International Journal of Mobile Communications
INBCT 4.1 Mobile Banking and Consumer Behavior – New Insights into the Diffusion Pattern Suoranta, Mari & Mattila, Minna
Abstract Technological advancement has challenged the providers of financial services; the very nature of selling and buying financial services has changed. The paradigm shift, from traditional branch banking to electronic banking and the newly emerged wireless delivery channel are the motivators of this study in which the focus is on studying diffusion and adopters of mobile banking services. In consequence we are able to state what is the influence of certain demographic characteristics of the customers on the adoption and usage of mobile banking services. A quantitative survey sheds more light on this researched issue. The data was collected in Finland during May-July 2002 and includes 1253 survey responses. Keywords: Innovation diffusion, mobile banking, adopter categories, demographics Mari Suoranta, Assistant Professor of Marketing E-mail:
[email protected] Minna Mattila, Professor of Marketing E-mail:
[email protected] University of Jyvaskyla School of Business and Economics P.O.Box 35 (MaE) 40014 Jyvaskyla, Finland Tel. +358-40-5085552 Fax +358-14-2602968
Mobile Banking and Consumer Behavior – New Insights into the Diffusion Pattern Biographical notes: Mari Suoranta (M.Sc. Econ) is an Assistant Professor of Marketing and Ph.D. Candidate at University of Jyvaskyla, Finland. Her current research interests include electronic service delivery channels, mobile phone services’ diffusion and e-customer behavior. She has participated in the research projects in these areas. She is also writer of the working paper “The Future Developments in Mobile Phone Services” (available only in Finnish). Minna Mattila (Ph.D. Econ) is a Professor of Marketing and Docent at Electronic Business at University of Jyvaskyla, Finland. Minna Mattila has specialized in e-customer behavior. Prior to her current position, she led a software business program as well as has been teaching in Europe and the US. She has been involved in several large research projects focusing on e-banking, electronic retail shopping and mobile business. She also consults for Finnish banks, which are among the world leaders in the area of e-banking. Her work has been published in several international journals and conference proceedings.
1. Introduction The paradigm shift, from traditional branch banking to electronic banking; the newly emerged channels and rapidly increasing penetration rates of mobile phones are the motivators of this study. Technology has become an increasingly vital element in the competitive landscape of the financial service industry. The recent developments have created a totally new service concept and service environment [1]. Technology has changed the very nature of selling and buying financial services. Innovations in telecommunications have led to usage of mobile devices in banking services. Mobile banking is among the newest electronic delivery channels to be offered by banks. As using the term electronic banking we refer to a definition, which explains it as the provision of information and services by a bank to its customers via electronic wired or wireless channels, for example Internet, telephone, mobile phone or interactive television [2]. Currently, conducting account balance and transaction history inquires, funds transfer, bill payments, stock trades and quotes, portfolio management as well as insurance ordering are technologically enabled via a mobile device. Even though technology and applications for these services are available, the usage rates internationally have been fairly low and, in fact, in most developed countries financial institutions have only recently begun to offer mobile services to customers. Mobile banking service market is still in its infancies [3, 4]. The newly emerged mobile banking services represent an innovation where both intangible service and an innovative medium of service delivery employing high technology are present. Thus, concepts of innovation and diffusion of innovation are even more intricate as technology and service aspects have an effect on the characteristics of mobile banking services [5]. As the technology has become increasingly more vital element of service delivery, managerial interest in understanding the adoption processes and different customers as adopters has led to calls for more academic research. This paper aims at answering that call by shedding light on the consumer behavior in mobile services era and in particular on influence of certain demographic characteristics on adoption. The survey was conducted among Finnish bank customers. The approach we employ is practical and provides insights drawn from the quantitative empirical survey. It is argued that because of the above mentioned complexity of the service models in general and the convergence of technologies and services, there are very little relevant research available to help us understand the adoption of mobile services [6]. However, adoption of basic mobile services as well as Internet services has received research attention in recent years. Pedersen and Ling [7] suggest that this research is highly relevant and can provide valuable starting points understanding more complex end-user services. In banking context much of the existing research cover tele-banking [e.g. 8,9] or Internet banking [e.g. 10,11,12] perspectives. Nevertheless, a lack of studies directly investigating the adoption and diffusion patterns of the mobile banking services is to be expected due to newness of the services. Customer behavior in mobile banking context has remained rather uncharted territory, which further raises the value of the contribution of this study. The paper is organised as follows: it begins with a brief review on the traditional standpoint of the diffusion and adoption research as well as on current state of mobile banking usage in Finland in order to provide research rationale for the study. Thereafter, the methodology and data collection are described and the relevant empirical implications of the survey presented. The paper concludes with a discussion of the findings and development of practical guidelines applicable to this case.
