The influence of communication source and mode on consumer adoption of technological innovations.
Technology is "a form of human activity that applies the principles of science and mechanics to the solution of problems," to the enhancement of performance, and the creation of competitive advantage (Bush 1981, p. 1). In today's world, technological advancements seem to occur constantly as companies introduce innovative products and services into the marketplace at an ever-increasing speed. During this era of accelerating technological advancement, product life cycles for many high tech innovations are rapidly decreasing. Facing the challenge of what may seem to be a bombardment of technological products and services, coupled with the rapid pace of change occurring in the technology industry, many consumers today seem to be overwhelmed with technological innovation (Cohen 1999; Hirschman 1987; Miles 2000). Without diffusion agents vigorously communicating useful information, these consumers are not always able to recognize the full advantages of these technological innovations (Campbell 1999; Nuttall 1998). As Allen (1971) rightly put it, technology is also "consumer information" (p. 2), the flow of which must be planned and managed through appropriate communication processes in order to facilitate the adoption by potential customers.
Rogers' Diffusion of Innovations Theory (1995) provides some conceptual guidance for understanding the adoption of products and services, and how technological innovation is proliferated across the consuming public (Gatignon and Robertson 1985). To date, the bulk of the diffusion studies in the consumer behavior literature have tilted heavily toward either individual predisposition (Hirschman 1980; Midgley and Dowling 1978) or innovation characteristics (Rogers 1995), as precursors of innovation adoption. It is important to note that communication is also a critical process factor for the diffusion of innovation (Mahajan, Muller, and Bass 1990). Yet, to date, consumer researchers have largely neglected the empirical study of communication in the diffusion process. Only a handful of studies have considered the notion that various types of communication can influence consumers' adoption of innovation (Mahajan, Muller, and Bass 1990; Mahajan, Muller, and Srivastava 1990).
This paper considers the diffusion of technological innovation as it travels through multiple communication sources employing various communication modes. In this study, "sources" including mass media (i.e., one-way communication from corporations or government to consumers) and word of mouth (i.e., communications with family and friends), are coupled with one or both conversation and/or print "modes." While previous studies have not differentiated the effects of written and conversational communication, this study explicitly recognizes and investigates the differential impacts of written and conversational modalities. An empirical study is presented examining the perceived usefulness of these varied strategic communication combinations in the diffusion and adoption of technology using the 1999 University of Michigan's Survey of Consumers data.
REVIEW OF LITERATURE
The communication literature has identified the various paths that information travels as it crosses different population segments within a social system. The two-step flow model of communication, for example, posits that information flows from mass media (e.g., radio and print) to opinion leaders (innovators), and that the less active members of the society (imitators) are subsequently influenced by communication with these innovative consumers (Katz and Lazarsfeld 1955; Katz 1957). Opinion leaders have been found to be highly exposed to formal communication media including marketer- and independent third party-provided information. On the other hand, interpersonal communication was found to mediate the flow of information from mass media to the less active segments of the population (imitators).
Critics of the two-step flow model have asserted that mass media can have a direct impact on both innovators and imitators (i.e., hypodermic theory of mass media effects) or that the communication process can involve more than two (multiple) steps under certain circumstances (Mattelart, Mattelart, and Taponier 1998). In spite of continuing criticisms, the two-step flow model of communication has provided the conceptual foundation for the Bass model of diffusion in marketing. The Bass model (1969) assumes two means of communication that affect adoption decisions: mass media and word of mouth. Innovators are influenced only by the mass media communication, or external influences, whereas imitators are typically influenced only by word-of-mouth communication (Mahajan, Muller, and Bass 1990, p. 2).
Communication Sources and Modality
Communication Sources. The diffusion of innovation literature suggests that information about technological innovations can travel through a variety of communication sources and modes to members of a social system (Rogers 1995). Extant literature on consumer information search helps to identify the types of consumer communication sources that might be related to the adoption of technological innovations. Although a variety of external information sources have been introduced in the consumer behavior literature, the three major societal sources of information have been identified as marketer-provided (e.g., corporation, industry), independent third-party (e.g., government, independent agencies), and interpersonal (e.g., family, friends) (Lee and Hogarth 2000, 2001; Newman and Staelin 1972; Mazis et al. 1981).
While information about technological innovations may come from a variety of institutional sources, consumers use the provided information selectively. The preferred information source may differ across individual consumers (Beales et al. 1981; Midgley and Dowling 1993; Thorelli 1971), and individuals may have different propensities for relying on marketer-provided information, independent third-party information, and information from personal sources (Bearden et al. 1989; Furse et al. 1984).
Several marketing researchers (Bayus, Carroll, and Rao 1985; Rogers 1995; Westbrook and Fornell 1979) have suggested that potential adopters tend to use information from various sources, such as marketers and independent third parties beyond close interpersonal networks, because they are often highly involved with the product category (Bloch, Sherrell, and Ridgway 1986). Innovators tend to be heavier users of professional communication sources, such as sellers, governments, and other third parties, than imitators and non-adopters (Bayus, Carroll, and Rao 1985; Midgley and Dowling 1993; Price, Feick, and Higie 1987). Innovators are also heavy users of interpersonal communication (Bayus, Carroll, and Rao 1985; Gatignon and Robertson 1985). Imitators tend to rely heavily on interpersonal communication (Gatignon and Robertson 1985). Based upon these inclinations, the following hypotheses are offered:
H1: Receiving information from an industry or corporate source will be positively associated with the probability of being an innovator.
H2: Receiving information from government/consumer agencies will be positively associated with the probability of being an innovator.
H3: Receiving information from family and/or friends will be positively associated with the probability of being an imitator.
