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The relationship between consumer innovativeness, personal characteristics, and online banking adoption Walfried M. Lassar
Received March 2004 Revised July 2004 Accepted October 2004
Florida International University, Miami, Florida, USA
Chris Manolis Xavier University, Cincinnati, Ohio, USA
Sharon S. Lassar Florida Atlantic University, Boca Raton, Florida, USA Abstract
International Journal of Bank Marketing Vol. 23 No. 2, 2005 pp. 176-199 q Emerald Group Publishing Limited 0265-2323 DOI 10.1108/02652320510584403
Purpose – This paper explores the relationships between consumer innovativeness, self-efficacy on the internet, internet attitudes and online banking adoption, while controlling for personal characteristics. Design/methodology/approach – The study integrates the technology acceptance model (TAM) and adoption of innovation framework to develop predictions of online banking acceptance. It distinguishes between innate consumer innovativeness, a generalized personality trait, and internet-domain-specific or actualized innovativeness in order to explore consumer characteristics’ impact on adoption. Data are analyzed using logistic regression. Findings – While results confirm the positive relationship between internet related innovativeness and online banking they also surprisingly show that general innovativeness is negatively related to online banking. Research limitations/implications – Results may or may not differ according to whether consumers are using online, telephone banking, electronic funds transfer (EFT) or direct bill payment. Our results may generalize to telephone banking and EFT as these products, like online banking, require an active consumer role in using the product. With direct bill payment, consumers need only set up the process initially and then monitor it on a semi-regular basis. Practical implications – Findings suggest that the type of consumer innovation matters in understanding the adoption of e-banking processes. This supports the notion that online shoppers are distinct from traditional non-online shoppers or highlight the unique nature of purchasing financial versus non-financial products. Banks offering e-banking need to recognize the importance of internet-specific consumer innovation characteristics. Originality/value – This paper closes a research gap as the model tested provides insights toward understanding the consumer-based phenomenon of e-banking, and serves to evaluate the TAM in this context. In contrast to previous research the study utilized an actual measure of e-banking adoption versus a measure of intention to use the technology. Keywords Innovation, Banking, Consumer behaviour, Electronic commerce Paper type Research paper
Introduction Practitioners and academics alike have noted the recent “revolution” in retail banking services across the US (Kolodinsky and Hogarth, 2001). The transformation from
traditional, “brick and mortar” banking to electronic banking (e-banking) has been momentous. Not since the advent of the automatic teller machine (ATM) has the retail banking industry witnessed such significant and extensive change. Formally, e-banking comprises various formats or technologies, including telephone (both landline and cell phones) banking, direct bill payment, electronic funds transfer (EFT), and, most recently, PC or online (internet) banking (Power, 2000; Weitzman, 2000). Similarly, Chou and Chou (2000) identified five basic services associated with online banking: (1) view account balances and transaction histories; (2) paying bills; (3) transferring funds between accounts; (4) requesting credit card advances; and (5) ordering checks. In 1999, no less than 85 percent of US households had at least one EFT feature on their bank accounts, and seven million households used online banking (at the time, this represented approximately one-fifth of households with online capabilities). Since that time, these numbers have increased significantly, and they are expected to increase considerably in the years ahead. Some account-to-account transfer services (Wells Fargo’s Billpoint, etc.), for instance, are said to attract more than 10,000 new users daily (Janik, 2000; Kolodinsky and Hogarth, 2001). By the end of 2002, some estimates suggested that as many as 30 percent of Americans were doing some sort of consumer banking online (Bruno, 2003). And, Bank of America experienced a 50 percent growth in online banking customers in 2003 (Ramasaran, 2003). Although some researchers have argued that e-banking has not lived up its potential as of yet, e.g. Sarel and Marmorstein (2003) and Wang et al. (2003), we maintain that e-banking has become a mainstay of modern society, and, in the future, will likely become an even larger and more significant element of the overall, retail banking experience. Given the current and likely future magnitude of e-banking, not only in the US but the world over (Mattilia et al., 2003), retail banks must gain a comprehensive understanding of this consumer-based, electronic revolution. Among other things, banks must understand who specifically is adopting and utilizing this new commercial technology and why. Ultimately, banks should be in a position to predict who the users of this new technology will be by, first, understanding important user characteristics, and, second, understanding how these characteristics interact with new e-banking processes and procedures. Although the reasons behind this electronic phenomenon are many and varied, there are likely consumer characteristics that can be identified and investigated in order to gain such an understanding. What are needed are theoretically sound, empirically tested models capable of assisting banks and their managers as they strive to understand which consumers will accept and use the new technology, and why these particular consumers are poised to adopt the new procedures. The purpose of this paper is to propose and test one such model. Since e-banking is largely a technology-based procedure or process, it is logical to consider the technology itself when trying to understand who is utilizing it. Importantly, from a consumer standpoint, the technology necessary to reliably and securely process online banking transactions has only recently become available and easily accessible to consumers. Technological innovations in and of themselves,
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however, do not necessitate the level or magnitude of change as seen recently in the retail banking industry. Beyond being available, a technology must offer clear-cut benefits for both buyers and sellers in order to be successfully adopted and utilized. E-banking offers numerous benefits for both banks and their retail customers. A primary benefit for the bank is cost savings; and, for the consumer, a primary benefit is convenience (Bruno, 2003; Gerrard and Cunningham, 2003). The bank can provide the customer with convenient, inexpensive access to the bank 24 hours a day, seven days a week. Although critically important, the benefits of this new technology do not, in and of themselves, explain why some consumers embrace the new technology and accompanying procedures and others do not. In this way, benefits may be seen as a necessary but insufficient condition toward understanding e-banking. What further explains and ultimately predicts the use and adoption of e-banking among retail consumers? Research investigating the expansive growth and use of the internet among consumers suggests that internet technology serves as an important source of consumer information that has become increasingly more user-friendly and accessible while at the same time less expensive (Bonn et al., 1999). Research in this area suggests a distinction between consumers who use the internet to browse and gather information and those who use it to actually make purchases (e-commerce, e-tailing, e-shopping, etc.). One study, for instance, found that 81 percent of those who browse the internet for goods and services do not actually make online purchases (Gupta, 1996), while another study found that, of those who initiated an online purchase transaction, only 25 percent followed through with the purchase (BizRate, 1999). Among the variables found to affect whether or not consumers engaged in online shopping were attitudes toward internet purchasing, perceived usefulness and ease of use of the internet for purchasing purposes, online experiences, and various personal characteristics such as buying impulsiveness and opinion leadership. In general, higher levels of these variables were associated with more online purchasing (O’Cass and Fenech, 2003). Research addressing new product adoption behavior appears particularly relevant to the e-banking phenomenon (Lokken et al., 2003). Indeed, Im et al. (2003) call specifically for research investigating the relationship between new product adoption characteristics among consumers and e-banking. Research on new product adoption behavior strives to unearth, among other things, why certain consumers adopt and use new products and services while others do not. In other words, why are certain consumers more innovative than others? Based largely on Roger’s pioneering work in the diffusion of innovation (Rogers, 1995; Rogers and Shoemaker, 1971), adoption studies have been conducted in the domain of e-banking. In a study looking at consumers in Singapore, for example, Gerrard and Cunningham (2003) found that those who adopted internet banking compared with those who did not believed internet banking to be more convenient, less complex, and more combatable. In another study, Liao and Cheung (2002) found that consumer expectations regarding accuracy, security, transaction speed, user-friendliness, and involvement were important as to whether or not consumers adopted internet-based, e-banking. Conceptually speaking, there are two primary theoretical orientations or explanations used to understand this adoption and use among consumers, and both concern innovation. The first relates personal consumer characteristics to new-product
