Adoption of a service innovation in the business market: An empirical test of supply-side variables

ELSEVIER I

I

I

Adoption of a Service Innovation in the Business Market An Empirical Test of Supply-Side Variables Ruud T. Frambach UNIVERSITYOF GHENT AND THE VLERICK ScHoOL OF MANAGEMENT, BELGIUM TILBURG UNIVERSITY

Harry G. Barkema TILBURG UNIVERSITY

Bart Nooteboom UNIVERSITYOF GRONINGEN

Michel Wedel UNIVERSITYOF GRONINGEN

The objective of this article is to assess the influence of variables over which suppliers have control (supply-side variables) on the adoption of innovations in addition to adopter-side variables. The empirical study focused on the adoption of electronic banking in the Dutch business market. A quantitative study was conducted to test hypotheses. The results show that the extent to which a supplier has pursued a strategy aimed at positioning the innovation in the marketplace or has focused on reducing the risk of adoption has a positive and significant effect on the probability of innovation adoption. The evidence corroborates that not only adopterside variables significantly influence innovation, but supply-side variables as well. 1BUSNRES1998. 41.161--174. © 1998 Elsevier Science Inc.

nvironmental conditions increasingly force organizations to innovate and bring new products and services to the market. Because only a fraction of new product ideas are successful, a thorough understanding of factors underlying the innovation adoption decision by potential adopters is necessary. Diffusion research has become an important tool in such studies in marketing theory and practice (Rogers, 1983, 1992; Shanklin and Ryans, 1984). Within diffusion research, two types of diffusion models are distinguished (Sinha and Chandrashekaran, 1992). First are models that aim to gain understanding of diffusion processes as a whole. These models are analytical representations

E

Address correspondence to Ruud T. Frambach, University of Ghent, Department of Marketing, Hoveniersberg 4, B-9000Ghent, Belgium (e-mail: Ruud. [email protected]). Journal of Business Research 41, 161-174 (1998) © 1998 Elsevier Science Inc. All rights reserved. 655 Avenue of the Americas, New York, NY 10010

of a diffusion process at the aggregate level (see e.g., Mansfield, 1968; Bass, 1969; Mahajan, Muller, and Bass, 1990; Lilien, Kotler, and Moorthy, 1992). They are often referred to as diffusion models. A second class of models has the objective to gain insight in the determinants of the individual adoption (or nonadoption) decision. These models take a disaggregate perspective and are generally referred to as adoption models. In diffusion theory several variables are identified to influence the adoption and diffusion of innovations. Whereas diffusion models have been used to investigate the role of both adopter-side and supply-side variables on the shape of the diffusion process as a whole (Mahajan, Muller, and Bass, 1990; Lilien, Kotler, and Moorthy, 1992), adoption models have almost exclusively focused on adopter-side variables in explaining individual adoption behavior (Rogers, 1983; Robertson and Gatignon, 1986; Clark and Staunton, 1989; Frambach, 1993). Preliminary research on the influence of supplier variables on the individual innovation adoption decision support the view that supply-side variables can play an important role in the individual adoption context (Brown, 1981; Stoneman and Ireland, 1983; Gatignon and Robertson, 1989). This article sets out to compare empirically a model of innovation adoption including supplier variables in addition to adopter-side variables with the type of adoption model commonly used including only adopter-side variables. The objective of this study is to assess the influence of variables over which suppliers have control (referred to as supply-side variables) and to examine the results of the full model relative to the adopter-side model. Consequently, this article is distinctive in the following ways. ISSN 0148-2963/98/$19.00 PII S0148-2963(97)00005-2

162

J Busn Res 1998:41:161-174

First, the influence of supplier characteristics on innovation adoption are investigated. Enhanced insight in the influence of the supply side on innovation adoption in the marketplace is interesting in its own right. The role of supplier variables in innovation adoption is considered one of the most untapped and potentially interesting research areas within diffusion research (Brown, 1981; Rogers, 1983; Clark and Staunton, 1989; Gatignon and Robertson, 1991). Furthermore, measuring the effect of supply-side variables on innovation adoption while controlling for adopter-side variables gives a more profound insight in the determinants of adoption. Omitting potentially powerful explanatory variables in the adoption model may lead to model misspecification and misinterpretation of empirical results. Second, this study focuses on adoption in the businessto-business context, while previous research has primarily focused on consumers as adopters of innovations. Some notable exceptions include Gatignon and Robertson (1989) and Gauvin and Sinha (1993). Gatignon and Robertson (1989) investigated the adoption of laptop computers among salespeople in business firms. They especially focused on the influence of the competitive environment on innovation adoption and found this to be significant. Gauvin and Sinha (1993) focused on the opportunities industrial organizations have for innovation adoption. They found that firm size has a positive impact on opportunity of adoption and that the opportunity of adoption as well as past adoptions positively influence the probability of adoption. Consumer adopters differ from organizational adopters in several ways (Day and Herbig, 1990). First, whereas personal characteristics determine the degree of innovativeness of consumers (see Gatignon and Robertson, 1985), organization size and organization structure are important determinants of organizational innovativeness (e.g., Kennedy, 1983). Second, innovation adoption by consumers will be primarily driven by the desire to satisfy individual needs, whereas business firms adopt innovations in order to carry out value-adding activities. Organizations aim to achieve and sustain competitive advantages (Chisnall, 1989). Consequently, adoption of innovations by businesses usually involves a long-term commitment with a higher degree of perceived risk involved than in the case of consumer products. Considering the above, findings about consumer markets cannot be generalized directly to business markets. Third, this study focuses on the adoption of a service innovation. In most innovation adoption and diffusion studies, the object of study is a tangible product. The adoption and diffusion of service innovations has received little attention (De Brentani, 1995), although the importance of services to most Western economies is strongly increasing. Due to the differences in product characteristics between tangible products and service products, it is not obvious that results from studies on the adoption of tangible products can be generalized to settings where services are considered. As is well known from the literature on services marketing, intangible products

R.T. Frambach et al.

such as services are often more difficult to evaluate for potential users, and in an effort to assess quality, attention may shift to peripheral, more tangible aspects of the product, or to prestige or reputation of the supplier (Shostack, 1977; Bateson, 1992). Therefore, we should expect a different constellation of marketing instruments and their effects. For example, although support for the influence of the perceived complexity of an innovation on the adoption of new tangible products has been weak (Rogers, 1983), this variable may well be an important determinant of the adoption of new services due to their intangibility. The same might apply to other marketing variables such as reduction of user risk, communication, and reputation. These variables are included in the present study. This study investigates the adoption of a service innovation, (i.e., electronic banking) in the business market. Electronic banking is a financial service innovation that offers banking firms opportunities to reduce the costs of banking transactions, enhance the intensity of the relationship with their customers, and to distribute electronically a wider range of (semi-) financial products. Thus, it is of strategic importance to the financial services sector. The next section discusses determinants of innovation adoption previously studied in adoption models (i.e., the adopter-side model). Because these variables generally have been found to influence innovation adoption, they will be incorporated in our adoption model as control variables. Hypothesis development will concentrate on the influence of supply-side variables that are discussed and integrated in the adoption model in the third section (this model is referred to as the full model). The research method of the study is discussed in the fourth section. The fifth section presents test results of both the adopter-side model and the full model. The sixth section provides a discussion, and the seventh concludes with implications.

Adopter-Side Model The following adopter-side variables generally have been found to influence the individual adoption decision.