Communication Modality. Another important aspect of communication is the mode of communication. Two communication modes--written information and conversation--are examined in this paper. Written communication modes can be broken down into print media, letters and memos, and e-mails, while conversational modes can be classified as telephone conversation and face-to-face communications (Maltz 2000; Rice 1993). This taxonomy of consumer communication is summarized in Table 1. Previous studies considering communications mode within an organization context (Maltz 2000; Moenaert and Souder 1996) provide strong evidence that written communication (e.g., print media advertisement) and conversational communication (e.g., discussion with salespeople) have very different impacts on consumers perceptions about the usefulness of the information being communicated. However, to date, no studies have been found in the literature that examine how communication modes actually influence consumers' final decisions to adopt techn ological innovation. Therefore, it is important to examine how different Modes of communication influence consumers' perceived usefulness of information about technological innovations, and consequently, their decisions to adopt.
The difference between written and conversational communication is that written communication is one-way communication from the source to the receiver. Thus, the success of written communication is based on the receiver's comprehension of the provided information. On the other hand, conversation is an interpersonal interaction that allows two speakers to exchange sequentially linked messages, which consequently develops mutual agreement on the issue (Thomas 1992). It is suggested in the consumer behavior literature that conversations present information more vividly than written communication (Herr, Kardes, and Kim 1991), which requires rather elaborate processing. Thus, conversational information tends to be more persuasive than written information, especially when consumers are not highly motivated to learn (Celsi and Olson, 1988; Petty and Cacioppo 1982; Thomas 1992).
In the communication literature, media richness theory (Daft and Lengel 1984, 1986; Westmyer, DiCioccio, and Rubin 1998) proposes different modes of communication vary in their capacity to convey "rich" information. Specifically, richness of information refers to "the ability to deal wit multiple cues, to facilitate rapid feedback, and establish a personal focus" (Lengel and Daft 1988, P. 226). Rich information is characterized by various simultaneous cues, rapid feedback, and personalization, and is believed to reduce information uncertainty and ambiguity (Carlson and Davis 1998; Daft and Lengel 1984, 1986). When positioned along the media richness hierarchy, conversational communication is richer than written communication since conversation involves many visual cues and advantages associated with the physical presence of both communicating parties (Lengel and Daft 1988; Rice 1993).
Furthermore, media richness theory presents communication strategies based on a contingency model (Keller 1994). According to the contingency model, the selection of communication mode should be based on the assessment of the richness of communicated information. And, the fit between the message and the communication mode influences the perceived utility of information (Maltz 2000; Moenaert and Souder 1996), performance (Keller 1994; Lengel and Daft 1988), and effectiveness (Westmyer, DiCiccio, and Rubin 1998). Thus, it is suggested that marketers should use a rich medium when communicating new and difficult messages and adopt a lean medium for simple, routine communication. Since learning about technological innovation involves exposures to new and sometimes difficult information, using the conversational mode rather than the written mode may increase the perceived usefulness of the information.
A study by Wilkie and Dickson (1985) revealed that 41% of the purchasers of electrical appliances rated conversations with salespeople as more useful than either print ads or information presented in Consumer Reports. A salesperson can customize information about a new product/service to help facilitate customer understanding. If a consumer has a better understanding of the technology, the probability of his/her adoption also increases (Davis 1989; Rogers 1995). Similarly, Wilton and Myers (1986) suggest that if consumers perceive that information as useful, then they are more likely to accept the message. Therefore, the mode of communication will influence consumers' perception of the usefulness of information when learning about innovations. Specifically,
H4: Conversational information will be perceived as more useful than written information when learning about innovations.
Relationships Among Communication Sources and Modality. Several researchers assert that the interaction among different communication sources influences the extent to which an innovation is diffused across individuals (Gatignon and Robertson 1985; Mahajan, Muller, and Bass 1990; Rogers 1995). Similarly, studies in consumer information search have suggested that information search activities might be interdependent (Katona and Mueller 1955; Lee and Hogarth 2000, 2001; Thorelli 1971). For example, if a consumer uses a particular source of information, he or she may be more or less likely to be exposed to other types of information sources.
Many studies have investigated consumer use of individual information sources; however, little is known about how individuals combine more than one channel of communication in their decision-making (Lee and Hogarth 2000, 2001). Acknowledging the gaps in the literature, researchers call for more study of the interrelationships among various communication sources (Beales et al. 1981; Mazis et al. 1981; Price et al. 1987). In addition, less is known about how communication channels and modes interact in influencing consumer adoption. Therefore, a further purpose of this study was to investigate the potential for the interdependent effects of channels and modes of communication on consumer adoption of technology.
Benefits and Costs of Electronic Banking
Electronic banking technologies refer to all the financial activities involving electronic media such as Automated Teller Machines (ATMs), debit cards, direct deposit, direct payment, smart cards, and computer banking (Lee and Lee 2000). The category of "electronic banking" includes various technological innovations that are currently at different diffusion stages. For example, automatic teller machines (ATMs) have been in the market over 30 years, whereas computer banking was introduced relatively recently. Temporal diversity within the category of electronic banking technologies provides an opportunity to explore the impact of communication on consumer adoption of technological innovations at different stages.
Recent technological changes in the financial market have altered the nature of consumer interactions with the providers of financial services, reducing the importance of physical location and face-to-face interactions (Lee 2001; White 1998). Benefits of electronic banking technologies include convenience and cost savings. Using these electronic banking technologies, consumers can conduct fast and convenient financial transaction activities and obtain their account information without direct face-to-face interaction with a customer representative (Lee and Lee 2000; Mester 2000; White 1998). For financial institutions, transaction cost of electronic banking is only a fraction of the expense for regular teller banking (Sheth and Sisodia 1999; Talmor 1995). Because check processing costs approximately $181 billion per year (Consumers' Research Magazine 1999), low cost is also an attractive incentive for governments to embrace electronic banking. In 1999, Congress finalized the Electronic Funds Transfer Act, whic h mandated electronic disbursement of social security benefits (Department of Treasury 1999).
As alternative electronic banking options become available, an increasing number of consumers are expected to use them. However, both the financial industry and government seem to face consumer resistance to electronic banking (Fain and Roberts, 1997; Lee 2001; Lee and Lee 2000; Mester 2000). Paper checks still dominate the U.S. payment system (Bank for International Settlements 1995), and the majority of consumers prefer to conduct their banking business in person as opposed to using electronic banking technologies (Kennickell and Kwast 1997).