adoption or innovation behavior and suggests that consumers who adopt new products – referred to as innovators or actual innovators – often have specific and identifiable characteristics. Compared with non-innovators, innovators, for example, have higher levels of income, education, opinion leadership, and both social mobility and participation, and are usually younger and have more favorable attitudes toward risk (Dickerson and Gentry, 1983; Gatignon and Robertson, 1991; Rogers, 1995). Importantly, innovators compared with non-innovators are also thought to make actual new product acquisitions. The second explanation stresses the identification of consumer innovators by way of a generalized, unobservable predisposition referred to as “innate consumer innovativeness” (Foxall, 1995; Hirschman, 1980a; Midgley and Dowling, 1993). In a recent study, Im et al. (2003) proposed and empirically tested a contingency model that combines these two theoretical orientations. In short, the researchers found that both personal characteristics and innate consumer innovativeness positively affect new product ownership. Rooted in technology, e-banking is a process or procedure and not a consumer product per se. Although it may have appeared and even behaved like a new product or service when it was initially introduced, e-banking is now most accurately portrayed as a relatively new, convenient, and technologically-oriented procedure whereby, consumers can accomplish customary banking tasks more quickly and easily than before. When online or internet banking was in its infancy, “pure-play e-banks” emerged. These banks were solely electronic, and, as such, did not have physical, brick-and-mortar facilities. Since that time, pure-play e-banks have all but disappeared; today most e-banking takes place by way of traditional brick-and-mortar banks who offer the new technology along with more conventional procedures. Thus, we are currently investigating whether or not consumers accept and/or adopt a new technologically-based procedure compared with a new product or service; consumers do not necessarily take ownership of or purchase anything. Accordingly, we now turn our attention to research that addresses specifically when and why people accept new technology. An influential research model in the fields of information technology and information systems, the technology acceptance model (TAM), suggests that a prospective user’s overall feelings or attitudes toward using a given technology-based system or procedure represent major determinants as to whether or not he/she will ultimately use the system (Davis, 1993). Adapted from the Theory of Reasoned Action (Azjen, 1980; Fishbein and Ajzen, 1975), TAM has been utilized in numerous settings involving varying forms of technological adoption (Venkatesh and Davis, 2000). O’Cass and Fenech (2003) suggest that, although TAM is specifically tailored to the acceptance of computer-based technologies, “its robust and parsimonious structure has allowed applications in other technological adoption situations with appropriate adjustments” (p. 82). Importantly for the current investigation, TAM has been utilized successfully to help understand and explain information systems/technology adoption in marketing contexts, including internet-based, retail consumer behavior (O’Cass and Fenech, 2003). Researchers have suggested that, in addition to utilizing feelings and/or attitudes to explain the use of a particular technology, “external variables” may be added to TAM as a way of improving the model’s predictive power (Davis et al., 1989; Davis, 1993). In marketing contexts, various external variables have been suggested and include a broad range of shopping motives (Eastlick and Feinberg, 1999),
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consumer skill/expertise, various demographic variables (Mattilia et al., 2003) personality characteristics, and computer anxiety (Harrison and Rainer, 1992). In a particular, Wang et al. (2003) used an extended TAM framework to model behavioral intentions with respect to using internet banking systems. In short, the researchers found that perceived usefulness, ease of use, and credibility affected directly intentions to use internet banking systems while computer self-efficacy had an indirect effect (Wang et al., 2003). Based on relevant, extant research, and steeped in the TAM theoretical tradition, we next propose a theoretically sound model useful toward understanding and explaining the consumer-based phenomenon of e-banking. To empirically test our model, we focus specifically on online (internet) or PC banking as this represents the newest and fasting-growing component of e-banking (Bruno, 2003). In the end, our model will assist banks and their managers as they strive to make strategically sound, consumer-related decisions. Conceptual model and hypotheses Consumer innovativeness As mentioned above, consumer innovativeness appears particularly useful in helping to understand the e-banking phenomenon, and past research has conceptualized consumer innovativeness in two primary ways (Im et al., 2003). On the one hand, consumer innovativeness is defined as actualized or domain-specific by the virtue of identifiable characteristics (e.g. opinion leadership, etc.) and actual acquisitions of new information, ideas, and products (Hirschman, 1980a; Midgley and Dowling, 1978). On the other hand, consumer innovators are identified based on their unobservable “innovative predisposition” across product classes (Midgley and Dowling, 1993), often referred to as innate or general innovativeness (Hirschman, 1980a). Both of these conceptualizations may be useful in depicting and understanding e-banking. The actualized innovativeness concept has received in-depth empirical attention within the diffusion of innovation framework (Rogers, 1995), and has been of particular interest in innovation diffusion research generally, and information technology (Agarwal and Prasad, 1998) and marketing research (Midgley and Dowling, 1978; Flynn and Goldsmith, 1993) specifically. According to this framework, a “personal innovativeness” construct is conceptualized as the degree and speed of adoption of innovation by an individual. In a marketing context, the construct has been measured via purchase intentions and opinions for certain new products, the number of new products owned, and the relative time of adoption for particular new products, and is usually applied to domain-specific products and services. The second innovativeness concept represents an innate phenomenon and is widely used in psychology to identify innovative characteristics of individuals (Kirton, 1976). According to this perspective, innovativeness is considered a generalized personality trait (also called “global innovativeness”) (Goldsmith and Hofacker, 1991; Goldsmith et al., 1995). This conceptualization of innovativeness has also been utilized in the marketing literature (Midgley and Dowling, 1978; Flynn and Goldsmith, 1993) and is thought to represent a highly abstract and generalized personality trait (Im et al., 2003). Examples as to the levels of abstraction inherent across the various literatures utilizing this perspective include “a willingness to change” (Hurt et al., 1977) and the receptivity to new experiences and novel stimuli (Goldsmith, 1984; Leavitt and Walton, 1975).
Of particular interest to the current study is Midgley and Dowling (1978) marketing-based conceptualization of innovativeness. Representing the innate type or variety of consumer innovation, this conceptualization incorporates communication independence which is determined as the degree to which a consumer’s decision process is independent of others’ personal influence within the social system. Feick and Price (1987) identified consumers who agglomerate marketplace information and initiate discussions that, in turn, influence other consumers. Referred to as “Market Mavens”, these consumers are at the forefront of new market information concerning products, places to shop, and other facets of the marketplace. Mavens are thought to initiate discussions with and respond to information requests from other consumers. Influencing versus being influenced by others, Market Mavens clearly possess innate innovation characteristics, or possess them to greater degrees than non-Market Mavens. Banking on the internet is a relatively innovative behavior that is more likely to be adopted by innovators than non-innovators. We hypothesize the following: H1.
Innate or general marketplace innovation characteristics will be positively related to online banking adoption.
The innate approach to innovativeness is limited to the extent that consumer innovation is more domain or product/service specific and less of an individual personality characteristic. As the name suggests, domain-specific (or actualized) innovation reflects the tendency to learn about and adopt innovations within a specific domain of interest, and, therefore, taps an innovativeness more specific to an area of interest (Citrin et al., 2000). Gatignon and Robertson (1985) found little overlap in innovativeness across domains or product categories suggesting that innovation is fairly product or domain specific. And, domain-specific measures of innovativeness have yielded useful predictions as far as the adoption of innovations by consumers is concerned (Goldsmith and Hofacker, 1991; Hirschman, 1980b). Domain-specific opinion leadership is a concept (construct) that is related to domain-specific innovativeness. Dickerson and Gentry (1983) identified the importance of opinion leaders in the diffusion process when studying the adoption of home computers. Two decades later, the personal computer is still at the center of the computing environment as per the internet, and some suggest that opinion leadership may still be significant (O’Cass and Fenech, 2003). In addition, opinion leadership has been associated with early adopters of electronic shopping technology, such as videotex (Korgaonkar and Moschis, 1987; Eastlick, 1993). In this way, we suggest that a domain-specific measure of opinion leadership represents a viable proxy for domain-specific innovation, and, as such, may be an indicator of internet or e-banking adoption. We expect that internet users who are opinion leaders on internet-related issues will utilize online banking via the world wide web (i.e. innovators) and accept the (perceived) risks associated with using this technology. H2.1. Opinion leadership in internet processes and issues will be positively related with online banking adoption. H2.2. Opinion seeking behavior in internet processes and issues will be inversely (negatively) related to online banking adoption.