Perceived Innovation Characteristics Various studies have found that the perceived relative advantage of an innovation, defined as the degree to which an innovation is perceived as being better than the idea it supersedes (Rogers, 1983, p. 213), is one of the best predictors of the rate of adoption of innovations (see Tornatzky and Klein, 1982; Rogers, 1983; Onkvisit and Shaw, 1989; Robinson, 1990). Especially in industrial markets, organizations will seek increased efficiency or effectiveness of their activities by adopting an innovation (Webster, 1969; Chisnall, 1989). Also, the perceived compatibility of an innovation, defined as the degree to which an innovation is perceived as consistent with the existing values, past experiences, and needs of potential adopters is positively related to adoption (Tornatzky and Klein,

Adoption of a Service Innovation

1982; Rogers, 1983). The perceived observability of an innovation, defined as the degree to which the results of an innovation are visible to others (Rogers, 1983, p. 232), and its perceived trialability, defined as the degree to which an innovation may be experimented with on a limited basis (Rogers, 1983, p. 231) are also found to be positively related to adoption (Tornatzky and Klein, 1982; Rogers, 1983). A significant negative influence on the probability of adoption is found to be exerted by the perceived complexity of the innovation (Tornatzky and Klein, 1982; Rogers, 1983), defined as the degree to which an innovation is perceived as relatively difficult to understand and use (Rogers, 1983, p. 230), and the perceived uncertainty regarding the innovation (Gatignon and Robertson, 1985; Nooteboom, 1989).

Adopter Characteristics One variable that most often has been found to be positively related to the adoption rate of innovations in the industrial context, is the size of the adopter (Kennedy, 1983). Although the significant influence of size on adoption may be attributable to its interdependence with other variables (Rogers, 1983, p. 359), there are good reasons to expect large firms to adopt an innovation before small firms, in particular if there are economies of scale in the use of the innovation (Kimberley and Evanisko, 1981; Brown, 1981). The receptiveness of an organization toward new ideas also encourages innovation adoption (Baldwin and Scott, 1987). Also, the age of the decision makers and/or the organization may (negatively) influence the degree to which new ideas and products are welcomed by the firm (Lancaster and Taylor, 1988). Furthermore, the organization structure may either encourage or discourage the acceptance of new ideas and products. A high degree of centralization may obstruct the opportunity for new products to be implemented in an organization (Zahman, Duncan, and Holbek, 1973).

Network Participation The interaction between members of a social system (network participation) can also enhance the speed and rate of the adoption and diffusion process (Zahman, Duncan, and Holbek, 1973). The participation of organization members in informal networks facilitates the spread of information about an innovation, which may positively influence the probability of an organization adopting the innovation. Such an informal network may either connect organizations within the industry or organizations in different industries. This variable further links the present adoption study to the logic of "contagion" in most diffusion models.

Competitive Environment Recently, the role of the competitive environment in innovation diffusion has received more attention (Robertson and Gatignon, 1986). Empirical studies in the industrial organization literature corroborate the hypothesis that the higher the

J Busn Res 1998:41:161-174

163

degree of competitiveness and intensity of innovative activities in an industry, the more likely organizations in that industry are to adopt an innovation (Kamien and Schwartz, 1982; Baldwin and Scott, 1987; also see Gatignon and Robertson, 1989).

Information Innovation adoption is largely an information processing activity (Rogers, 1983). Therefore, the extent to which potential adopters of an innovation have processed information on the innovation can be expected to influence the probability of adoption (Webster, 1969). This is highly dependent on the degree to which suppliers have been involved in providing information on the innovation (Clark and Staunton, 1989).

Incorporating Supplier Variables in the Adoption Model: Hypotheses In the past decade, some diffusion researchers have included supply-side variables in adoption models to some extent. Brown (1981) incorporated the physical distribution of innovations in the diffusion model. This approach is known as the "market and infrastructure perspective." Robertson and Gatignon (1986) incorporated variables pertaining to the competitive environment of the supplier of the innovation. Although these models give some insight about the influence of supplier variables on innovation adoption, they do not explicitly consider two potentially major supplier related determinants of innovation adoption. First, the marketing strategy pursued by the supplier of an innovation can be hypothesized to influence the probability of adoption (Sultan, Farley, and Lehmann, 1990). Although the marketing activities undertaken by the supply side can be expected to be a major factor in determining the rate of adoption of an innovation in the marketplace (Shanklin and Ryans, 1984), the influence of marketing strategic variables have hardly been investigated in adoption studies (Gatignon and Robertson, 1991). Consequently, in diffusion research this area is considered one that needs further attention. Second, Rogers (1983, p. 134) advocated the incorporation of innovation development activities by the supply side in the adoption model. Innovation development activities, such as the extent to which the supplier has made a substantial effort in meeting customer needs, can have a major influence on the success of the new product (see e.g., Cooper, 1983; Zirger and Maidique, 1990). Ultimately, this is dependent on the perceived degree of customization of the innovation in the marketplace (i.e. the extent to which potential customers perceive the innovation as one that satisfies individual needs). Based on the above and consistent with empirical findings in the literature of industrial marketing and innovation management, this study maintains that the marketing strategies pursued by suppliers and the innovation development process conducted by suppliers influence innovation adoption and

164

J Busn Res 1998:41:161-174

should therefore be incorporated in the adoption model. In particular for service products, which due to their intangibility tend to be more difficult to judge by potential adopters, marketing instruments related to communication and reputation are expected to be especially relevant.

Marketing Strategy The marketing strategy of a supplier of an innovation is aimed at increasing the rate of adoption and diffusion of that innovation (Sultan, Farley, and Lehmann, 1990, p. 73), next to financial objectives in terms of price, revenue, and profit. Marketing activities pursued by suppliers can be considered to have a direct influence on the probability of innovation adoption, since these activities are to some extent aimed at influencing overt behavior (Shanklin and Ryans, 1984). The following groups of market launch strategies of new industrial products aimed at accelerating the rate of early adoption can be identified. First, marketing efforts can be concentrated on positioning the innovation in the marketplace (Easingwood and Beard, 1989). Organizations such as innovative adopters, heavy users of the product category, or heavy users of the preceding technology may be more receptive to the innovation than others. For suppliers it is important to achieve a certain level of adoption of the innovation in order to get the innovation accepted in different social systems (e.g., different industries) through interaction and contamination effects (Mansfield, 1961). The probability of organizations adopting an innovation will increase with the suppliers being more active in marketing the innovation and communicating its properties more explicitly (Webster, 1969; Clark and Staunton, 1989, p. 113). This is expected to apply particularly for service products. This leads to the following hypothesis: HI: The probability of innovation adoption by an organiza-

tion is higher if that organization was exposed to the supplier's communication activities more extensively. Second, marketing strategy can be directed at reducing the risks associated with early adoption, stimulating adoption of the innovation on a larger scale (Easingwood and Beard, 1989). In this respect the innovation may be given on trial to the customer for a certain period of time or the supplier may decide to absorb major risks of adoption by offering the potential adopter the innovation at a low introduction price (Kotler, 1991, p. 355). In some cases of high technology marketing this may be necessary to gain market acceptance, and the variable is expected to be relevant in particular for service products. The following can be hypothesized: H2: The probability of innovation adoption by an organiza-

tion is higher if that organization perceives a reduction of the risk of adoption invoked by the supplier. Third, diffusion of an innovation may be stimulated by winning market support for the innovation. This may be achieved in several ways (Easingwood and Beard, 1989). One

R.T. Frambach et al.

approach is to win the endorsement of opinion leaders. In the business market these will include key decision makers within firms and key influencers outside the firm (i.e., consultants and accountants). Another approach is to establish a "winner" image in the marketplace by creating instant success (e.g., by investing substantial resources in launching the new product). The aim of this approach is to accelerate the process of building a positive reputation in the marketplace. This may also be pursued by publicizing the names of organizations that adopted the innovation, whose endorsement contributes to an air of "legitimacy."Ideally, this would create a substantial positive word-of-mouth communication from adopters to potential adopters. Again, this variable is expected to be relevant in particular for a service product. Based on the foregoing, we formulate the following hypothesis: H3: The probability of innovation adoption by an organiza-

tion is higher if that organization has a positive perception of the supplier's reputation.

