University of Jyväskylä
School of Business and Economics
Mari Suoranta
ADOPTION OF MOBILE BANKING IN FINLAND
DOCTORAL DISSERTATION MANUSCRIPT
Author’s address Mari Suoranta
University
of Jyväskylä
School
of Business and Economics
P.O.Box 35
40014
University of Jyväskylä
Finland
Tel.:
+358-40-5085552
E-mail: suoranta@econ.jyu.fi
Supervisor Professor Minna Mattila
University of Jyväskylä
School of Business and Economics
Reviewers Professor George Giaglis
Department of Management Science
and Technology
Athens University of Economics
and Business
Professor Bharat
Rao
Department of Management
Polytechnic University of New
York
Opponent Professor George Giaglis
Department of Management Science
and Technology
Athens University of Economics
and Business
TABLE OF
CONTENTS
ABSTRACT
ACKNOWLEDGEMENTS
LIST OF FIGURES AND TABLES
PART I
Page
I INTRODUCTION 11
1.1 Motivation 12
1.2 Electronic
banking 15
1.1.1 Mobile banking 17
1.1.2 The Finnish mobile banking
environment 18
1.3 Research
objectives and questions 20
1.4 The structure
of the dissertation 21
II RESEARCH
METHODOLOGY AND DESIGN OF THE STUDY 23
2.1 Research design 23 2.1.1
Using a descriptive research approach 24
2.2
Data source and sampling 26
2.2.1 Questionnaire design 28
2.3 Method of
analysis 31
2.4
Reliability and validity of the study 32
III
THEORETICAL BACKGROUND
34
3.1 Research into innovation diffusion and adoption 34
3.1.1 Attributes of innovation 36
3.1.2 Adopter categories: individual
differences 39
3.1.3 Communication 40 3.2 Electronic banking research 43
3.3
Convergence of technology and services 45
3.3.1. Technology-based services 45
3.4 The framework
of the dissertation 47
3.4.1 Model development 47
IV REVIEW OF
THE SURVEY DATA 49
4.1
Descriptive statistics 49
4.2
Demographics of the study participants 52
4.3 Current
usage of mobile banking services 58
V CONCLUDING
DISCUSSION 61
5.1
Contributions 61
5.1.1 Hypotheses tested 62
5.1.2 Theoretical contributions 64
5.1.3 Implications for practitioners 65
5.1.4 The revised model 67
5.2
Limitations of the study 68
5.3 Future
research 69
REFERENCES 72
PART II
RESEARCH ARTICLES 79
I “Usage of
Mobile Services: Empirical Findings from a Bank Customer Survey”
2003 American Marketing Association Educators’ Proceedings 79
II
“Modelling Mobile Banking Adoption: An Empirical Investigation”
2003 European Marketing Academy Conference Proceedings 105
International Journal of
Innovation Management
III
“Technology-based Service Products – a Study on the Drivers and Inhibitors
of Mobile Banking”
The Second International Conference on Mobile Business 134
International Journal of Mobile Communication
(Special Issue)
IV “Mobile Banking and
Consumer Behaviour – New Insights
into the
Diffusion Pattern”
Journal of Financial Services Marketing 159
YHTEENVETO (FINNISH SUMMARY) 188
I INTRODUCTION
The
banking environment is constantly changing.
The
financial services industry is in transition.
Technology
is profoundly changing the nature of financial services.
Statements
such as these occur both in the academic literature of banking and in trade
literature within the banking. Technology and change became a mantra of the
1990s – and one which was by no means unjustified. It is evident that the
financial services industry has been undergoing a profound transformation.
Rapid changes in the banking environment, increased competition by new players
from the non-banking sector, product innovations, globalisation and
technological advancement – all these have led to a market situation in which
the battle for customers is intense. As a consequence, banks have started to
offer services through various delivery channels. In the name of increased
customer satisfaction and efficiency, they have developed innovative service
products and offered a wider range of services. The delivery of multi-channel
services forms a part of these efforts. One step in achieving the goal of the
banks is the provision of banking services through electronic delivery
channels. Among the newest services to be offered is a wireless delivery
channel, with banking services being available via mobile phones or Personal
Digital Assistants (PDAs). Clearly, mobile banking
services form an important innovation in the banking sector, and it is mobile
banking as a phenomenon which is under investigation in the present research.
This introductory chapter aims at familiarising the reader with the overall
nature of the research phenomenon, presenting the basic concepts, and the way
in which they are to be understood and defined for the purposes of this
dissertation.
1.1 Motivation
The
emergence of new forms of technology has created highly competitive market
conditions, and these have had a critical impact upon consumer behaviour. Not
surprisingly, this competition puts a premium on innovation and precision in
delivering services. These two constructs have become the basis for the new
competencies required by banks. In other words, innovation and service
delivery, and more importantly, managing the opportunities offered by current
technological advancement, can be seen as critical factors for success at the
present time.
Technological innovation is a complex
notion for various reasons, one of these being the generic breadth of the
concept itself. In fact, many kinds of technology and definitions of technology
are applicable to the concept. Betz (1998, 9) defines technology as "the
knowledge of the manipulation of nature for human purposes”. Carlell (2002) cites in her thesis Joerges’
(1988, 221) definition of technology: technology is said to refer to “artificial
things, and more particularly modern machines: artificial things that require
engineering knowledge for their design and production, and perform a large
amount of operations by themselves”. Both definitions refer to knowledge,
thus harking back to the etymological root of the word. The Creek word “technos” means the process of doing something and “ology” a systematic understanding of something (Betz 1998).
Technology acts as a mediating device for bringing life into realisation, for
bringing the world to us. In present-day society technology also focuses on a
different kind of mediation, since it is supposed to ease people's efforts and
to speed them up (Carlell 2002). Innovation, for its
part, can be defined following Rogers (1995, 11) as “an idea, practice, or
object that is perceived as new by an individual”. Here the research
interest is on consumer behaviour, with the emphasis being placed on the
perceived newness of the idea and rather than on its objective newness;
furthermore the unit of adoption is taken to be the individual, rather than,
for example, an organisation.
New technologies create new markets and
opportunities for the banking sector, and thus managing and satisfying the
customers in this new banking environment has become a key issue for the
players in the industry (Jayawardhena and Foley
2000). Opposing possibilities exist: of exploiting advanced technologies
successfully, or by contrast, investing in an unsatisfactory technology and
drowning in a technological chasm. The question is how to select and exploit
new forms of technology in the right way and at the right time so that the
banks can compete successfully: developing new processes without having their
returns threatened as a result of wasteful expenditure. Bank managers must be increasingly aware of
the opportunities that come with technological change. In order to rise to
these challenges, service providers are finding it ever more vital to improve
their understanding of consumer behaviour patterns in banking, and consumers' adoption
of new banking technology.
Mattila and Pento (2002) present their own view of the problem of
combining marketing knowledge with technological development, regarding it as
the “black hole” of the marketing-technology interface (see Figure 1). The upper
part of the figure indicates marketing knowledge (information on customer’s
needs, the employment of different marketing tools, choices on distribution
channels) and the lower part the technical approach, i.e. one that has regard
to available technologies and their feasibility. The point is that though both
customer-specific and market information are essential for determining market
needs and profit opportunities, in most cases they seem to vanish as if into a
black hole, somewhere between the marketing and the technical approaches. Given
this situation, such information asymmetries may result in very low adoption
rates of new financial products. Until recent years, mobile application
innovations such as Wireless Application Protocol services followed a
technology-driven model of design. Failures in the introduction of these new
services have taught valuable lessons, not least to providers of financial
services. In order to succeed, innovations need to be market-driven and
customer-centric.
FIGURE 1 The ”black hole” of the marketing-technology interface (Mattila and Pento 2002)
The use of
technology in banking activities is by no means something totally new; Vesala (2000) distinguishes in his thesis the effects of
two types of technological development. The internal wave of
technological development in banking began as far back as the 1960s and 1970s.
This involved the development of an information technology which substituted
computers for paper-based and labour -intensive methods of accounting; the
areas it affected included customer deposits, withdrawals and other
transactions, and many internal operations. This resulted in significant
increases in efficiency and productivity, and lowered banks' costs. However,
more recent developments in information technology have provided the
opportunity for customers to access banking services without direct
face-to-face contact with bank personnel. This external wave of
technological development has intensified in recent years and will further
reduce financial institutions’ costs.
At
the present time, we are witnessing a revolution in one particular
technological development, namely wireless technology. Mobile communication is
an emerging information technology that makes “anytime-to-anyplace”
communication possible. In order to exploit these opportunities, companies in
various industries have rapidly begun to integrate mobile communication
technologies into their business models, and the financial services sector is no
exception (Yen and Chou 2000). Mobile technologies for communications, for
accessing the Internet and for mobile commerce transactions are being adopted
extremely rapidly; indeed, over the few last years mobile devices have become
the consumer product that has been most quickly adopted to date (Dholakia et al. 2003). Siau and Shen (2003) have set out the features of mobile services that
can be seen as key drivers as well: mobility, reachability,
localisation and personalisation. The essence of the popularity of the services
involves the capability of reaching consumers, and of being able to deliver the
right information to the right place at the right time – features which are in
fact fundamental to marketing in general.
In the financial services industry, the
major changes brought about by developments in information technology involve
particularly the link between consumers and firms, and the generation of new
service products (Devlin and Wright 1995). Undoubtedly, there has been a
reshaping of the behavioural patterns that exist between consumers and their
financial institutions. Today, customers can easily access and obtain
information on different suppliers of banking services and hence make
comparisons, and one might expect customer loyalty to diminish as a
consequence. Yet although consumer
empowerment is discernible at a general level, it is
debatable to what extent the shift is truly evident in banking because of the
nature of financial services. One is led to consider whether reluctance to
change banks is based on strong feelings of loyalty, or merely to high costs
deriving from the need to compare various service offerings (often of
considerable complexity), to change bank cards and electronic banking key
codes, and so on (Harrison 2002). However, consumers are all the time becoming
more technologically aware, and their distrust of technological innovation may
be lessening. All in all, one can say that the infusion of new technologies
into the services sector is ubiquitous, and that it will continue to increase (Bitner et al. 2000).
1.2 Electronic banking
The drivers
for the take-up of information and communication technologies in the financial
services industry cover a wide spectrum. The basic trends affecting the
financial market are globalisation, deregulation, liberalisation, mergers and
acquisitions, competition, technology and new demographic trends (Vesala 2000; Koch and MacDonald 2000). Nowadays, technology
runs through every part of the banking business. And why not?
This is precisely what one would expect: the banking industry has provided
fertile ground for the application of technological innovation due to the fact
that banking activities are easily digitised and automated (Bradley and Stewart
2002; Daniel 1997). Most of the players in the financial services industry have
recognised the fact that electronic banking is an area of major importance, and
various versions of online banking are offered by all the major banks. This is
the case throughout developed markets, but also increasingly in emerging
markets (Karjaluoto et al. 2002; Datta
et al. 2001).