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Internet/technology self-efficacy and experience In the TAM tradition (see earlier discussion), self-efficacy with respect to internet-related tasks can be an important factor in considering whether or not a new process is adopted (O’Cass and Fenech, 2003). Davis et al. (1989) and Venkatesh and Davis (1996) suggest that self-efficacy is an antecedent of perceived ease-of-use and object use-ability. Translated to computer and internet use, computer-related self-efficacy is a natural precursor to using the internet for commerce (Rampoldi-Hnilo, 1996; Maitland, 1996). As alluded to earlier, Wang et al. (2003) found computer self-efficacy to have an indirect effect on consumer intentions to use internet banking systems. Perceived self-efficacy refers to the beliefs in one’s capability to organize and execute the courses of action required to produce a given accomplishment or outcome (Bandura, 1997a), and originates from various sources including performance accomplishments, vicarious experience, verbal persuasion, and psychological states (Bandura, 1997b). In the case of computer-related self-efficacy, internet experience is likely important in understanding how self-efficacy influences e-shopping and/or e-banking. As O’Cass and Fenech (2003) point out, when internet users have accumulated sufficient personal experience via their adoption of computer technology, it creates a belief in their ability to use the internet for commercial purposes. Applied to the online banking scenario, we propose H3.1. The extent of internet users’ web experience will positively influence online banking adoption. H3.2. Internet users’ intensity of internet use will positively influence online banking adoption. H3.3. Internet users’ comfort with internet technology will positively influence online banking adoption. Type of web/internet use With respect to online shopping, Moschis et al. (1985) state that, whatever the delivery method and location of access, internet shopping will need to be compatible with internet-user lifestyles, experiences, and buying habits if it is to be adopted. This belief has been confirmed in the area of internet banking specifically as researchers have found positive relationships between perceptions of convenience and the use of internet banking (Gerrard and Cunningham, 2003; Polatoglu and Ekin, 2001). Overall, compatibility is the degree to which consumers perceive an innovation to be consistent with their needs, values, past experiences, and routines (Rogers, 1995; Tornatzky and Klien, 1982). Research suggests that compatibility has a large and direct positive impact on purchase intentions (Holak and Lehmann, 1990), and that, from a consumer’s perspective, retailing technology is most convenient when it matches shopping and media habits (Burke, 1997). Internet retailing processes need to provide users with real benefits and not just an alternative retail environment (O’Cass and Fenech, 2003). Over three decades ago, behavioral shopping research suggested that consumer shopping motivations could be thought of as the personal purposeful seeking of solutions to needs. In this way, transactions are thought to incorporate a social experience, enabling consumers to interact with others outside the home environment (Tauber, 1972). Bellenger et al. (1977) classified consumers into two groups they termed
“recreational” and “economic” shoppers. Under the Bellenger et al. (1977) typology, economic shoppers do not necessarily search for value and shop out of necessity. Recreational shoppers are not after discounts; instead, these shoppers seek-out the atmosphere and services associated with more prestigious stores (Williams et al., 1985). The conceptual framework of the recreational versus economic shoppers was refined in the 1980s and 1990s when researchers contrasted between consumers who see shopping in purely utilitarian terms and those who have hedonistic associations with the activity (Babin et al., 1994; Batra and Ahtola, 1991; Baumann et al., 1981). Put another way, shopping may involve both instrumental and experiential outcomes (Crowley et al., 1992). As far as the transaction-oriented task of online banking is concerned, the activity seems more closely related to purposeful and efficient utilitarian approaches to seeking and using commercial information than to hedonic, experiential, and self-expressive commercial activities. That is, online banking benefits appear functional and economic in nature and likely suit utilitarian internet users who regard internet use as a form of work, or a necessary means of serving their functional ends. H4.1. Internet users’ “utilitarian” internet use will be positively related to online banking adoption. H4.2. Internet users’ “hedonistic” internet use will be negatively related to online banking adoption. Demographic characteristics As noted earlier, personal characteristics, like socio-demographics, have also been widely used to profile innovators. Household income, education, and age are the most widely adopted identifiers for innovators (for a review on personal characteristics and innovation research see Im et al., 2003). In the area of internet banking specifically, Mattilia et al. (2003) found that household income and education predicted whether or not consumers in Finland adopted internet banking, and Sathye (1999) indicated that young, educated, and wealthy consumers were among those most likely to adopt to internet banking in Australia. Although some research indicates that demographic effects are weak (Ostlundt, 1974), consumer innovators are generally thought to have higher levels of income and education, and are younger (Im et al., 2003). Therefore, we establish the following hypotheses for the adoption of online banking: H5.
Personal characteristics will influence online banking adoption.
H5.1. Household income levels will be positively related to online banking adoption. H5.2. Education levels will be positively related to online banking adoption. H5.3. Age levels will be negatively related to online banking adoption.
Research methodology Data collection We recruited 349 participants from three college campuses in the eastern United States. The participants represented a wide variety of demographic segments with varying
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computer expertise and consisted of professionals participating in certificate courses and undergraduate or graduate students taking business courses. Our sample represented a cross-section of age, gender, income, and education. The male to female distribution was 40.4 and 59.6 percent, respectively. Respondents’ age ranged from 19 to 48 years with an average of 26. Roughly half (54.5 percent) of our respondents were 23 years of age or younger, 41.5 percent were in the Generation-X age range (between 24 and 38 years), and four percent were older than 38 years. In terms of education, 12.2 percent of the respondents had post-graduate education, 45.2 percent held graduate degrees, 33.3 percent held undergraduate degrees, and 9.3 percent had some undergraduate education. Finally, in terms of household income, roughly a fifth (19 percent) reported household incomes of less than US $20K, 36.7 percent reported incomes between $20K and 50K, 28.7 percent reported incomes between $50K and 100K, and the remaining 15.6 percent reported household incomes of more than $100K. The use of business school students as surrogates might raise some issue of external validity. However, (Gordon et al., 1986), Hughes and Gibson (1991) point out that the suitability of students as surrogates depends on case-specific circumstances. The use of students can show external validity providing the students’ profiles and performances are similar to the studied population. While the use of a student sample limits the generalizability of our research, this group does represent web-educated and computer-skilled consumers. And, both of these characteristics are requirements for internet and online banking use. Computer users who are not experienced in using their browser or feel uncomfortable with the internet will be less likely to use the web for commercial purposes. Our survey instrument was administered on the internet. Specifically, subjects were instructed to go to a particular website that was constructed for the purposes of this study and resided on a University server. The website included the survey, and, upon entering the site, respondents were provided with appropriate instructions. Subjects were asked to complete some simple tasks in order to ensure computer/internet proficiency. In the event that a subject could not complete these tasks, he/she did not participate further in the study. In this way, we controlled for web proficiency among respondents as research has shown that expertise and proficiency influence the use of technology (Novak et al., 2000; Ghani and Deshpande, 1994). Every respondent saw the same website and questionnaire, and all respondents had the same information to guide them. The survey items dealt with diffusion of innovation, internet attitudes, general internet usage issues, and demographic characteristics (see forthcoming discussion). After filling-out and submitting the questionnaire online, respondents were shown a “thank you” page and contact information for a debriefing of the study. Operationalization of measures Adoption measure. Following Rogers (1995) innovation diffusion theory, the logic implied throughout the paper suggests that the extent to which consumers adopt or use the internet to bank online indicates the extent to which the internet – as an innovation – has been “diffused”. The adopter/non-adopter category has been used in prior online shopping research (Eastlick and Lotz, 1999; Venkatraman, 1991; Shim and Drake, 1990). In traditional TAM research, the dependent variable is actual usage although some have
