Intermediating technologies and multi-group adoption

The Journal of Product Innovation Management 18 (2001) 65– 81

Intermediating technologies and multi-group adoption: A comparison of consumer and merchant adoption intentions toward a new electronic payment system Christopher R. Plouffe*, Mark Vandenbosch, John Hulland Ivey Business School, University of Western Ontario, London, Ontario N6A 3K7, Canada accepted 1 November 2000

Abstract Traditional technology adoption research has assumed a single adopting group. However, there are many settings in which multiple groups must jointly adopt an innovation in order for it to succeed. This is particularly true for new information technology innovations that mediate the relationship between two groups. For example, online exchanges (e.g., Freemarkets, GoFish) must attract both suppliers and buyers in order to be successful. The same is true for providers of hardware/software solutions for electronic data interchange and supply chain management. This article describes the phenomenon of multigroup adoption with a particular focus on applications within the financial services and retailing industries. Empirically, the article reports findings from a study that illustrates the importance of evaluating and managing multigroup technology adoption in the specific context of an in-market trial of a new smart card-based electronic payment system. Two distinct groups critical to the smart card’s success are studied: consumers (who must decide to use the new card) and retailers (who must agree to adopt and use new technology needed to process smart card transactions). The study identifies which characteristics of the smart card innovation are most closely linked to intention to adopt for each group, and examines how these key characteristics differ by group. Perceptual data were collected via a mail survey from consumers and merchants living in the city where a one-year market trial of the new card was taking place. Four separate sampling frames were established for both consumers and merchants who were participating in the trial as well as both consumers and merchants who were not participating in the trial. Random samples were then drawn from these frames. More than 350 consumers and over 250 merchants completed and returned the survey. Responses were analyzed separately for each of the four groups sampled. The most important characteristic leading to adoption identified by all four groups was relative advantage—the smart card had to demonstrate a clear competitive advantage over what they currently used. Compatibility (i.e., the degree to which the smart card fit with their current preferences) was also noted as important to all but the nonparticipating merchant group. Beyond this, the key drivers of adoption differed considerably by group. Participating consumers and participating merchants appeared to possess different perspectives when assessing their decision to adopt the smart card technology. Consumers seemed to value the notion that the adoption decision is under their control, whereas merchants seemed to place more value on the antecedents that had the potential to add to their bottom line. This suggests that it is necessary to institute different marketing tactics to attract the early adopting groups. In addition, significant differences in the importance of antecedents between participating and nonparticipating consumers and participating and nonparticipating merchants suggest that, over time, it may also be necessary to develop and use different marketing tactics for later adopters. © 2001 Elsevier Science Inc. All rights reserved.

1. Introduction Forrester Research estimates that E-commerce transactions over the Internet will grow to over $300 billion by 2002 and 6% of US GDP by 2005 [30]. Add to this the * Corresponding author. Tel.: ⫹1-519-661-2111 ext. 85129; fax: ⫹1519-661-3959. E-mail address: [email protected] (C. Plouffe).

explosive growth of electronic funds transfer through vehicles like direct debit and smart card payment systems, and the importance of new product development in this arena becomes obvious. However, many of the products and services developed for E-commerce applications have characteristics that are different from traditional new products and thus warrant further study. Rather than a product or service that is sold to an end user, many E-commerce-related offerings are designed to intermediate the exchange relation-

0737-6782/01/$ – see front matter © 2001 Elsevier Science Inc. All rights reserved. PII: S 0 7 3 7 - 6 7 8 2 ( 0 0 ) 0 0 0 7 2 - 2

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C.R. Plouffe et al. / The Journal of Product Innovation Management 18 (2001) 65– 81

ships between buyers and sellers. These offerings have the potential to radically alter the exchange process through the development of new channels of distribution, the introduction of new electronic financial instruments, and the empowerment of customers through the use of efficient information resources. As intermediating products and services appear with increasing frequency, it is critical that managers and researchers understand what drives adoption behavior for these new forms of technological innovation. Adoption has been an important area of research inquiry in marketing for many years (for example, [4,43,50]). Virtually all adoption research has made the implicit assumption that there is a single adopting group—the end user (for example, [37]). However, intermediating technologies, by definition, require more than one group to adopt the offering in sufficient numbers before market success is assured. For example: Y Freemarkets conducts online competitive bidding events in which potential suppliers of industrial products conduct live Internet bidding in order to secure orders from industrial buyers for manufactured parts and components. To be successful, industrial buyers must be willing to adopt the idea of auctioning off their supply contracts and potential suppliers must be willing to adopt the competitive buying process. Other Internet-based companies marketing intermediating technologies and services include IMX Exchange (Mortgages), GoFish (Seafood) and Trade’Ex (Computer equipment and components) [31]. Y Financial institutions throughout the world have launched or are experimenting with new forms of electronic payment like direct-debit and smart cards. These systems are designed to simplify cash management and reduce transaction costs. Since these systems require consumers to adopt and use cards and merchants to install transaction processing hardware, system viability will not be assured until both consumers and merchants adopt these technologies in sufficiently large numbers [1]. The preceding examples highlight the need to examine in detail a multigroup adoption process for intermediating innovations and technologies. In order for the innovation itself to be successful, it must be adopted by all relevant groups. However, the perceptions that each group has of the intermediating technology can vary considerably, since each group is likely to have different needs and/or applications in mind for the innovation. Because the costs of launching new products can be very high [59], and because it is often difficult for firms to know when to “pull the plug” on an unsuccessful offering [9,49], clear managerial understanding of the factors driving new product adoption within multiple groups is required to both correctly position complex offerings and ensure their commercial viability [15]. In this article, we illustrate the importance of considering multiple groups in the technology adoption process. We

examine the antecedents of adoption for two distinct groups evaluating an intermediating technological innovation. Our results demonstrate managerially significant differences and highlight the importance of within- and between-group analyses prior to finalizing marketing mix and launch decisions. Using data from a large, in-market trial of a smart card-based electronic payment system, we assess similarities and differences in adoption antecedents between consumers (card holders) and merchants (card acceptors). The remainder of the article is organized as follows. In the next section we develop the concept of multigroup adoption within the context of intermediating technologies. Next, we describe the research model. We then outline our empirical setting, including a discussion of sampling and measurement issues. This is followed by a description of the collected data and its analysis using partial least squares (PLS). The last sections of the article provide a more detailed discussion of our findings, their implications, and suggestions for subsequent research.