Electronic
banking can be defined in various ways. In this dissertation as using the term electronic banking it is referred 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 (Daniel 1999). In other words, electronic banking is a
high-order construct consisting of several delivery platforms of which mobile
devices are an example; thus mobile banking as a construct is a subset of
electronic banking. In this dissertation, mobile banking is taken to involve
the use of a traditional banking service via a mobile device (e.g. a account balance inquiry), but not, for example the use of
mobile devices for instant payment of vending machine items. It is obvious that electronic banking cannot be
detached from the context of Internet, which has had and will continue have to
a significant impact on the diffusion of electronic banking. It is interesting
to note that on a global level the first Internet-based banking service system
was in fact launched in Finland; this was in 1996 (Karjaluoto
et al. 2002), around the time when the use of the Internet was just starting to
become widespread within the country.
Clearly, there
is growth potential in electronic banking, as its overall growth is likely to
follow the curves of personal computer and Internet usage. Similarly, the
ongoing expansion of delivery channels should enhance future market growth.
According to Deutsche Bank Research (2001) the share of on-line banking could
rise from 8.5 percent to 50 percent in developed countries. In the Nordic
countries electronic banking could even reach nearly 80 percent by year 2005,
while UK and US penetration would approach 50 percent. In emerging markets the
share could rise from 1 percent to 10 percent. Figure 2 outlines electronic
banking customer uptake estimates for Europe, US, South Korea and Japan in the
years 2001-2004.
FIGURE 2 Electronic banking customer uptake (Centeno 2003) (Western Europe: Benelux, France, Germany,
Italy, Nordics, Spain, Switzerland, UK)
Bissola (2003) has outlined the projected evolution of
online banking activities, as shown in Figure 3 below. Various categories of
financial products and services are distinguished. The difference between these
categories is not only the level of interaction and personalisation allowed by
the technologies, but also the value perceived by customers. Bissola (2003) believes that the European market has
reached the end of the second step and that the first attempts at an evolution
towards the personalisation of services have already been made. In Finland this
evolution has gone beyond that in Europe; thus the banks in Finland already
offer services that can be categorised as belonging to step three in the
framework
Value creation
for
the customer
Relationship build
Transactional
Informational
Time
FIGURE 3 Evolution
of online banking activities (adapted from Bissola
2003)
1.2.1 Mobile banking
In addition to
offering branch-based services via new delivery channels, technology allows
banks to offer new value-added services which are only available in an
electronic environment, such as personalised financial information menus, Short
Messaging Services alerts, real-time brokerage. The
existing and envisaged changes in the technologies of service delivery have the
potential to affect the full range of retail services (Vesala
2000). Recent innovations in telecommunications have opened up an additional
channel for electronic banking. The market potential exists for mobile banking
which would enable customers to bank virtually anywhere, and at any time.
Wireless devices may outpace personal computers in market penetration, and many
are sophisticated enough to serve as access points to the Internet and to
private networks. They may even function as handheld PCs in their own right (Kiesnoski 2000).
The
uptake of mobile banking will probably be further encouraged by the
improvements in mobile service and application space anticipated with the
arrival of 2.5G and 3G networks. Those improvements include the ability of
mobile devices to provide location-specific information and new means of
personalisation, together with enhanced availability and immediacy of service.
It is this immediacy that is likely to contribute most to the predicted shift
from wired Internet connections to wireless mobile services in banking (Wah 1999). Immediacy of information access will be enhanced
by always-on functionality; this will support the provision of the
time-critical information needed to conduct high value transactions, including
participation in mobile auctions and the execution of mobile stock-trading
deals. It has been argued that these types of value-added mobile services are
bound to take their place among the most interesting revenue-generating
services, simply because of the economic value attached to them (Durlacher Report 2001).
In
Finland, payments and account management transactions have been available as an
SMS service via mobile GSM phones for more than a decade (since 1992, to be
precise); television-based banking has
been available since 1998, and banking via mobile internet WAP since 1999 (Mattila and Pento 2002).
Furthermore, mobile phone penetration amounted to 94 percent in 2002. On these
grounds, the diffusion of mobile banking in Finland would seem to have a bright
future. Already at the time of writing, the technology of mobile banking
enables services such as checking account balances and transactions, the
transfer of funds, bill payments, share dealing and the obtaining of share
quotations, portfolio management, and the purchase of insurance.
1.2.2 The Finnish mobile banking environment
Finland is
often considered to have been one of the pioneers in mobile communication and
in conducting mobile business; it is a country of early technological adoption, known for its
strong mobile phone industry and the development of that industry. According
to Peltonen and Dholakia
(2002), the roots for this development actually date back to 19th
century; this was when the first telecom networks were built in the country, an
undertaking initiated by the Russian Czar and by the
Finnish autonomous government of the time. Finland’s telecom industry, led by
the national monopoly company Telecom Finland (which initially operated as a
division of the Finnish national Post Office, changed its name to Sonera in 1997, and is now merged with Sweden’s Telia) gradually developed one of the most sophisticated
networks in Western Europe, a sophistication which increased further along with
the processes of liberalisation and digitalisation
The structures of Finnish society –
including its information infrastructure – have over the years developed in
ways which are favourable to the adoption of technology-based products and
services. Finland has a history of building an information infrastructure in
order to connect its geographically dispersed population. Additional factors
include a well-educated workforce, an effective policy environment and a
sophisticated use of information and communication technologies (Ratnathicam 2002). The fact that Finns have information
networks as a part of their everyday lives has lowered the threshold for using
electronic services; for example, at the end of 2002, more than 73 percent of individuals had
access to an Internet connection (Finnish Bankers Association 2003). The banks
have made over 2.8 million agreements on electronic banking with their
customers (see Figure 4). 67 percent of invoices were paid via information
networks in 2002, while only some five percent of payments were made in branch
offices (see Figure 5). The rapidity of the change in this area is striking; at
the beginning of the 1990s almost 20 percent of payments were still being
carried out in branch offices (Böhle et al. 2000;
Finnish Bankers’ Association 2003).
FIGURE 4
Bank distribution network in Finland (Finnish Bankers’ Association 2003)
FIGURE 5 Number
of banks’ payment transactions (Finnish Bankers’ Association 2003)
Starting in
the early 1990s, the financial services industry set up internal information
technology services that would allow advanced payment, security and
verification procedures; this has enabled Finland to be among the first country
in the world to offer online and mobile services. Along with other Scandinavian
countries Finland has championed the technological development and employment
of these new technologies. It is worth noting that the Scandinavian countries
as a whole are among the most advanced in adapting to and using various new
mobile and technological appliances (Statistics Finland 2002). Finnish
consumers have been relatively eager to try out new mobile applications such as
SMS chatting, SMS dating, and television voting (Pelkonen
and Dholokia 2002). This has carried over into the
banking sector: consumers have been provided with increasingly versatile means
of using banking services, and have been willing to use them.
The
sections above have outlined in general terms the current situation in
electronic banking. From a more theoretical perspective, the aim of the
research reported here is to contribute to the study of consumer behaviour in
the context of technology. Thus, the perspective taken by the dissertation is
one that focuses on consumption, and on the consumer. The study will provide new information about
consumer behaviour in the rapidly changing financial services industry. The
more we know about consumer behaviour in the context of the research phenomenon
in question, the more we are likely to gain insights which are useful to
practitioners investigating the acceptance and potential diffusion rate of the
various new types of services.
The
framework and the models presented in the dissertation will aim at
conceptualising various factors that influence the electronic banking
environment and
delivering potential to generalise them to introduction of future products
along this line. It is clear that mobile banking,
and mobile commerce in general, can be facilitated through the availability of
more data concerning customers’ behavioural patterns and profiles. Among other
consequences, one direct result is that advertising will become even more
targeted and customised. As Mattila and Hanin (2000) have argued, information about the behaviour
of bank customers is of value in itself.
1.3 Research objectives and questions
As mentioned
in the sections above, the purpose of this study is to provide a better
understanding of the adoption of technology-based services, and to model
customer behaviour in the mobile banking context. On the basis of a review of
the literature (see Chapter III and Research Articles) and the hypotheses
developed from the theory as well as the various general objectives regarded as
pertinent to this research, the research questions listed below were
formulated.
The primary
academic question to be addressed is:
What
are the dimensions affecting the adoption of mobile banking services in
Finland?
To
gain a comprehensive understanding of the phenomenon under investigation, and
in order to be able to provide a sufficient justification for answering that
question, a number of subordinate questions need to be addressed. For the
purposes of the present research, these questions are:
-
What are the attributes of innovation, and how
are they constituted in the modelling of mobile banking adoption? Why is
Everett M. Rogers’ work applicable in this context?
-
How do the following variables explain the
adoption of mobile banking:
Demographics, technology
perceptions, communication?
-
What are the drivers and the inhibitors of
mobile banking?
-
What are future trends in consumer behaviour in
the context of mobile banking?
1.4 The structure of the dissertation
The
dissertation is divided into two parts. The first part introduces the research phenomenon,
the methodological approach, the steps taken in conducting the survey, the
theoretical foundations, formulation of the hypotheses, and also a review of
some descriptive statistical data from the survey. In other words, the first
part serves as an introduction to the research articles in the second part of
the dissertation. It is recommended
that the reader should become acquainted with the four research articles before
proceeding to the final chapter, Chapter V Concluding Discussion, of part one.
Introductory
Chapter I presents the background against which this research has been undertaken, and hence provides the motivation for the
research. The objectives and research questions are also laid out in this
chapter.
Chapter II outlines the research
approach and the methodological standpoints taken in the research. It explains
the method of data collection and the techniques used for quantitative data
analysis as well as the variables chosen for the questionnaires. The reliability and validity of the study are
briefly discussed.
Chapter III reviews the literature
concerning the research topic and presents a synthesis of the constructs
relevant to formulating the hypotheses and to building a framework for the
research. This chapter proposes the use of three theoretical approaches as
lenses for understanding the phenomenon under investigation.
Chapter IV presents a review of some
statistics of the survey data in order to give statistical descriptions of the
characteristics of the survey participants and of their electronic banking
usage.
Chapter V discusses the main
conclusions that can be drawn from the research. In addition, results of the
hypotheses testing are presented herein. The limitations of the study and
suggestions for future research are considered. Note that the empirical
findings and their linkages to theory, and also the concluding arguments, are
mainly presented later, within the research articles.
The collection
of research articles forms the second part of the dissertation. Each paper
adopts a different viewpoint with regard to the research topic. These research
articles have been presented at international conferences and published in
conference proceedings, or accepted for publication in a scientific journal, or
else are under review for publishing in international scientific journals (for
details see the opening page of each article). Note that since Chapters III and
V discuss issues that are presented in detail within the articles, references
are made throughout the text to these articles. Each paper is included in the
dissertation in the format in which it has been submitted.
Article I “Usage of Mobile Services:
Empirical Findings from a Bank Customer Survey” studies the usage of mobile
services in a fairly general sense. It focuses on demographic variables as
indicators of certain type of consumer behaviour, presents the profile of a
typical mobile banking user and describes the relationships that exist between
variables such as technology perceptions and usage.
Article II
“Modelling Mobile Banking Adoption: An Empirical Investigation” identifies and
explains the variables that exist within a mobile banking adoption framework.
The starting point in this is the list of attributes of innovation defined by
Everett M. Rogers.