Innovation Development A large body of research on the development of innovations has investigated the determinants of success of new industrial products (see e.g., Cooper, 1983; Zirger and Maidique, 1990). Lilien and Yoon's (1989) synthesis of empirical research emphasizes the following four determinants. First, business strategic and organizational factors influence new product success, including the support and involvement of general managers, the fit between the innovation development project and other business activities, and the degree of interaction between R&D, manufacturing, and marketing. Second, R&D and production factors influence new product success. These include the superiority or uniqueness of the innovation, the level of experience and synergy in R&D and production, the degree of user benefit or economic advantage of the innovation, the role of the product champion, and patent protection. Third, marketing factors determine product performance. These include experience and efficiency in marketing and interaction with potential customers. Finally, market and environmental factors determine new product success. This involves the degree of competition in the market and the market size and growth rate. Summarizing, the probability of adoption of an innovation in the marketplace will depend on the degree to which the supplier has succeeded in developing an innovation that is unique and satisfies specific (latent) needs of potential adopters. Although this may seem obvious, it should be recognized that most innovations fail because of a lack of offering distinctive benefits to the potential adopter. So, the perceived customization of the innovation, (i.e., the extent to which the potential adopter perceives the innovation as adjusted to the specific needs of the organization) will influence the probability of adoption. This leads to the following hypothesis: H4: The more a potential adopter of an innovation per-

Adoption of a Service Innovation

165

J Busn Res 1998:41:161-174

Adopter Side

Supply Side

Perceived Innovation Characteristics • relative advantage ,,compatibility ,,complexity • obse rvabill~J ,trialability

-uncertainty Mm'keting Strategy ,positioning • risk reduction ,market support

• size Adopter ¢harecteHetice .age -structure .receptiveness Network participation •within industry • outside industry

Innovation Development *perceived customization

Competitive environment • intensity of competition • intensity of innovation

Information

r

adopter side model y

full model

Figure 1. Research model.

ceives the innovation to be customized by the supplier to the organization's needs, the more likely its adoption. The discussion in the previous two sections leads to the formulation of the research model as depicted in Figure 1. Next, the empirical test of the research hypotheses will be discussed.

Method Procedure Our study focused on the adoption of electronic banking in the Dutch business market. The first electronic banking system was introduced on the Dutch business market in 1985. Although our data gathering took place six years later (August 1991), the penetration rate of electronic banking in the business market was still less than 5% by 1991, although rapid growth was expected in the industry. Thus, electronic banking seemed to be shifting from the introduction phase to the growth phase of the product life cycle at the time of our study. The empirical research was divided into two steps. The first phase consisted of qualitative research in the Dutch banking sector. By conducting in-depth personal interviews with 11

representatives of five Dutch banking firms (including the four largest of the industry), it was investigated whether the variables incorporated in the research model were applicable to the sector of empirical research and whether relevant variables were left out. The results did not give cause for adjustments of the research model. The second phase of the empirical research concerns the test of both the adopter-side model and the full model. The research methodology will be discussed in this section. First, attention will be paid to the sampling procedure and the sample. Second, we will discuss the measurement of the var/ables. Finally, the methods of analysis will be addressed.

Sample The sample was drawn from the NIPO Business Monitor, a database of 20,000 organizations operating in The Netherlands, representative of the population of organizations with respect to the main variables of interest. Imposed restrictions were that organizations in the sample should have access to at least one personal computer, were no subsidiary (in order to assure independent decision-making opportunity), and did not have the Postbank as their primary banking firm, because

166

J Busn Res 1998:41:161-174

this bank had relatively few customers in the business market at the time of the study. The data collection was carried out by a professional marketing research agency by means of computer-aided telephone interviewing (CATI). Pre-tests were used to investigate whether the respondents had difficulties with the questionnaire. Some minor adjustments to the questionnaire were made on the basis of the results. In general the method proved to be working well and the technology turned out to enable an extended scope of telephone interviewing, in terms of number and complexity of questions asked and processed. Interviewers asked for the key decision maker in the field of financial services within the company (Gatignon and Robertson, 1989; Gauvin and Sinha, 1993). In 53% of the cases the interviewee was the CEO or owner of the organization. In other cases, the respondent was the administrator (32%), or held some other financial/economic position (15%). Considering the respondents' occupations and given that the majority of the respondents either were major decision makers themselves, or at least likely to be well acquainted with the decision process, it is reasonable to assume that the interviewees had an in-depth insight in the (non-) adoption decision of electronic banking by the organization and that these persons are pre-eminently capable of answering questions about the innovation adoption decision. In a comparative study on the use of single informants versus multiple informants, Wilson and Lilien (1992, p. 302) found that "it matters little who is chosen as the i n f o r m a n t . . , as long as the informant is reasonably knowledgeable about the buying process." Consequently, the use of key informants in the present study seems to be appropriate. Furthermore, Day and Herbig (1990) point out that although industrial buying decisions often involve group decisions, the use of one personal interview per organization can be justified by realizing that within the industrial purchase process each individual subverts much of his own preferences to the group agent. While adoption is to be seen as a process with different stages (Rogers, 1992), we focus for two reasons on the outcome of the process in the form of an implemented decision to adopt. First, adoption can be observed in a clear cut fashion, in contrast with preceding stages of awareness and evaluation. Second, it can be observed by interviewing only one person, in spite of the adoption by organizations being a group process. A single member of the group, if s/he has a position of sufficient authority in the group, will know of the outcome of the adoption process and the main considerations for it. A disproportionally stratified sample of 593 organizations was drawn. Stratification was based on the variables size (the following categories were distinguished: 1 to 19 employees, 20 to 99 employees, and 100 or more employees), industry (categories: manufacturing and construction, trade and hospitality, transport and repair, and professional services), and adoption (adoption versus nonadoption of electronic banking). This stratification scheme was deemed desirable, for random sampling would yield a too small number of adopters

R.T. Frambach et al.

of the innovation (due to the low penetration level), and to an overrepresentation of small organizations. Stratification on the dependent variable as applied in the present study is common in case control studies and biased results are not expected if the proportion of respondents belonging to one of the two categories of the dependent variable is reasonably close to one-half (see Ben-Akiva and Lerman, 1985). A sample of 259 respondents resulted, representing a nonresponse rate of 56%. From those who responded, 12 had never heard of electronic banking, leaving a sample of 247 organizations (101 adopters of electronic banking and 146 nonadopters of the innovation). To test for nonrepresentativeness of the sample due to nonresponse bias, a logit analysis was conducted with size, industry, and adoption as the independent variables and response as the dependent variable. The results showed that response among adopters of electronic banking was significantly higher (.05 level) than among nonadopters. Relatively more nonadopters indicated they were too occupied to answer any questions than adopters. This may be attributable to the higher level of involvement of adopters with the product. Because there were no significant differences between adopters and nonadopters for the other motives of refusal of cooperation with the study, it seems plausible to conclude that no specific reasons that may cause structural nonresponse bias in the results underly the differences found. Further, the analysis showed relatively high nonresponse rates in the industries transport/repair and trade/hospitality. At the time of the research these industries showed relatively low penetration rates of electronic banking, which may result in lower involvement with the product than in other industries. Consequently, the respondents may be more familiar with electronic banking than the persons who did not respond. Although this implies that we should be somewhat careful with the generalization of specific results on electronic banking to the population, it has no influence on the validity of the results concerning the determinants of the (non) adoption decision.