utilized measures of behavioral intention (Liao et al., 1999; Wang et al., 2003). Thus, adoption of the web for banking was measured via a yes/no response to the question: “Do you have an online banking account?” Whilst this is a limited behavioral measure, we deem it acceptable as it taps the actual behavior in which we are interested. Consumer innovativeness. Six items measuring general marketplace opinion leadership were used to represent innate consumer innovativeness. In a departure from psychology-based research that incorporates innate consumer innovativeness, our research is not focused on personal traits per se as these traits are difficult to link to marketing campaigns. Instead, we are interested in a construct and/or measure that can more easily be linked to marketing strategy and therefore used general or overall marketplace opinion leadership as a proxy for innate consumer innovation (see earlier discussion). The specific measure we used is based on the innovation adoption or leadership scale by Feick and Price (1987). This well-known scale is often referred to as the “Market Maven” scale. One of the advantages of the scale is that it has been shown to be independent of socioeconomic and demographic profiles, and persons identified as “Market Mavens” are known to influence a wide range of actions and reactions on behalf of consumers (see Appendix). The six items, each measuring a perception on a seven-point scale anchored by strongly agree/disagree, were combined. Cronbach’s alpha for the six scale items was 0.89. To represent domain-specific or actualized innovativeness (internet-related, domain-specific consumer innovativeness specifically), we used six items that assess both internet opinion leadership and internet opinion seeking behavior. Scale items developed originally by Reynolds and Darden (1971) were adopted for this purpose. Four items represented opinion leadership and had a Cronbach alpha of 0.93, and two items represented opinion seeking and had an alpha of 0.78. Although we are predicting opinion leadership and opinion seeking to have opposite effects (positive and negative, respectively) on the adoption of e-banking procedures, the two variables are generally thought to be distinct constructs. For example, there are likely opinion seekers who are not opinion leaders (Feick et al., 1986; Flynn et al., 1996). At the same time, however, there may be some overlap across the two variables as, for instance, there are likely opinion leaders who are also opinion seekers because of their interest in the domain. We conducted an exploratory factor analysis to assess the discriminate validity of the multi-item innovativeness measures. Utilizing principle components analysis with varimax rotation, we found that the scale items loaded according to three factors: the dimension of general marketplace leadership that represents innate consumer innovativeness, and the measures of internet opinion leadership and opinion seeking that both represent domain-specific or actualized consumer involvement. All measurement items aligned with their respective factors, with no cross-loadings exceeding 0.21. Eigenvalues were 3.9 and 3.3 for general market leadership and internet opinion leadership, respectively, and 1.7 for internet opinion seeking (Table I). In sum, the results appear to demonstrate satisfactory levels of reliability and validity. Self-efficacy-related measures. In order to assess respondents’ self-efficacy per the internet, we relied on three measures: (1) web usage intensity; (2) length of web usage; and (3) technology comfort.
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Table I. Exploratory factor analysis
Factor 1 Factor 2 Factor 3 General market Internet opinion Internet opinion leader leader seeker My friends think of me as a good source of information when it comes to new products or sales Like helping people by providing them with information about many kinds of products I like introducing new brands and products to my friends and co-workers If someone asked where to get the best buy on several types of products, I could tell him or her where to shop People ask me for information about products, places to shop, or sales Think about a person who has information about a variety of products and likes to share this information with others. This person knows about new products, sales, stores, and so on (e.g. websites, etc.), but does not necessarily feel he or she is an expert on one particular product. How well would you say this description fits you? My friends and co-workers come to me more often than I go to them for information about the internet and/or website(s) I feel that I am generally regarded by my friends and co-workers as a good source of advice and/or information about the internet My friends and co-workers often ask my advice about the internet and/or particular website(s) I sometimes influence the website(s) that my friends and co-workers visit I often seek out the advice of my friends and co-workers regarding the internet My friends and co-workers usually give me good advice as far as the internet is concerned Variance Explained Coefficient a
0.83789 0.83594 0.81456 0.81114 0.80450
0.65247 0.91743 0.90787 0.87895 0.86709 0.89187 3.893 0.89
3.313 0.93
0.88760 1.672 0.78
Using the internet for commercial purposes requires web-browser action. Thus, two six-point items were used to gauge the intensity of web-browser usage: the first item asked about the average time of use (responses ranged from using the internet less than an hour a week to using the internet over 40 hours a week), and the second asked how often a respondent used a web-browser per day (responses ranged from more than 9 times a day to once a month; see Appendix). The two intensity items were significantly and positively correlated ðr ¼ 0:51; p , 0:00Þ and were combined to form the web usage intensity variable. Length of time that someone has been using the internet is a known indicator of expertise (Novak et al., 2000). To measure length of web usage we utilized an item that asked how long the respondent had been using the internet (responses ranged from “less than 6 months” to “seven years or more”).
Technology comfort was measured by three items utilizing a five-point scale (anchors were 1 ¼ very uncomfortable and 5 ¼ very comfortable). The three items measured respondents’ comfort in regard to (1) using computers in general; (2) using the internet; and (3) their satisfaction with their current skills for using the internet.
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All items for the three measures representing self-efficacy (web usage intensity, web usage length, and technology comfort), were taken from the Ninth WWW User Survey sponsored by the Graphic, Visualization, and Usability (GVU) Center at the Georgia Institute for Technology (1998). The survey was intended to investigate behaviors as well as attitudes towards the internet in general. GVU ran the survey as a public service, and the survey was endorsed by the World Wide Web Consortium, which exists to develop common standards for the evolution of the web (Novak et al., 2000). Utilitarian versus hedonic internet use. Type of internet use was measured by asking respondents to rate their web usage in terms of usage experience: utilitarian information searchers/collectors versus using the web for self-expression and fun (hedonic use). Two items from the Ninth WWW User Survey (1998) were used to measure type of internet use. The first item measured “using the internet for information search” and the second item measured “using the internet for self expression” (the items had a four-point response format – 1 for “never” through 4 for “most of the time”). Based on TAM literature, three demographic characteristics were included in our analysis as independent variables: education, income, and age (see Table II). Given our Dimension