Learning an unfamiliar practice of banking using technology as opposed to face-to-face interactions may incur some cognitively effortful costs (Dickerson and Gentry 1983; Hirschman 1980). Furthermore, for those consumers who perceive electronic banking as complex and unreliable, using electronic banking may likely present substantial security and financial costs concerns. Finally, the loss of human contact may likely outweigh the increased benefits of electronic banking if consumers strongly prefer personal interaction to electronic formats (Dabbolkar 1996).
In fact, it has been reported that many consumers are not well informed about the benefits of electronic banking innovations (Barczac, Scholder, and Pilling 1997) and perceive security risks regarding financial transactions via electronic media (O'Connell 1999). Therefore, creating positive awareness among potential adopters (Mahajan, Muller, and Kerin 1984) by communicating the increased benefits and reduced costs of electronic banking becomes critical in the successful diffusion of electronic banking innovations (Lockett 1999). Generally, the print media is the most widely used by marketers to promote consumer adoption of electronic banking innovations. Alternatively, information about electronic banking can be delivered through conversations with salespeople, employees at government/consumer agencies, and family and friends.
The data set employed for this study was the 1999 Survey of Consumers commissioned by the University of Michigan Institute of Social Research Survey Research Center. The Surveys of Consumers were initiated in the late 1940s by the Survey Research Center at the University of Michigan. The purpose of these surveys is to measure changes in consumer attitudes and expectations, to explain why such changes occur, and to evaluate how these changes relate to consumer decisions to save, borrow, or make discretionary purchases. In September and October of 1999, the Federal Reserve Board commissioned additional questions on the Surveys of Consumers, including specific questions on consumers' adoption of electronic banking innovations. The authors of this study contributed to the development of specific questions addressing electronic banking innovations and consumer communication patterns. Using a telephone survey, the researchers queried a national sample of 1,000 adults.
Respondents were asked several questions in order to identify consumers' communication patterns and their adoption of electronic banking innovations. Demographic variables comprise another set of explanatory variables and were used as control variables in the analysis.
Channels of Communication. Three different channels of communication regarding electronic banking were included: financial institutions, government/consumer agencies, and family and friends. The consumers who had received information regarding electronic banking from each of the three channels were categorized into receivers on a binary scale (1 = received, 0 = not received).
Modes of Communication. Two major communication modes--written and conversational--were recorded for information from financial institutions and information from government/consumer agencies, respectively. Thus, five different combinations of channels and modes of communication were investigated in this study: (1) written information from financial institutions, (2) conversational information from financial institutions, (3) written information from government/consumer agencies, (4) conversational information from government/consumer agencies, and finally, (5) information from family and friends.
Perceived Usefulness. To measure their perception of each of the above five types of communication, respondents were asked the following question: "How useful was this information from _____ in making decisions to use or not to use electronic banking services?" Each response was recorded on 3-point Likert scale ranging from "not at all useful (= 1)," "somewhat useful (= 2)' to "very useful (= 3)."
Adoption of Electronic Banking. Consumer adoption of electronic banking was measured for ATM, computer banking, direct deposit, and direct payment, respectively. Adoption was recorded for each technology for the respondents who had used the technology over the past 12 months.
Demographics. Age was measured according to the following five ordinal categories: 18-24 years, 25-34 years, 35-44 years, 45-54 years, 55-64 years, and over 65. Midpoints were used in multivariate analyses. The respondent's education level was recorded on five levels: Less than High School (= 1), High School (= 2), Some College (= 3), Bachelor's Degree (= 4), and Graduate Degree (= 5). Fourteen income levels were created: $10,000 or less (= 1), $10,000-$14,999 (= 2), $15,000-$19,999 (= 3), $20,000-$24,999 (= 4), $25,000-$29,999 (= 5), $30,000-$34,999 (= 6), $35,000-$39,999 (= 7), $40,000-$44,999 (= 8), $45,000-$49,999 (=9), $50,000-$59,999 (= 10), $60,000-$74,999 (= 11), $75,000-$99,999 (= 12), $l00,000-$124,999 (= 13), and $125,000 or more (= 14). Four categories of race were included in the survey: white, black, Asian, and Hispanic. However, only the binary variable of "black" was used in the multivariate model (1 = black, 0 = other), because Asian and Hispanic race categories were less than five percent in cell counts. Finally, each respondent's marital status was recorded into one of the following four categories: married, separated or divorced, widowed, and never married. A binary variable of "married" was created and used in the multivariate analysis (1 = married, 0 = not married).
In this study, adopters of electronic banking innovations are grouped based on Bass and his colleagues' conceptualization of innovators, imitators, and non-adopters (1969, 1990). In categorizing adopters of electronic banking innovations, we considered how recently each innovation was first introduced to general consumers. Among the four electronic banking innovations (ATM, computer banking, direct deposit, and direct payment), computer banking is the most recent innovation. Thus, we define those who have adopted computer banking as Innovators. ATMs and direct deposit/payment services have been available to consumers for quite sometime now. Those who have adopted ATMs, direct deposit or direct payment services, but not computer banking, are considered Imitators, since they are not innovative enough to adopt the recent innovation but have adopted innovations after some introductory time in the market. Finally, those who have not adopted any of the four electronic banking innovations were deemed Non-Adopters. T he three identified adopter categories were then used as dependent variables in the subsequent multinominal logit analysis to investigate the effects of communication factors and demographic differences on consumer adoption of electronic banking.
Bivariate analyses, including [chi square] tests and analysis of variance (ANOVA), were used to examine cluster differences in communication patterns as well as in demographics. Specifically, Hypothesis 4, which is the relationship between communication mode and perceived usefulness, was examined using a contingency table and the associated [chi square] tests. Also, differences in channel preferences among consumers of different adoption stages were investigated by pair-wise tests using the LS (Least Squares) Means option in the SAS GLM (General Linear Model) procedure. In doing so, Bonferroni adjustments were made to reduce the type 1 error.