Control Variables Variables found to be determinants of the innovation adoption decision in previous adoption studies are incorporated in the present study as control variables. The influence of these variables on adoption was discussed in the second section.

MeasRrement Variables are measured at the level of the (non) adopter. Because it is hypothesized that adoption is influenced by the extent to which potential adopters are aware of marketing activities by the supply side, measurement of perceptions is preferred over measurement of actual supplier activities. Marketing strategy was operationalized by measurement of the extent to which the (potential) adopter was exposed to a marketing strategy aimed at positioning the innovation in the SUPPLIER VARIABLES.

Adoption of a Service Innovation

marketplace, reducing the risk of adoption, and/or winning market support by means of the supplier's reputation. Operationalizations were based on Easingwood and Beard (1989). With respect to the strategy of positioning the innovation in the marketplace, respondents were asked whether the supplier pre-announced the innovation, engaged in personal selling, offered direct mail or organized trade exhibitions. Also, the respondents were asked whether the supplier explicitly communicated either one or more of the innovation's product features, price level, possibility of integration with the potential adopter's financial reporting system, user friendliness, and the supplier's service organization. With respect to the strategy of risk reduction, respondents were asked whether a low introduction price or a trial period was offered by the supplier. The marketing strategy of winning market support was operationalized by measurement of the potential adopter's perception of the supplier's reputation with respect to the domestic market, the international market, and the market for small and medium sized firms. Respondents were also asked whether the supplier had pointed out other adopters of the innovation. Innovation development was operationalized by assessing the customers' perception of the degree to which the supplier of the innovation can satisfy the organization's needs. More specifically, the perceived customization of the innovation and the perceived expertise of the supplier in the domain of the innovation were assessed. Both were measured by means of 5-point Likert scales, based on Lilien and Yoon (1989) and Maidique and Zirger (1984). CONTROL VARIABLES. Operationalizations of perceived innova-

tion characteristics are widely available in the diffusion literature. Tomatzky and Klein (1982) provided an overview of the operationalizations used for these variables (also see Rogers, 1983). Perceived innovation characteristics are usually measured by Likert-type scales. Statements were selected and adjusted to the case of electronic banking, based on information on electronic banking from different sources (expert interviews, qualitative research in the financial sector, and publications on electronic banking, e.g., Reistad, 1991; Balakirsky, 1990). Adopter characteristics were operationalized according to prior studies. Size was measured directly using three different indicators (i.e., sales per year, number of white-collar workers, and average number of transactions with the bank per week) (Kimberley and Evanisko, 1981). Age was measured directly as well. Centralization and receptiveness were measured using 5-point Likert scales (Zaltman, Duncan, and Holbek, 1973; Robertson and Wind, 1980; Cohn and Turyn, 1984; Gatignon and Robertson, 1989). Operationalization of network participation within the industry is similar to the Likert-type statements used by Zaltman, Duncan, and Holbek (1973). Measurement of network participation outside the industry involved direct assessment of the number of interactions with other parties as well as the use of a Likert-type statement (Zaltman, Duncan, and Holbek, 1973). Operationalization of the competitive environment of the

J Busn Res 1998:41:161-174

167

adopter focused on the perceived competitiveness of the environment, because organizational behavior will be especially driven by the degree of competitiveness as perceived by the decision maker(s) instead of actual levels of competition (Gatignon and Robertson, 1989; Jaworski and Kohli, 1993). It is hypothesized that the more a potential adopter perceives the environment as competitive, the more likely adoption of innovations is in order to sustain or create competitive advantage (Stoneman, 1983, p. 95; Kennedy, 1983). Five statements were generated to measure the degree of competitiveness, based on Gatignon and Robertson (1989). Information processing activity was assessed by measurement of the number of meetings the (potential) adopter had with an employee of the supplier organization in which electronic banking was discussed, the number of brochures on electronic banking read by the potential adopter, and the number of exhibitions visited where electronic banking was demonstrated (Gatignon and Robertson, 1989). A questionnaire was composed using the operationalizations described above. The questionnaire was screened by a research director of a large marketing research agency for inconsistencies and unclarities, and routing. Because the questionnaire was computer-controlled, statements were randomized in order to avoid sequence effects and statements and questions were adapted to the respondent being an adopter or a nonadopter of electronic banking. The questionnaire was implemented in a CATI-system. Questionnaire items and descriptive statistics are available on request.

Analysis Both adopter-side model and full model were tested following the two-step approach recommended by Anderson and Gerbing (1988; see also Butt, 1976). First, the measurement model was estimated. For this purpose confirmatory factor analysis is appropriate (Aaker and Bagozzi, 1979). Based on the a priori specified measurement model, this method estimates the extent to which the indicator variables (measurements) are related to the underlying constructs (the latent variables or factors). LISREI_ (J~3reskog and S~3rbom, 1989) is a widely used program to carry out the necessary computations and is able to generate estimates of the scores of respondents on the latent variables, the factor scores (Steenkamp and van Trijp, 1991). For each of the main variables (marketing strategy, innovation development, perceived innovation characteristics, adopter characteristics, network participation, competitive emqronment, and information) a confirmatory factor analytical model was estimated. Using PRELIS (Joreskog and Sorbom, 1988) a correlation matrix was computed for each of the variables. The factor analytic models were estimated using a maximum likelihood procedure. Based on the estimation of the constructs, factor scores for each latent variable for each organization were computed. Second, the estimated factor scores were used to test the relationships in the models. The objective is to assess which variables have a significant influence on the binary (adoption

168

J Busn Res 1998:41:161-174

or nonadoption) dependent variable. For this purpose binomial logit analysis was used (Gatignon and Robertson, 1989; O'Catlaghan, Kaufmann, and Kosynski, 1992). The logit model was estimated using the method of maximum likelihood (see e.g., Cramer, 1991, p. 18; Malhotra, 1984, p. 22). The independent variables are represented by the respondent scores on the latent variables. Multicollinearity between the independent variables was investigated by examining both the correlations between the latent variables and the tolerance levels of the latent variables (see Norugis, 1988). Tolerance levels were computed by estimation of multiple regression equations with successively each one of the latent variables as dependent variable (thus multiple tolerance levels were obtained for each latent variable). The results showed no indications of multicollinearity, except for the latent variables perceived compatibility of the innovation and perceived expertise of the supplier. Therefore, these variables were eliminated from the analysis. Also, the latent variables perceived observability and perceived trialability had to be eliminated due to highpercentages of missing values. To compare the full model with the adopter-side model, we estimate two logit models. In the first model, only the adopterside variables commonly used in diffusion research are included. In the second full model, all the variables identified in the second and third sections of this article are included. In addition to testing the influence of both supply-side and adopter-side variables on adoption, we investigated whether interactions between these variables significantly influenced the adoption decision. Specifically, we expected the interaction terms between marketing strategy and perceived innovation characteristics, and between innovation development and perceived innovation characteristics to be significant. In other words, marketing activities may at least to some extent operate through, rather than beside, perceived innovation Characteristics. However, no significant effects were found, so that the final model specification in terms of separate effects is justified.

R.T. Frambach et al.