Segment
Gender
Female Male ,21 years 21 years 22-23 years 24-28 years 29-38 years .38 years Post-Graduate degree Graduate degree Undergraduate degree Some college , US$10,000 US$10-20,000 US$20-30,000 US$30-50,000 US$50-75,000 US$75-100,000 . US$100,000
Age
Education
Household income
Overall sample n/(percent)a 195/59.5 132/40.4 46/13.2 54/15.5 90/25.8 81/23.2 65/18.3 14/4.0 32/12.2 115/45.2 156/33.3 42/9.3 16/6.8 29/12.2 35/14.8 52/21.9 44/18.6 24/10.1 37/15.6
t-valueb
p,
1.00 (325)
0.32
2.90 (347)
0.004
2.25 (343)
0.02
2.54 (235)
0.01
Notes: Overall sample size n ¼ 349; acells show number and percentage of sub-sample; and bcells show t-value and degrees of freedom in parenthesis for the comparison between groups
Table II. Demographic information
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sample, we developed the following categories for the education and income measures: education was classified by four categories (post-graduate degree, graduate degree, undergraduate degree, and some college experience) and household income was classified utilizing seven categories (, US$10,000; $10-20K; $20-30K; $30-50K; $50-75K; $75-100K or . $100,000). A correlation matrix of all variables is provided in Table III. As seen in the table, the correlations between the multi-item constructs of internet opinion leadership, technology comfort, and web expertise are reasonably strong and significant. These findings are not surprising as it can be expected that web usage will increase the comfort level of users ðr ¼ 0:44; p , 0:01Þ: Furthermore, internet opinion leaders are likely people who use the web often ðr ¼ 0:38; p , 0:01Þ and feel comfortable with the technology ðr ¼ 0:41; p , 0:01Þ: Analysis and results Logistic regression was used in the analysis since the dependent variable is a dichotomous variable (online account/no online account) and the probability of purchase must lie between 0 and 1 (Press and Wilson, 1978). Logistic regression assigns purchase probabilities between 0 and 1, whereas linear regression would predict probability of purchase of less than 0 and greater than 1. The a priori probabilities for the logistic regression models were calculated based on adoption behavior as the proportion who actually had an online banking account. Three measures of model performance are available. Using the standard Lehmann-Chernoff goodness-of-fit test, model chi-square is compared to chi-square(1) (Lehmann, 1959). For our model utilizing adoption as the dependent variable and the 11 independent variables noted above (“Market Maven,” opinion leadership and seekers, comfort with technology, web intensity, length of time using the web, utilitarian and hedonic web use, education, income, and age), chi-square was greater than chi-square(1) leading to the rejection of the hypothesis that there is no fit ðp , 0:05Þ: Next, the p-value associated with model chi-square is examined (Table IV). For our model, the chi-square value is significant ðp , 0:01Þ: Finally, model classification performance is examined. The number of adopters correctly classified is greater than 80 percent, leading to the conclusion that classification performance is acceptable. Based on these three model performance criteria, we conclude that, overall, model performance is acceptable. The independent variables were considered together since, for any specific adopter, these characteristics come into play simultaneously in influencing adoption behavior. To identify significant coefficients the Wald test was applied by comparing coefficient chi-square to chi-squared(1) (Engel, 1984). If the coefficient chi-square is greater than chi-square(1), then the hypothesis that the coefficient is not significant is rejected ðp , 0:05Þ: Personal innovativeness characteristics are all significantly related to online banking adoption. The innate measure of general market innovation (“Market Maven”), however, shows an unexpected relationship. Contrary to H1, general market innovation has a negative impact on adoption. Domain-specific innovativeness per internet issues, on the other hand, supports H2 (both 2.1 and 2.2) as opinion leadership and opinion seeking positively and negatively, respectively, relate to online banking adoption. With regard to internet self-efficacy, the three variables of technology comfort and length and intensity of web usage reveal the expected directional (i.e. positive)
1.000 20.256* 0.412* 0.382* 0.170* 0.038 20.060 20.050 0.264* 0.250* 3.26 0.95 329
0.345*
20.225*
0.038
0.056 0.040 0.024 20.015 20.069 0.111** 0.053 4.59 1.32 336
1.000
Opinion leading
2 0.102*** 0.116** 2 0.081 0.013 0.026 0.017 2 0.137** 2.70 0.83 329
2 0.045
1.000
Opinion seeking
Notes: *p , 0:01; **p , 0:05; and ***p , 0:10
MKT Maven Opinion leading Opinion seeking Tech. comfort Web intensity Web long Educate Income Age Utilit-arian Hedonistic Mean Std N
MKT Maven
0.441* 0.178* 0.027 0.139** 2 0.080 0.303* 0.147* 3.39 0.73 328
1.000
Tech comfort
Web long Educate
Income
Age
1.000 2.56 0.89 335
Utilitarian Hedonistic
1.000 0.290* 1.000 0.177* 0.082 1.000 0.131** 0.162** 0.183* 1.000 0.054 20.066 0.303* 0.288* 1.000 0.285* 0.143* 2 0.086 0.106 20.008 1.000 0.191* 0.062 0.022 2 0.170* 20.104*** 0.104*** 3.60 2.70 2.40 4.81 25.81 3.73 1.07 0.71 0.82 2.21 5.72 0.46 326 341 345 237 326 335
Web intensty
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Table III. Means, standard-deviation and correlations
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Construct
Estimate
Standard error
Wald chi-square
Pr . ChiSq
Intercept MKT Maven Opinlead Opinseek Techcomf Webintensity Weblong Educate Income Age Utilitarian Hedonistic
29.4711 20.3175 0.8394 20.4120 0.1923 0.4788 0.1816 20.3672 0.2105 0.0118 1.6342 20.4361
2.4960 0.1422 0.2492 0.2225 0.3325 0.2110 0.2756 0.2500 0.0848 0.0293 0.5263 0.2130
14.3979 4.9876 11.3455 3.4303 0.3346 5.1514 0.4341 2.1570 6.1663 0.1635 9.6396 4.1902
0.0001 0.0255 0.0008 0.0640 0.5630 0.0232 0.5100 0.1419 0.0130 0.6860 0.0019 0.0407
relationships to online banking adoption. Unexpectedly, however, only intensity of web usage is significant ðp , 0:05Þ: Only H3.2, therefore, is fully supported. For the two “type of web use” measures, results are as predicted in H3.1 and 3.2: utilitarian information search behavior is positively related ðp , 0:01Þ and hedonistic use (fun, self-expression, etc.) is negatively related ðp , 0:05Þ to online banking adoption. Findings involving the demographic characteristics both supported and failed to support the hypotheses. Contrary to H4.1 and 4.3, neither education nor age significantly affected online banking adoption. In support of H4.2, on the other hand, income is positively and significantly related to online banking adoption ðp , 0:05Þ: Discussion This study examines the adoption of e-banking and how personal innovation attitudes, internet-related self-efficacy, type of web use, and demographic characteristics affect adoption. Rooted in the TAM theoretical tradition, the empirical model tested herein not only provides important insights toward understanding and explaining the consumer-based phenomenon of e-banking, but also serves to empirically evaluate the TAM framework in this emerging and important context. Overall, the model supports the TAM perspective in that prospective users’ overall feelings or attitudes toward using an on-line banking system represent significant determinants as to whether or not they will ultimately use the internet-based banking procedure. And, in contrast to the previous internet banking research utilizing the TAM framework (Wang et al., 2003), our study utilized an actual measure of e-banking adoption versus a measure of (behavioral) intention to use the technology. Next, we recap our findings. Consistent with extant research, we predicted and found that domain-specific or actualized consumer innovation significantly and positively affects the adoption of online banking, as does the self-efficacy-related measure of web use intensity, utilitarian-based web use (in contrast, the hedonic-based use measure had a predicted negative effect), and income. Variables that did not affect the e-banking adoption process, and, as such, did not support prior predictions, include the self-efficacy-related measures of technology comfort and length of web usage, and the age and education demographic characteristics. In direct contrast to predictions, the innate (general) consumer innovation variable had a significant negative effect on e-banking adoption.