2. Diffusion of Mobile Banking Services Diffusion Research Theoretical framework of this paper is based on the traditional innovation diffusion research. Rogers defines diffusion as the adoption of an innovation “over time by the given social system”, as a consequence diffusion processes result in the acceptance or penetration of a new idea, behavior, or physical innovation [13]. The classical diffusion study typically contrasts different user categories to describe the adoption process of an innovation a posteriori. Several aggregate mechanisms are proposed to explain the observed diffusion process [7]. In marketing the main impetus underlying diffusion research is the Bass model which focuses on how information is communicated in media and interpersonally, and how the two mechanisms of communication result in an S-shaped aggregate adoption rate often observed in studies of innovation diffusion [14]. Consequently, in diffusion research interest is in aggregates of individual users, typically identified as user segments or as other aggregate communities of users. Diffusion research mainly focuses on describing and explaining the adoption process as a process of innovation diffusion at the aggregate level. Studies focusing on description typically characterize user segments along the diffusion process, such as early adopters, early majority users and laggards using demographic and socioeconomic variables [7]. This is also our main research interest. Valuable research avenue examples exist, e.g. Mattila et al. [15] studied Internet adoption among mature customers or Wei [16] studied the socioeconomic characteristics of mobile phone laggards. These studies do not concentrate to explain the observed segment differences. Rogers suggested that explanation can be found in attributes of innovations being adopted. Research that has investigated the product characteristics of innovation has generally endorsed evaluating the innovation along the product characteristics that involve five constructs; relative advantage, compatibility, complexity, trialability and observability [17]. Concept of perceived risk is often included as augmented by Bauer [18]. In mobile banking context our findings reveal relative advantage to be the most important trigger for adoption of mobile banking services and it constitutes of availability, 24/7 access, independence of time and place and portability. According to Keen and Mackintosh [19], the key value proposition of mobility is creation of choice or new freedom for customers. If mobility aspect will be the most valued feature by customers in the future as suggested by the survey data, the wireless connections gain advantage over wired connections in banking too. Bass diffusion model assumes that potential adopters of an innovation are influenced by two types of communication channels: mass media (external influence) and interpersonal worth-of-mouth (internal influence) channels, with the latter much more important. Individuals adopting based on mass media messages occur continually throughout the diffusion process, but are concentrated in the relatively early time periods. Individuals adopting as a result of interpersonal messages about the new idea expand in numbers during the first half of the diffusion process, and thereafter declines in numbers per time period, creating the S-shaped diffusion curve. Further Bass model assumes that the rate of adoption during the first half of the diffusion process is symmetrical with that in the second half, as necessary for a S-shaped diffusion curve [13,14]. Place here: FIGURE 1 Adoptions due to external and internal influence in the Bass model [20] Traditionally, the Rogers’ adoption continuum recognizes five categories of consumers that differ in terms of adoption rate and, as the findings of this study reveal, in terms of certain socioeconomic characteristics. Innovators who are the first adopters, interested in technology itself with positive technology attitudes; early adopters who are also interested in technology and willing to take risk; early majority who can be considered pragmatist and process oriented; late majority who are more or less skeptical with negative technology attitudes; laggards who have extremely negative technology attitudes and hence never adopt technology among the main stream [5,13]. Each category of adopters has unique characteristics. Adopter Category Differences