Table 1. Construct Reliabilities and Goodness of Fit of Measure-

ment Models Measurement Model/ Construct

Goodness of Fit" Reliability AGFI RMR (%)

Marketing Strategy Positioning Risk reduction Market support Informationb

0.90 0.74 0.68 0.59

0.652

Innovation Development Perceived customization Expertise supplieff

0.80 0.70

Perceived Innovation Characteristics Relative advantage Complexity Compatibilitya Uncertainty

0.73 0.48 0.60 0.60

Adopter Characteristics Size Centralization Age~ Receptiveness

0.86 0.62 0.32 0.69

Network Participation Within industry Outside industry

0.57 0.56

Competitive Environment Intensity of competition Intensity of innovative activities

0.69 0.76

13

0.870

5.9

0.813

7.8

0.878

6.8

0.901

6.1

0.989

1.6

The adjusted goodness of fit index (AGFI) is a measure of the percentage of total variance explained by the model. The closer the measure is to 1, the better the fit of the model. The root mean square residual (RMR) relates to the percentage of residual variance not explained by the model. The smaller the RMR, the better the fit. Chi-square is another generally used goodness of fit measure; however, this measure is not c o r r e c t if correlation matrices are used in the confirmatory factor analyses (Joreskog and Sorbom, 1989), as is the case in the present study. Therefore, this indicator is not reported. This latent variable was estimated in one measurement model together with the marketing strategy variables. This variable was eliminated in subsequent analyses due to its high correlation with "perceived customization" (0.72). d This variable was eliminated in subsequent analyses due to muhicollinearity. This variable was eliminated in subsequent analyses due to its very low reliability.

Results The goodness of fit indicators of the measurement models as well as the reliability measures of the estimated constructs are shown in Table 1 (construct reliabilities are computed according to Hair, Anderson, Tatham, and Black, 1992). Based on the construct reliabilities and goodness of fit indices, the operationalization of most constructs seems satisfactory, although the reliabilities of perceived complexity, age, network participation, and information are below 0.6, which is somewhat low. The two indicators of complexity focused on the required knowledge to use the innovation and the perceived ease of use of the innovation, respectively. Because the former relates to a large extent to the decision-making unit considering adoption, while the latter relates directly to the innovation itself, combining these two indicators may have resulted in the low reliability of the construct. However, taking into account that the low reliability may have a negative influence on the significance

of the construct in subsequent analysis, and considering that perceived complexity has received only very limited attention in previous empirical research, the variable was not dropped. Age shows a very low reliability and is therefore eliminated. All other variables hypothesized to influence innovation adoption are included in the logit analyses in order to maintain the confirmatory character of the present study (Joreskog and Sorbom, 1989). The results of the binomial logit analyses are reported in Table 2. The main indicator of the influence of an independent variable on adoption is the level of significance (based on the Wald statistic). The effect of a variable is considered to be significant if the significance level does not exceed .05. The goodness of fit of both logit models are satisfactory. Based on the significance level of the model chi-square (p < .001), it can be concluded that not all coefficients in the models

Adoption of a Service Innovation

J Busn Res 1998:41:161-174

169

Table 2. Results Estimation Binomial Logit Models (n = 247) Variable

13

Adopter-Side Model SE

p

13

SE

p

Intercept

- 6.39

1.6437

0.000

-5.20

2.1769

0.017

1.81 2.16 0.11

0.8270 0.6927 0.8543

0.029 0.002 0.901

-0.39

0.3028

0.199

Full Model

Marketing Strategy Positioning Risk reduction Market support Innovation Development Perceived customization Perceived Innovation Characteristics Relative advantage Complexity Uncertainty

0.75 - 1.44 0.23

0.2042 0.3280 0.2352

0.000 0.000 0.333

0.85 - 1.62 0.20

0.2266 0.3676 0.2581

0.000 0.000 0.438

Adopter Characteristics Size Centralization Receptiveness

0.29 - 0.20 -0.13

0.1239 0.1917 0.2403

0.018 0.308 0.604

0.23 -0.27 -0.30

0.1447 0.2106 0.2808

0.108 0.201 0.286

Network Participation Within industry Outside industry

0.27 -0.29

0.2652 0.4101

0.315 0.486

0.18 -0.48

0.2892 0.4800

0.532 0.319

0.17 0.13

0.1968 O.1410

0.396 0.368

0.25 0.13

0.2178 0.1595

0.252 0.428

-0.82 X2 114.42

0.4832 df 15

0.090 p 0.000 76.9% 219.75

97.76

4

0.000 74.9% 236.41

Competitive Environment Intensity of competition Intensity of innovation Information Goodness of Fit Model chi-square % correct classification - 2 log likelihood Goodness of Fit Trimmed Model (significant effects only) model chi-square % correct classification - 2 log likelihood

X2 76.28

df 10

p 0.000 71.7% 257.89

70.98

"3

0.000 70.8% 263.19

(except for the intercept) equal 0. The percentage of correctly classified observations is 71.7 for the adopter-side logit model and 76.9 for the full logit model. The percentage of correct classification in case of a random model is oL2 + (1-002, where ot represents the a priori probability of adoption (Morrison, 1969). c~ is estimated on the basis of the observed percentage of adopters (Rao and McLaughlin, 1989, p. 85). Both percentages of correct classification found in the present study are satisfactory, as there is an improvement of information of 20% for the adopter-side model and 25.2% for the full model as compared to the random model (that shows a correct classification percentage of 51.7). To test whether the full model, including supplier variables, provides a significant improvement in explanatory power over the adopter-side model, the model chisquares were compared. Because the improvement in chi-square (114.42-76.28 with 1 5 - 1 0 dt) is significant with p < .01, it can be concluded that the full model performs better. As both the adopter-side model and the full model show

relatively large numbers of insignificant variables, both models were also estimated on the independent variables that were significant only in order to investigate the fit of the trimmed models and to identify a possible improvement over the original theory-based versions. It is found that, as was the case with the theory-based versions, the trimmed version of the full model performs better than the adopter-side model (the additional supplier variables provide here significant improvement [p < 0.01] of explanatory power as well). Because there is no significant loss of explanatory power with the fewer variables, the trimmed version of the models can be accepted. However, it should be noted that the other variables in the theory-based model may indeed influence adoption decisions significantly in other contexts than the one studied here.

Adopter-Side Model The results of the adopter-side model show that the variables most commonly assessed in diffusion research to influence inno-

170

J Busn Res 1998:41:161-174

vation adoption behavior in the industrial context (i.e., perceived innovation characteristics and size ) (Rogers, 1983), are also significant in this study. More specifically, the perceived relative advantage of the innovation and the firm size of the (potential) adopter have a significantly positive effect on innovation adoption. The perceived complexity of the innovation, operationalized as the degree to which the product is perceived as difficult to handle, has a significantly negative effect on adoption. This result is noteworthy, because Rogers (1983, p. 231) concludes that empirical support for this relation is not very extensive. We do not find significant effects of the perceived uncertainty of the innovation, adopter characteristics other than size, network participation, and the competitive environment of the adopter. Perceived uncertainty was operationalized as the extent to which potential adopters were unsecure about the functioning of the innovation within the organization. However, the results indicate that almost 70% of both adopters and nonadopters of electronic banking have no doubts about the implementation of electronic banking, explaining the insignificance of this variable in the present study. The finding that other adopter characteristics than size had no significant influence on adoption is in line with findings of previous research in the businessto-business context, showing ambiguous results for adopter characteristics other than size (Zaltman et al., 1973; Rogers, 1983; Kennedy, 1983). This may be explained by the fact that these characteristics, such as the degree of centralization can both have a stimulating and an inhibiting effect on the adoption process. It was surprising that network participation had no significant influence on adoption. An explanation could be that the most important function of networking (i.e., reducing the uncertainty concerning the functioning of the innovation within the organization) was not relevant due to the lack of perceived uncertainty among potential adopters. Also, given the early stage of the diffusion process electronic banking was in at the time of the study, even organizations that participate in networks extensively did not have the opportunity to come into contact with users of electronic banking frequently. Finally, the influence of the competitive environment also had no significant influence on adoption. Although in general it can be expected that competitive pressures influence the adoption of innovations, it may be argued that electronic banking does not have any significant direct effect on the competitiveness of a firm, thus explaining the fact that the (potential) adopter's competitive environment has no influence on the adoption of this particular innovation. However, for the other innovations this may be well the case.