Managerial implications Consumer innovation. Our findings suggest that levels of consumer innovation do matter when it comes to adapting and utilizing e-banking products and procedures. As far as domain-specific (internet) innovation is concerned, consumers who are internet opinion leaders are significantly more likely to adopt or utilize online banking. And, conversely, those consumers who are internet opinion seekers are significantly less likely to utilize the online technology. These findings support a well established stream of marketing research suggesting that those who have knowledge and therefore opinions about a particular domain (product or product class, etc.) – and are willing to share their opinions with others – are likely to be (early) adopters and users of commercial innovations (Dickerson and Gentry, 1983; Gatignon and Robertson, 1991; Rogers, 1995). Furthermore, the findings suggest that online banking as a technology is likely still in the earlier stages of adoption (e.g. innovators, earlier adopters and earlier majority), and, as such, will likely see more adopters in the years ahead (e.g. late majority and laggards) (Rogers, 1995). In trying to understand who does and does not use or adopt e-banking systems and processes, banks and their subsidiaries who offer e-banking products need to recognize and appreciate the importance of internet-specific consumer innovation levels and characteristics. In addition to levels of consumer innovation, our research also suggests that the type of consumer innovation matters when it comes to understanding the adoption and utilization of e-banking systems and processes. That is, we found disparate results across our two consumer innovation measures. In a rather unexpected yet intriguing finding, the innate or general consumer innovativeness measure had a significantly negative effect on online banking. This finding is in direct contrast to the finding involving the domain-specific consumer innovation variable. Apparently, consumers who are general or overall marketplace opinion leaders (i.e. Market Mavens) are significantly less likely to use or adopt online banking. Had the innate consumer innovativeness variable simply failed to have a significant effect on e-banking adoption, the finding and subsequent interpretation would be far less clear and/or engaging. Yet, the fact that this particular form of consumer innovation had a significantly negative effect on online banking adoption levels perhaps suggests something more. Among other things, this finding may: (1) support the notion that online shoppers are somehow distinct compared with traditional non-online (i.e. brick-and-mortar) shoppers (Lokken et al., 2003); (2) highlight the distinct or unique nature of purchasing financial (versus non-financial) products (Beckett et al., 2000; Javalgi and Dion, 1999; Moskowitz and Krieger, 2001); and (3) some combination thereof whereby financial services purchased online are inherently unique (Ramaswami et al., 2000-2001). Recent qualitative research offers additional insight on why innate consumer innovativeness is inversely related to online banking adoption. That is, consumers do not view online banking as an exciting innovation (Sarel and Marmorstein, 2003). Even active users do not communicate their experiences with others. This is in sharp contrast to the communicative and persuasive behavior of Market Mavens who are depicted as “marketplace influencers” and whose preponderance is not necessarily based on knowledge or expertise in particular product categories, but, instead, on more
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general knowledge and experience with markets (Feick and Price, 1987, p. 83). Active e-bankers view online banking as their primary method of banking and as a natural extension of their online world. They appreciate the control and convenience of online banking but view banking as noticeably less valuable than other online services. The domain-specific innovativeness of online bankers has helped them get the most benefit from a relatively unexciting service. In the end, the findings across both the innovation variables suggest that, in terms of consumer innovation characteristics, the selling and/or targeting of e-banking products is a fairly unique and/or distinctive proposition as compared with other more general (traditional) consumer-oriented products and markets. The prominent role of the internet domain in online banking adoption is further emphasized by the results regarding web use and intensity. Web use and intensity. Together, the findings pertaining to the self-efficacy-related measure of web use intensity and the two measures of web use (i.e. utilitarian-and hedonic-based) suggest a rather distinct portrayal of e-banking patrons. That is, these consumers believe in and draw on their abilities to use the internet in a concentrated if not determined manner for specific purposes and do so intensively. In this way, the internet serves as an essential and fundamental tool to accomplish specific goals and tasks. Not only must the “core” online banking product adhere to this user (usage) platform, but the entire or “augmented” online banking product as well (the augmented product includes the core or fundamental product as well as other peripheral aspects of the product offering including facilitating and supportive products, image, marketing communications, etc. (Gro¨nroos, 1990)). The need for online banking products to be purposeful is also supported by the recent qualitative research by Sarel and Marmorstein (2003) that tries to ascertain the reasons for non-adoption. The researchers interview only those who use the internet regularly and characterize subjects as active, light or non-users of online banking. Those characterized as light users do not view online banking as a significant enhancement to their banking capabilities. Most light users have not even tried the bill payment feature of online banking, whereas most active users view this feature as the greatest benefit to banking online. Active users are those that had searched for the best mix of services, rates and fees and then made best use of the features of convenience, control and integration with other financial services. Web use intensity and utilitarian internet helps explain both the empirical and qualitative results. Demographic factors. Interestingly, the only demographic characteristic to significantly affect the adoption of e-banking in our study was income. In a finding common across both innovation- and internet-based marketing studies (Im et al., 2003 and Kolodinsky and Hogarth, 2001, respectively), income was found to positively affect the use or adoption of online banking. As an object variable (characteristic), income serves as a powerful and straightforward means by which marketers can target potential e-banking users. Limitations and future research As with the findings reported in any study, the findings reported here may be limited to the population and/or type of product investigated in our research. Internet or online banking, for instance, is only one of the various types or forms of e-banking products available (see earlier discussion). Thus, the relationships found in this study may or
may not differ according to whether consumers are using telephone banking, EFT, or direct bill payment (Kolodinsky and Hogarth, 2001). Our results may generalize to telephone banking and EFT as these products/systems, like online banking, require an active role on behalf of consumers in using the product. With direct bill payment, on the other hand, consumers need only set up the process initially and then monitor it on a semi-regular basis. In addition to replicating our findings across different e-banking products, future research might also address aspects of marketing strategy beyond the product itself. Promotional and communication issues, for instance, are likely important in terms of understanding the relationship between consumers and e-banking. What promotional messages are effective in acquiring new customers? And, what forms or types of communications are necessary and effective in maintaining existing customer relationships? As an emerging and consequential commercial phenomenon, e-banking can represent both a challenge and an opportunity for marketers of financial products and services. Those marketers armed with a thorough understanding of e-consumers will be the most competitive. References Agarwal, R. and Prasad, J. (1998), “A conceptual and operational definition of personal innovativeness in the domain of information technology”, Information System Research, Vol. 9 No. 2, pp. 204-15, June. Azjen, I. (1980), Attitudes, Personality and Behavior, Dorsey Press, Chicago, IL. Babin, B.J., Darden, W.R. and Griffin, M. (1994), “Work and/or fun: measuring hedonic and utilitarian shopping value”, Journal of Consumer Research, Vol. 26 No. 4, pp. 644-56, March. Bandura, A. (1997a), Self-Efficacy: The Exercise of Control, W.H. Freeman and Company, New York, NY. Bandura, A. (1997b), “Self-efficacy: toward a unifying theory of behavioral change”, Psychological Review, Vol. 82 No. 2, pp. 191-215. Batra, R. and Ahtola, O.T. (1991), “Measuring the hedonic and utilitarian sources of consumer attitudes”, Marketing Letters, Vol. 2, pp. 159-70. Baumann, D.J., Cialdini, R.B. and Kenrick, D.T. (1981), “Altruism as hedonism: helping and self-gratification as equivalent responses”, Journal of Personality and Social Psychology, Vol. 40, pp. 1039-46, June. Beckett, A., Hewer, P. and Howcroft, B. (2000), “An exposition of consumer behavior in the financial services industry”, International Journal of Bank Marketing, Vol. 18 No. 1, pp. 15-26. Bellenger, D.N., Robertson, D.H. and Greenberg, B.A. (1977), “Shopping center patronage motives”, Journal of Retailing, Vol. 56 No. 3, pp. 29-38, Summer. BizRate (1999), “75 Percent of shoppers abandon carts”, Cyber Atlas, available at: http:// cyberatlas.internet.com/bigpicture/demographics/article/0,1323,6061230231,00.html Bonn, M.A., Furr, H.L. and Susskind, A.M. (1999), “Predicting a behavioral profile for pleasure travelers on the basis of internet use segmentation”, Journal of Travel Research, Vol. 37, pp. 333-40. Bruno, M.A. (2003), “BofA’s climb to the top of the online world”, US Banker, Vol. 113 No. 6, pp. 24-5.