Article III
“Technology-based Service Products – a Study on the Drivers and Inhibitors of
Mobile Banking” analyses the forces that encourage or discourage the use of
mobile banking services, employing a practical approach. The model presented
summarises the most important underlying constructs.
Article IV “Mobile Banking and Consumer
Behaviour – New Insights into the Diffusion Pattern” examines traditional
diffusion constructs, such as the Bass Model of communication flow and the
differences between adopter categories. New insights are obtained regarding
diffusion patterns, incorporating the empirical implications of the survey
data. Following on from this, predictions can be made with respect to the
future usage of mobile banking services.
II
RESEARCH METHODOLOGY AND DESING OF THE
STUDY
2.1 Research design
A research
design is more than just a method of data collection and analysis. It
governs the overall configuration and organisation of the research activity.
The research design determines the type of evidence that is collected and
interpreted in order to provide acceptable answers to the research questions.
In this endeavour, some understanding of the philosophy of science is needed,
so that one can recognise which methodological approaches will be appropriate
and applicable to the investigation in question. The suitability of the
methodology ultimately stems from the research tradition of the discipline, and
its scientific norms and principles, as much as from the research questions and
problems to be addressed. Clearly, the whole area involves debates on
underlying philosophies, schools, and concepts of epistemology, as well as on
the categorisation of different research disciplines (e.g. Hall and Elliot
1999; Foxall 1995; Hirsjärvi
et al. 2001).
Even
though it would not be practical within this dissertation to examine the
various schools of thought in minute detail, one can usefully refer to the
general division of the philosophy of science into two major approaches, i.e.
the positivistic approach and the hermeneutical approach (Riggs 1992). In the
positivistic approach the researcher tries to explain and predict the
phenomenon under investigation. This will often involve the formulation of a
tentative theory of the phenomenon, working out the implied consequences, and
controlling the events in a situation in such a way as to observe the validity
of empirical deductions (Bonoma 1985). This type of
research is typically based on obtaining data through surveys with a large
sample size and analysing structured data quantitatively, using statistical
methods. In the hermeneutical approach, the interest lies more in exploring and
understanding the phenomenon than in explaining and predicting it. This
approach involves reasoning from individual and naturally occurring but largely
uncontrollable observations towards generalisable
inductive principles (Bonoma 1985). Research within
this approach is typically based on unstructured data obtained through
fieldwork studies and case research methods.
A
further means of distinguishing research designs is in terms of deductive
reasoning as opposed to inductive reasoning (Riggs 1992). Deductive reasoning
is defined as working from the more general to the more specific. Inductive
reasoning works the other way round, moving from specific observations to
broader generalisations and theories. Inductive reasoning, by its nature, is
more open-ended and exploratory, especially in its initial stages. In inductive
reasoning, the researcher begins with various specific observations and
measures, and continues by detecting patterns and regularities, formulating
some tentative hypotheses that can be explored. Finally, the researcher may
develop some general conclusions or theories. Deductive reasoning is narrower
in nature and is concerned with testing or confirming hypotheses (Trochim 2000). Yet even though a particular study may look
as if it is purely deductive or inductive, most social research involves both
inductive and deductive reasoning processes in the course of the project. This
is true in the present research also.
Methodological
approaches are often distinguished with respect to the novelty of the
phenomenon under study. If the area of investigation is new or vague, a
researcher may need to carry out an exploration simply in order to learn
something about the problem. In the exploratory research approach, the
important variables may not be known or thoroughly defined; the emphasis is on
the discovery of ideas and insights. Exploration relies more heavily on
qualitative techniques. By contrast, the objective of a descriptive study is to
learn the who, what, when, where and how of the topic
in order to provide an accurate snapshot of some phenomenon, and to determine
the relationships between variables. In descriptive research, hypotheses often
exist but they may be tentative and speculative in nature; moreover the
relationships under study may not be causal. A causal research design is
concerned with determining possible cause-and-effect relationships. Causality
involves but may not be proved by the strength of association between two or
more variables that are measured; thus one may discover that a change in
variable A appears to cause a change in variable B (Cooper and Emory 1995; Churchill
and Iacobucci 2001; Aaker
and Day 1983; Bagozzi 1980).
The present dissertation will, in the
main, follow the path of a descriptive research design. Yet the fact that this is a descriptive study
in no way means that it is simply a fact-gathering expedition. This argument
will be discussed in detail in the next section.
2.1.1 Using a descriptive
research approach
A descriptive
research approach is justified in so far as one attempts to identify and
explain the variables that exist in a given situation, and to describe the
relationship that exists between these variables, the intention being to
provide a picture of a particular phenomenon rather than to ferret out
cause-effect relationships (Christensen 1991). The descriptive study is
typically guided by initial hypotheses as in this research too. Descriptive
research is used when the purpose of the study is to describe the
characteristics of certain groups. For example, based on information gathered
from known users of a service, it was attempted to develop a profile of the
“average or typical” user with respect to demographic variables. Furthermore,
an attempt was made to estimate and describe the proportion of people in a
specific population who behave in a certain way and find out explanations for
their behavioural patterns.
The phenomenon to be studied here,
mobile banking, is comparatively new in the field of academic research, and for
this reason the study aims at gaining insights into current consumer behaviour
patterns in the electronic services era. It is with this in view when it can be
said that an excursion into explorative research is taken to some degree. In
concrete terms the brief exploratory part of this study was conducted when we
sought information from persons experienced, both practitioners and academics,
in the area of the study before designing the questionnaire variables. However,
since research into the diffusion of technological innovation has a long
tradition, and since electronic banking (especially Internet banking) has
received a good deal of research attention in recent years, one cannot assert
that the research area is totally vague or new. Given this situation, it did
not seem appropriate to rely purely on exploratory research techniques. It
might be argued that this research is also to some extent causal, since it was
aimed at explaining relationships among the variables using regression analysis,
and answering some of the “why” questions concerning the phenomenon. However, a
study to be purely causal one, the relationships, causality, should have been
studied by using even more rigorous analytical tools, such as structural
equation modelling (Cooper and Emory 1995).
As mentioned previously, the objective
was not simply to gather facts but rather to increase understanding. In this
regard, the comment of Ferber et al. (1964, 153)
seems appropriate:
“Facts
do not lead anywhere… Anyone with a questionnaire can gather thousands of facts
a day – and probably not find much real use for them. What makes facts practical
and valuable is the glue of explanation and understanding, the framework of the
theory, the tie-rod of conjecture. Only when facts can be fleshed to a skeletal
theory do they become meaningful in the solution of problems.”
According
to Churchill and Iacobucci (2001), descriptive
studies can be divided into two types of studies: longitudinal and
cross-sectional designs. A cross-sectional study, as in the present case,
typically involves a sample of elements from the population of interest, the
various characteristics of which are measured once. In other words, it provides
a snapshot of the variables of interest at a single point of time, in contrast
to a longitudinal study, which examines the variables over a period of time. A great deal of emphasis is placed on
selecting a representative sample of the phenomenon and this is often done, as
in this study, by employing a probability sampling plan.
2.2 Data source and sampling
Often the
research design is a combination of both qualitative and quantitative
techniques. First an exploratory research is conducted, for example in form of
interviewing in order to provide clarification of the research problem, or to
assist with the formulation of a questionnaire. Then the formal study begins
where the exploration leaves off. Methods of data collection can be
distinguished for example between observational studies and the survey mode.
The survey type of research gathers the data from interview or telephone
conversations; self-administered or self-report instruments via the mail, left
in convenient locations, or transmitted electronically; or instruments
presented before and after a treatment in an experiment (Cooper and Emory 1995).
In this study the survey method, a
large postal survey, was selected for its ability to encompass the phenomenon
on a larger scale. Additional justifying reasons in comparison with
observational studies were the possibility for improved geographical
accessibility of customers (in Finland), increased flexibility for the
availability of the respondents’ time, ability to maintain the anonymity of the
respondent, and comparative inexpensiveness and efficiency besides allowing for
a larger sample size to be selected (Zikmund
1991). Typically, questionnaire survey
method sacrifices some of the depth of the research results as compared to more
qualitative methods, but in this case breadth was more of interest since the
aim was to obtain findings that are generalisable.
Traditionally, qualitative research has said (e.g. Cooper and Emory 1995) to
often have just the bias, such as subjectiveness and nonpresentativeness, which would have been
non-acceptable. Interviewing the bank customers in a large
scale might have been an option, but the constraints of budget and time impose
the need for self-administered instrument sent through the mail, and for the
same reason the study was conducted as a single cross-sectional study. Obviously
delivering the questionnaire in the bank branches (informed questionnaire)
would have overcome the low response rate problem faced often in postal surveys.
A problem in postal surveys is often a
low response rate and a non-response bias. However, in this study the response
rate was satisfactory; obviously the steps taken in the design process had had
a positive effect. The response rate was increased by sending a covering letter
informing respondents about the content, the purpose of the survey and a
guarantee that responses would be confidential; however, no incentives were
offered to encourage participation in the survey. Follow-up mailing can further increase the
response rate, as noted in the literature (Churchill and Iacobucci
2001). No significant non-response bias was detected during the analysis of the
survey data. Possible response biases resulting from response style (meaning,
for example, non-contingent or extreme responding), or response set (meaning
responding according to what is considered to be desirable and acceptable),
were taken into account when checking for indiscreet questionnaires prior to data
analysis.
Questionnaire surveys are indeed widely
used in large scale investigations of both management practices, and of consumer
beliefs, preferences and behaviours (Easterby-Smith
1993; Tull and Albaum
1973).
If considering
more qualitative methods such as in-depth interviewing Justification for
selecting this particular method was the previous knowledge that research
community had on consumers behaviour in context of was selected to obtain.
The research data was collected during
the summer of 2002. Pre-tested questionnaires with a covering letter from the
University of Jyväskylä and a postage-paid return
envelope were sent to a cross section of 3000 bank customers in Finland. The
questionnaire was administered to a stratified sample of Finnish bank
customers, selected in terms of their banking habits. The use of stratified
sampling meant first dividing the parent population into mutually exclusive and
exhaustive subsets, then drawing a simple random sample independently from each
stratum. The reasons for using stratified sampling stemmed from the research
objectives: stratification also allowed the separate investigation of
characteristics of interest for particular user segments. The sampling frame
from which the sample elements were drawn was the customer database of one
major Finnish bank, OKO Bank Group. After two follow-up mailings, 1303
responses were received, of which 1253 were usable. The usable response rate
amounted to 41.8 percent, which was highly satisfactory, and above the 20-30 percent
rate considered acceptable in economics research.
The survey sample consisted of three
equal-sized groups (1000 questionnaires to each segment) that were selected
according to mobile banking usage experience and density. When deciding on the
sample size we had to take into account the need for subgroup analysis, and the
need for precision when analysing separately just one user segment. Generally speaking, the larger the population, the greater the
potential for variation in the characteristics of the sample. Churchill
and Iacobucci (2001) suggest some typical sample
sizes: in a national study of a human population, where many subgroup analyses
are needed, the recommended sample size is over 2500. Furthermore, Thornton and
White (2001) cite to Sudman’s (1976) guideline of
sample sizes which suggests that a study’s sample should be large enough so
that there are at least 100 units in each category of the major breakdowns. The
major breakdowns for this study relates to the three user groups.