Full Model The full model represents an extension of the adopter-side model with variables related to the supplier of the innovation. It was hypothesized that both the marketing activities undertaken by the supply side and the innovation development process have a significant influence on innovation adoption by

R.T. Frambach et al.

organizations, in particular in the case of an intangible, service product. The results of the estimated model show that the marketing strategy pursued by the supplier of the innovation has indeed a significant and positive influence on adoption. The extent to which a supplier has implemented a strategy aimed at positioning the innovation in the marketplace or has been committed to reducing the risk of adoption of the innovation has a positive and significant effect on the probability of innovation adoption. Therefore, Hypotheses 1 and 2 are corroborated. This means that marketing strategy, and therefore the supplier of an innovation, can exert a significant influence on the adoption decision (Sultan, Farley, and Lehmann, 1990), particularly for a service product. Thus one of the central premises of this article that the supply side plays an important role in the adoption process of an innovation and therefore should be incorporated in the adoption model explicitly, is supported. No support is found for Hypothesis 3 that the supplier's reputation positively influences the probability of adoption. Note that the effect of adopter size is no longer significant in the full model, and that the coefficients of both perceived relative advantage and complexity are higher. Table 3 shows the results of t-tests for equality of means of the marketing activities distinguished for each one of the three marketing strategies tested for the adopters of electronic banking versus nonadopters. The results in Table 3 reflect the significant influence of marketing strategies aimed at positioning the innovation in the marketplace and reduction of adoption risks on the adoption decision. With respect to the former, the extent to which adopters have been confronted with personal selling and communication of innovation characteristics by the supplier is significantly higher than for nonadopters. This may be a result of the supplier segmenting the potential adopter market and targeting the potentially most successful market segments (based on characteristics such as innovativeness, personal contacts, etc.). In our qualitative research, some banks indicated that they focused first on what they perceived to be "hot prospects," thus implementing the marketing strategy of "positioning the innovation in the marketplace" that proved to be successful according to the results of our quantitative study. Also, adopters have been offered low introduction prices and trial periods to a larger extent than nonadopters, reflecting the significant influence of the marketing strategy of risk reduction. Supplier's reputation does not seem to play an important role in the adoption of electronic banking. However, the adoption decision seems to be influenced positively by the extent to which the supplier has made the organization aware of adopters of the innovation. The variable related to the innovation development process (i.e., perceived customization of the innovation) is not found to influence the innovation adoption decision significantly. Consequently, Hypothesis 4 is not confirmed. Considering the research findings in innovation management, this result was not expected (Lilien and Yoon, 1989). Taking into account that previous research indicates the relevance of the influence of

Adoption of a Service Innovation

J Busn Res 1998:41 :I 6 I - I 74

Table 3. t-tests for Equality of Means of Marketing Activities as

Experienced by Adopters and Nonadopters

Marketing Strategy Positioning the Innovation in the Marketplace Pre-announcement Personal selling Direct mail Trade exhibitions Communication of product features Communication of price level Communication of integration possibilities Communication of user friendliness Communication of service organization Reduction of Risk of Adoption Low introduction price Trial period Winning Market Support Reputation as market leader Reputation as international supplier Reputation as small/ medium sized firm specialist Make non adopters aware of adopters *p < .05 **p < 0 1 ' M i n i m u m value =

O,

Meansa Adopters Nonadopters Significance

171

tion first to those organizations that are expected to adopt the innovation first and have the largest potential. This supports the idea that size can function as a proxy or catch-all variable for other explanatory variables in diffusion research. We con= clude from this that neglecting potential powerful determinants of innovation adoption in the adoption model may lead to specification error, which may result in erroneous interpretation of empirical results. The approach used in this study, that investigated the effects of supplier variables on adoption in addition to the effects of adopter variables commonly studied in diffusion research, is supported.

0.539 0.750 0.345 0.411

0.646 0.500 0.515 0.328

0.117 0.000"* 0.013" 0.204

0.750

0.521

0.000"*

0.895

0.472

0.000"*

Conclusions and Limitations

0.790

0.564

0.000"*

0.835

0.607

0.000"*

0.755

0.442

0.000"*

0.415 0.444

0.133 0.188

0.000"* 0.000" *

0.231

0.135

0.093

0.181

0.126

0.346

0.312

0.208

0.116

0.351

0.199

0.009**

This study examined the influence of supplier variables on organizational adoption of innovations by comparing a model incorporating only adopter-side variables with a model that includes both supply-side and adopter-side variables. Both models were tested on the adoption of electronic banking in the business market in The Netherlands. The central premise of this study, that supply-side factors as perceived by adopters are important determinants of innovation adoption in addition to adopter-side variables, in particular for a service product, is supported by the empirical results. It was found that probability of adoption was positively influenced by the degree to which the supplier of the innovation pursued a marketing strategy aimed at positioning the product in the marketplace by explicitly communicating the innovation's distinctive properties or focused on reducing the risk of adoption for potential adopters by offering a low introduction price or a free trial period. This was expected in particular for a service product, which is relatively difficult to judge by potential adopters, and requires more support especially in the form of communication. Also, as expected, adoption was found to be positively influenced by the perceived relative advantage of the innovation, whereas its perceived complexity showed a negative effect on adoption. The results of the present study support the plea of diffusion researchers (Brown, 1981; Rogers, 1983; Gatignon and Robertson, 1991) that supplier variables could be major determinants of innovation adoption and, therefore, should be studied in more depth. Also some limitations of this study have to be noted. First, the adoption of only one specific service innovation was examined. The validity of the formulated model of organizational innovation adoption should be investigated further by replicating the study for innovations other than the one chosen here. Second, we interviewed only one key decision maker within each responding organization concerning the outcome of the adoption process. More insight in the adoption process in the business market may be obtained by considering different stages preceding decisions in the adoption process as a group process and studying the effect of personal interactions within the buying center on adoption probability. By considering the determinants of each one of the different stages within the adoption

m a x i m u m value -

I.

innovation development on the adoption process, the finding of this study may be attributable to the innovation chosen for this study. Specifically, it may be due to the fact that electronic banking applies to supporting activities rather than the primary process of an organization. As a result, involvement of organizations regarding this innovation may be relatively low. Additional research should reveal whether effects are product specific. The results on the influence of adopter-side variables on adoption show some interesting differences between the adopter-side model and the model proposed in this study. Perceived innovation characteristics are also found to have a significant effect on adoption in the full model, but their effects are underestimated in the adopter-side model by over 10%. Most important, the firm size of the (potential) adopter does not significantly influence adoption in the full model. Apparently, the effect of size of the adopter is confounded with that of supply-side marketing strategy. Large organizations were approached more often by suppliers than smaller ones. This reflects the marketing strategy of suppliers to devote their atten-

17).