Consumer innovativeness
193
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194
Burke, R.R. (1997), “Do you see what I see? The future of virtual shopping”, Journal of the Academy of Science, Vol. 25 No. 4, pp. 352-60. Chou, D. and Chou, A.Y. (2000), “A guide to the internet revolution in banking”, Information Systems Management, Vol. 17 No. 2, pp. 51-7. Citrin, A.V., Sprott, D.E., Silverman, S.N. and Stem, D.E. Jr (2000), “Adoption of internet shopping: the role of consumer innovativeness”, Industrial Management and Data Systems, Vol. 100 No. 7, pp. 294-300. Crowley, A.E., Spangenberg, E.R. and Hughes, K.R. (1992), “Measuring the hedonic and utilitarian dimensions of attitudes toward product categories”, Marketing Letters, Vol. 3, pp. 239-49. Davis, F.D. (1993), “User acceptance of information technology: system characteristics, user perceptions and behavioral impacts”, International Journal of Man-Machine Studies, Vol. 38, pp. 475-87. Davis, F.D., Bagozzi, R.P. and Warshaw, P.R. (1989), “User acceptance of computer technology: a comparison of two theoretical models”, Management Science, Vol. 35 No. 8, pp. 982-1003. Dickerson, M.D. and Gentry, J.W. (1983), “Characteristics of adopter and non-adopters of home computers”, Journal of Consumer Research, Vol. 10, pp. 225-35. Eastlick, M.A. (1993), “Predictors of videotex adoption”, Journal of Direct Marketing, Vol. 7 No. 3, pp. 66-74. Eastlick, M.A. and Feinberg, R.A. (1999), “Shopping motives for mail catalog shoppers”, Journal of Business Research, Vol. 45, pp. 281-90. Eastlick, M.A. and Lotz, S. (1999), “Profiling potential adopters and nonadopters of an interactive electronic shopping medium”, International Journal of Retail and Distribution Management, Vol. 27 No. 6, pp. 209-23. Engel, R.F. (1984), “Wald, likelihood ratio, and Lagrange multiplier tests in econometrics”, in Griliches, Z. and Intriligator, M.D. (Eds), Handbook of Econometrics, Vol. 2, North Holland, New York, NY. Feick, L.F. and Price, L.L. (1987), “The Market Maven: a diffuser of marketplace information”, Journal of Marketing, Vol. 51 No. 1, pp. 83-97. Feick, L.F., Price, L.L. and Higie, R.A. (1986), “People who use people: the other side of opinion leadership”, in Lutz, R. (Ed.), Advances in Consumer Research, Vol. 8, Association of Consumer Research, Provo, UT. Fishbein, M. and Ajzen, I. (1975), Belief, Attitude and Behavior: An Introduction to Theory and Research, Addison-Wesley, Reading, MA. Flynn, L.R. and Goldsmith, R.E. (1993), “A validation of the Goldsmith and Hofacker innovativeness scale”, Educational and Psychological Measurement, Vol. 53, pp. 1105-16. Flynn, L.R., Goldsmith, R.E. and Eastman, J. (1996), “Opinion leaders and opinion seekers: two new measurement scales”, Journal of the Academy of Marketing Science, Vol. 24 No. 2, pp. 137-47. Foxall, G.R. (1995), “Cognitive styles of consumer initiators”, Technovation, Vol. 15 No. 5, pp. 269-88. Gatignon, H. and Robertson, T.S. (1985), “A propositional inventory for new diffusion research”, Journal of Consumer Research, Vol. 11, pp. 849-67. Gatignon, H. and Robertson, T.S. (1991), “Innovative decision processes”, in Robertson, T.S. and Kassarjian, H.H. (Eds), Handbook of Consumer Behavior, Prentice-Hall, Englewood Cliffs, NJ.
Gerrard, P. and Cunningham, J. B. (2003), “The diffusion of internet banking among Singapore consumers”, International Journal of Bank Marketing, Vol. 21 No. 1, pp. 16-28. Ghani, J.A. and Deshpande, S.P. (1994), “Task characteristics and the experience of optimal flow in human-computer interaction”, The Journal of Psychology, Vol. 128 No. 4, pp. 381-91. Goldsmith, R.E. (1984), “Personality characteristics associated with adoption-innovation”, Journal of Psychology, Vol. 117, pp. 159-65. Goldsmith, R.E. and Hofacker, C.F. (1991), “Measuring consumer innovativeness”, Journal of the Academy of Marketing Science, Vol. 19, pp. 1004-16. Goldsmith, R.E., Freiden, J.B. and Eastman, J.K. (1995), “The generality/specificity issue in consumer innovativeness research”, Technovation, Vol. 15 No. 10, pp. 601-12. Gordon, M.E., Slade, A. and Schmitt, N. (1986), “The ‘Science of the Sophomore’ revisited: from conjecture to empiricism”, Academy of Management Review, Vol. 11 No. 1, pp. 191-207. Gro¨nroos, C. (1990), Service Management and Marketing: Managing the Moments of Truth in Service Competition, Lexington Books, Lexington, MA. Gupta, S. (1996), “The fourth WWW consumer survey: a Hermes project”, in collaboration with GVU Centre’s 4th WWW User Survey, available at: www.umich.edu/Bsgupta/hermes Harrison, A.W. and Rainer, K.R. Jnr (1992), “The influence of individual differences on skill in end-user computing”, Journal of Management Information Systems, Vol. 9 No. 1, pp. 93-111. Hirschman, E.C. (1980a), “Innovativeness, novelty seeking, and consumer creativity”, Journal of Consumer Research, Vol. 7, pp. 283-95. Hirschman, E.C. (1980b), “Black ethnicity and innovative communication”, Journal of the Academy of Marketing Science, Vol. 8, pp. 100-18. Holak, S.L. and Lehmann, D.R. (1990), “Purchase intentions and the dimensions of innovation: an exploratory model”, The Journal of Product Innovation Management, Vol. 7 No. 1, pp. 59-75. Hughes, C.T. and Gibson, M.L. (1991), “Students as surrogates for managers in a decision-making environment: an experimental study”, Journal of MIS, Vol. 8 No. 2, pp. 153-66. Hurt, H.T., Joseph, K. and Cook, C.D. (1977), “Scale for the measurement of innovativeness”, Human Communication Research, Vol. 4, pp. 58-65. Im, S., Bayus, B.L. and Mason, C.H. (2003), “An empirical study of innate consumer innovativeness, personal characteristics and new product adoption behavior”, Journal of the Academy of Marketing Science, Vol. 31 No. 1, pp. 61-73. Janik, A. (2000), “A new way to send cash over the Net”, Money Magazine, p. 177. Javalgi, R. and Dion, D. (1999), “A life cycle segmentation approach to marketing financial products and services”, Services Industries Journal, Vol. 19 No. 3, pp. 74-96. Korgaonkar, P.K. and Moschis, G.P. (1987), “Consumer adoption of videotex services”, Journal of Direct Marketing, Vol. 1, pp. 63-71. Kirton, M. (1976), “Adaptors and innovators: a description and measure”, Journal of Applied Psychology, Vol. 61 No. 5, pp. 622-9. Kolodinsky, J. and Hogarth, J.M. (2001), “The adoption of electronic banking technologies by American consumers”, Consumer Interests Annual, Vol. 47, pp. 1-9. Lehmann, E.L. (1959), Testing Statistical Hypotheses, Wiley, New York, NY. Leavitt, C. and Walton, J. (1975), “Development of a scale for innovativeness”, in Schlinger, M.J. (Ed.), Advances in Consumer Research, Vol. 2, Association of Consumer Research, Ann Arbor, MI.
Consumer innovativeness
195
IJBM 23,2
196
Liao, Z. and Cheung, M.T. (2002), “Internet-based e-banking and consumer attitudes: an empirical study”, Information and Management, Vol. 39 No. 4, pp. 283-95. Liao, S., Shao, Y.P., Wang, H. and Chen, A. (1999), “The adoption of virtual banking: an empirical study”, International Journal of Information Management, Vol. 19 No. 1, pp. 63-74. Lokken, S.L., Cross, G.W., Halbert, L.K., Lindsey, G., Derby, C. and Stanford, C. (2003), “Comparing online and non-online shoppers”, International Journal of Consumer Studies, Vol. 27 No. 2, March pp. 126-33. Maitland, C. (1996), “Measurement of computer/internet self-efficacy: a preliminary analysis of computer self-efficacy and internet self-efficacy measurement instruments”, Department of Telecommunication, Michigan State University, East Lansing, MI, available at: www.tc. msu.edu/TC960/CSE.htm Mattilia, M., Karjaluoto, H. and Pento, T. (2003), “Internet banking adoption among mature customers: early majority or laggards?”, Journal of Services Marketing, Vol. 17 No. 5, pp. 514-28. Midgley, F.D. and Dowling, G.R. (1978), “Innovativeness: the concept and its measurement”, Journal of Consumer Research, Vol. 4 No. 4, pp. 229-35. Midgley, F.D. and Dowling, G.R. (1993), “A longitudinal study of product form innovation: the interaction between predisposition and social messages”, Journal of Consumer Research, Vol. 19, pp. 611-25. Moskowitz, H.R. and Krieger, B. (2001), “Financial products: rapid, iterative and segmented development by conjoint measurement and self-authoring iterative procedures”, Journal of Financial Services Marketing, Vol. 5 No. 4, pp. 343-55. Moschis, G.P., Goldstucker, J.L. and Stanley, T.J. (1985), “At-home shopping: will consumers let their computers do the walking?”, Business Horizons, Vol. 28 No. 2, pp. 22-9. Ninth WWW User Survey (1998), sponsored by the Graphic, Visualization, and Usability (GVU) Center at the Georgia Institute of Technology, Atlanta, GA, available at: www.gvu.gatech. edu/ user_surveys/survey-1998-04/ Novak, T.P., Hoffman, D.L. and Yung, Y. (2000), “Measuring the customer experience in online environments: a structural modeling approach”, Marketing Science, Vol. 19 No. 1, pp. 22-42. O’Cass, A. and Fenech, T. (2003), “Webretailing adoption: exploring the nature of internet users’ Webretailing behaviour”, Journal of Retailing and Consumer Services, Vol. 10, pp. 81-94. Ostlundt, L.E. (1974), “Perceived innovation attributes as predictors of innovativeness”, Journal of Consumer Research, Vol. 1, pp. 23-9. Polatoglu, V.N. and Ekin, S. (2001), “An empirical investigation of the Turkish consumers’ acceptance of internet banking services”, International Journal of Bank Marketing, Vol. 19 No. 4, pp. 156-65. Power, C. (2000), “Finnish bank brings digital certificates to mobile phones”, American Banker, Vol. 165 No. 10, p. 20. Press, S.J. and Wilson, S. (1978), “Choosing between logistic regression and discriminant analysis”, Journal of the American Statistical Association, Vol. 73, pp. 699-705. Ramsaran, C. (2003), “Online banking comes of age”, Bank Systems and Technology, Vol. 40 No. 11, p. 29. Ramaswami, S.N., Strader, T.J. and Brett, K. (2000-2001), “Determinants of online channel use for purchasing financial products”, International Journal of Electronic Commerce, Vol. 5 No. 2, pp. 95-118.