Considering the user groups in the
survey, the non-users had never used any form of mobile banking service
on a permanent basis, the occasional users had started to use some form
of mobile service, and the regular users had been using the services for
a longer period of time. The questionnaires were partly tailored according to
the characteristics of each group. The first round of mailings was conducted in
May 2002, and the follow up mailings were mailed in June and July. The regular
users returned 351 responses, giving an overall response rate of 28 percent for
this group. From the occasional users 416 responses were received giving
response rate of 33.2 percent. The largest number of responses came from the
group of non-users. They returned 486 responses with a 38.8 percent response
rate. Figure 6 depicts this distribution. The objective was to gather responses
from every part of Finland; this was indeed achieved, since the sample proved
to be geographically representative of the entire country.
FIGURE 6 Distribution of the respondents
according to user group
2.2.1 Questionnaire
design
Questionnaire
design may have significant effect on the reliability and validity of the
responses obtained, and a number of steps were taken with this aspect in mind. As
mentioned above the questionnaire was designed in a close cooperation with
experts involved both in practice and research. Before mailing, the
questionnaire was pre-tested on a reference group who did not participate in
the real survey, but who matched the composition of the true sample. During the
pre-testing each question was discussed and analysed in order to check the
readability and comprehensibility of the questionnaire. In most of the
questions the points on the scales were labelled with words; it has been found
that the use of such labels can significantly improve reliability and validity,
since they clarify the meaning of the scale points (Krosnick
1999).
Most of the questions were multichotomous questions; only the questionnaire designed
for the regular users included four open-ended questions. The questionnaire
designed for regular users included 25 questions and four open-ended questions,
whereas questionnaires for occasional and non-users included 26 standardised
questions. All of the questions were not relevant for academic purposes of this
dissertation, thus they were excluded from the analysis.
The variables presented in the
research framework were operationalised following the
methodological approach outlined for example by Kerlinger
(1973), Peter (1981), DeVellis (1991) and Fishbein (1967). The recommended statistical tests were
conducted before final variable/question construction. The theories and
constructs behind the variables, each antecedent behind the framework are described
and justified in detail in the literature review in Chapter III and in Research
Articles. Multiple-item measures were developed by generating item pools based
on instruments published in the literature on innovation adoption,
technology-based services as well as on proper empirical findings in electronic
banking literature (see the references in Table 1). The items were modified to
reflect the phenomenon under investigation.
The number of the items comprising a variable ranged from two to fifteen.
The five to seven point Likert scale was used to
elicit responses on the questionnaire. Table 1 presents the variables and examples
of items and their descriptions since the actual questionnaires has been
decided to keep confidential.
Prior to data analysis, the research
instrument was assessed for its reliability as well as construct validity. In
Table 2 Cronbach’s alpha is reported for scale
reliability. Nunnally
(1967) has suggested that a minimum alpha of 0.6 sufficed for early stages of
research. Furthermore, the choice of variables that had been validated by other
researchers and pre-tested with practitioners establishes the construct
validity for the items.
TABLE 2 Reliability analysis
for scale items
Variable Cronbach’s Alpha
Relative
advantage .860
Complexity .953
Compatibility .622
Observability/Communication .711
Trialability .644
Risk .630
Technology
perceptions .778
TABLE 1 Operationalisation
of the variables
Variable Item example Description example References
Each of the
questionnaire consisted of three sections. Section one gathered information
about the respondents’ banking habits; which services do they use, how often
they use certain services etc. Section two solicits respondents’ views on their
feelings towards the different technological products and services. There were
some questions concerning various delivery channels and banking mode choice,
information source preferences and mode of communications used. Questionnaires
included questions on future use of financial services and use of mobile
services in general. The last section in the questionnaire gathered demographic
information about respondents’ gender, age, marital status, education level,
household income, occupation and line of business.
A tailored questionnaire made it
possible to put slightly different questions to the different user segments,
i.e. questions that only individuals in that segment were able to answer. Thus,
for example, non-users gave information on factors inhibiting the adoption of a
service. The specific questions cannot be discussed herein in more detail due
to the confidential nature of the questionnaire.
2.3 Method of analysis
In accordance
with the methodological approach that was chosen, the quantitative data were
statistically analysed using the SPSS-program. In addition to written
explanations of the analyses, the results were incorporated into tables and
figures which readers would probably find easier to comprehend. Statistical methods such as means,
standard deviations, ANOVA, rotated exploratory factor analysis, regression and
correlation analysis were found to be suitable for the data (e.g. Steward 1981;
DeVellis 1992), and for reaching the answers to the
research questions, with most of the data consisting of interval
scale variables on a seven-point Likert attitude
scale.
The simplest form of analysis of
variance (ANOVA) was used, namely a one-way analysis of variance which is an
appropriate statistical test for analysing data collected on the basis of
randomised subject designs. This statistical procedure makes it possible to
estimate the probability that the observed difference between the means of
groups is the result of chance factors (Christensen 1997). By contrast, exploratory factor analysis is
appropriate in cases where the underlying dimensions of the data set are not
known, and where one seeks to find a new set of variables, fewer in number than
the original variables, which express that which is common among the original
variables (Steward 1981). In the present
study, factor analysis was used as a way of determining the nature of the
underlying variables among a large number of measures (Cohen and Manion 1986).
Exploratory factor analysis was also
used, due to newness of the phenomena under investigation. Even though there
are several studies on electronic banking in the context of Internet banking,
there was almost no knowledge or theory available in advance concerning the
factor structure of mobile banking. Thus there was nothing that would enable
one to determine, for example, the number of factors or dimensions, or the
number of indicators of each factor, or which indicators might be connected with
which factor. Given this situation, it seems reasonable to use exploratory
factor analysis in the search for a factor structure which could explain the
correlations among the indicators; clearly we are not in the domain of
confirmatory factor analysis, which would assume that the factor structure is
known or can be hypothesised a priori.
To further elaborate and measure the
relationships between variables in the framework regression and correlation
analyses were applied. Correlation analysis involves measuring the closeness of
the relationship between two and more variables; it considers the joint
variation of two measures (Churchill and Iacobucci
2002). Regression analysis refers to the techniques used to derive an equation
(a relationship of the type Y=a+βX+є) that
relates the criterion (dependent) variable to one or more predictor
(independent) variables (Zikmund 1991).
2.4 Reliability and validity of the study
Reliability
has to do with the accuracy and precision of a measurement procedure. It refers
to the degree to which a measure is free of variable error (DeVellis
1991; Kerlinger 1980) and to the accuracy,
consistency and reproducibility of a measuring instrument. The most common type
of reliability measurement evaluates the internal consistency, which is
concerned with homogeneity of items comprising a scale (DeVellis
1991). The scales used in the present research in order to measure beliefs were
based on previous research, and on existing scales (see section 2.2 above).
Previous research suggests that the seven-point bipolar scales used in the
semantic differential have relatively high reliabilities. Thus, responses to
the probability scales of the semantic differential (for example,
likely-unlikely), tend to yield highly reliable measures of strength of belief
and intention (Fishbein 1967).
Internal consistency is typically
determined through a statistical examination of the results obtained, equated
with Cronbach’s coefficient alpha (DeVellis 1991). Thus, the present research uses Cronbach’s alpha to determine the reliability of the scales
and the results. According to Nunnally (1967) the
alpha of a scale should be greater than 0.7 for items that are to be used
together as a scale. Considering the present study as a whole, Cronbach’s alpha varied from 0.6209 to 0.9538, which is
considered acceptable for this type of research. The alpha values are presented
along with the research results in the relevant research articles. Kerlinger (1981) suggests various means to improve the
reliability of results, such as phrasing the measuring instruments
unambiguously, determining carefully the number of items (adding more items of
the same type and quality decreases the change error , and adding to the number
of items increases the probability of accurate measurement), and providing
clear and standard instructions for questions. These strategies were taken into
consideration in designing the questionnaire. Reliability has been identified
as a contributor to validity, and is a necessary but not sufficient condition
for validity (Nunnally 1967; Cooper and Emory 1995).
Historically, the most common
definition of validity is that it refers to the extent to which a test or a set
of operations measures what it is supposed to measure (Chiselli
et al. 1981). Internal validity refers to the results of the study being a
consequence of the studied systems or phenomena, or being true (Tuckmann 1988). External validity refers to possibilities
for generalising the results to other situations or groups. This validity can
be ensured by scrutinising the effects of testing, of selection bias, of
experimental arrangements and of multiple treatment interference (Lynch 1999).
Internal validity has been enhanced in the present study by a careful review of
the relevant literature. The external validity can be considered to be high as
a result of the large sample size.
A number of other forms of validity are
mentioned in the literature on research methodology. The categories most
relevant to this study are touched on below, namely content, criterion-related
and construct validity (DeVellis 1991).
Content validity concerns item sampling
adequacy – that is, the extent to which a specific set of items reflects a
content domain. In behavioural research, when one is measuring attributes such
as beliefs and attitudes, the issue of content validity is more subtle, since
it is difficult to determine exactly what the range of potential items is.
Nevertheless, one recommended method (e.g. Chiselli
et al. 1981) of enhancing content validity was employed in the survey. The
items were viewed and judged by colleagues familiar with the context of the
research; these experts judged the extent to which each item was representative
of the domain of interest, and the extent to which the item pool adequately
sampled all the relevant parts of the domain.
In order to
achieve criterion-related validity, an item or scale is required only to have
an empirical association with some criterion or “gold standard”; this aspect is
not concerned with understanding a process but merely with predicting it. Thus,
criterion-related validity is often referred to as predictive validity when it
reflects the success of measures used for prediction of estimation. Criterion
validity is studied by comparing scores with one or more external variables, or
criteria, that are known or believed to measure the
attribute in the study.
Construct validity is defined by Peter
(1981) as the degree to which the scores achieved by a measure perform as they
should, according to a substantive theory postulate. In other words, construct
validity is directly concerned with the theoretical relationship of a variable
with respect to other variables. Through construct validation, an attempt is
made to identify the underlying constructs that are being measured and to
determine how well the test represents them. Cooper and Emory (1995) present
methods for achieving this kind of validation, including factor analysis and multitrait-multimethod analysis. In this study the former
was used.
III THEORETICAL BACKGROUND
This chapter
reviews the literature which has formed the theoretical background for the
research articles. The purpose here is not to go through each underlying theory
in detail, since this is done in the Research Articles. Rather, this chapter
concentrates on justifying the theoretical choices made during the research and
on developing formal hypotheses. The chapter discusses various theoretical
viewpoints, i.e. viewpoints that have been selected to shed light on the main
objectives of this research. The literature review itself is presented in three
major sections (3.1, 3.2, 3.3); these deal with
theories which have wide currency at the present time, and which provide a
solid foundation for the chosen variables and constructs presented in the
previous chapter, and for the empirical investigations of the present study.
The chapter ends with a section (3.4) setting out the overall framework of the
dissertation.