Research literature states that information about technological innovations can travel through a variety of communications sources and modes to members of a social system [13]. For example, the two-step model of communication posits that information flows from mass media (e.g. commercial advertisements) to opinion leaders (innovators), and that the less active members of the society (imitators) are subsequently influenced by interpersonal communication with these innovative consumers [21]. One of the basic assumptions is that innovators tent to be heavier users of professional communication sources, such as sellers, governments and other third parties, than imitators and non-adopters. Thus, preferred information source may differ across different adopter categories and individuals may have different propensities for relying on marketer-provided information, independent third-party information, and information from personal sources [22]. Lee et al. [21] have found that communication factors are indeed significant predictors of consumer adoption of electronic banking innovations too. Voluminous research literature has accumulated about variables, such as socioeconomic or personality characteristics of the potential adopters, related to innovativeness [13]. Earlier adopters of technological innovations are often stated to be relative young, have higher income, more education, and higher social status (professional, technical and managerial) occupations. According to Polatoglu and Ekin [23] and Howcroft et al. [9] demographic factors that describe typical electronic banking services adopter include young, affluent and highly educated. In earlier Finnish studies findings of the typical Internet banking user were somewhat similar and in some respect contradictory. A Finnish study [12] reported Internet banking user is middle-aged, relative wealthy and highly educated. Gatignon and Robertson [24] made an interesting finding on the basis of their review of adoption research. New product innovators in technology-based products are likely to be drawn from heavy users of other products within the product category. Adopters who adopt earlier than others are likely to have more gain from the use of the product and hence have a greater usage propensity. Additionally, it is argued that adoption of complex products depends on adopter’s ability to develop new knowledge and new patterns of experience. This ability can be enhanced by the knowledge gained from related products. In Finland usage of Internet banking has already diffused to masses of banking customers, on that basis a conclusion might be drawn that Internet banking services can serve as related service products to mobile banking services and that innovators of mobile banking are drawn from the heavy-users of Internet banking. Mobile Banking in Finland Before discussing methodological standpoints and the empirical evidences of the survey relating to above discussed issues it is worthwhile to give some insights into the current state of mobile banking activities in Finland. Northern European countries are among the most advanced ones in the adoption to and use of different new mobile and technological appliances [25]. In Finland payments and account management products over mobile GSM phones as SMS service have been available over one decade, exactly since 1992, television-based banking since 1998 and banking via mobile Internet WAP since 1999 [3]. Finnish customers conduct their routine banking mainly via Internet, over 70 % of the customers visit a branch office less than twice a year. The number of bank branches in Finland has been shrinking in rhythm with increased electronic banking usage [26]. Mobile phone penetration amounted to 94 % in year 2002. The structures of Finnish society comprising information infrastructure have developed over years to be favourable for adopting technology-based products and services. Finland has a history of building out information infrastructure to connect its geographically dispersed population, in addition well educated work force, effective policy environment and sophisticated use of information and communication technologies explain that further. The financial services industry implemented advanced payment, security and verification internal IT systems in the early 1990s, enabling Finland to be among the first in the world to offer online and mobile services [25].