The SAS CATMOD (Categorical Data Modeling) procedure was then used for a multinominal logit analysis. In a multinominal logit analysis, the likelihood ratio indicates the goodness of fit of the tested model. The channel and the mode of communication and their interactions, as well as individual demographic variables, comprised the explanatory variables to predict consumer adoption/usage of electronic banking innovations. Specifically, the significance and magnitude of relationships provided bases for testing hypothesesl through 3. In addition, interdependencies between the channel and the mode of communication were tested by the interaction terms among the independent variables in the multinominal logit analysis.
The adoption patterns of four electronic banking innovations and demographic characteristics are presented in Tables 2 and 3. Profiles of each adopter category are presented next.
The "Non-Adopter" category was composed of 224 individuals (22.4%) who did not use electronic banking innovation in any form. Post hoc pair-wise comparisons with Bonferroni adjustment showed that an average Non-Adopter respondent (age 47) was older than the average Innovator. In terms of social attainment, the "Non-Adopter" category had a significantly lower income level than Imitators and Innovators. Non-Adopters also had a significantly lower education level than Imitators and Innovators. They had the highest proportion of non-white population.
The "Imitators" category was composed of 665 individuals (66.5%) who use ATMs, direct deposit, and/or direct payment but not computer banking. The average Imitator respondent was 46 years of age, significantly older than the average Innovator. The average income level of Imitators was higher than Non-Adopters', but lower than Innovators'. Similarly, the education level was higher than the educational level of Non-Adopters but lower than that of Innovators.
Finally, the "Innovators" were composed of users of computer banking (N = 111, 11.1%). Every respondent in this cluster used computer banking. About 90% used ATMs, and 76% used direct deposit. Just over half the Innovators used direct payment (51.35%). This Innovator cluster was composed of the young (average age = 40), higher income (midpoint $55,000) individuals with the highest level of education among the three consumer clusters. Specifically, an average Innovator consumer was younger than an average Imitator or Non-Adopter. Innovators had higher income and education levels compared to the other categories. About 68% of the Innovators were married.
Table 4 represents the percentage of respondents who received a particular type of information. Overall, written information from financial institutions was more available to respondents (42.1%) than conversational information (15.5%). The next most available communication was interpersonal communication with family and friends (25.2%). Information from government/consumer agencies reached about 12% of the respondents through written communication and about 6% of respondents through conversational communication.
Table 5 represents the perceived usefulness of the different types of Communication among respondents who received information. Overall, conversational communication from government/consumer agencies was perceived as most useful (mean 2.21), closely followed by conversational information from financial institutions (2.19) and interpersonal communication with family and friends (2.02). On average, written communication from both financial institutions (1.82) and government/consumer agencies (1.81) was perceived somewhere between "not at all useful" and "somewhat useful." A discussion of the communication pattern of each adopter group is presented next.
First, the Non-Adopters reported getting the least information of all five types of communication categories. Only 13% of the Non-Adopters received written information from financial institutions, and only 5% ever discussed the information with employees of financial institutions. While 5% reported having received written information from government/consumer agencies, only 3% ever had personal contacts with employees at government/consumer agencies. They also appeared to be less likely to discuss electronic banking with their family and friends than the Imitators and the Innovators.
The Imitators more actively sought information from financial institutions compared to the Non-Adopters, but less actively so than the Innovators. Specifically, about 47% of the Imitators received written information from financial institutions, 15% had discussed electronic banking with employees of financial institutions, 14% had received written information from government/consumer agencies, and 7% had discussed the information with employees at government/consumer agencies. Finally, 28% had received information from their family and friends.
The Innovators were found to be active information seekers who employed all five types of communication extensively. About 68% of the Innovators had received written information about electronic banking from financial institutions, while 36% had discussed electronic banking with employees of financial institutions. Twelve percent had received information from government/consumer agencies, and 9% had discussed the information with employees at government/consumer agencies. Slightly less than 40% had received information from family and friends.
The Innovators perceived most types of communication useful compared to the consumers in the other two categories.
Perceived usefulness of the five different types of communication by cluster is presented in Table 5. The results show that there were significant differences in the perceived usefulness of written (F = 7.50, p = 0.0006) and conversational information (F = 4.37, p = 0.0143) from financial institutions. Specifically, post-hoc comparisons revealed that the Innovators perceived written information from financial institutions to be more useful than did the Imitators and the Non-Adopters. However, the Non-Adopters were not significantly different from the Imitators in their perception of the usefulness of conversational information provided by financial institutions.
No significant difference across adopter categories was found in the perceptions of written (F = 1.18, p = 0.3097) and conversational communication (F = 2.62, p 0.0812) initiated by government/consumer agencies and information from family and friends (F = 2.99, p = 0.0523). Interestingly, although the Innovators and the Non-Adopters differed in their perceptions about the usefulness of written information from financial institutions, no differences were found between the Innovators and the Non-Adopters concerning the value of conversational communications from all three sources.
The mode of communication was found to significantly influence consumer perception of usefulness. When [chi square] tests were conducted on written versus conversational communication, the conversational mode was perceived as more useful than the written mode for communications initiated by financial institutions and communications initiated by government/consumer agencies (p <0.0001 and p = 0.0002, respectively). The effectiveness of conversational information was evident when the two channels were collapsed ([chi square] = 44.5745, p <0.0001), thereby supporting Hypothesis 4 (see Table 6). About 85% of the respondents who had received conversational information thought that the information was useful in making decisions to use or not to use electronic banking services, whereas only 64 percent of the respondents who had received written information perceived it as useful.
Multinominal Logit Analysis
In order to observe distinct differences between the adopter groups, (1) the Innovators and (2) the Imitators were compared to (3) the Non-Adopters; two log odds ratios were estimated in the subsequent multinominal logit analysis:
ln ([P.sub.1]/[P.sub.3]) and ln ([P.sub.2]/[P.sub.3]),
[P.sub.1]/[P.sub.3] = probability of an individual being an Innovator over a Non-Adopter
[P.sub.2]/[P.sub.3] = probability of an individual being an Imitator over a Non-Adopter.