Rampoldi-Hnilo, L.A. (1996), “The hierarchy of self-efficacy and development of an internet self-efficacy scale”, Department of Telecommunication, Michigan State University, East Lansing, MI, available at: www.tc.msu.edu/TC960/self.htm Reynolds, F.D. and Darden, W.R. (1971), “Mutually adaptive effects of interpersonal communication”, Journal of Marketing Research, Vol. 8 No. 4, pp. 449-54 (pre-1986). Rogers, E.M. (1995), The Diffusion of Innovations, 4th ed., The Free Press, New York, NY. Rogers, E.M. and Shoemaker, F.F. (1971), Communication of Innovations, The Free Press, New York, NY. Sarel, D. and Marmorstein, H. (2003), “Marketing online banking services: the voice of the consumer”, Journal of Financial Services Marketing, Vol. 8 No. 2, pp. 106-18. Sathye, M. (1999), “Adoption of internet banking by Australian consumers: an empirical investigation”, International Journal of Bank Marketing, Vol. 17 No. 7, pp. 324-34. Shim, S. and Drake, M.F. (1990), “Consumer intention to utilize electronic shopping”, Journal of Direct Marketing, Vol. 4 No. 3, pp. 22-52. Tauber, E.M. (1972), “Why do people shop?”, Journal of Marketing, Vol. 36, pp. 46-59. Tornatzky, L.G. and Klien, K.J. (1982), “Innovation characteristics and innovation adoption – implementation a meta-analysis of findings”, IEEE Transactions on Engineering Management, Vol. 29 No. 1, pp. 28-45. Venkatesh, V. and Davis, F.D. (1996), “A model of the antecedents of perceived ease of use: development and test”, Decision Sciences, Vol. 27 No. 3, pp. 451-81. Venkatesh, V. and Davis, F.D. (2000), “A theoretical extension of the technology acceptance model: four longitudinal field studies”, Management Science, Vol. 46 No. 2, pp. 186-205. Venkatraman, M.P. (1991), “The impact of innovativeness and innovation type on adoption”, Journal of Retailing, Vol. 67 No. 1, pp. 51-67. Wang, Y., Wang, Y., Lin, H. and Tang, T. (2003), “Determinants of user acceptance of internet banking: an empirical study”, International Journal of Service Industry Management, Vol. 14 No. 5, pp. 501-19. Weitzman, J. (2000), “Cendant unit helps banks offer internet access”, American Banker, Vol. 165 No. 2, p. 11. Williams, T., Slama, M. and Rogers, J. (1985), “Behavioral characteristics of the recreational shopper and implications for retail management”, Journal of the Academy of Marketing Science, Vol. 13 No. 3, Summer pp. 307-16.
Appendix Measures Diffusion of innovation – online banking variable. Adoption of the web for banking was measured via yes/no response to the question: “Do you have an online banking account?” Personal consumer innovation variables. Innate (general) marketplace leadership – “Market Maven” Below are a number of statements. Please click on the response that most accurately describes you. Please assign a rating on a scale from 1 to 7 based on which number you consider to be the most appropriate for yourself where 1 ¼ “the description does NOT fit me at all” and 7 ¼ “the description fits me VERY well.” . My friends think of me as a good source of information when it comes to new products or sales. . I like helping people by providing them with information about many kinds of products.
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I like introducing new brands and products to my friends and co-workers. If someone asked where to get the best buy on several types of products, I could tell him or her where to shop. People ask me for information about products, places to shop, or sales. Think about a person who has information about a variety of products and likes to share this information with others. This person knows about new products, sales, stores, and so on (e.g. web sites, etc.), but does not necessarily feel he or she is an expert on one particular product. How well would you say this description fits you?
Domain-specific (internet) or actualized innovation – opinion leadership. All questions were administered on a five-point scale: “Please click on the response that best represents your agreement with the following statements [Strongly Agree – Agree – Neutral – Disagree – Strongly Disagree]” (1) My friends and co-workers come to me more often than I go to them for information about the internet and/or web site(s). (2) I feel that I am generally regarded by my friends and co-workers as a good source of advice and/or information about the internet. (3) My friends and co-workers often ask my advice about the internet and/or particular website(s). (4) I sometimes influence the web site(s) that my friends and co-workers visit. Domain-specific (internet) or actualized innovation – opinion seeking. All questions were administered on a five-point scale: “Please click on the response that best represents your agreement with the following statements (strongly agree – agree – neutral – disagree – strongly disagree)” (1) I often seek out the advice of my friends and co-workers regarding the internet. (2) My friends and co-workers usually give me good advice as far as the internet is concerned.
Self-efficacy-related (experience) measures Technology comfort level. (a) How comfortable do you feel using computers in general? Very comfortable Somewhat comfortable Neither comfortable nor uncomfortable Somewhat uncomfortable Very uncomfortable (b) How comfortable do you feel using the internet? Very comfortable Somewhat comfortable Neither comfortable nor uncomfortable Somewhat uncomfortable Very uncomfortable
(c) How satisfied are you with your current skills for using the internet? Very satisfied – I can do everything that I want to do Somewhat satisfied – I can do most things I want to do Neither satisfied nor unsatisfied Somewhat unsatisfied – I can’t do many things I would like to do Very unsatisfied – I can’t do most of the things I would like to do Length of internet use. How long have you been using the internet (including using e-mail, ftp, gopher, etc.)? Less than 6 months 6-12 months 1-3 years 4-6 years 7 years or more Web usage intensity. (a) On average, how many hours a week do you use your web-browser? Less than 1hour 1 to 5 hours 6 to 10 hours 11 to 20 hours 21 to 40 hours Over 40 hours (b) On average, how often do you use your web-browser (e.g. netscape communicator, internet explorer, etc.)? More than 9 times a day 5 to 8 times a day 1 to 4 times a day A few times a week Once a week Once a month Type of web use Utilitarian. We would like to explore the extent to which you use the internet for specific activities. To what extent would you say you use the internet to search for specific information? Would you say. . . Most of the time ¼ 4; Sometimes ¼ 3; Seldom ¼ 2; Never ¼ 1. Hedonistic. To what extent would you say you use the internet to express yourself: to help you convey the right impression to others (either on- or off-line)? Would you say. . . Most of the time ¼ 4; Sometimes ¼ 3; Seldom ¼ 2; Never ¼ 1.
Consumer innovativeness
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