .42 2.45 .008** .62 2.50 .007** Implementation -.13 -2.25 .014* -.09 -1.02 .155 Satisfaction -.03 -.95 .174 .06 1.26 .106 Interaction Adoption level with: - Knowledge -.91 -2.59 .006** - Potential value -.13 -.41 .342 - Implementation -.00 -.01 .496 - Satisfaction -.16 -2.56 .006** Adoption level 5.01 4.26 .000** Company size .05 .42 .337 .08 .71 .239 Industry .00 .01 .497 .24 1.15 .127 Market share position .03 .24 .405 -.08 -.57 .284 Adjusted R Square .08 .26 F (1-tailed p-value) 2.13 (.024*) 3.73 (.000**)

* p<0.05, ** p<0.01

22

Figure 1. E-commerce adoption considered as a series of adoption processes in order to reach the next levels of e-commerce.

Figure 2. Conceptual model including the factors influencing the intention to

(further) adopt e-commerce and the moderating and direct effect of the current adoption level.

Ado

ptio

n le

vel

(Sop

hist

icat

ion

leve

l of e

-com

mer

ce)

Time(Series of innovation adoption processes)

Adoption process

Adoption process

Adoption process

Moderating effects

Potential Value

Adoption Level

Knowledge

Implementation

Satisfaction

Intention to(further) adopt

+

+

+

+

+

- + ? -

Moderating effects

Potential Value

Adoption Level

Knowledge

Implementation

Satisfaction

Intention to(further) adopt

+

+

+

+

+

- + ? -

23

Figure 3a (above) and 3b (under). A simplified illustration of the significant interaction effect of adoption level with knowledge (a) and satisfaction (b).

Basic Adoption Level

Advanced Adoption Level

Knowledge

Intention

to adopt

Advanced Adoption Level

Satisfaction

Intention

to adopt

24

Brief biographical statements: Maryse J. Brand is Associate Professor of Small Business and Entrepreneurship at the University of Groningen, the Netherlands. She holds a PhD in Economics and worked for several years as a small business consultant. She published work in various academic and professional journals on topics including: industrial marketing, organizational buying behaviour, organizational structures of SMEs, small business HRM, entrepreneurship education and small business internationalisation. Her current research interests focus on small business management. Eelko K.R.E. Huizingh is Associate Professor of Business Development at the University of Groningen, the Netherlands. His research focuses on the intersection of innovation, marketing and information technology. He is involved in studies on e-commerce, new business development, web site performance, marketing decision support systems, and database marketing. Eelko Huizingh has published over 250 articles in various academic and professional journals, and is (co-)author of several text books on E-commerce, Marketing Management, the use of SPSS, and Marketing Information Systems.