3. Methodology and Data Collection The methodological approach in this study is descriptive, the phenomenon to be studied is comparatively new in the field of academic research and thereby study aims at increasing the understanding of the current consumer behaviour pattern in electronic services era, and particularly in mobile banking [27]. The research data was collected by means of a traditional postal survey. The pre-tested questionnaire was sent to a gross section of 3.000 bank customers in Finland. This resulted to 1303 responses of which 1253 were usable i.e. usable response rate amounted to 41.8 percent, which was really satisfactory and above the 20-30 percent rate considered acceptable in economics research. The objective was to gather a highly representative sample that was also attained as the sample represents geographically Finland and the respondents were chosen in terms of their banking habits. The survey sample consisted of three equal-sized segments that were selected according to mobile banking usage experience and density. The non-users have not ever used permanently any form of mobile banking services, the occasional users had started to use some form of mobile services and the regular users had been using services for a longer period of time. The questionnaires were also partly tailored respectively. This data forms the basis of the whole research of which this paper is one part. Only the selected sections of the survey data will be used in the present paper. According to the chosen methodological research approach the quantitative data was analysed using statistical methods by SPSS-program. The demographic profile of the respondents is summarized in TABLE 1. Place here: TABLE 1 Demographic profile of the respondents
4. Empirical Implications Information Sources Referring to the Bass model of diffusion we discuss the information sources (external and internal) influencing and contributing to the adoption of mobile banking services. Based on the information received from out empirical data, we know the respondents’ main sources of information about mobile banking services; and why the customers tried mobile services in first place. Research results among so called occasional users were consistent with Bass model arguments (FIGURE 2). Most of the occasional users, 46.7 %, had been exposed to interpersonal influence, namely recommendations by bank’s personnel. Importance of mass media exposure was not equally significant, 16.4 % of respondents were influenced by bank’s direct marketing activity (letter) and 15.7 % by bank’s advertisement. In the very beginning of the diffusion process it is typical that adoptions are more due to external influence, i.e. mass media, and as the process continues internal influences gain in importance. Occasional users of this survey may be characterized to consist of both innovator and imitators as defined by Bass. In the group of so called non-users 36.3 % of the respondents had heard about mobile banking services through mass media, banks’ advertisements. And 26.1 % of respondents have had bank’s letter as information source and 19.5 % bank’s personnel, in other words, the results confirm the communication source and mode pattern presented in the literature. Our findings are consistent with that of Lee’s et al. [21], financial institutions are currently the most active diffusion agents for customers as well as receiving written information from financial institutions is likely to increase the probability of adopting electronic banking innovations such as mobile banking services. Place here: FIGURE 2 Information sources Adopter Category Differences: Age and Household Income Following the rationale of diffusion model, our research interest focused on investigating intentions of customers of different age and income category to begin the usage of mobile services in the future or of customers who already use those services to continue and increase their usage. As considering the intentions of the respondents in the non-users group to adopt mobile services, it seems that 35 to 49 years old (29.4 %) are the next ones to begin the usage of mobile banking services; it is also the largest age category among the non-users of this survey. In the occasional users group the most eager to begin regular usage of the services are 25 to 34 (38.3 %) years old customers. As we examined the results this way, it seemed that mobile banking service innovation is adopted as diffusion models often suggest; younger consumers first followed by older customers. However, in order to gain a realistic picture of adoption intentions of the respondents it is more worthwhile to discuss internal percentages of intentions in the age category of the user groups of occasional/regular/non-users respectively, i.e. to study proportional percentages, instead of mutually comparing groups of different sizes. The FIGURE 3 depicts the results. Place here: FIGURE 3 Intention to begin regular usage of mobile banking services presented proportional among the user groups by age category As it can be seen from the FIGURE 3 in the current non-users group the most eager ones to begin the usage are the 50 years or over old customers. Two of three respondents (30.2 % in age group 50-64 years old and 35.5 % in age group 65 years and over) stated that they will begin to use mobile banking in the near future. Another very interesting implication from the