3.1 Research into innovation diffusion and adoption
Mobile banking
services represent both an innovative service, considered as something
intangible, and an innovative medium of service delivery employing high
technology. Thus, in order to arrive at a framework for the present study, it
was necessary to use as key underlying notions the concept of innovation
adoption and also the concept of the diffusion of technology-based services.
The
diffusion of an innovation has traditionally been defined as the process by
which that innovation “is communicated through certain channels over time
among the members of a social system” (Rogers 1995, 5). Considered in this
way, there are four key elements in the diffusion process: the innovation
itself, channels of communication, time, and the social system. Innovation was
defined in Section 1.1. Communication channels for their part are the means by
which information is transmitted to or within the social system. Time relates
to the rate at which the innovation is diffused, or the relative speed with
which it is adopted by member of the social system. The social system consists
of those individuals, organisations, or agencies that share a common “culture”
and are potential adopters of an innovation (Mahajan
and Peterson 1985).
Within
this research area, one of the most frequently cited studies is that by Everett
M. Rogers (e.g. Moore and Benbasat 1991; Howcroft et al. 2002; Tan and Teo
2000) which originally dates back to 1962. The theoretical framework of the
present dissertation is based on this traditional approach to innovation
diffusion. There are several overviews of diffusion and adoption theories in
the literature of marketing (e.g. Mahajan et al. 1990a), and in the literature of information
systems (e.g. Lu et al. 2003: Puumalainen and Sundqvist 2000). The diffusion and adoption process has
also been viewed from various perspectives within academic research on
electronic banking, for example from an organisational perspective (e.g. Daniel
1999) or from a distribution channel perspective (e.g. Black et al. 2002; Mols 2001). In addition, there is a large body of current
research on consumers as adopters, and on their behaviour. In attempts to
understand consumers’ adoption of technology (at least where the research has
focused on the consumer perspective) investigators have often explicitly
employed Rogers’ diffusion model.
The present research also builds on the
work of Rogers, in so far as mobile banking adoption can be explained and
described in terms of five innovation attributes. Furthermore, Rogers’
conceptualisation of the various characteristics of adopters is of interest in
the study. An additional rationale stems from the fact that Rogers’ model has
been successfully applied in producing various forecasts, and various
descriptive accounts.
In
considering the applicability of traditional diffusion research to the present
study, it is worth going over certain points in Rogers’ (1995) argument.
According to Rogers, the diffusion model is a conceptual paradigm with
relevance to many disciplines; thus the diffusion approach provides common
conceptual ground. Rogers further suggests that most social scientists are
interested in social change, and that diffusion research offers a particularly
useful means of gaining an understanding of change, since innovations are a type
of communication message whose effects are relatively easy to isolate. Thus,
when studying mobile banking adoption one is dealing with a change in human
behaviour, or in marketing terms a change in consumer behaviour. One of the key
concepts in diffusion research is that change in consumer behaviour is affected
by different forces, which can be driving or inhibiting, and which can lead to
the adoption or non-adoption of a particular innovation. The research
methodology implied by the classical diffusion model is clear-cut and
relatively simple. Diffusion scholars have often emphasised quantitative
research approaches; they have focused especially on characteristics related to
individual innovativeness that can be arrived at through cross-sectional
analysis (Rogers 1995).
3.1.1 Attributes of innovation
In accordance
with Rogers’ suggestion that the explanation for different diffusion paths can
be found in the attributes of the innovations being adopted, researchers have
generally been in favour of evaluating the innovation according to product
characteristics involving five constructs. These are listed as relative
advantage, compatibility, complexity, trialability
and observability. An additional concept of perceived
risk is often included, as suggested by Bauer (1960). According to Moore
and Benbasat (1991), measuring potential adopters’
perceptions and intentions regarding innovations is a “classic issue in the
innovation literature” and a “potential key” for integrating various findings
within diffusion research. Furthermore, empirical research has shown user
perceptions to account for a substantial proportion of the variance in current
use and future intentions to use (Agalwal
and Prasad 1997). Marketing research has been particularly concerned with predicting
the rate of adoption of new products, with studies on how the perceived
attributes of an innovation affect its purchase.
One
of the most frequently cited reviews of perceived characteristics literature is
that by Rogers. It was for this reason that one of the research articles in the
present dissertation set out to test the set of characteristics listed in the
paragraph above, within the context of mobile banking adoption. A brief
discussion of these attributes follows.
Each of the six different attributes of innovation is to some extent
empirically interrelated to the remaining five. However, following the thinking
of Rogers (1995) they will be regarded here as conceptually distinct.
Relative
advantage is concerned with the degree to which an innovation
is perceived as being better than the idea it supersedes. The degree of
relative advantage is often expressed as economic profitability, social
prestige, a saving in time and effort, immediacy of the reward, or as a
decrease in discomfort (Rogers 1995). The construct of relative advantage is
highly domain-specific and thus advantage can be viewed differently according
to the innovation in question, and also according to the consumer in question.
In general, perceived relative advantage of an innovation is positively related
to its rate of adoption. As electronic banking services allow customers to
access their bank accounts from any location, at any time of the day, it
provides tremendous advantage and convenience to users. It also (Tan and Teo 2000) Based on this line of reasoning, it can be
hypothesised that:
H 1
The greater the perceived relative
advantage of using mobile banking services,
the more likely that mobile banking will be adopted
Complexity is the degree to
which an innovation is perceived as being difficult to use and understand. It
has been often measured in relation to perceptions about the purpose of the
respective innovation, its intended use and ease with which it can be used (Gerrard and Cunningham 2003). Previous studies on
technology-based innovations have indicated that more complex an innovation is to use,
and the greater the skill and effort needed for adopting it, the less likely
that it will be adopted (Tan and Teo 2000).
Complexity is also a subjective concept and not an innate attribute of a
product or service, and can be perceived differently by different individuals (Agarwal and Prasad 1997). The perception
of the complexity involved in conducting a financial transaction via a mobile
channel is can be argued to be inversely related to the consumer’s experience
with technology in general.
H2 The lower the perceived complexity of using
mobile banking services, the more
likely that mobile banking will be adopted
Compatibility refers to the
degree to which an innovative channel such as a mobile device is compatible
with the individual’s past experiences and values; it appears to have a
significant impact on a person's willingness to adopt (Rogers 1995; Moore and Bensabat 1991). Consumer behaviour tent to be based on
known solutions and past successes, for example computer literacy has been
proven to affect adoption of Internet banking (Tan and Teo
2000). Nevertheless, not all experience is necessarily efficacious in the
acceptance of technology-based products, because the switching costs associated
with moving to a very dissimilar technology may offset any positive gains due
to experience (Agalwal and Prasad 1999). A consumer’s
innovativeness is often measured as how technology-oriented, change oriented or
convenience oriented she is (e.g. Thornton and White 2001).
H3 The greater the perceived compatibility of
mobile banking with one’s values, the
more likely that mobile banking will be adopted
The observability
of an innovation describes the extent to which an innovation is visible to
other members of a social system, and how easily the benefits can be observed
and communicated. In the present case, the lack of a physical domain in service
products may present some problems, even though the service delivery medium,
i.e. the mobile phone itself, may provide physical evidence for the innovation.
This research investigated observability as
communication process meaning the effects of used channel and mode of
communication on adoption of mobile banking under the theoretical framework of
diffusion of innovations following the approach outlined for example by Lee at
al. (2002), Mahajan et al. (1990a) and discussed in
detail in Section 3.1.1. Moore and Bensabat (1991)
refer also to Tornatzky and Klein’ (1982) discussion
in which they noted that communication, or communicability as they term, was
closely related to observability. Therefore, it is proposed that:
H4 Media channels are relatively more important
than interpersonal channels for earlier
adopters than for later adopters
Rogers (1995) argues that potential
adopters who are allowed to experiment with an innovation will feel more
comfortable with it and are more likely to adopt it. Consequently, if consumers
are given the opportunity to try out the innovation, fears of the unknown and
of being unable to use the innovation can be reduced. A trial can further
convince that even mistakes can be rectified, thus providing a more predictable
situation and give necessary confidence for using an innovation (Gerrard and Cunningham 2003). This attribute is labelled trialability. This leads to the hypothesis:
H5 The greater the trialability
of mobile banking services, the more likely that mobile banking will be adopted
An additional attribute augmented by
Bauer appears to be that of perceived risk. Particularly in banking
services, consumers consider the risk associated with the financial service
product itself, and also with the electronic delivery channel, to be higher
than with basic consumer goods; hence the importance of this attribute is
emphasised (Harrison 2000). In the context of Internet banking, Black et al.
(2001) have suggested that errors and the security afforded might be considered
as measures of risk. Hewer and Howcroft
(1997) refer to the term trust in this context.
H6 The lower the perceived risk of using mobile
banking services, the more likely
that mobile banking will be adopted
In addition to these attributes
included in Rogers’ model, empirical research on the adoption of financial services
has suggested the existence of a number of other factors; these include
societal concerns, involving for example job losses, the development of a lazy
society or “sense of fatalism” meaning the feeling customers had that
technology-based channels are forced upon customers (Black et al. 2001).
One
major objective in the research is to determine to what extent and why these
dimensions actually do account for the adoption of mobile banking. Thereby attributes of mobile
banking services are examined through the lenses of the proposed framework
described in detail later in Section 3.4. Investigating
mobile banking adoption through the lenses of these theoretical concepts
follows the path chosen in various studies on electronic banking, including
those of Tan and Teo (2000), Black et al. (2001), Polatoglu and Ekin (2001).
3.1.2 Adopter categories: individual
differences
The notion
that individual differences play a crucial role in the implementation of any
technological innovation has been a recurrent research theme in a wide variety
of disciplines – not only in marketing, but also in information systems and in
production. Numerous individual difference variables have been studied,
including demographic and situational variables, cognitive variables, personality-related
variables and the communication behaviour of potential adopters (Agarwal and Prasad 1999; Rogers 1995). It has been found
that all these factors are indeed significant predictors of the consumer
adoption of technology-based innovations (e.g.Polatoglu
and Ekin 2001; Mattila
2001; Lee et al. 2002). Previous
research has also explored each of these variables in relation to the financial
services sector.
The demographical variables are all
potentially critical to our understanding of adoption, since they could play an
important role in determining how consumers make their decisions about adopting
and using new technology-based services. Indeed, there is already a significant
body of evidence from empirical studies. Thus, for example, gender differences
have been investigated in the context of individual adoption and the sustained
use of technology (Morris and Venkatesh 2000), and
investigators have looked at individual levels of education and the prior
similar experience of adopters (Agalwal and Prasad
1999). It can be proposed that:
H7 Individual differences will have an effect
on adoption of mobile banking services
in terms of a) technology perceptions and b) demographics
Furthermore, in diffusion research, the
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 characterise user segments along the diffusion process, such as innovators, early adopters, early majority users, late majority users
and laggards. Demographic and socio-economic variables are used for this
purpose (Pedersen and Ling 2002). This is also one of the main research
interests of the current study. Note that examples of valuable avenues of
research already exist: for example, Mattila et al.
(2003) studied Internet adoption among mature customers, and Wei (2001) studied the socio-economic characteristics of
mobile phone laggards.