J Busn Res 1998:41:161-174

process separately instead of focusing on the adoption/nonadoption decision per se, marketing activities may be formulated according to the typical characteristics of these different stages and thus may be allocated more effectively. This is in line with the recognition in the marketing literature that the focus of marketing effort is shifting from transaction-based activities (such as the adoption decision) to relationship-based activities (related to buyer/seller interactions during the different stages of the adoption process) (Webster, 1992). Third, the results of this study showed that the influence of firm size on adoption may be dependent on the other variables considered. In this respect, it has to be pointed out that the significance of supplier variables may to some extent be attributable to suppliers targeting potentially most interesting customers in addition to adoption being a function of supplier activity. Also, interactive effects between adopter-side variables and supply-side variables may exist (e.g., marketing strategy and perceived innovation characteristics), but these were found not to be significant in the present study. Finally, adoption was measured as a dichotomous decision. In order to gain more insight in innovation adoption behavior in future studies, one could focus on aspects like the time of adoption, the extent of adoption (Van Everdingen, 1995), and nonadoption of innovations (Gatignon and Robertson, 1989; Stevens, Warren, and Martin, 1989).

Implications The aim of the present study was to contribute to both theory and managerial decision making on innovation adoption and diffusion by demonstrating that in addition to the adopter-side factors commonly assessed in adoption and diffusion research, supply-side factors are important determinants of innovation adoption as well. First, it was found that marketing strategy was one of the determinants of innovation adoption. Adoption probability was positively influenced by the strategies of positioning the innovation and reducing the risk of adoption. Next to these marketing strategies, others may significantly influence adoption as well. In this respect, the impact of different launch strategies may be evaluated by assessing their influence on adoption behavior, thus contributing to the more recent work within marketing on identifying successful new product launch strategies (see e.g., Hultink and Schoormans, 1995; Beard and Easingwood, 1996). Second, pre-diffusion activities undertaken by suppliers, such as innovation development activities, were identified as potentially important determinants of adoption (Rogers, 1983). However, the influence of such activities is still not very clear, because the present study found no significant effect of the degree of perceived customization of the innovation on the adoption decision. The study has made it clear that future research should focus on the methods that can be used most effectively to investigate the relationship between new product development activities and the probability of innovation adoption in the marketplace. Although a lot of research

R.T. Frambach et al.

has been done on identifying factors that determine the success and failure of new products (see e.g., Cooper and Kleinschmidt, 1987), the direct influence of innovation development activities and processes on adoption remains under-researched. Based on the findings presented here for a service product, suppliers should develop an integrative perspective of industrial innovation adoption in order not to overlook important determinants of innovation diffusion, thus improving the marketing performance of new products. Suppliers focusing either on adopter variables or supplier variables, are likely to achieve suboptimal results. This is supported by the different outcomes of the models with and without supplier variables. Specifically, the effect of firm size in the adopter-side model appeared to be. caused by supplier variables, while the effects of perceived innovation characteristics were underestimated. The lack of an effect of firm size in the full model confirms suspicions that this variable may act as a catch-all variable of omitted variables. Our findings suggest that suppliers of electronic banking (and potentially other innovations) should focus on clearly positioning their innovation in the marketplace by making potential adopters more familiar with the new product and by facilitating early adoption. Potential barriers of adoption should be overcome by the supply side. Effective tools in the present study proved to be offering the potential adopter a free trial period of the innovation or a low introduction price. Suppliers should also be aware of the necessity of explicitly communicating the relative advantages of the innovation and reducing the perceived complexity of the new product. Suppliers may easily think that potential adopters are well aware of the relative advantages of an innovation and its ease of use, although reality may prove different. In the present study, for example, several banks indicated that they believed potential adopters of electronic banking did not perceive the innovation as complex, although the empirical study showed perceived complexity to have a significant negative effect on adoption. Finally, the results of this study indicate that nonadoption to a large extent may be attributable to the supplier marketing the new product instead of the organization considering adoption (referring to the "individual blame bias" within adoption and diffusion research). This being the case, management should be well aware of the group of nonadopters and should take appropriate action in communicating with this segment. The authors acknowledge the financial support of N1PO, Rabobank and Digital. The valuable comments of two reviewers are very much appreciated.

References Aaker, David A., and Bagozzi, Richard P.: Unobservable Variables in Structural Equation Modelwith an Applicationin IndustrialSelling. Journal of Marketing Research 16 (May 1979): 147-158. Anderson, James C., and Gerbing, David W.: Structural Equation Modeling in Practice: A Reviewand RecommendedTwo-Step Approach. Psychological Bulletin 103 (1988): 411-423. Balakirsky, Ph. J. R.: Electronic Data Interchange: A Challenge to

Adoption of a Service Innovation

Bankers /in Dutch]. Bank- en Effectenbedrijf (December 1990): 34-37. Baldwin, William L., and Scott, John T.: Market Structure and Technological Change, Harwood Academic Publishers, Chur, Switzerland. 1987. Bass, Frank M.: A New Product Growth Model for Consumer Durables. Management Science 15 (January 1969): 215-227. Bateson, John E. G.: ManagingServices Marketing, 2nd ed., The Dryden Press, Fort Worth. 1992. Beard, Charles, and Easingwood, Chris: New Product Launch; Marketing Action and Launch Tactics for High-Technology Products. Industrial Marketing Management 25 (March 1996): 87-103. Ben-Akiva, Moshe E., and Lerman, Steven R.: Discrete Choice Analysis, MIT Press, Cambridge, MA. 1985. Brown, Lawrence A.: Innovation Diffusion, Methuen & Co., London/ New York. 1981. Burr, R. S.: Interpretational Confounding of Unobserved Variables in Structural Equation Models. Sociolo~cal Methods & Research 5 (January 1976): 3-52. Chisnall, Peter M.: Strategic Industrial Marketing, Prentice Hall, Englewood Cliffs, NJ. 1989. Clark, Peter, and Staunton, Neih Innovationin Technologyand Organization, Routledge, London/New York. 1989. Cohn, Steven F., and Turyn, Romaine M: Organizational Structure, Decision-Making Procedures, and the Adoption of Innovations. IEEE Transactions on En~neering Management 31 (November 1984): 154-161. Cooper, Robert G.: A Process Model for Industrial New Product Development. IEEE Transactions on En~neering Management 30 (February 1983): 2-11. Cooper, Robert G., and Kleinschmidt, E. J.: New Products: What Separates Winners from Losers?Journal of Product Innovation Management 4 (March 1987): 169-184. Cramer, J. S.: The LOGIT Model: An Introductionfor Economists, Edward Arnold, London. 1991. Day, Ralph L., and Herbig, Paul A.: How the Diffusion of Industrial Innovations is Different from New Retail Products. Industrial Marketing Management 19 (1990): 261-266. De Brentani, Ulrike: New Industrial Service Development. Journal of Business Research 32 (February 1995): 93-103. Easingwood, Chris, and Beard, Charles: High Technology Launch Strategies in the U.K. Industrial Marketing Management 18 (1989): 125-138. Frambach, Ruud T.: An Integrated Model of Organizational Adoption and Diffusion of Innovations. European Journal of Marketing 27 (May 1993): 22-41. Gatignon, Hubert, and Robertson, Thomas S.: A Propositional Inventory for New Diffusion Research. Journal of Consumer Research 11 (March 1985): 849-867. Gatignon, Hubert, and Robertson, Thomas S.: Technology Diffusion: An Empirical Test of Competitive Effects. Journal of Marketing 53 (January 1989): 35-49. Gatignon, Hubert, and Robertson, Thomas S.: Innovative Decision Processes, in Handbook of Consumer Behavior, Thomas S. Robertson and Harold H. Kassarjian, eds., Prentice Hall International, Englewood Cliffs, NJ. 1991. Gauvin, Stephane, and Sinha, Rajiv K.: Innovativeness in Industrial Organizations: A Two-stage Model of Adoption. InternationalJournal of Research in Marketing 10 (February 1993): 165-183.