2. Intermediating technologies and multigroup adoption The power of the Internet and electronic data interchange as both disintermediating (removing layers or middlemen that stand between parties in an exchange relationship) and remediating (changing the type of layers or middlemen) technologies has been well documented (for example, [10, 56,57]). In this article we focus on the technologies (and related products and services) that are used as part of an exchange process and are not concerned as to whether these technologies add, subtract or change the level of mediation. Thus, we define intermediating technologies as technologies that use electronic data storage, transfer and communications to add value to the exchange relationship between at least two groups. Most often, these groups consist of buyers and sellers of products or services. The successful launch and implementation of an intermediating technology requires a thorough understanding of buyer behavior. Though individual innovations will vary in the benefits that are provided to potential adopters, understanding the most important antecedents of adoption in these multigroup adoption settings is a critical first step in the process of developing a successful launch strategy. A typical exchange relationship involves the transfer of goods and/or services between a set of buyers and sellers. When evaluating an intermediating technology, it is quite likely that the two groups would value different aspects of the technology. Buyers using the FreeMarkets Online reverse-auction system value the cost savings that they are able to achieve, while adopting suppliers—though not at all happy with the increased price competition–value the increased visibility the system provides and the access to a wider array of potential buyers [45]. Other business-tobusiness intermediating technologies (e.g., Trade’ex) let


sellers anonymously dispose of excess inventory or experiment with pricing strategies while buyers (e.g., computer resellers) benefit from the ability to search for lower prices on everyday stock items [34]. Although the adopting groups are likely to value different aspects of the offering, existing adoption research does not give us a clear indication of which antecedents will be important to each group. Tornatzky and Klein [54], through a meta-analysis of existing adoption studies, found that the innovation-based antecedents of relative advantage, compatibility and to a lesser degree, complexity, were the most important predictors of adoption. However, all of the studies included in their meta-analysis focused on a single group of adopters. In the context of an intermediating technology, there are multiple adopters who may have very different antecedents for adoption. Existing research provides only limited direction as to what the differences may be. To explore this issue, we take the position that “value,” broadly defined, determines the likelihood of adoption. New technologies and information processing innovations have long been heralded as being capable of increasing both seller and buyer welfare [29]— or value, as we define it here. However, value may mean very different things to buyers and sellers in an exchange relationship. Value of an innovation to buyers, the traditional target group in adoption research, would likely be based on the degree to which the new innovation improves the utility they derive from the consumption experience. This utility would be positively affected by the improvement the new innovation provides over existing alternatives and the ease with which the new innovation can be incorporated into their behavior patterns or routines [23]. The value of an innovation to sellers depends primarily on whether the innovation improves profit potential. This means that both cost-side and revenue-side influences may be important antecedents to adoption. Thus, the value of an innovation will be positively affected by enhancements in operational efficiency and the degree to which the innovation enhances the attractiveness of the seller’s offering to the consumer (relative to competitive offerings). Rogers’ [48] work has been widely cited and applied in diffusion and adoption research. Specifically, he proposed that five constructs would potentially act as antecedent predictors to an adoption decision: 1) relative advantage, 2) compatibility, 3) trialability, 4) complexity, and 5) observability. According to diffusion of innovations theory, depending upon a) the specific nature of the innovation in question, and b) the specific characteristics of the adopting group (or groups, as is the case here), some subset of these antecedents should frame the technology adoption decision in question and form the overall value proposition for each adopting group. In the case of an electronic payment system studied here, one would expect a priori that different sets of Rogers’ antecedents would drive the perceptions of value for each of the two groups involved: merchants and consumers. Con-

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sistent with El Sawy [17], Gatignon and Robertson [24] and Robertson and Gatignon [46], it is expected that merchant value will be formed primarily through relative advantage (the degree to which the technology bestows the adopter with enhanced functionality or features over current offerings) as well as observability (the degree to which the innovation in question can be seen being successfully applied by others). These categories address both the operational efficiency and customer attractiveness dimensions. For consumers, a priori it is expected that relative advantage would drive the value proposition. Compatibility (or how the innovation gels with the adopter’s habits and preferences) should also be an important consideration in whether or not the electronic payment system would add value to their retail consumption experiences and tasks [23]. Markets for new technologies, such as smart card-based electronic payment systems, typically include both adopters (i.e., those who have begun using the innovation) and nonadopters. The antecedents that these two groups use to assess value are likely to differ (for example, [39]). In addition to differences in perceptions of relative advantage [43], the perceived degree of complexity associated with the new technology is likely to differ considerably across these two groups. In a premarket trial setting, one would expect similar differences to appear among those respondents who have or have not participated in the actual trial. In the next section, we develop (and subsequently test) a model of adoption that we apply to all groups concerned in the adoption of an intermediating technology. The results of this model can then be used to assess the nature of the marketing task required to gain adoption and to provide suggestions for appropriate marketing mix decisions.

3. Research model Although firms are ultimately interested in assessing whether or not all relevant groups will adopt a technological innovation, it is generally not possible to examine actual adoption behaviors prior to product launch. Instead, managers in these firms must look at measures that are thought to be closely related to adoption behavior, such as overall attitude towards the innovation or intention to adopt. In this article, we focus on intention to adopt as a key outcome construct, since it represents the final precursor to actual adoption behavior (which cannot be observed prior to product launch for the technological innovation studied here). Previous research has suggested that the perceived characteristics of an innovation are closely linked to adoption (for example, [23,40,48]). Ostlund ([43], p. 28) suggested that “the perceptions of innovations by potential adopters can be very effective predictors of innovativeness, more so than personal characteristic variables.” Similarly, Labay and Kinnear [35] found that perceptions of innovations provided better predictions of innovativeness than did demographic variables. Finally, Tornatzky and Klein [54] found that the

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Fig. 1. Intention to adopt model using PCI measures.

perceived characteristics of innovations were consistently the most important predictors of adoption. Within the IS field, the Technology Acceptance Model (TAM) proposed by Davis [13] has been a popular and very effective means of predicting technology adoption (for example, [14,52]). TAM-based models have also been used to explore multigroup adoption issues as they pertain to multiple adopting constituencies within the context of international trade [12,44]. Davis’ TAM is an extremely parsimonious model which characterizes the adoption process with just two antecedent constructs: perceived ease-of-use and perceived usefulness. In an attempt to broaden the domain of innovation characteristics studied, Moore and Benbasat [40] developed a measurement instrument known as the Perceived Characteristics of Innovating (PCI). PCI was derived from earlier conceptual work by Rogers [48]. PCI expands Rogers’ conceptual framework by adding several additional constructs that can also influence individuals’ adoption decisions (Fig. 1). Relative advantage represents the degree to which an innovation is perceived to be superior to current offerings. Compatibility is the degree to which an innovation is perceived as being congruent with an adopter’s current preferences and habits. Trialability represents the extent to which a potential adopter believes that the innovation can be adequately tried prior to the adoption decision. All three of these constructs are employed as originally proposed by Rogers [48]. A fourth construct— complexity—was also transferred directly from Rogers’ original framework into

PCI but was renamed “ease-of-use” to be consistent with other views of the adoption process (for example, [13]). Ease-of-use represents the degree to which an innovation is perceived to be easy to use. The final construct included in Rogers’ original model is observability. Moore and Benbasat [40] argue that observability is too broad a construct for use in many technology adoption contexts. In its place, they proposed two more specific constructs: visibility and result demonstrability. Visibility is the extent to which an innovation is perceived to be widely diffused in the relevant adoption setting, while result demonstrability captures the degree to which the unique features and benefits of an innovation are readily discerned by the potential adopter. Completing the eight antecedent constructs in the PCI measurement instrument are image and voluntariness. Image represents the degree to which an individual believes that an innovation will bestow them with added prestige or status in their relevant community. Moore and Benbasat argue for the inclusion of image in the PCI model because various diffusion researchers (for example, [54]) have demonstrated or argued that the social approval associated with the adoption of an innovation is important and distinct from other constructs, namely relative advantage. Finally, voluntariness reflects the extent to which the adoption of an innovation is perceived to be under an individual’s volitional control. Here Moore and Benbasat argue that because innovations are sometimes forced on individuals in ISbased, organizational adoption contexts, that the perceived voluntariness associated with adoption might be an important predictor of adoption intentions. Although Moore and Benbasat [40,41] provide empirical support for their proposed measurement instrument, including strongly reliable measures for the eight antecedent constructs, other researchers have neither tested nor refined PCI. This absence of subsequent work seems surprising, since as Moore and Benbasat suggest ([40], p. 194): “It was our intention . . . that any scales developed should also be generally applicable to a wide variety of innovations.” PCI is intuitively appealing, reliable, and descriptively rich, providing more detailed information than other measures about how an innovation’s characteristics are perceived and how these perceptions affect intent to adopt. In the next section, we describe a study that operationalized PCI in a multigroup adoption setting involving an intermediating technology. Data were collected from test market respondents who were actively considering whether or not to adopt an electronic smart card-based payment system. Two distinct groups of respondents are incorporated into the design: consumers (who must decide whether or not to use the smart card to make purchases on a regular basis), and merchants (who must decide whether or not to install and use a new card processing device).