Traditionally, the Rogers adoption
continuum recognises five categories of consumers, differing in terms of
adoption rate and (as the findings of this study will reveal) in terms of
certain socio-economic characteristics. The common extrapolation (e.g. Rogers
1995; Mohr 2001) characterises adopter categories as follows:
- Innovators:
the first adopters, interested in technology itself
with positive technology attitudes;
- Early adopters: also interested in
technology and willing to take a risk;
- The
early majority: these are people who can be considered pragmatist and process
oriented;
- The
late majority: these are people who are more or less sceptical, with negative
technology attitudes;
- Laggards:
these have extremely negative technology attitudes and hence never adopt
technology, at least within the main stream.
3.1.3
Communication
In
the early 1970s, diffusion studies started to pay attention to the behavioural
theories behind the innovation adoption processes. This aspect is clearly
presented in the diffusion model by Bass (Puumalainen
2002). In marketing, one of the main impetuses underlying diffusion research
has in fact been the Bass model (see Figure 7), which focuses on how
information is communicated in the media and interpersonally. It shows how the
two mechanisms of communication result in an S-shaped aggregate adoption rate,
a phenomenon often observed in studies on innovation diffusion. The key
elements in the Bass model are adopter due to media messages (p), adopters due to interpersonal
messages (q) and an index of market
potential (m) for the new product.
The upper figure shows the number of new adopters per time unit is due to mass
media channels and to interpersonal communication. The lower figure on the left
shows that the crucial variable to predict is the number of adopters from the
time of the prediction to the mean time of adoption, when a point of inflection
occurs in the diffusion curve. The figure on the right shows that the
cumulative number of adopters can then be estimated because the S-shaped
diffusion curve is symmetrical around the mean year of adoption (Rogers 1995). Hence,
a theory of diffusion can also be characterised as a theory of communication (Mahajan et al. 1990a).
It is important to note that
communication is also a critical process factor for the diffusion of innovation
in electronic banking. In accordance with the studies of Lee et al. (2002) and Lievens and Moenart (2000)
communication was highlighted as a significant predictor of the consumer
adoption of technological innovations, the present study will also address this
area. The Bass diffusion model assumes that the potential adopters of an
innovation are influenced by two types of communication channel: mass media
channels (external influence), and interpersonal worth-of-mouth channels
(internal influence). 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.
Rogers
(1995) argues that perhaps interpersonal influence is not so necessary to
motivate earlier adopters to decide favourable because they possess a more
venturesome orientation, and the mass media message stimulus is enough to move
them over the mental threshold to adopt. But the less change-oriented later
adopters require a stronger and more immediate influence, such as that from
interpersonal networks.
FIGURE 7 The Bass new product diffusion model (Mahajan et al. 1990a)
Similarly,
Link (1998) presents a two-step communication process on which the initial
exponential or logistic shape of the diffusion of an innovation is built (see
Figure 8). In the first step, information is transferred by the mass media from
a marketing company to potential adopters who eventually adopt. Thereafter, in
the second step, the actual adopters influence other potential adopters who in
turn adopt the innovation. These considerations lead to the hypothesis:
H4 Mass media channels are relatively more
important than interpersonal channels
for earlier adopters than for later adopters
Step one Transfer of
information via mass media
Influence
vie interpersonal channels
Step two
FIGURE 8 Two-step communication-of-experiences
model (Link 1998)
In Rogers’ (1995) conceptualisation of the
innovation-decision process, communication channels have a central role, as can
be seen in Figure 9. Communication is defined as effective if it generates
changes in the receiver’s behaviour that were intended by the information
sources (Rogers and Shoemaker 1973). This change has its manifestation in the
decision stage in the model. Employing
these models, the present research aimed to determine which sources of
communication, employing which communication mode, have affected the adoption
of mobile banking.
Communication channels
Adoption
Characteristics Perceived characteristics
Rejection
of an individual of an
innovation
FIGURE 9 Stages in the innovation-decision
process (adapted from Rogers 1995)
3.2 Electronic banking
research
The definition
of mobile banking discussed in Section 1.1 offers a straightforward
justification for reviewing this field of research. Electronic banking,
sometimes referred as on-line banking, is the high-order construct and it has
created research enthusiasm among academic community of the banking and
services scholars.
According to Lievens
and Moenaert (2000) the study of innovation in the
financial service industry is a relatively new area of research. They consider
the research in this area to have got under way around the mid 1980s. An
analysis of the literature on financial service innovations brings up several
research topics. Thus there have been
fairly recent studies on the diffusion and adoption of tele-banking
(e.g. Al-Ashban and Burney 2001; Howcroft
et al. 2002) and on Internet banking (e.g. Jayawardhena
and Foley 2000; Mattila 2001). The focus in those investigations has been to
a large extent on the consumers using the services in question.
Sathye
(1999), Milesworth and Sourtti
(2002), and Fain and Roberts (1997) have investigated the adoption of an
innovation from a reverse research angle, namely consumer resistance to
innovations. Rogers (1995) also warns that diffusion literature may be
subjected to pro-innovation bias, were researchers assume that an innovation should be diffused. The resistance to
adopt innovations has received relatively little marketing attention, even
though understanding it is critical to success of an innovation. Ram and Sheth’s (1989) conceptualisation of innovation resistance
provides justification for inclusion of adoption barrier discussion,
investigation of the factors inhibiting the adoption of mobile banking, in this
research (see Figure 10).
Functional barriers Consumer resistance
to innovations Psychological barriers
FIGURE
10 Typology of innovation
resistance (adapted from Ram and Sheth 1989)
Research
on Internet banking, and to some extent research on mobile services adoption,
can act as a valuable starting point for examining the adoption of mobile
banking, a point made also by Pedersen and Ling (2003). In this context it is
useful to refer also to Gatignon and Robertson’s
(1985) findings based on a review of adoption research. They take the view that,
in a case where no prior data on a totally new product or service concept
exists, some conclusions can be drawn from the adoption of other products
within the same product category. Similarly, Hirschman (1980) has suggested
that prior experience with a product category (e.g. Internet banking) may lead
to greater acceptance of new products (e.g. mobile banking), hence increasing
the likelihood that they will be adopted. This rationale can be linked both
with the discussion of compatibility of an innovation with the values of an
adopter, and why general technology orientation change behaviour (Thornton and
White 2001).
In fact, one soon discovers that there
is no lack of research material on Internet banking services (e.g. Karjaluoto et al. 2002; Bradley and Steward 2002), and more
specifically, research material applying the Rogers model to Internet banking
(see e.g. Black et al. 2001; Polatoglu and Ekin 2001; Tan and Teo 2000).
However, in the case of mobile phones, and consequently in the case of mobile
banking services, we can observe characteristics that differ sharply from such
wired line devices such as personal computers or televisions. Siau and Shen
(2003) list some key drivers for mobile services which further explicate the
differences between wireless and wired service environment.
- Mobility: The primary advantage of mobile
services. Users can get any information they want, whenever they want,
regardless of their location. Mobile services fulfil the need for real-time
information and for communication anytime.
- Reachability:
Through mobile devices, business/government entities are able to
reach customers/constituents anywhere, anytime. With a mobile terminal a user
can be in touch with and accessible to other people anywhere anytime. The user
can also limit her “reachability” to particular
persons or at particular times.
- Localisation: The knowledge of a user´s physical location at a particular moment also adds
significant value to mobile services. With location information available, many
location-based services can be provided. For example the mobile application can
alert a user quickly when a friend or colleague is nearby or to help with the
user locate the nearest restaurant or ATM.
- Personalisation: Although enormous amounts of information,
services and applications are currently available on Internet, not all
information is relevant to all users. Mobile services can be personalised to filter information or provide services in
ways appropriate to a tailored user.
Computer as a device has often many users; the ownership
is publicly shared (e.g. library’s computers) whereas mobile phone is
proprietary owned and used by a consumer. Additionally, since the concerns of
security are often raised a mobile device can be more easily identified and
even with the latest biometric technology, a person using mobile phone can be
identified (Yuan and
Zhang 2003).
The
expected improvements for the mobile service and application space arriving
with 2.5G and 3G networks can act as a trigger for acceptance. These include
ability of mobile devices to provide location-specific information, new ways of
personalisation, enhanced availability and immediacy of service. Particularly
the last mentioned feature can contribute to the predicted shift from wired
Internet connections to wireless mobile services in banking too (Wah 1999). The immediacy of information access will be
enhanced by the always-on functionality, which supports the provision of
time-critical information conducting high value transactions, such as
participating in mobile auctions and executing mobile stock trading deals.
These type of value-added mobile services will without doubt become some of the
most interesting, revenue-generating services simply because of the economic
value attached to them (Durlacher Report 2001).
Given this situation, the research
findings on marketing and consumer behaviour patterns that may have applied in
the wired line environment do not necessarily hold good for the wireless
environment. This is, in fact, one paramount reason why research into mobile
banking is needed: consumer behaviour in the mobile context has up to now
remained more or less uncharted territory.
3.3 Convergence of technology and services
As suggested
previously Section 3.1, mobile banking is an innovation involving both an
intangible service and an innovative medium of service delivery employing high
technology. From this perspective, it is obviously useful to examine research
into technology and services.
3.3.1
Technology-based services
Concepts of
innovation and the diffusion of innovation become particularly intricate in a
case such as the present one, where both technology and service aspects affect
the characteristics of mobile banking services. Here, indeed, we have a complex
interaction between an intangible service and technology-based service delivery
(Black et al. 2001). The impact of technology on the financial services
environment was already discussed in Chapter I. Today’s banking industry is, to
large extent, driven by technological innovations. The banking industry shares
the common characteristics of a high-technology industry discussed in e.g. Mohr
(2001), most notably in terms of market uncertainty, technology uncertainty and
competitive volatility.
Technology-based self-service is
growing at a tremendous rate all over the world, but it appears that a strong
unifying theory which would encompass this form of service is lacking. Bobbit and Dabholkar (2001) seek
to explain the pivotal role of attitudes in influencing intentions and
behaviour relating to technology-based services. They propose a conceptual
framework that incorporates several well-known attitudinal theories. The hope
would be that frameworks of this kind might help to make better predictions of
consumer decisions regarding technology-based products.
In fact, it is only fairly recently that academic researchers have recognised the critical
importance of technology in the delivery of services (e.g. Bitner
et al. 2000; Dabholkar 1996; Lee and Allaway 2002). It has been suggested that the traditional
marketplace interaction has been replaced by a marketspace
transaction. Meuter et al. (2000) cite Rayport
and Sviokla’s (1995, 14) definition of marketspace as “a virtual realm where products and
services exist as digital information and can be delivered through information
based channels”. Electronic banking services, and thus mobile banking also,
are a typical example of marketspace transactions
that require no personal interaction.
In order to better understand the role
of technology, it became apparent that there was a need for a wider focus,
considering the role of technology in banking in ways that went beyond those
academic banking articles (e.g. Durkin and Howcroft
2003) and trade literature (e.g. Koch and MacDonald 2000; Seymann
1998; Keyes 1999) which directly discuss the development of banking technology.