The tested model had a good fit. The probability associated with the likelihood ratio was 1.0, p = 1.00, indicating that the membership of adopter categories is very well explained by the communication factors and the demographic variables included in the model.
The results of the multinominal logit analysis are presented in Table 7. These results reflect that receiving written and conversational information about electronic banking from financial institutions was, in fact, positively associated with the probability of being an Innovator, ln ([P.sub.1]/[P.sub.3]), thereby supporting Hypothesis 1. The results also revealed that written information provided by financial institutions was positively associated with the probability of belonging to the Imitators over the Non-Adopters, ln ([P.sub.2]/[P.sub.3]). However, receiving conversational information from financial institutions did not significantly influence the probability of belonging to the Imitator group.
Receiving information from government/consumer agencies did not influence the probability of being an Innovator over a Non-Adopter; thus Hypothesis 2 was not supported. Receiving information from government/consumer agencies also did not influence the probability of being an Imitator over a Non-Adopter.
The main effect of receiving information from family and friends was not a significant differentiator between the Imitators and the Non-Adopters. In other words, the effect of interpersonal communication on the probability of being an Imitator over a Non-Adopter, ln ([P.sub.2]/[P.sub.3]), was not significant by itself ([beta] = 0.0835, p = 0.65).
However, receiving information from family and friends did increase the likelihood of the Imitator's adoption through the interaction with other communication effects, thereby providing a partial support for Hypothesis 3. For example, the effect of conversational information from financial institutions is not significant by itself for the Imitators (b = 0.1372, p = 0.46). Figures 1 and 2 illustrate how the odds ratios increase when the two effects are combined. However, when it is combined with information received from family and friends, the odds of belonging to the Imitators over the Non-Adopters increases from 1.32 to a significant 4.02 (see Figure 1).
The combined effects of multiple channels and modes of communication on consumer adoption of electronic banking innovations were examined by including interaction terms in the model. Only one interaction term--the interaction between receiving conversational information from financial institutions and interpersonal communication with family and friends--was found to be significant. Conversational information from financial institutions and interpersonal communications were not found to be significant predictors of consumer adoption by themselves; however, their effects on consumer adoption, when combined, became significant. When a consumer receives conversational information from family and friends and also from financial institutions, the odds ratio of being an Innovator over a Non-Adopter increases from 1.57 to 4.02 (see Figure 1).
Likewise, for consumers who receive both types of conversational information compared to those who receive information only from financial institutions, the odds of belonging to the Innovators over the Non-Adopters increase from 3.64 to 9.36 (see Figure 2).
Demographic variables were included as control variables and their effects are reported briefly below. It was found that the effect of age was negative for consumer adoption of electronic banking. The quadratic term of age was significant on ln ([P.sub.2]/[P.sub.3]), suggesting a non-linear relationship between age and the probability of belonging to the Imitators over the Non-Adopters. Income and education had positive effects on consumer adoption of electronic banking innovations, whereas being black or married did not have any significant impact on adoption.
DISCUSSION, CONCLUSIONS, AND IMPLICATIONS
Despite the importance of the communication process in the successful diffusion of new technology, few studies have examined how channels and modes of communication can affect consumer adoption. This study investigated the effects of channel and mode of communication on consumer adoption of electronic banking innovations under the theoretical framework of Diffusion of Innovations as communication process.
We found that communication factors are indeed significant predictors of consumer adoption of electronic banking innovations. Among the three channels identified from the proposed taxonomy, financial institutions are currently the most active diffusion agents for consumers. Specifically, receiving written information from financial institutions is likely to increase the probability of adopting electronic banking innovations, which indicates that written communication devices issued by financial institutions are effective tools.
As the previous literature suggests, the government's effort to enhance information flow can help consumers make more informed decisions (Brown, Berry, and Goel 1990). The government's diffusion-facilitating efforts were found to be positively associated with industry's adoption of the innovation (Moon and Bretschneider 1997). However, the effect of receiving information from a third party was not significant in our study. It appears that, presently, the role of government in the diffusion of electronic banking innovations is limited, because only a small portion of the population has received any electronic banking information from government agencies. Despite significant efforts made by the government under the Electronic Funds Transfer Act, only a small number of consumers have been influenced by this initiative.
The importance of personal communication networks for the successful diffusion of innovations has been emphasized in the literature. Successful diffusion and its full penetration to the entire population depends on whether the development of diffusion can reach the critical mass by activating interpersonal communication channels. Interestingly, we found that a significant interaction effect between interpersonal communications and conversational communications initiated by financial institutions. That is, the combination of conversational information from financial institutions and family and friends' advice has a strong impact on their adoption decision. Therefore, diffusion agents need to promote innovations by not only talking about the new technology to potential adopters but also encouraging them to make recommendations to their close family, neighbors, and friends.
Currently, the Non-Adopters seem to be isolated from the scope of communication activities because they do not appear to receive sufficient information from financial institutions, government/consumer agencies, and family and friends. Although this group of consumers needs the most assistance to adopt innovation, they are getting the least attention from the diffusion agents in practice. Such uneven distribution of information and lack of communications can widen the gaps between the audience segments high and low in socioeconomic status (Rogers 1995, p.432). Targeting communication campaigns, especially for this Non-Adopter audience, is necessary to narrow such gaps. For that purpose, our findings indicate that professional information in literature might not be the best way to reach the Non-Adopter consumers, as they perceive conversational communication as more useful than written information. Therefore, there is a need to promote the conversational mode of communication in developing communication program s aiming at the Non-Adopter population.
Midgley and Dowling (1993) proposed that even non-innovative individuals could adopt a technology at the early stage of diffusion when they are exposed to the right communication. This paper provides empirical evidence that an individual consumer's adoption is indeed influenced by communication factors. In addition, we suggest those communication strategies for technological innovations would be more effective by adopting the notion of segmentation; disseminating information through the right channel and the right mode of communication for different consumer segments will likely increase each segment's probability to adopt technological innovations. Specifically, innovators are likely to use both lean (written) and rich (conversational) information from various channels, and thus, reinforcing the intended message through multiple channels is recommended. We also found that, in general, the conversational communication mode is perceived as more useful than the written communication mode when learning about tec hnological innovations.