4. Research method 4.1. The smart card context A smart card is a small credit card-sized financial instrument that can be used for a wide variety of transactionbased applications. The key difference between a smart card and a conventional credit card is that the former contains a tiny embedded microprocessor. Thus, the smart card can be credited or debited directly with electronic funds. This allows the smart card to be used (at least potentially) for more sophisticated tasks than allowed for by traditional financial services cards (e.g., credit cards, ATM cards). One primary application for smart card technology is in the retailing sector. If adopted widely by both consumers and retailers, smart cards could be used as a substitute for cash in everyday retail consumption situations [1]. To use the system, a consumer must first receive a smart card from her/his financial institution and then “load” it with money through electronic funds transfer. Funds stored on the card can subsequently be used to purchase goods or services from any merchant equipped with a smart card machine, just as normally would occur with cash, checks, or credit and debit cards. When the smart card is empty, the consumer can reload it at a bank machine, payphone, or even remotely using a PC. For consumers, one of the primary attractions of smartcard based payment is the convenience of not having to carry cash or coinage. For banks, the appeal is reduced longer-term cash management operating costs, plus the ability to levy new transaction charges on a wide array of transaction types across many purchasing contexts. For merchants, smart card-based payment is expected to be attractive because of speedier financial account reconciliation and a decreased need to handle cash and coinage at the point-of-sale. In order for this new system to succeed, many consumers must use their smart cards for many products and services across many purchase occasions. At the same time, many merchants must adopt the required point-of-sale technology needed to process smart card payments from consumers. If too few consumers adopt the technology, merchants will have little incentive to invest in the required equipment, to alter their business practices, or to train their employees in the system’s operation. If too few merchants adopt the system, consumers will have little use for the cards. Thus, adoption must take place simultaneously within both groups. 4.2. The Exact card In mid-1997, we approached three large Canadian banks that were in the midst of conducting a geographic market trial of a smart card-based payment system in one midsized city (population of about 100,000). Their smart card system, known as Exact, was test marketed for a full calendar year

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with a national roll-out and full market deployment planned thereafter. At the peak of the trial, over 400 merchants had equipped their retail establishments with the Exact system’s point-of-sale equipment, and some 5,000 consumers possessed an Exact smart card. The Exact card trial represents a strong test of our adoption model. Consumers are frequently resistant to accepting new ways of conducting their financial transactions, particularly when they believe that they may lose control of personal information, perceive an increase in task complexity, or worry about losing money due to system malfunctions [61]. Such consumer resistance should ensure greater variance in expressed intention to adopt the Exact technology. Furthermore, the large scope of the Exact card system’s market trial allowed us to draw samples of reasonable size from participating and nonparticipating consumer and merchant groups. 4.3. Measures Measures for the intention to adopt model were operationalized separately for the consumer and merchant groups, although every attempt was made to keep these measures as similar as possible. For the consumer group measures, we started by examining carefully the 25 items suggested by Moore and Benbasat as a short-form of the PCI constructs ([40], pp. 216 –217). Four of these items were dropped because their content placed too great an emphasis on organizational-level factors not relevant to our empirical context. We then added five PCI items originally recommended by Moore and Benbasat [40] but not included in their short-form scale. The items needed to be modified slightly to capture the merchant’s perceptions of the Exact card system as a potential retail point-of-sale technology. Thus, the key difference between the consumer and merchant items is that the consumer items measure only individual-level perceptions of the Exact card system, whereas the merchant items attempt to assess key informant perceptions of how the innovation relates to a specific business. The Appendix describes the measures employed for both the consumer and merchant groups. All items were measured using 7-point Likert scales, anchored by “strongly disagree” and “strongly agree.” To develop adequate measures for the dependent intention to adopt construct, we began by reviewing previous studies that have attempted to measure this construct (for example, [38,53]). An examination of these existing scales plus further reflection on the construct’s domain lead to the identification of four measurement items for the intention to adopt construct (see Appendix). These four items are taskfocused, and are designed to capture the respondent’s sense of urgency to formally adopt the innovation once it becomes widely available. The proposed measures were initially tested in a smallscale pilot study well in advance of the full survey admin-

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Table 1 Sampling frame sources, number of respondents, & response rates Group Participating Consumer Source: A random sample of 400⫹ consumers who possessed an Exact card and who were clients of one of the three participating banks. Non-Participating Consumer Source: A random sample of 800⫹ consumers living in the Exact card market trial city. This sampling frame was drawn from a publicly available and current electronic CD-ROM-based phonebook called “CanadaPhone ‘96”. Participating Merchant Source: 400⫹ merchants who were actively involved in the Exact trial at the 10-month point of the test market. These merchants were institutional clients of all three banks conducting the market trial. Non-Participating Merchant Source: A sample of 475⫹ merchants and retail businesses located in the city of the market trial drawn from a current Dun & Bradstreet business database. This sample was cross-referenced against the ‘participating merchant’ sample obtained from the three banks for redundancies. A few were identified, and in these cases, they remained in the ‘participating merchant’ group. TOTALS

Total Surveys Outgoing

Effective

Total Returns

Response Rate Percentage

407

344

167

48.6%

819

515

185

35.9%

405

379

172

45.4%

479

385

80

20.8%

2,110

1,623

604

37.2%

* Note: Effective sample denotes those consumer subjects with whom contact could be established (e.g., their addresses had not changed, they had not moved, they had not passed-away). In the case of the merchant samples, the effective sample is all those merchants with whom contact could be established and who were still going concerns (i.e., they had not gone bankrup, been purchased by another firm or otherwise amalgamated).