It was deemed necessary to review the literature on services marketing which
focuses on the impact of technology on services from a number of different research
angles. Thus, it proved useful to make a small excursion into services
marketing theories too. Indeed, the change in the services environment has been
discussed in the literature of relationship marketing, as well as in other
domains. Reflecting the changing landscape of services, and capturing the
complexities resulting from the growing infusion of technology, Parasuraman (1996) proposed a now well-known pyramid model
of services marketing, which was an extension of Kotler’s
(1994) triangle model of services marketing. The traditional triangle was
modified, with technology included as the crucial fourth end-point (see Figure
10). Through the base of the pyramid, services are now seen as involving a
dynamic relationship between employees, customers and technology. The adapted
pyramid model can be regarded as one likely to encourage and direct research,
that is, research that could encompass the important and growing role of
technology in the delivery of services (Bitner et al.
2000; Parasuraman and Greval
2000).
Company
Technology
Employees
Customer
FIGURE
10 Pyramid model
of services marketing (Parasuraman 1996)
Dabholkar
(1994) explicates further the convergence of technology and services. Services
marketing literature has traditionally distinguished between technological
services and non-technological services. However, with today’s technological
advances, this distinction is increasingly difficult to make; the line between
technological and non-technological services is becoming blurred. Across
different industries, service firms are offering and delivering similar
technology-based services, with similar implications for marketing. In
consequence, it is impossible for research to overlook these developments.
3.4
The framework of
the dissertation
Drawing on the literature discussed above, and
having considered the applicability of a large body of empirical studies, the
various theoretical constructs and models that seemed likely to be helpful in
reaching answers to the research questions were selected and the hypotheses
formulated. Following on from this, an underlying framework for the dissertation
was arrived at (see Figure 11).
3.4.1 Model development
The work
presented in the dissertation represents a synthesis and extension of the three
research streams that were identified in discussion above. The model is based
on the work of Rogers (1995) and comprises five constructs of innovation
attributes, augmented with a perceived risk factor, plus 'external' factors
such as social system, time, communication channels and the demographics of the
adopters. The six constructs (relative advantage, complexity, compatibility,
trialability, observability,
risk) are the attributes which define the perceived characteristics of an
innovation. These perceived attributes constitute one important explanation of
the rate of adoption of an innovation. A particular construct is often highly
domain-specific; thus its influence on adoption can be seen differently in the
context of different innovations and different consumers. In the present work,
the domain under study is the adoption of mobile banking services. The
dimensions highlighted in the framework present the constructs that form the
main research interests in this dissertation. In addition to innovation
attributes, the characteristics of the adopters, in respect of demographical variables and communication channels, have been
investigated. The arrows indicate the influencing effect of a dimension on
adoption.
Social
System
Time
Time
FIGURE 11 The framework of the dissertation
IV REVIEW OF THE SURVEY
DATA
The chapter
reviews some results of the survey data, concentrating on providing statistical
descriptions of the survey participants and their electronic banking usage. In
addition to demographical profiles and a comparison of different user groups,
some interesting data are presented concerning current usage of various service
delivery channels. However, it does not
at this point go into an exhaustive analysis of the empirical implications, or
the linkages that exist between the survey variables. This is because these
more detailed considerations are given in-depth treatment in the Research Articles
and additionally in the concluding Chapter V.
4.1
Descriptive statistics
The questionnaire was sent to a
sample of 3000 banking customers. The sample was further divided into three
user segments, each of which received 1000 questionnaires. The user segments
were based on the customer database information received from the OKO Bank
Group, each segment being defined in terms of experience with mobile banking
services. Thus, the segments were labelled as regular users, occasional users
and non-users of mobile banking.
Altogether 1301
responses were received, out of which 1253 were accepted for analysis. The
greatest number of responses came from the group of non-users. Among the
non-user segment, the response rate was 38.8 percent, among occasional users it
was 33.2 percent and among regular users it was 28 percent. This kind of
distribution among respondents appears to be typical, since research has
indicated (e.g. Mattila 2001) that customers who
prefer personal services and personal contact in service encounters are more
willing to respond to survey questionnaires. In contrast, current users of
technology-based services are described as being 'homing'; respondents in this
category have even stated that avoiding contact with service providers is a
reason for choosing the electronic banking delivery channel – which could also
indicate an unwillingness to respond to questionnaires.
The structure of the questionnaire made it possible to analyse
and compare mobile banking users according to whether they used SMS or WAP
services; these users are here referred to as SMS or WAP customers. In the
present study, they are compared in relation to age distribution (see Figure
12), and later in relation to the channels they are using (Figures 20 and 21).
In addition, some correlation coefficients were studied between using SMS
or WAP service and the demographic variables.
Respondents indicated the postal code
of their home address. They were dispersed geographically as shown in Figure
11. Most of the respondent came from the Southern part of Finland; Oulu and Lapland counties are in Northern Finland. This
distribution of respondents is geographically consistent with the population
distribution of Finland. It is interesting to notice that there are more
customers using mobile banking services via the WAP service in Southern and
Western Finland, whereas in Northern Finland SMS services are more commonly
used.
FIGURE
11 Geographical distributions of the
respondents
Figure 12
depicts the age distributions of mobile banking users. As can be seen from the
figure, the distribution is not consistent with mean age distribution in
Finland. This can be partly explained by the fact that certain age groups are
traditionally more responsible than others for taking care of the financial
issues in a household. According to these results they are persons belonging to
the age group 25-34 years. The distribution is also to some extent biased,
since in the sample, the proportion of current users of mobile banking services
is higher. The only correlation found was between age and the use of WAP
services (rwap=0.107, p<0.01); this can
be interpreted as an indication that customers in the 25-34 age group are more
likely to use these services.
FIGURE
12 Age distributions of mobile banking
users in relation to Finnish mean
Slightly over
half (51.6 %) of the respondents were male and 47.1 percent female. Figure 13
presents the gender distribution of each user group. In the regular users group
there were 10 percent more men than women, and also slightly more men in the
occasional users group, but this difference was not statistically significant (Χ2 = 2.15, p =
.341). However, the results of the survey are consistent with the common
belief that the early adopters of new products are male in most technology-led
markets (Lu et al. 2003). No correlation was found between gender and the use
of SMS services; however, there was a correlation between gender and the use of
WAP services (rwap = -0.057, p<0.05), indicating
that men are more likely to use WAP services.
Χ2
= 2.15, p = .341
FIGURE 13 Gender
distributions of the user groups
4.2
Demographics of the study participants
Previous
studies on electronic banking as well on theories of consumer behaviour have
shown demographics to be a factor influencing the adoption of technology-based
products and services (e.g. Agarwal and Prasad 1999).
One of the major interests in the survey was to study the differences between
the users and current non-users of mobile banking, and this was made possible
by the stratified sampling method employed. At first, an overview of all the study
participants is presented, and then this will be followed by a more detailed
description of the differences between the three user groups. The demographic
profile of the respondents is summarised in Table 1.
TABLE
1 Summarised
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
(continues)
TABLE 1 (continues)
Entrepreneur 74 5.9 95
Public servant 49 3.9 98.9
Other 5 0.5 99.4
Missing 7 0.6 100
Standard
deviation 2.183
Education
Elementary school 196 15.6 15.6
Commercial school 163 13 28.6
Vocational school 316 25.2 53.8
Technical school 113 9 62.8
Polytechnic 119 9.5 72.3
University 169 13.5 85.8
Secondary school 135 10.8 96.6
Other 27 2.2 98.8
Missing 15 1.2 100
Standard
deviation 2.094
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
Field
of employment
Industry 237 18.9 18.9
Administration 158 12.6 31.5
Logistics 82 6.5 38
Services 267 23.7 61.7
Banking /insurance 14 1.1 62.8
IT 73 5.8 68.6
Trade and commerce 73 5.8 74.4
Primary production 45 3.6 78
Other 2 0.2 78.2
Missing 273 21.8 100
Standard
deviation 2.084
Figure 14
depicts the age distributions of the respondents according to the three user
groups. As the figure indicates, the
majority of the regular users belonged to the 24-34 age group,
as did the majority of the occasional users. Non-users were somewhat older with
about one third (31.7 %) of them belonging to the 35-49 age group. It has often
been suggested that the adopters of technology-based services are relatively
young, even though Internet banking studies (e.g. Mattila
2001) have shown that the typical Internet banking user is more likely to be
middle-aged. In this study there was no predominance of middle-aged users
Χ2 = 84.22, p = .000
FIGURE 14 Age distributions of the user groups
The
questionnaire asked about income level, in terms of annual household income. In
this connection one must remember that household size may affect the overall
household income per year (see Figure 15). Almost a fifth (19.1 %) of all
respondents belonged to the annual household income category of 20 001-30 000
euros, and 16.5 percent of the respondents had an income of over 60 000 Euros.
The respondents were distributed fairly evenly over all the income categories,
and there were no striking differences in the size of the user groups. Surprisingly, the respondents
earning most (18.3 % have annual income of 40001 to 50000 euros) can be found
in the user group of non-users, which may be considered as an interesting
finding. When correlation coefficients between household income and the use
of SMS or WAP services were calculated, it was found that the use of WAP
services correlates with income (rwap=0.076, p<0.01).
Χ2
= 50.78, p = .000
FIGURE 15 Annual household income distributions of
the user groups
The
other interesting variables in tracing the demographic characteristics of the
different user groups are occupation, education and field of employment. Figure
16 depicts occupation distribution; it shows that worker was the largest occupational category in each user
category (53.3 % of regular users, 38 % of occasional users, 32.5
% of non-users. The second largest category was that of white-collar workers; in the non-users group as much as 24.3
percent of the respondents belonged to this category. This result is consistent
with the picture that emerges from Figure 17, which depicts the educational
background of the respondents. In most cases this was vocational school (35.9 %
of regular users, 25.5 % of occasional users), but 20 percent of the non-users
had a university degree. Traditionally, the innovators in the use of
technological products are often characterised as being at a higher
professional and educational level, and it has been found, for example, that
the non-users of Internet banking are often pensioners (e.g. Rogers 1995; Polatoglu and Ekin 2001).
However, the results of this survey would appear to contradict this
characterisation.
Χ2 = 90.88, p = .000
FIGURE 15 Occupation distributions of the user
groups
Χ2
= 86.43, p = .000
FIGURE 16 Education distributions of the user groups
Most
of the respondents worked in services (23.7 %), as can also be observed from
Figure 17. The next largest fields of employment were industry (18.9 %) and
administration (12.6 %). In relation to this variable there was no significant
difference between the user groups.
Χ2
= 37.78, p = .002
FIGURE 17 Field of employment distribution of the
user segments
Summarising the descriptions in the paragraphs above, one can say
that demographics do have an impact on the use of mobile banking, even though
the variances between the user groups were not as high as one might have
expected from previous research (see Chi square statistics). Nevertheless, the review yielded
an explicit description of the users of mobile banking in term of demographical
variables. There were 10 percent more men in the regular users’ group. Users of
mobile banking services typically belonged to the 25-34 age group. The majority
of regular users (43.6 %) were aged 25-34 years, as were the majority (36.8 %)
of the occasional users. Non-users tended to be older than the other two
groups, with a third being in age group 35-49, and 25.9 percent in age group
50-64.