Perhaps understanding the characteristics of channels may explain why a particular communication channel or modality is highly effective in influencing consumers' adoption decisions. In general, written communications are unsolicited because they are initiated by corporations or the government/consumer agencies. Consumers incur low search costs from this type of unsolicited communication (i.e., receiving information) (Stigler 1961). However, because of weak relevance of unsolicited information to individual needs, the perceived usefulness of the information may be limited (Wilton and Myers 1986). On the other hand, word-of-mouth communications or discussions with employees at financial institutions allow consumers to take a more interactive role as communication partners. Greater involvement during the conversation may yield greater learning effects that can lead to behavioral changes after the communication (Herr, Kardes, and Kim 1991).
Conversational communication is especially effective in reaching the Non-Adopter consumers since they do not tend to perceive written communication as useful when learning about complex technological innovations. Technological innovations might require a rich (conversational) communication mode that enables instant feedback and personalized learning for these consumers. Therefore, consumer education efforts should promote the conversation information dissemination strategy, especially to reach the Non-Adopters.
Importantly, our finding that the interaction between commercial and personal communications is a more significant predictor of the Imitators' adoption than the effect of interpersonal communication alone needs further scrutiny in the light of Bass and his colleagues' specification of the Imitators relating primarily to interpersonal sources of influence. In today's complex and competitive market, consumers are exposed to a multitude of communications from a variety of channels at the same time; thus the interdependent nature of commercial and interpersonal communications and its influence on consumers' decision-making processes warrant future research attention.
Limitations of this study include the fact that only one direction of flow of information was used, mass communication from marketers or government consumer/agencies to consumers. However, consumers may seek, instead of receive, information about technological innovations depending on their motivations and needs, and actively use the information in making adoption decisions. Understanding consumer needs and their motivations to use a specific channel and/or modality of communication will likely shed light on why one type of information is more effective in influencing consumers' adoption decisions. Further specification of the direction of information flow coupled with specific consumer motivations might shed additional light on the perceived usefulness of different types of communication. In addition, our examination of communication modes does not include the case of multimedia information, where graphic, video, text, and sound are all linked to create a richest form of information and the enhanced interact ivity of new media technology allows consumers to both receive and seek information in real time (e.g., Internet communication). As technological advances will allow such interactive, high-tech multimedia communications in the near future, future research needs to investigate the effectiveness of interactive, multimedia communication.
Table 1 Taxonomy of Sources and Communication Modality Marketer-Provided Independent Third-Party Written Print media Publications by Consumer reports marketers brochures Government publications or print advertisement) Consumer reports E-mail Promotional messages Government publications by marketers Letters & Promotional letters Letters by government/ Memos by marketers consumer agencies Conversational Phone Telephone discussions Telephone discussions with with employees employees at government or of the business consumer agencies Face-to-face Face-to-face discussions Face-to-face discussions with employees with employees of the business at government or consumer agencies Interpersonal Written Print media N/A E-mail E-mail correspondence with family and friends Letters & Written correspondence Memos with family and friends Conversational Phone Telephone discussion with family and friends Face-to-face Face-to-face discussion with family and friends Table 2 Percentage of Respondents Who Used a Particular Electronic Banking Technology During the Past 12 Months Electronic Banking Usage Non-Adopters Imitators Innovators All ATM 0% 68.12% 88.29% 55.1% Computer Banking 0% 48.70% 100.00% 11.1% Direct Deposit 0% 73.23% 75.68% 57.1% Direct Bill Payment 0% 35.04% 51.35% 29.0% N 224 665 111 1000 Percent 22.4% 66.5% 11.10% 100.0% Table 3 Demographic Characteristics by Adopter Category Demographics Non-Adopters Imitators (N = 224) (%) (N = 665) (%) Age 18-24 11.61 8.42 25-34 12.95 18.20 35-44 22.77 23.46 45-54 18.30 18.20 55-64 17.86 13.08 over 65 16.52 18.65 100 100 Mean (Median) F = 9.07 (p = 0.0001) 46.80 (b) 46.30 (b) Income $10,000 or less (= 1) 9.38 2.86 $10,000-$14,999 (= 2) 9.38 4.96 $15,000-$19,999 (= 3) 8.93 5.41 $20,000-$24,999 (= 4) 9.38 6.17 $25,000-$29,999 (= 5) 3.57 5.86 $30,000-$34,999 (= 6) 5.80 4.96 $35,000-$39,999 (= 7) 6.25 5.11 $40,000-$44,999 (= 8) 4.46 5.86 $45,000-$49,999 (= 9) 19.64 