istration.1 The pilot study lead to a few minor changes in item wordings and helped to clarify related survey design issues for the main study. 4.4. Consumer and merchant samples To enable a comparison of adoption antecedents across the groups targeted by the intermediating technology, we surveyed both consumers and merchants who were participating in the trial. In addition, to get a better understanding of some of the differences in adoption antecedents across triers and nontriers of the smart card technology, we sampled both nonparticipating consumers and nonparticipating merchants. Table 1 describes briefly the four frames used to sample respondents, and provides information about the number of initial surveys mailed to each group and the resulting response rates. For both the participating consumer and participating merchant groups, sampling frame lists were obtained directly from the participating banks. These lists were found to be highly accurate, up-to-date, and reliable. However, to ensure that surveys and cover letters were addressed to the correct person in each merchant organization, all participating merchants were first contacted by telephone to confirm that the listed contact person was in fact the individual who had made the original deci-

sion to participate in the Exact trial and who would ultimately be responsible for a post-test market adoption decision. Surveys were mailed out at the 10-month point of the year-long Exact card trial. This ensured that both the consumer and merchant subjects had had substantial opportunity to consider and use the technological innovation, yet also allowed us to finish the survey’s administration and follow-up procedures before the end of the market trial. The survey design and implementation procedures outlined by Dillman [16] were used as the basis for the survey design and field execution approach.2 In all, 151 participating consumers responded to the survey (for an effective response rate of 43.9%), as did 172 participating merchants (45.7%). The nonparticipating consumer and nonparticipating merchant sampling frames were drawn from two publicly available electronic databases. In the case of the consumers, a random sample of 819 consumers was generated from an electronic phonebook that contained nearly 47,000 residential telephone listings within the trial city. A larger initial sample size was used for this group because we believed that these consumers would be notably less inclined to respond to our survey than the other groups. For the nonparticipating merchants, we began with a database of all businesses in the trial city sorted by SIC codes, and then,


with the banks’ assistance, identified all those businesses that were likely to have a potential need for the Exact card system. Next, we manually eliminated all businesses already participating in the trial, leaving 479 nonparticipating merchants in this sampling frame.3 We received responses from 201 randomly selected consumers (yielding an effective response rate of 39.0%). However, sixteen of these respondents indicated that they had used a smart card, and were therefore dropped from the nonparticipating consumer group, leaving 185 respondents.4 The proportion of trial participants in this sample (8%) was similar to the overall estimated penetration rate of smart cards in the trial city. A total of 80 nonparticipating merchants (20.8%) responded to the survey. Although these response rate levels are lower than those obtained for the two participating groups, given the lack of a vested interest in our research by both of these groups and the absence of strong incentives to participate, the achieved response rates are actually quite encouraging. In order to determine if nonresponse bias was an issue for any of the four groups, we used the procedure outlined by Armstrong and Overton [3] to compare early and late respondents. No significant differences were noted. Demographic data collected from consumers indicated a very diverse profile of respondents in terms of variables such as age, income, and highest educational attainment in both the participating and nonparticipating groups. Mean values for these three measures are reported, by consumer group, in the top half of Table 2, along with the proportion of male respondents in each group. Comparisons between these measures for the two groups indicated that neither education nor income differed significantly (t(350) ⫽ 1.10 and 0.53 respectively). However, both age (t(350) ⫽ 9.96, p ⬍ .0001) and gender (t(350) ⫽ 6.28, p ⬍ .0001) were notably different. Specifically, the participating consumer respondents were both significantly younger and significantly more likely to be male. For the merchant groups, information about annual sales revenues, the number of employees, and the overall retail space occupied was collected for each business. Mean or median values for these measures are reported in the lower half of Table 2. No significant differences were found between the participating and nonparticipating groups. The diversity of businesses responding to the survey was quite extensive. 4.5. Missing data For the most part, missing data were not an issue across any of the four sample groups. A total of 13 responses across the four groups were initially removed from further consideration due to an unacceptably large number of missing responses on items capturing the constructs of interest. This left our final sample at 605 respondents across all four groups, as reported in Table 1. For 29 of these cases (4.8% of the total sample), respondents had completed most— but

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Table 2 Analysis of consumer and merchant samples on selected descriptive measures Descriptive Measure

Participating Groups

Non-Participating Groups

CONSUMERS Age1 Gender (% Male) Income2 Educational Attainment3

(n ⫽ 167) 3.2 47.3% 5.0 4.7/7

(n ⫽ 185) 4.6 18.4% 5.1 4.5/7

MERCHANTS Annual Revenues4 Number of Employees (Median) Retail Square Footage (Median)

(n ⫽ 172) 3.28 6 1,500

(n ⫽ 80) 3.35 7 1,500

Notes: 1 Six response categories were used to assess consumer respondents’ ages: (1) 19 years of age and under, (2) 20 –29, (3) 30 –39, (4) 40 – 49, (5) 50 –59, or (6) over 60. 2 Eight response categories were used to assess consumers’ total average annual household incomes: (1) $20,000 or less, (2) $20,001 to $30,000, (3) $30,001 to $40,000, (4) $40,001 to $50,000, (5) $50,001 to $60,000, (6) $60,001 to $70,000, (7) $70,001 to $80,000, or (8) $80,001 or more. 3 Seven response categories were used to determine highest educational attainment. 4 Six response categories were used to assess each businesses’ approximate gross sales in the previous year: (1) less than $100,000, (2) $100,000 to $249,999, (3) $250,000 to $499,999, (4) $500,000 to $999,999, (5) $1 million to $5 million, or (6) more than $5 million.

not all—items pertaining to the constructs of interest. Because the analytic technique we employ (PLS) cannot accommodate missing data, we followed the data imputation procedures recommended by Hair et al. ([27, pp. 43–57) in the coding of these cases.5 4.6. Analysis approach In order to estimate the adoption model described previously, we used partial least squares, or PLS [60]. Although PLS is similar in many respects to covariance analysis, the former is more appropriate when measures are not wellestablished or are employed in a new measurement context, and when the primary objective of the research is the explanation of model variance for one or more endogenous constructs [7,20]. Because the adoption model used here has not been previously tested in a marketing context, and because we are primarily interested in maximizing the variance explained in the dependent intention to adopt construct, we used PLS for the current study. PLS has been successfully applied in marketing to examine customer satisfaction [22], organizational buying [6], product category evolution [26], and consumer information processing [33]. PLS path estimates are standardized regression coefficients, while the loadings of items on the constructs can be thought of as factor loadings. PLS also produces R2 values for all endogenous constructs that can be interpreted in the same manner as R2 values produced by regression analyses.

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consumer groups. However, while this information may be useful to the banks in segmenting likely early adopters from late or nonadopters of the Exact card,6 it provides little information about the considerations that form their intentions to adopt the new technology. In contrast, an examination of differences in the perceived characteristics of the new technology across the two groups can be used to develop more focused and effective marketing plans, leading to higher overall adoption of the new technology. Finally, the participating merchant sample results are compared to those obtained from the nonparticipating merchants. From the banks’ perspective, it is important to understand the motives of both participants and nonparticipants in order to achieve maximum adoption of the new technology by retailers. As already noted, the participating and nonparticipating merchant groups cannot be differentiated in terms of their basic firm characteristics (e.g., number of employees). Thus, any significant differences in the two groups’ perceptions of the Exact card provides important information that can be used to develop more effective marketing programs addressing the concerns of all merchants. Fig. 2. Summary of between-group comparisons conducted in analysis.