J Bush Res 1998:41 : 161-174

173

Hair, J. F., Anderson, R. E., Tatham, R. L., and Black. W. C.: Multivariate Data Analysis, MacMillan Publishing Company, New York. 1992. Hultink, E. J., and Schoormans, J. P. L.: How to Launch a High-tech Product Successfully: An Analysis of Marketing Managers' Strategy Choices. Journal of High-Technology Management Research 2 (1995). Jaworski, Bernard J., and Kohli, Ajay K.: Market Orientation: Antecedents and Consequences.Journal of Marketing 57 (July 1993): 53-70. JOreskog, Karl G., and Sorbom, Dag: PRELIS,A Programfor Multivariate Data Screening and Data Summarization, 2nd ed., University of Uppsala, Uppsala. 1988. Joreskog, Karl G., and S6rbom, Dag: LISREL 7 User's Reference Guide, ]st ed., Scientific Software Inc., Mooresville. 1989. Kamien, Morton I., and Schwartz, Nancy L.: Market Structure and Innovation, Cambridge University Press, Cambridge. 1982. Kennedy, Anita M.: The Adoption and Diffusion of New Industrial Products: A Literature Review. European Journal of Marketing 17 (1983): 31-88. Kimberley, John R., and Evanisko, Michael J.: Organizational Innovation: The Influence of Individual, Organizational, and Contextual Factors on Hospital Adoption of Technological and Administrative Innovations. Academy of ManagementJournal 24 (April 1981): 689713. Kotler, Philip: Marketing Management, Analysis, Planning, Implementation, and Control, Prentice Hall, Inc. Englewood Cliffs, NJ. 1991. Lancaster, G. A., and Taylor, C. T.: A Study of Diffusion of Innovations in Respect of the High Speed Train. EuropeanJournal of Marketing (March 1988): 21-47. Lilien, Gary L., and Yoon, Eunsang: Determinants of New Industrial Product Performance: A Strategic Reexamination of the Empirical Literature." IEEE Transactions on Enffmeering Management 36 (February 1989): 3-10. Lilien, Gary L., Kotler, Philip, and Moorthy, K. S.: Marketing Models, Prentice Hall, Inc., Englewood Cliffs, NJ. 1992. Mahajan, Vijay, Muller, Eitan, and Bass, Frank M.: New Product Diffusion Models in Marketing: A Review and Directions for Research. Journal of Marketing 54 (January 1990): 1-26. Maidique, Modesto A., and Zirger, Billie Jo: A Study of Success and Failure in Product Innovation: The Case of the U.S. Electronics Industry. IEEE Transactions on En~neering Management 31 (November 1984): 192-203. Malhotra, Naresh K.: The Use of Linear Logit Models in Marketing Research. Journal of Marketing Research 21 (February 1984): 20-31. Mansfield, Edwin: Technical Change and the Rate of Imitation. Econometrica 29 (October 1961): 741-766. Mansfield, Edwin: The Economicsof Technical Change, Longmans, Green and Co Ltd., London. 1968. Morrison, Donald G.: On the Interpretation of Discriminant Analysis. Journal of Marketing Research, 6 (May 1969): 156-163. Nooteboom, Bart: Diffusion, Uncertainty and Firm Size. International Journal of Research in Marketing 6 (1989): 109-128. Noru~is, Marija J.: SPSS/PC+ Advanced Statistics V2.0, SPSS, Inc., Chicago, IL. 1988. O'Callaghan, Ramon, Kaufmann, Patrick J., and Konsynski, Benn R.: Adoption Correlates and Share Effects of Electronic Data Interchange Systems in Marketing Channels. Journal of Marketing 56 (April 1992): 45-56. Onkvisit, Sak, and Shaw, John J.: The Diffusion of Innovations Theory: Some Research Questions and Ideas. Akron Business and Economic Review 20 (Spring 1989): 46-55.

174

J Busn Res 1998:41:161-174

Rao, Vithala R., and McLaughlin, Edward W.: Modeling the Decision to Add New Products by Channel Intermediaries. Journal of Marketing 53 (January 1989): 80-88. Reistad, Dale L.: The Outlook for Emerging Technologies. The Bankers Magazine (January/February 1991): 62-66. Robertson, Thomas S., and Gatignon, Hubert: Competitive Effects on Technology Diffusion, Journal of Marketing 50 (July 1986): 1-12. Robertson, Thomas S., and Wind, Yoram: Organizational Psychographics and Innovativeness. Journal of Consumer Research 7 (June 1980): 24-31. Robinson, William T.: Product Innovation and Start-Up Business Market Share Performance. Management Science 36 (October 1990): 1279-1289. Rogers, Everett M.: Diffusion of Innovations, 3rd. ed., The Free Press, New York. 1983. Rogers, Everett M.: The Diffusion of Innovations Model. Paper presented at NATO Advanced Research Workshop on Modeling the Use and Diffusion of Geographic Information Technologies, Sounion, Greece, April 8-11. 1992. Shanklin, William L., and Ryans, John K., Jr.: Marketing High Technology, Lexington Books, Lexington, MA. Lexington Books, 1984. Shostack, G. Lynn: Breaking Free from Product Marketing. Journal of Marketing 41 (April 1977): 73-80. Sinha, Rajiv K., and Chandrashekaran, Murali: A Split Hazard Model for Analyzing the Diffusion of Innovations. Journal of Marketing Research 29 (February 1992): 116-127. Steenkamp, Jan-Benedict E. M., and van Trijp, Hans C. M.: The Use of LISRELin Validating Marketing Constructs. InternationalJournal of Research in Marketing 8 (1991): 283-299.

R.T. Frambach et al.

Stevens, Robert E., Warren, William E., and Martin, Rinne T.: Nonadopters Of Automatic Teller Machines. Akron Businessand Economic Review 20 (March 1989): 55-63. Stoneman, Paul: The Economic Analysis of Technologcal Change, Oxford University Press, Oxford. 1983. Stoneman, Paul, and Ireland, N.: The Role of Supply Factors in the Diffusion of New Process Technology. EconomicJournal 93 (1983). Sultan, Fareena, Farley, John U., and Lehmann, Donald R.: A MetaAnalysis of Applications of Diffusion Models. Journal of Marketing Research, 27 (February 1990): 70-77. Tomatzky, Louis G., and Klein, Katherine J.: Innovation Characteristics and Innovation Adoption Implementation: A Meta-Analysisof Findings. IEEE Transactions on Engineering Management 29 (February 1982): 28-45. Van Everdingen, Yvonne M.: Adoption and Diffusion of the European Currency Unit. An EmpiricalStudy among European Companies, Thesis Publishers, Amsterdam. 1995. Webster, Frederick E.: New Product Adoption in Industrial Markets: A Framework for Analysis. Journal of Marketing 33 (July 1969): 34-39. Webster, Frederick E.: The Changing Role of Marketing in the Corporation. Journal of Marketing 56 (October 1992): 1-17. Wilson, Elizabeth M., and Lilien, Gary L.: Using Single Informants to Study Group Choice: An Examination of Research Practice in Organizational Buying. Marketing Letters 3 (1992): 297-305. Zaltman, G., Duncan, R., and Holbek, J.: Innovations and Organizations, Wiley, New York. 1973. Zirger, Billie Jo, and Maidique, Modesto A.: A Model of New Product Development: An Empirical Test. Management Science 36 (July 1990): 867-883.