13.23 $50,000-$59,999 (= 10) 6.70 12.48 $60,000-$74,999 (= 11) 7.14 10.38 $75,000-$99,999 (= 12) 3.57 11.43 $100,000-$124,999 (= 13) 2.68 5.56 $125,000 or more (= 14) 3.13 5.71 100 100 Mean (Median) F = 41.21 (p<0.0001) 6.68 (a) 8.43 (b) Education Less than High School (= 1) 19.20 6.02 High School (= 2) 37.50 29.92 Some College (= 3) 20.09 24.66 Bachelor's Degree (= 4) 19.20 24.66 Graduate Degree (= 5) 4.02 14.74 100 100 Mean (Median) F = 36.79 (p<0.0001) 2.51 (a) 3.12 (b) Ethnicity White 68.75 80.6 Black 14.73 9.77 Other 16.52 9.62 100 100 [chi square] = 16.26 (0.0027) Household Composition Married 45.54 59.40 Male Single 22.77 14.59 Female Single 31.70 26.02 100 100 [chi square] = 23.59 (<0.0001) Demographics Innovators TOTAL (N) (N = 111) (%) (N) Age 18-24 8.11 91 25-34 30.63 184 35-44 30.63 241 45-54 17.12 181 55-64 9.91 138 over 65 3.60 165 100 1,000 Mean (Median) F = 9.07 (p = 0.0001) 39.77 (a) 45.63 (39.5) Income $10,000 or less (= 1) 0 40 $10,000-$14,999 (= 2) 0.90 55 $15,000-$19,999 (= 3) 0.90 57 $20,000-$24,999 (= 4) 3.60 66 $25,000-$29,999 (= 5) 3.60 51 $30,000-$34,999 (= 6) 2.70 49 $35,000-$39,999 (= 7) 6.31 55 $40,000-$44,999 (= 8) 8.11 58 $45,000-$49,999 (= 9) 9.01 142 $50,000-$59,999 (= 10) 8.11 107 $60,000-$74,999 (= 11) 14.41 101 $75,000-$99,999 (= 12) 15.32 101 $100,000-$124,999 (= 13) 11.71 56 $125,000 or more (= 14) 15.32 62 100 1,000 Mean (Median) F = 41.21 (p<0.0001) 10.33 (c) 8.25 (9) Education Less than High School (= 1) 2.70 86 High School (= 2) 16.22 301 Some College (= 3) 26.13 238 Bachelor's Degree (= 4) 31.53 242 Graduate Degree (= 5) 23.42 133 100 1,000 Mean (Median) F = 36.79 (p<0.0001) 3.57 (c) 3.03 (3.00) Ethnicity White 81.98 781 Black 6.31 105 Other 11.71 114 100 1,000 [chi square] = 16.26 (0.0027) Household Composition Married 68.47 573 Male Single 17.12 167 Female Single 14.41 260 100 1,000 [chi square] = 23.59 (<0.0001) Note: The superscripts a, b, and c present the results of pair-wise tests using Bonferroni adjustment with an alpha level of 0.05. Values with the same subscript are not significantly different. For instance, for age, Non-Adopters (a) and Imitators (a) are not significantly different from each other, However, the average age of Innovators (a) is significantly different from those of Imitators (b) and Non-Adopters (b). Table 4 Percentage of Respondents Who Reported Receiving a Particular Type of Information Non-Adopters Imitators Innovators Communication (N = 224) (N = 665) (N = 111) Financial Institutions--Written 13.39% (a) 47.37% (b) 68.47% (c) [chi square = 114.9636(<.001) Financial Institutions-- Conversations 5.36% (a) 15.49% (b) 36.04% (c) [chi square] = 53.3358 (<.0001) Government/ Consumer Agencies-- Written 5.36% (b) 13.68% (a) 11.71% (b) [chi square] = 11.3318(0.0035) Government/Consumer Agencies-- Conversational 2.68% (a) 6.92% (a) 9.01% (a) [chi square] = 6.8707(0.0322) Family/Friends 9.82% (a) 28.12% (b) 38.74% (c) [chi square] = 41.9072(<.0001) All Communication (N = 1000) Financial Institutions--Written 42.1% [chi square = 114.9636(<.001) Financial Institutions-- Conversations 15.5% [chi square] = 53.3358 (<.0001) Government/ Consumer Agencies-- Written 11.6% [chi square] = 11.3318(0.0035) Government/Consumer Agencies-- Conversational 6.2% [chi square] = 6.8707(0.0322) Family/Friends 25.2% [chi square] = 41.9072(<.0001) Note: The supercripts a, b, c present the results of pair-wise tests using Bonferroni adjustment with an alpha level of 0.05. Table 5 Perceived Usefulness of Information Among Consumers Who Received Information Communication Non-Adopters Imitators Innovators Financial Institutions--Written Received (N) 30 315 76 Not at all Useful 40.00 38.41 18.42 Somewhat Useful 46.67 47.30 53.95 Very Useful 13.33 14.29 27.63 [chi square] (*) = 14.5538 (0.0007) F = 7.50 (0.0006) Mean 100 100 100 1.73 (a) l.65 (a) 2.09 (b) Financial Institutions-- Conversational Received (N) 12 103 40 Not at all Useful 16.67 21.36 7.50 Somewhat Useful 33.33 49.51 40.00 Very Useful 7.74 29.13 52.50 [chi square] (*) = 8.3668 (0.0152) F = 4.37 (0.0143) Mean 100 100 100 2.33 (ab) 2.08 (a) 2.45 (b) Government/Consumer Agencies-- Written Received (N) 12 91 13 Not at all Useful 16.67 38.46 30.77 Somewhat Useful 66.67 47.25 38.46 Very Useful 16.67 14.29 30.77 [chi square] (*) = 2.3612 (0.3071) F = 1.18 (0.3097) Mean 100 100 100 2.00 (a) 1.76 (a) 2.00 (a) Government/Consumer Agencies-- Conversational Received (N) 6 46 10 Not at all Useful 0 13.04 0 Somewhat Useful 50.00 63.04 50.00 Very Useful 50.00 23.91 50.00 [chi square] (*) = 4.9779 (0.0830) F = 2.62 (0.0812) Mean 100 100 100 2.50 (a) 2.11 (a) 2.50 (a) Family/Friends Received (N) 22 187 43 Not at all Useful 31.82 26.20 6.98 Somewhat Useful 45.45 49.20 60.47 Very Useful 22.73 24.60 32.56 [chi square](*) = 5.8790 (0.0529) F = 2.99 (0.0523) Mean 100 100 100 1.91 (a) 1.98 (a) 2.26 (a) Communication All Financial Institutions--Written Received (N) 421 Not at all Useful 34.92 Somewhat Useful 48.46 Very Useful 16.63 [chi square] (*) = 14.5538 (0.0007) F = 7.50 (0.0006) Mean 100 1.82 Financial Institutions-- Conversational Received (N) 155 Not at all Useful 13.42 Somewhat Useful 45.81 Very Useful 36.77 [chi square] (*) = 8.3668 (0.0152) F = 4.37 (0.0143) Mean 100 2.19 Government/Consumer Agencies-- Written Received (N) 116 Not at all Useful 35.34 Somewhat Useful 48.28 Very Useful 16.38 [chi square] (*) = 2.3612 (0.3071) F = 1.18 (0.3097) Mean 