5. Analysis and results 5.1. Overview As noted previously, we are interested in looking at differences in the perceived characteristics of a technological innovation under consideration by consumers and merchants, and how these perceptions affect their intentions to adopt the new Exact card system. In this section, we make three intergroup comparisons, as summarized in Fig. 2. First, we compare results from the sample of participating consumers to those from the sample of participating merchants. This comparison is undertaken to assess the baseline attractiveness of the innovation to both groups and their corresponding models of intention to adopt. Note that members of both of these groups had already made a decision to participate in the year long trial. As a result of this participation, these “lead users” [55] had numerous opportunities over the 10 months already elapsed to try and evaluate the new transaction system. Thus, any differences in perceptions of the characteristics of the Exact card technology across these two groups are likely to reflect true differences in respective adoption needs and perspectives. From a managerial perspective, identifying differences between these two groups is an important first step in developing a focused, winning marketing program for the new technology. We then compare the results obtained from the participating and nonparticipating consumer samples. As described previously, there are notable demographic differences between the participating and nonparticipating

5.2. Measurement model results Although PLS estimates parameters for both the links between measures and constructs (i.e., loadings) and the links between different constructs (i.e., path coefficients) at the same time, a PLS model is typically analyzed and interpreted sequentially in two stages: (1) an assessment of the reliability and validity of the measurement model, followed by (2) an assessment of the structural model. This sequence ensures that the researcher has reliable and valid measures of constructs before attempting to draw conclusions about interconstruct relationships. The adequacy of a measurement model can be assessed by looking at: (1) individual item reliabilities, (2) the convergent validity of the measures associated with individual constructs, and (3) discriminant validity [32]. In PLS, individual item reliability is assessed by examining the loadings of the measures with their respective constructs. One frequently cited rule-of-thumb suggests retaining only items with loadings of 0.70 or more [11], since items with lower loadings have a random error component that exceeds the explanatory component. Nonremoval of such items is likely to lead to attenuated estimates of the true relationships between constructs [42]. However, when the link between a given construct and specific measures has been established in previous studies (as is the case here), the researcher should attempt to eliminate as few measures as possible, since any item deletion may change the domain of the underlying construct. A preliminary analysis of the measurement models for the consumer and merchant samples indicated only one truly problematic item in terms of item-to-construct loadings. This item was one of the four used to measure result

C.R. Plouffe et al. / The Journal of Product Innovation Management 18 (2001) 65– 81 Table 3 Constructs, number of items, and internal consistencies Construct

No. of Items

CONSUMER GROUPS Relative Advantage Ease-of-Use Compatibility Image Result Demonstrability Visibility Trialability Voluntariness Intention to Adopt

5 4 3 3 3 2 2 2 4

MERCHANT GROUPS Relative Advantage Ease-to-Use Compatibility Image Result Demonstrability Visibility Trialability Voluntariness Intention to Adopt

5 4 3 3 3 2 2 2 4

Internal Consistency

Internal Consistency

Participating

Non-participating

.93 .89 .96 .97 .83 .85 .72 .90 .95

.94 .90 .95 .94 .86 .78 .82 .74 .93

Participating

Non-participating

.95 .93 .94 .97 .82 .83 .76 .91 .94

.94 .91 .94 .97 .85 .84 .64 .86 .93

Note: Internal consistency measure is taken from Fornell and Larcker [21].

demonstrability. As the loading for this item was consistently below 0.50 for all four groups, it was omitted from all subsequent analyses (only the three remaining result de-

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monstrability items are reported in the Appendix). All but six of the remaining construct items had loadings of greater than of 0.70 or above. However, since no one item had a loading of lower than 0.70 in more than two data sets, it was decided to retain the remaining items so that all of the results are based on identical measurement instruments. The internal consistency of the measures used for each of the constructs was assessed using a measure proposed by Fornell and Larcker [21]. These values are reported in Table 3. Following the guidelines offered by Nunnally [42], constructs with internal consistencies of at least 0.70 may be considered acceptable. As Table 3 shows, the trialability measures were the only ones that failed to meet this threshold, and even then, this was only the case for the nonparticipating merchant group. This may be due in part to the small sample size for this group. Nonetheless, the low internal consistency observed here for trialability should be kept in mind when interpreting the PLS structural results. To assess discriminant validity, Fornell and Larcker [21] suggest the use of an average variance extracted measure (AVE) for each construct. The square roots of these measures are shown along the diagonal of the matrices reported in Tables 4 and 5. For adequate discriminant validity, these diagonal elements should be greater than the off-diagonal elements (i.e., the between-construct correlations) in the corresponding rows and columns [7]. This is true for all constructs in all four data samples. Thus, discriminant validity among constructs does not appear to be a major problem in our study. However, it is possible (particularly

Table 4 Correlations among constructs & average variance extracted consumer samples PARTICIPATING CONSUMER Construct 1. 2. 3. 4. 5. 6. 7. 8. 9.

Relative Advantage Compatibility Trialability Ease-of-Use Visibility Result Demonstrability Image Voluntariness Intention to Adopt

1

2 .71 .60 .06 .42 .33 .11 .45 .01 .62

3 .89 .04 .42 .23 .24 .42 .11 .59

4

.56 .19 .00 .00 .03 .11 .06

5

.67 .06 .16 .06 .20 .32

6

.74 .02 .27 .11 .25

7

.74 .12 .46 .15

8

.91 .13 .40

9

.81 .14

.82

NON-PARTICIPATING CONSUMER Construct 1. 2. 3. 4. 5. 6. 7. 8. 9.


1

2 .76 .67 .37 .25 .30 .18 .59 .10 .68

3 .87 .42 .34 .25 .37 .51 .19 .59

4

.69 .44 .12 .49 .28 .28 .40

5

.68 .17 .54 .04 .24 .20

6

.64 .11 .21 .03 .34

7

.78 .21 .25 .24

8

.83 .00 .48

9

.61 .12

Note: Diagonal numbers are Average Variance Extracted (AVE)—Fornell and Larcker, [21]. Bolded numbers are significant at p ⬍ .05.

.75

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Table 5 Correlations among constructs & average variance extracted merchant samples PARTICIPATING MERCHANT Construct 1. 2. 3. 4. 5. 6. 7. 8. 9.


1

2 .78 .77 .33 .48 .16 .31 .60 .15 .60

3 .85 .34 .49 .19 .33 .58 .18 .58

4

.61 .30 .14 .37 .24 .02 .36

5

.77 .18 .28 .23 .26 .34

6

.72 .13 .19 .06 .26

7

.72 .24 .20 .30

8

.91 .05 .50

9

.84 .14

.78

NON-PARTICIPATING MERCHANT Construct 1. 2. 3. 4. 5. 6. 7. 8. 9.


1

2 .77 .76 .25 .45 .34 .36 .60 .19 .58

3 .84 .20 .52 .26 .47 .58 .36 .53

4

.49 .19 .09 .40 .18 .08 .34

5

.72 .28 .25 .24 .21 .40

6

.73 .21 .38 .06 .26

7

.77 .26 .28 .34

8

.91 .16 .44

9

.75 .25

.77

Note: Diagonal numbers are Average Variance Extracted (AVE)—Fornell and Larcker, [21]. Bolded numbers are significant at p ⬍ .05.

given the relatively high between-construct correlations) that individual measurement items may not exhibit adequate discriminant validity. To check this, a matrix of loadings and cross-loadings was constructed for each of the samples.7 For each of the constructs across all four groups, the items loaded more highly on their intended constructs than on other constructs. Thus, discriminant validity can be considered adequate in all cases.

5.3. Structural model results Given the acceptable measurement model results reported above, it is appropriate to turn to the structural model results. Path coefficients from the estimated structural models are reported for both the consumer and merchant samples in Table 6, along with R2 values for the intention to adopt construct. The statistical significance of the standard-

Table 6 PLS structural model results, path significance levels, and percentage of explained variance in intention to adopt Construct

Participating Consumer Group (n ⫽ 167) Path Coefficient (␤)

Non-Participating Consumer Group (n ⫽ 185) Path Coefficient (␤)

Participating Merchant Group (n ⫽ 172) Path Coefficient (␤)

Non-Participating Merchant Group (n ⫽ 80) Path Coefficient (␤)

1. 2. 3. 4. 5. 6. 7. 8.