figure is that middle-aged are not very willing to begin usage of the services. According to other Finnish studies [e.g. 12] on electronic banking middle-aged customers are just the main users of Internet banking. In terms of age category intentions of occasional users to adopt mobile banking seem to follow the traditional adoption and diffusion pattern as mentioned already in the above discussion. As considering household income category it seems that the next customers among the non-users group to begin usage of the services are those who have annual household income of 30.001 to 40.000 euros (20.5 %) and respectively among the occasional users those who have annual household income of 20.001 to 30.000 euros (21.3 %). Again investigating the proportional percentages is more meaningful. The FIGURE 4 illustrates that among the occasional users group customers who have annual household income level fewer than 50.000 euros are more willing to begin usage, whereas customers earning more have lower intention to use mobile delivery channel. The non-users are in general willing to a lesser degree. Averagely half of the all customers in household income category are going to conduct their banking through mobile channel. Place here: FIGURE 4 Intention to begin regular usage of mobile banking services presented proportional among the user groups by household income category Our research interest included also the current mobile delivery channel usage of so called regular users of the survey and diffusion development among this user group. Even though regular users can be considered to be the customers who have already made favorable adoption decision, we cannot forget that individual’s innovation decision is not only an instantaneous act but rather a process consisting of a series of actions and decisions in which reversing a previous decision may occur. Rogers [13] defines that as discontinuance which is a decision to reject an innovation after having previously adopted it. Mattila and Pento [28] have studied adopters of Internet banking as well as electronic grocery shopping in this context. Their results confirm that it is possible though rare that regular users who have made the innovation adoption decision may stop using an innovation and “drop down” to the group of people, which they labeled T and characterized “have tried, but not users of an innovation anymore”. FIGURES 5 and 6 illustrate the findings in this survey. In FIGURE 5 proportional comparison inside the age category makes the figure optical fallacious, because in this user group there is no respondents under 18 years old and only one respondent over 65 years old. However, excluding these age categories from investigation, we can make an alarming conclusion. Proportional share indicating intentions to continue and increase usage of mobile banking services amounted only to approximately 50 % of the customers in each age category. Place here: FIGURE 5 Intention to continue regular usage of mobile banking services presented proportional among the user group by age category The same trend of future usage intentions among regular users can be seen in FIGURE 6, in terms of household income category approximately half of the customers are going to use mobile delivery channel as their main banking service delivery channel. Interesting question arises: why are today’s mobile banking services users not more willing to continue and increase their usage? And what are the underlying factors causing that development? Answers to these considerations of high importance have been covered in the scope of the whole study, but are not discussed in detail in this paper. Anyway, figures indicate indisputably that the mobile banking services are not yet fully institutionalized and routinized into the ongoing practice and way of life of the adopters. Place here: FIGURE 6 Intention to continue regular usage of mobile banking services presented proportional among the user group by household income category
5. Conclusions The paper provided new interesting insights into the diffusion pattern of mobile banking services adopters - and maybe not so surprisingly. As Pedersen and Ling [7] have noticed within the research area of these new services, whereby technology, service as well as human-interaction aspects convergence, traditional diffusion models need to be extended and modified. In consequence, we are able to gain more comprehensive understanding of value-added mobile services, such as mobile banking, as well as draw conclusions that contribute not only to the theory but also to practice of mobile service development. In a management context, several of our findings are apposite to financial services providers to better understand diffusion of mobile banking services and characteristics of the prospective adopters. Number of issues is worth mentioning. High hopes for the diffusion of mobile services in banking are not completely unfound. With the high market penetration of mobile phones and the optimally designed marketing tactics of service providers, exposure to mobile technology increases and such exposure is founded to be likely to facilitate the adoption [29]. Several expected adopter category differences in diffusion of mobile banking services innovation were discussed and indicators thereby included adopters’ communication source, age and household income. Service providers’ communication style should be