100 1.81 Government/Consumer Agencies-- Conversational Received (N) 62 Not at all Useful 9.68 Somewhat Useful 59.68 Very Useful 30.65 [chi square] (*) = 4.9779 (0.0830) F = 2.62 (0.0812) Mean 100 2.21 Family/Friends Received (N) 252 Not at all Useful 23.41 Somewhat Useful 50.79 Very Useful 25.79 [chi square](*) = 5.8790 (0.0529) F = 2.99 (0.0523) Mean 100 2.02 Notes: (1)The superscript (*) indicates that Cochran-Mantel-Haenszel statistics are used to test cluster differences in mean scores of perceived usefulness. (2)The superscripts a and b present the results of pair-wise tests using Bonferroni adjustment with an alpha level of 0.05. Table 6 Communication Mode and Perceived Usefulness Financial Government/ Institutions Consumer Agencies Communication Mode Written Conversational Written Not at all useful 147 27 41 34.92% 17.42% 35.34% Somewhat useful 204 71 56 48.46% 45.81% 48.28% Very useful 70 57 19 16.63% 36.77% 16.38% Total(N) 421 155 115 [chi square] (*) 31.064 13.489 p-value <.0001 0.0002 Government/ Consumer Combined Agencies Communication Mode Conversational Written Conversational Not at all useful 6 188 33 9.68% 35.61% 15.21% Somewhat useful 37 260 108 59.68% 47.54% 49.77% Very useful 19 89 76 30.65% 16.86% 35.02% Total(N) 62 528 217 [chi square] (*) 44,574 p-value <.0001 Note: (*)Cochran-Mantel-Haenszel statistics are used to test differences in mean scores of preceived usefulness by communication mode. Table 7 Results of Multinominal Logit Analysis: Maximum Likelihood Analysis 1n ([P.sub.1]/[P.sub.3]) = probability of being an Innovator over a Non-Adopter 1n ([P.sub.2]/[P.sub.3]) = probability of being an Imitator over a Non-Adopter 1n([P.sub.1]/[P.sub.3]) Intercept 0.5696 (0.6569) Communication Financial Institutions-Written 0.9211 (***) (<0.0001) Financial Institutions-- Conversational 0.6463 (**) (0.0024) Government/Consumer Agencies 0.0650 (0.7588) Family/Friends 0.2243 (0.2814) Interaction between Financial Institutions--Conversational and 0.4719 (*) Family/Friends (0.0237) Demographic Age -0.1046 (0.0930) [Age.sup.2] 0.0009 (**) (0.1822) Income 0.2144 (**) (<0.0001) Education 0.4705 (***) (0.0002) Male Single (Base = Married) -0.0055 (0.9815) Female Single (Base = Married) 0.3276 (0.1703) Likelihood Ratio 0.002 ln([P.sub.2]/[P.sub.3]) Intercept 2.2760 (0.0059) Communication Financial Institutions-Written 0.7094 (**) (<0.0001) Financial Institutions-- Conversational 0.1372 (0.4639) Government/Consumer Agencies 0.0670 (0.6850) Family/Friends 0.0835 (0.6499) Interaction between Financial Institutions--Conversational and 0.5586 (*) Family/Friends (0.0024) Demographic Age -0.1025 (*) (0.0061) [Age.sup.2] 0.0012 (0.0019) Income 0.0755 (**) (0.0043) Education 0.3102 (**) (0.0002) Male Single (Base = Married) 0.2368 (0.1040) Female Single (Base = Married) 0.0219 (0.8706) Likelihood Ratio [chi square] Intercept 13.05 (0.0015) Communication Financial Institutions-Written 43.59 (***) (<0.0001) Financial Institutions-- Conversational 17.98 (***) (0.0001) Government/Consumer Agencies 0.88 (0.6425) Family/Friends 1.71 (0.4258) Interaction between Financial Institutions--Conversational and 9.36 (**) Family/Friends (0.0093) Demographic Age 7.64 (*) (0.0219) [Age.sup.2] 9.64 (**) (0.0081) Income 22.92 (***) (<0.0001) Education 18.03 (**) (0.0001) Male Single (Base = Married) 6.48 (0.1659) Female Single (Base = Married) Likelihood Ratio 1177.15 (1.0000) Note: [Age.sup.2] Age Squared (***)p<0.001 (**)p<0.01 (*)p<0.05 Figure 1 Odds Ratios: The Effect of Receiving Information from Family and Friends on Adoption of Electronic Banking Technologies Odds Ratio P1/P3 P2/P3 When a Consumer Didn't Receive 1.57 1.18 Conversational Information from Financial Institutions When a Consumer Received 4.02 3.61 Conversational Information from Financial Institutions ln ([P.sub.1]/[P.sub.3]) = probability of being an Innovator over a Non-Adopter ln ([P.sub.2]/[P.sub.3]) = probability of being an Imitator over a Non-Adopter Note: Table made from line graph Figure 2 Odds Ratios: The Effect of Receiving Conversational Information from Financial Institutions on Adoption of Electronic Banking Technologies Odds Ratio P1/P3 P2/P3 When a Consumer Didn't Receive 3.64 1.32 Information from Family/Friends When a Consumer Received 9.36 4.02 Information from Family/Friends In ([P.sub.1]/[P.sub.3]) = probability of being an Innovator over a Non-Adopter In ([P.sub.2]/[P.sub.3]) = Probability of being an Imitator over a Non-Adopter Note: Table made from Line graph
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Eun-Ju Lee is an assistant professor of Marketing at the California State University, Los Angeles; Jinkook Lee is Associate Professor at the University of Georgia; and David w. Schumann is Associate Dean and Professor of Marketing at the University of Tennessee, Knoxville.
This article is based on a part of the first author's dissertation, which received the 2001 ACCI Dissertation Award. The funding for this project was provided for the second author from the Federal Reserve Board and U.S. Department of Agriculture. The authors thank Jeanne Hogarth, Jane Kolodinsky, and Jeffrey Shue for their input on the instrument development.
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|Author:||Lee, Eun-Ju; Lee, Jinkook; Schumann, David W.|
|Publication:||Journal of Consumer Affairs|
|Date:||Jun 22, 2002|
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