.387*** .293*** .017 .006 .032 .033 .111* .135*

.447*** .181* .138* .087 .146* .044* .052* .010*

.288** .168* .134 .004 .127* .049 .165* .036

.313* .075 .188 .116 .048 .021 .108 .100

.483

.535

.452

.423

Relative Advantage Compatibility Trialability Ease-of-Use Visibility Result Demonstrability Image Voluntariness

R2-Intention to Adopt Path Significance Levels: *** p ⬍ .001 ** p ⬍ .01 * p ⬍ .05


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Table 7 Percentage of explained variance in intention to adopt by significant antecedents Participating Consumers

Non-Participating Consumers

Participating Merchants

Non-Participating Merchants

Relative Advantage (38.2%) Compatibility (28.9%) Voluntariness (13.3%) Image (10.9%)

Relative Advantage (40.5%) Compatibility (16.4%) Visibility (13.2%) Trialability (12.5%) Image (4.7%) Result Demonstrability (4.0%) Voluntariness (0.9%)

Relative Advantage (29.7%) Compatibility (17.3%) Image (17.0%) Visibility (13.1%)

Relative Advantage (32.3%)

Note: Antecedents significant in Table 6 are reported here, by group, in order of variance explained. The number in brackets beneath each antecedent indicates the proportion of variance explained by that antecedent relative to the variance explained by all antecedent constructs for that group.

ized path coefficients were determined using jackknife analysis [19,58]. As Table 6 indicates, the R2 values for all models are quite high, indicating that the PCI model may have considerable utility in assessing adoption intentions for new innovations. Moreover, the high explanatory values achieved by many of the model’s adoption antecedents are consistent with past research [23,43].8 Table 7 indicates, by group, the significant antecedents noted in Table 6, ranked according to their relative explanatory power. As this table shows, perceived relative advantage is important to all four groups. Compatibility and image are also important antecedents to adoption for three of the four groups. However, there are also important differences between the groups. These are discussed in turn below. 5.3.1. Participating consumer sample versus participating merchant sample. For the participating consumers, four significant antecedents (relative advantage, compatibility, voluntariness and image) accounted for more than 91% of explanatory effects on the intent to adopt construct. Relative advantage and compatibility were by far the most important predictors, accounting for more than two thirds of the explanatory power in the model. The participating merchants also had four significant antecedents (relative advantage, compatibility, image and visibility) that accounted for the majority of the explanatory power in the model (77.1%). The two most important antecedents (in terms of explanatory power)—relative advantage and compatibility—accounted for only 47% of the explanatory effects in the model, a full 20% less than in the participating consumer model. For both participating groups, ease-of-use, result demonstrability and trialability were not significantly related to adoption intent. We tested whether or not the individual path coefficients differed significantly across these two groups. Visibility was significantly more important to the participating merchants than to the participating consumers (t(337) ⫽ 2.21, p ⬍ .05). Both compatibility (t(337) ⫽ 1.70, p ⬍ .10) and voluntariness (t(337) ⫽ 1.66, p ⬍ .10) were marginally more important predictors of adoption intention among par-

ticipating consumers. None of the other paths differed significantly from one another across the two groups. The participating consumers and participating merchants appear to possess somewhat different perspectives when assessing their intention to adopt the smart card technology. The consumers place heavy emphasis on two constructs (compatibility and relative advantage) and less on all others. These findings are consistent with past adoption research. In addition, voluntariness was an important antecedent of adoption for consumers, but not merchants. Consumers seem to value the notion that the adoption decision is under their control, whereas merchants seem to place more value on antecedents that have the potential to add to their bottom line. The participating merchants valued a variety of adoption antecedents with no one construct accounting for more than a 30% of the explanatory power. The merchants were concerned with elements such as image and visibility of the innovation. These antecedents are concerned with different aspects of how potential customers assess value in the exchange relationship. These results are also consistent with our hypothesis that sellers in a mediated exchange relationship will be concerned with both the “revenue” (customer evaluation effects) and “cost” (operational efficiency) elements of an innovation. 5.3.2. Participating consumer sample versus non-participating consumer sample. Four antecedents were significant for both consumer groups (relative advantage, compatibility, voluntariness and image) with relative advantage accounting for about 40% of the explanatory effects in each model. For the nonparticipating consumer group, result demonstrability, visibility, and voluntariness have smaller, but still significant effects on intent to adopt. Ease-of-use was the only antecedent that was not significantly related to intent to adopt for either consumer group. When the path coefficients from the two groups are compared, three significant differences emerge. First, voluntariness is significantly more important to the participating group (t(350) ⫽ 2.74, p ⬍ .01). In addition, visibility was significantly more important to the nonparticipating consumers (t(350) ⫽ 2.41, p ⬍ .05). Finally, image is

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marginally more important to the participating consumers (t(350) ⫽ 1.66, p ⬍ .10). 5.3.3. Participating merchant sample versus non-participating merchant sample. Among nonparticipating merchants, the only significant antecedent was relative advantage. Relative advantage was also important to the participating merchants. However, compatibility, image and visibility were also strongly associated with intent to adopt the Exact card for the latter group. Ease-of-use, result demonstrability, trialability, and voluntariness have nonsignificant impacts in both groups. There were no significant differences in the path coefficients between these groups.

6. Discussion The primary objective of our study was to estimate group-specific models of adoption intent and to then use these model results to illustrate the extent and managerial significance of differences in the antecedents of adoption between two groups simultaneously considering an intermediating technological innovation. The antecedents of adoption we used were based on the PCI measurement instrument developed by Moore and Benbasat [40], an instrument that provides a rich description of key antecedent constructs that can be extremely insightful when developing the marketing mix associated with a technological innovation’s market launch. 6.1. Intermediating technologies and multigroup adoption The smart card study presented in this article illustrates the importance of addressing all groups in a multigroup adoption setting. In the Exact smart card trial, consumers and merchants clearly differed in their perceptions of the smart card-based payment system. This empirical insight has implications for the marketing of intermediating products and services. Specifically, our results demonstrate that when marketing novel technological products or services to multiple constituencies, the perceptions and needs of each group must be assessed, understood, and acted upon, often simultaneously. The approach outlined here provides one means for quantifiably and reliably garnering the insights necessary to achieve these ends. For many innovations, firms choose to concentrate on improving the price-performance ratio (relative advantage) to enhance the probability of adoption. However, our results indicate that time and effort may be well spent by focusing on other elements in the marketing mix. Product or service design changes not directly related to price-performance may have significant payoffs. For example, compatibility issues—an important driver of adoption intent among participating merchants—might be addressed by developing a means of integrating the point-of-sale equipment used for all electronic payment systems (credit, debit and smart