compatible with the information processing styles of potential adopters. Applying the notion of segmentation is also useful in this context; disseminating information through the right channel and the right mode of communication for different consumer segment will likely increase each segment’s probability to adopt technological innovations. It has been found age to be related to attitudes and adoption of technology. The growing market segment, the elderly, have traditionally been considered resistant to change and having negative attitudes towards technology [30]. And then again Rogers [13] states that earlier adopters are not different from later adopters in age, this study both verifies and on the other hand is in contrast with his argument. Future users of mobile banking services will likely be older than the researchers and public opinion have expected. Mature customers are usually not seen as innovators or early adopters of new technologies but rather belong to the late majority or even laggards in terms of adoption rates of technology-based new services [31]. In the light of the findings of this study the next customers willing to adopt mobile banking are the ones over 50 years old. Thus, issue is not that straightforward. Literature on elderly customers is replete with examples of problems such as marketers’ lack of genuine awareness of the scope and multidimensionality of the mature market and the stereotyping of this market’s members. However, innumerable opportunities and challenges for marketers to appropriately respond to this market remain. Internet banking surveys state that wealthier customers are more willing to adopt and use technology-based services. Traditional literature suggests that wealthier people are more likely to adopt innovations earlier [13]. In this survey the clear indication was that wealthier respondents were less willing to adopt the new mobile banking services. The adoption framework that we were able to form in the survey context implies that it is not just the paradigm of service environment that is changing but also the typology of electronic service user. It seems that typical Internet banking users will continue the usage of wired delivery channel and the current users of bill payment automates and branch offices will more likely “leap” to usage of mobile banking. Internet banking is obviously not the related service product category in a way as suggested by Gatignon and Robertson [24]. Furthermore, new mobile banking innovators are not likely to be drawn from heavy users of Internet banking services; they will more probably stick to Internet. The conclusion provides further reasoning why Internet designed services and strategies cannot directly be converted into mobile service environment: differentiation is needed. It should be noted that this study examined mobile banking only in Finland which can be regarded as one of the most advanced countries technologically and where technological advancement have been extended in banking services too. Research perspective was focused on only consumer and on a certain, limited number of adopter characteristics. These elements narrow the scope of generalization the findings. Even though the sky of mobile banking in now blue and clear the thunderclouds may arise if the questions we raised in the end of empirical evidence section are not thoroughly investigated. And this is the next research avenue we will step on.
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TABLE 1 Demographic profile of the respondents Demographic Frequency Percentage Cumulative Characteristics percentage Gender Male 634 50.6 50.6 Female 590 47.1 97.7 Missing 29 2.3 100 Standard deviation 0.499 Age Under 18 4 0.3 0.3 18-24 years 226 18 18.3 25-34 years 418 33.4 51.7 35-49 years 370 29.5 81.2 50-64 years 212 16.9 98.1 65 years and over 17 1.4 99.5 Missing 6 0.5 100 Standard deviation 1.026 Marital status Married 488 38.9 38.9 Cohabitation 337 26.9 65.8 Single 322 25.7 91.5 Widow 13 1 92.5 Divorced 75 6 98.5 Missing 18 1.5 100 Standard deviation 1.113 Occupation Executive 70 5.6 5.6 Worker 503 40.1 45.7 Not at work 84 6.7 52.4 White-collar worker 246 19.6 72 Student 132 10.5 82.5 Farmer 29 2.3 84.8 Pensioner 54 4.3 89.1 Entrepreneur 74 5.9 95 Public servant 49 3.9 98.9 Other 5 0.5 99.4 Missing 7 0.6 99.9 Standard deviation 2.183 Household income Under 10.000 euros 109 8.7 8.7 10.001-20.000 euros 191 15.2 23.9 20.001-30.000 euros 239 19.1 43 30.001-40.000 euros 195 15.6 58.6 40.001-50.000 euros 181 14.4 73 50.001-60.000 euros 130 10.4 83.4 60.001-70.000 euros 67 5.3 88.7 70.001-80-000 euros 34 2.7 91.4 Over 80.001 euros 33 2.7 94.1 Missing 74 5.9 100 Standard deviation 1.988
FIGURE 1 Adoptions due to external and internal influences in the Bass Model [20]
FIGURE 2 Information sources
FIGURE 3 Intention to begin regular usage of mobile banking services presented proportional among the user group by age category
Note! Under 18 years old presents only 0.3 % of all respondents
FIGURE 4 Intention to begin regular usage of mobile banking services presented proportional among the user gropus by household income category
FIGURE 5 Intention to continue regular usage of mobile banking services presented proportional among the user group by age category
FIGURE 6 Intention to continue regular usage of mobile banking services presented proportional among the user group by household income category
EUROOPAN UNIONI