card). For consumers, compatibility issues may be addressed by including the smart card payment features on other cards the are already in the consumer’s wallet (e.g., ATM or telephone cards). Similarly, an image-building campaign using smart card system logos, in-store promotions and point-of-sale signage might increase the likelihood of adoption among merchants. Indeed, this might be a more cost-effective way of increasing adoption among merchants than attempting to improve relative advantage through reduced transaction processing fees. In our analysis, we viewed retailers as an adopting group whose support of the smart card-based payment system would be critical to its broader market acceptance and continued viability. Others may view this group as a member of the distribution channel that can be encouraged to participate through the use of financial and nonfinancial incentives. Managers often take this latter perspective and consider only end users as the adopting group. The results reported here strongly suggest otherwise. We found that participating retailers’ adoption intent was influenced by a variety of antecedents. In fact, relative advantage—which would capture the financial incentives provided in a manufacturer-distributor relationship— explained less than 30% of the variance in participating merchants’ intentions to adopt. Other factors (such as those captured by our models) are also important drivers of adoption intent. These factors can only be addressed by the target firm through nonincentive elements of the marketing mix. Thus, our findings lend credence to the classification of smart card payment systems as an intermediating technology. Comparisons among different segments of an adopting group can also offer insights as to how to attract both early and later adopters. In our study, we collected information from both a random sample of consumers in the test community and a sample of those participating in the market trial. Compared to the participating sample of consumers, the nonparticipating group’s intention to adopt was more heavily influenced by relative advantage and visibility of the adoption among other consumers or the community at large. At the same time, nonparticipating consumers were less influenced by the image afforded by the innovation and the compatibility of the smart card system with their current behaviors. The implication of this is that potential early adopters may not require the smart card system to provide as large a relative advantage vis-a-vis other payment options so long as the value of the offering is enhanced through image and increased compatibility with their personal consumption habits and preferences. However, diffusion beyond the early adopter segment may be difficult as significant advantages over existing systems appears to be of concern to later adopters. These findings indicate that marketers may be well advised to look beyond trial consumers to assess the potential long-term success of an innovation. This is consistent with both Moore’s [39] description of the ‘chasm’ between visionaries and pragmatists and the dis-


cussion by Mahajan and Muller [36] about whether or not to target the majority instead of early adopters. Within-group segmentation comparisons across consumers and merchants indicate that strategies employed for one adopting group may not be appropriate for the other. If nonparticipating consumers are to be considered consistent with later adopters, it is clear that they require a whole host of benefits before they become willing to adopt. At the same time, late adopters among the merchants (nonparticipating merchants) seem to be concerned solely with relative advantage. These differences may be related to the way the later adopting groups assess the perceived risk associated with adoption [43]. Consumers, who are likely to be more concerned with social risk, will require a variety of assurances that the smart card is viable before adoption occurs. This can be evidenced through the importance that nonparticipating consumers place on constructs like visibility and result demonstrability. Merchants, on the other hand, are more likely to be concerned with financial risk. The nonparticipating merchants’ sole concern with relative advantage (price/performance) is consistent with this view. Therefore to attract these later adopters, strategies which work to lower the perceived risk associated with adoption are required. For consumers, this may imply advertising or promotion schedules that enhances the visibility of the smart card. For merchants, a targeted economic value to the customer (EVC) analysis may be more effective. 6.2. Limitations Although we believe that our findings are provocative, they were obtained in only one specific empirical context. In other settings, very different patterns of important drivers across adoption groups are likely to emerge. Although the importance of the various antecedents to adoption intent will almost certainly be innovation-specific, we believe use of the PCI measures will continue to offer reliable and valid insights into adoption differences across different groups adopting an intermediating technology.9 It can be argued that a new payment system represents more of an incremental change to existing consumer spending habits and retail payment systems than a radical intermediating technological innovation. While the Exact card does not represent a radical departure from existing payment systems, it is also more than an incremental change. The three banks conducting the market trial were sufficiently concerned about both merchant resistance to accepting new transaction equipment and consumer resistance to carrying “yet another card” to justify the substantial costs of conducting an extensive, one year, in-market test. In addition, poor results from similar smart card payment system trials elsewhere in Canada [51], the United States [28], and England and Ireland [8] indicate that the move to smart card-based payment systems is not an easy transition for the adopting groups. Finally, a potential concern centers around the issue of

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whether or not the nonparticipating consumers and merchants were really familiar enough with Exact smart card and associated retail payment system to intelligently and competently respond to our questions [18]. We do not believe that this was a major problem in the Exact card case. This market trial was quite comprehensive. More than 5,000 consumers held an Exact card and over 400 merchants installed the facilitating point-of-sale equipment during the well-promoted, year-long trial. The total population within the trial city was approximately 100,000 people. Rogers’ [47,48] arguments regarding the dispersion of knowledge about an innovation through informal contacts (e.g., family, friends, co-workers), “weak ties” theory [25], the notion of “critical mass” [2], and social learning theory [5] are all consistent with our position that the innovation studied in this research was both heavily diffused and widely recognized in the test market, and that even members of the general population were in a position to competently offer their perceptions of the technology undergoing market trial.

7. Conclusion The key objective of this article is to highlight the frequency of intermediating technology adoption settings and the importance of understanding the similarities and differences among adopting groups. We use an intuitive yet reliable approach to empirically assess the antecedents of adoption for both buyers and sellers involved in evaluating an intermediating technology. In addition, our results and discussion illustrate that a detailed analysis of adoption antecedents can lead to marketing mix strategies that improve the likelihood of adoption of a technological innovation among target segments and groups. It is our hope that this research will encourage managers to act upon the requirements of each of the groups adopting their innovations while stimulating researchers to work toward a more refined understanding of intermediating technologies and the multigroup adoption phenomenon.

Notes 1. The pilot study was conducted on a convenience sample of undergraduate and MBA-level students who were given preliminary versions of survey instruments as well as news clippings and press releases describing the Exact trial. They were asked to respond to all survey items by envisioning themselves as either consumers or retailers, respectively, who were considering adoption of the Exact technology as a possible point-of-sale and customer payment innovation. 2. Specifically, initial survey booklets were mailed complete with a hand-signed cover letter and business reply envelope. Two weeks later, reminder postcards were mailed, and finally, five weeks after the initial

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3.

4.

5.

6.

7.

8.

9.

survey packages were mailed, a second and final “reminder” survey package was sent to all remaining nonresponders. Conceptually, it is important to underscore that while we believe our nonparticipating consumer and merchant sampling frame sources are reasonably good proxies for Exact trial “nontriers,” the fact that individuals and business owners in these groups did not voluntarily participate in the year-long market trial does not necessarily imply that they would eventually become “late adopters” given full market deployment of a smart card-based payment technology (i.e., as per a diffusion of innovations perspective). The sixteen respondents in the random consumer sample who indicated that they had used a smart card were dropped from the nonparticipating group and added to the participating consumer group, increasing the total sample size for the latter to 167. A two stage imputation process was employed. In the first stage, we used a within-respondent/within-construct mean substitution approach. For those few cases where more than one item was missing from a particular construct, we used the across-respondent item averages. The nonparticipating consumer and nonparticipating merchant groups cannot be strictly classified as late or nonadopters. It is also possible that they were unable to participate in the trial for other reasons. However, open-ended responses by subjects in these groups lead us to believe that many will not be early adopters of the smart card technology. The matrix of loadings and crossloadings is analogous to a factor analysis completed within the PLS modeling framework. The loading/cross loading matrices are available from the first author upon request. Past research suggests that personal characteristics are not important in predicting adoption behavior [43]. To test this in the current context, we conducted additional analyses that also included a number of personal characteristics for the consumer groups (personal innovativeness, age, income and education) and organizational characteristics for the merchant groups (annual revenues, number of employees and innovativeness). None of these factors were significant predictors of adoption intent and they are therefore not discussed further in this article. One caveat here relates to the ease-of-use construct that was not found to be a significant predictor of adoption across all groups. As one reviewer pointed out, though conceptualized as the opposite of Roger’s complexity construct, this may not be the case. Easeof-use deals with the functional use of the product whereas this may only be a subset of the complexity issues involved in adoption. For example, a retailer adopting a smart card system needs to be concerned with maintenance, repair, uploading of information,

safety, and so forth that may not completely be captured in the ease-of-use construct. Future comparative research should be undertaken to assess the merits of using complexity versus ease-of-use constructs.

Acknowledgments All three authors gratefully acknowledge the financial support of the Richard Ivey School of Business, and the cooperation of the Bank of Montreal, the Toronto-Dominion Bank, and the Canada Trust Corporation for their assistance in the data gathering stages of this research. A grant for this research was provided to the first author by the American Marketing Association’s Technology and Innovation Special Interest Group. The second and third authors also appreciate support from the Social Sciences and Humanities Research Council of Canada. Finally, we have benefited immeasurably from the helpful comments provided by Don Barclay, Niraj Dawar, and Robert Fisher on an earlier version of this article.

Appendix Measures and operationalizations Notes: For each construct, the first version of a measure’s wording (wording “a”) is that which appeared on the “participating consumer” version of the survey. The second wording (wording “b”) is that which was used for the “participating merchant” group. The wordings for the two “non-participating” groups (i.e., the non-participating consumer and merchant samples) are not included due to space considerations. They can be obtained by contacting the lead author, or by simply changing the tense of each item from present to future. For example, with the first Relative Advantage item below, the non-participating consumer item read “Using an Exact card would improve the quality of my transactions with merchants”. Relative advantage 1) a - Using an Exact card improves the quality of my transactions with merchants. 1) b - Using the Exact card system improves the quality of the sales transactions my staff and I conduct in our business. 2) a - Using an Exact card gives me greater control over my purchasing transactions. 2) b - Using the Exact card system gives me and my staff greater control over our business’ sales transactions. 3) a - Using an Exact card enables me to make purchases more quickly.


3) b - Using the Exact card system enables me and my staff to process payments more quickly. 4) a - Using an Exact card enhances my effectiveness in making purchases. 4) b - Using the Exact card system enhances the on-thejob effectiveness of me and my staff. 5) a - Using an Exact card makes shopping easier for me. 5) b - Using the Exact card system makes it easier for me and my staff to do our jobs. Ease-of-use 1) a - Learning to operate the Exact card was easy for me. 1) b - Learning to operate the Exact card system was easy for me and my staff. 2) a - I find it easy to get the Exact card to do what I want it to do. 2) b - My staff and I find it easy to get the Exact card system to do what we want it to do. 3) a - Using an Exact card is clear and understandable. 3) b - Using the Exact card system is clear and understandable for me and my staff. 4) a - I find the Exact card easy to use. 4) b - My staff and I find the Exact card system easy to use. Compatibility 1) a - Using an Exact card is compatible with all the ways I like to pay for purchases. 1) b - Using the Exact card system is compatible with all aspects of my business’ sales transactions. 2) a - I think that using an Exact card fits well with the way I like to pay for goods and services. 2) b - I think that using the Exact card system fits well with the way my staff and I like to receive payment for goods and services. 3) a - Using an Exact card fits with my style as a consumer. 3) b - Using the Exact card system fits with our business’ work style. Image 1) a - People who use an Exact card have more prestige than those who do not. 1) b - Merchants who use the Exact card system have more prestige than those who do not. 2) a - People who use an Exact card have a higher profile than those who do not. 2) b - Merchants who use the Exact card system have a higher profile than those who do not. 3) a - Having an Exact card is a status symbol. 3) b - Having an Exact card system is a status symbol among the merchants I know.

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Result demonstrability 1) a - I would have no difficulty telling others about my experience using an Exact card. 1) b - My staff and I would have no difficulty telling others about our experience using the Exact card system. 2) a - I believe I could communicate to others the consequences of using an Exact card. 2) b - My staff and I could communicate to others the consequences of using the Exact card system. 3) a - The impact of using an Exact card is apparent to me. 3) b - The impact of using the Exact card system is apparent to my staff and me. Visibility 1) a - In my community, I see many people using Exact cards. 1) b - In my community, I see many merchants using the Exact card system. 2) a - The Exact card is not very visible in my community. 2) b - The Exact card system is not very visible in my community. Trialability 1) a - I’ve had a great deal of opportunity to try the Exact card in various situations. 1) b - My staff and I have had a great deal of opportunity to try the Exact card system in various situations (e.g., a customer asks to pay for a purchase with a combination of Exact and cash). 2) a - Before deciding whether to use an Exact card, I was able to properly try it out. 2) b - Before deciding whether to use the Exact card system, my staff and I were able to properly try it out. Voluntariness 1) a - My use of an Exact card is voluntary. 1) b - My business’ use of the Exact card system was voluntary. 2) a - While it was suggested to me, using an Exact card certainly is not compulsory. 2) b - Although suggested to my business, using the Exact card system was not compulsory. Intention to adopt 1) a - Once the trial period is over, I will be interested in continuing to use smartcard payment systems. 1) b - Once the trial period is over, I will be interested in continuing to use a smartcard payment system in my business.

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2) a - Once the trial period is over, I will arrange to become a permanent smartcard holder as soon as possible. 2) b - Once the trial period is over, I will arrange to permanently adopt a smartcard payment system as soon as possible. 3) a - Once the trial period is over, I won’t see much need to continue to use smartcard-based payment systems. 3) b - Once the trial period is over, I won’t see much need to continue to use a smartcard payment system in my business. 4) a - Once the trial period is over, I will recommend that my friends get a smartcard for paying for goods and services. 4) b - Once the trial period is over, I will recommend that my fellow merchants get a smartcard payment system.

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Biographical Sketches Christopher R. Plouffe is a PhD Candidate in Marketing at the Richard Ivey School of Business at the University of Western Ontario. He holds an MBA and an Honors Bachelor of Arts from Queen’s University. Prior to his PhD studies, Christopher spent several years in the computer industry selling high-end commercial and technical computing solutions for Hewlett-Packard (Canada). Christopher’s research interests focus on the ongoing challenges associated with the planning, launch, and selling of innovative products, services and technologies. He has published or has work forthcoming in Information Systems Research, The International Journal of Bank Marketing, and the Proceedings of the American Marketing Association Educators’ Conferences. Mark Vandenbosch is an Associate Professor of Marketing at the Richard Ivey School of Business at the University of Western Ontario, and, during 2000 –2001, is a Professor of Marketing at IMD in Lausanne Switzerland. He received his PhD from the University of British Columbia and his HBA from the University of Western Ontario. Vandenbosch’s research interests center around competitive strategy, product positioning and marketing research, primarily in technology-based markets. His work has appeared in Marketing Science, Organization Science, Information Systems Research, International Journal of Research in Marketing, Journal of Retailing, Marketing Letters and the Journal of Business Research. John Hulland is an Associate Professor of Marketing at the Richard Ivey School of Business at the University of Western Ontario, and, during 2000 –2001, is a Visiting Associate Professor of Marketing at the Wharton School, University of Pennsylvania. He received his PhD from MIT, his MBA from Queen’s University, and his BSc from the University of Guelph. Hulland has research interests in a number of areas, including the study of marketing resource management issues and the application of causal modeling techniques to strategic marketing problems. He is currently conducting a study of how cooperation between the sales and marketing groups within organizations affect their ability to deal with external customers. He has also written nearly 40 business cases.