Married persons comprised 38.9 percent of the
respondents. The majority of the respondents (19.1 %) belonged to household
income category 20 001-30 000 euros per annum, a figure in line with the
average yearly income for two persons in Finland. The majority of the
respondents were workers (40.1%), the second largest category being that of
white-collar workers (19.6 %) and the third largest that of students (10.5 %). These
results are compatible with the background education of the respondents, which
in most cases (25.2 %) was secondary level vocational school. However, the
results differ from previous findings on electronic (Internet) banking users,
who have previously been found to have had a university level education and to
belong to the higher professions (e.g. Jayawardhena
and Foley 2000).
4.3 Current usage of mobile
banking services
The survey also viewed the
participants' ways of conducting their banking: how often, via which mobile
banking mode, usage of several services in parallel, preferred channels for
certain banking services. As shown in Figure 18, the majority of the
respondents (58.3 %) reported that they conducted banking 1-3 times a week. For
32 percent the figure was less than once a week and for a very small minority
more than 7 times a week.
At the present moment, mobile
banking essentially means using the SMS services. This, the “simplest” mode of
mobile banking services, is still the one most used (by 71 % of the respondents, see Figure 19).
FIGURE 18 Distribution according to banking frequency
FIGURE 19 Distribution of mobile banking service
mode
The following Figures (20 and 21)
shed light on the distributions of usage of different service delivery
channels. The WAP customers use the Internet banking channel (80.5 %) and also
SMS services (73.6 %). These two service delivery channels are thus
alternatives for this customer group, with considerable usage for both
channels. By contrast, customers who use SMS services use both Internet banking
services (72.2 %) and ATM services (41.3 %). This group does not use WAP
services as an alternative channel. These findings are a clear indication that multi-channel
management is needed by banks already today!
FIGURE
20 WAP customers: usage of different
service delivery channels
FIGURE
21 SMS customers: usage of various
service delivery channels
Figure 22 below
gives an overview of respondents' perceptions of the suitability of different
channels for different banking services. In branch offices, consumers would be
most willing to deal with a loan application (86.5 %), obtain legal advisory
services (80 %) and order currency (78.4 %). Given the nature of these services
these results were not surprising. Services such as making an account balance
inquiry (35.7 %), making a transaction inquiry (15.9 %), paying a bill (7.2 %)
and making a card credit balance inquiry (5.2 %) are regarded as suitable also
for mobile banking services. Notice that these services are precisely those
which are used in everyday routines, i.e. in cases where the advantage of
mobile banking “access while on road” becomes more noticeable. Furthermore, the
findings emphasise the fact that, along with traditional branch banking, Internet
banking has a firm foothold in Finnish banking habits.
FIGURE 22 Channels used for particular banking services
V CONCLUDING DISCUSSION
This chapter
discusses the general contributions of the present study, the results from
hypotheses testing together with its limitations, and the future avenues for
research arising from it.
5.1 Contributions
If
banks are to successfully integrate new technology into their service delivery
platform, it is essential that they should understand the impact of
technology-based delivery channels on customer perceptions and behaviour. The
present research provides without dispute new information on consumer behaviour
and on the changes taking place in the sector. The dissertation makes a
contribution to electronic banking research; however, the findings can also be
applied to other similar types of innovations.
Despite all the possibilities offered
by the new electronic channels for banking services, there are various
psychological and behavioural issues which appear to influence the acceptance
of mobile banking, and these need to be better understood. In
addition to extending our understanding of consumer behaviour in mobile banking
context and innovation adoption from a theoretical stance, the research
presented also has practical implications for managers who
have to make decisions about the new technology-based services.
5.1.1 Hypotheses tested
The
theory-driven hypotheses were formulated and tested using linear regression
analysis. The independent variables were regressed on dependent variable
“adoption of mobile banking services”. Multicollinearity
was ruled out because the VIFs (variance inflation
factor) were all less than 10. The scatter plots were drawn and examined to
avoid the problems of non-linear relationships, extreme scores and combined
groups. All the hypotheses, with the
exception of H2 (complexity) and H6 (risk), were supported (see Table 3).
Past literature (e.g. Tan and Teo) has consistently shown that relative advantage (H1)
has a significant and positive influence on the adoption electronic banking,
that was the case also in this research (β= .212, p=.000). Reversely, the lack of support for H2,
complexity, (β= -.049, p=.218) is in contrast with previous findings which
indicated that more complex an innovation is to use, and the greater the skill
and effort needed for adopting it, the less likely that it will be adopted.
Even though in regard to Internet banking Tan and Teo’s
(2000) investigation yielded the same result. They explained the finding by
referring to Moore and Bensabat (1991) who have
suggested that this innovation attribute begins to play a more instrumental
role only after one has started hands-on trial. In Finnish setting explanation
could be found in the fact that diffusion of mobile banking is still in a
relatively early phase and those consumer who have already adopted mobile
banking can be categorised to belong in innovators in accordance with Rogers’
continuum of adoption. Innovators are often technology enthusiasts, who are
willing to tolerate initial glitches and problems that so typically accompany a
technology-based innovation just launched to market.
The
support for H3, compatibility with values and earlier experiences (β=
.215, p=.000) is
similar to previous findings. Consumers who feel that conducting banking via
wireless channel is compatible with they values and way of life are more inclined
to adopt. The infrastructural development in Finnish society had been very favourable
increasing general acceptance of technology-based services. The idea of “Nokia-land” is not just something
that foreign journalists have invented. The H4, observability/communication,
was supported (β= -.118, p=.005) which proved the assumption of the Bass
communication model to be relevant in the context of this phenomenon. In
similar vein, H5, trialability, was supported (β=
.169, p=.000) which
can be explained as Rogers (1995) argued that potential adopters who are able
to experiment with an innovation are more likely to adopt the innovation.
The
fact that H6, risk, (β= .017, p=.655) was not supported might be considered as
surprising if reflected to previous findings and the public opinion. The
non-significant effect of perceived risk on adoption of mobile banking services
indicates that user are not concerned about the risk related to conducting
banking via a wireless channel which are measured as the overall security and
trustworthiness of the offered services.
The
support for H7a, technology perceptions, (β= .154,
p=.000) is in line
with e.g. Gatignon and Robertson (1985). The
significant effect on technology perceptions to adoption of mobile banking
means that users will consider the usage of mobile banking more positively if
they are accustomed to mobile phones, computers and other technology-based
products and services, and they are having a positive attitude towards
technology.
TABLE
3 The results of the regression
analyses
β p Support(yes/no)
H1 .212 .000 Y
H2 -.049 .218 N
H3 .215 .000 Y
H4 -.118 .005 Y
H5 .169 .000 Y
H6 .017 .655 N
H7 a .154 .000 Y
The H7 b, demographics, was tested using
correlation analysis; the results are outlined in Table 4. These variables were
included on the basis of previous research which suggests that demographic
variables have an influence on adoption of electronic banking services. The
positive coefficient on age (r=.085**) is consistent with the finding presented
in Chapter IV since the adoption of mobile banking services was measured among
other as the usage of WAP-services. Interestingly,
the only other significant demographical variable in this analysis was
education (r=-.183**).
TABLE
4 Demographics: Correlation
coefficients
Gender
Age Marital status Education Household income
Adoption of mobile
banking services -.038 .085** .010 -.183** .052
Sig. .190 .003 .731 .000 .077
Notes: ** Correlation is significant at the 0.01 level (2-tailed)
* Correlation is significant at the 0.05
level (2-tailed)
(measured by using
Pearson’s Rho)
5.1.2 Theoretical contributions
As Poincare´ (1983) so aptly noted: “Science is just facts,
just as houses are made of stone… But a pile of stones is not a house, and a
collection of facts is not necessarily science” (Whetten
1989). Thus, theoretical insights come from demonstrating how the addition of a
new variable significantly alters our understanding of a phenomenon. The new
information stems from inconsistencies between the quantitative or qualitative
data gathered and conventional wisdom. This holds true in the present research
also.
The
theoretical relationships outlined in innovation diffusion research, between
perceived innovation attributes and adoption behaviour, were in fact supported
by the survey findings. In line with previous studies, the present research
gave support to Rogers’ model as an adequate and parsimonious conceptualisation
of adoption behaviour in the mobile banking domain. However, the model used,
which was based on Rogers’ work, and the results obtained from it, yielded some
new insights into consumer behaviour patterns. For example, the issues of
complexity, trialability and perceived risk are not
as straightforward as might have been expected in this domain. Furthermore,
some aspects of the Rogers model may call for refinement: Rogers (1995) viewed
many innovation attributes as being conceptually unique and unidimensional;
by contrast, the empirical evidence of this study suggests that certain
innovation attribute constructs might usefully be sub-divided. Furthermore, not
all innovation characteristics exert a similar steady influence on adoption,
although this might have been the a priori assumption. As in many other
studies, relative advantage appears in the present survey to be overwhelmingly
important for user acceptance (e.g. Agarwal and
Prasad 1997).
In terms of advancing theoretical
perspectives, the study provides empirical support for a theory of how
individual differences drive the adoption of new technology-based products. The
results suggest that certain demographical variables have an impact on adoption
behaviour; they also suggest that the use of certain communication styles and
modes makes consumers more receptive to information concerning an innovation.
Overall, the conclusions drawn from the results suggest that it is not simply
the paradigm of service environment that is changing, but also the typology of
the electronic service user. It appears that the profile of a typical mobile
banking user differs from that of an Internet banking user.
From the theoretical point of view, the
study confirmed that the traditional models used in adoption research require a
degree of modification and extension. The findings indicated herein are consistent with those of
Petersen and Ling (2002), in the sense that there appears to be a need to
develop different versions of adoption models; the constructs in these models
will have to be specific to the service or user segment in question. The
results suggest that models of technology adoption should take the nature of
the technology into account, since commonly-held perceptions may not in fact be
applicable to every technology.
In consumer research it is generally
believed that past experience of using a similar technology contributes greatly
to favourable attitudes to a new technology, and to the actual adoption of this
technology (e.g. Dabholkar 1996). The findings in the
present research revealed that positive technology perceptions may also enhance
the adoption of mobile banking; nevertheless, the adoption framework that
emerged in the context of the study implies that experience with Internet
banking services – which can be regarded to some extent as similar to mobile
banking services – do not necessarily encourage mobile banking. Internet banking
is apparently not a related service product in the ways suggested by Gatignon and Robertson (1985). It seems that the typical Internet
banking user will continue to use a wired delivery channel, whereas customers
who currently pay bills by automatic means and via branch offices are more
likely to make the “leap” to mobile banking. Extrapolating from this, it can be
argued that banks should not invest large sums of money in order to convince
regular Internet banking users to change from one electronic channel to
another; instead, they should try to convince customers outside this segment
about the advantages of mobile banking. And on a general level, the conclusion
provides further reasoning why strategies and services designed for the
Internet cannot be directly converted into the mobile service environment:
differentiation is needed.
In addition to these considerations,
the findings give some interesting insights into marketing and management
practice. The implications drawn from the findings could help service
providers to understand consumers better and to make more rationally-based
decisions. The results provide concrete tools for managers concerning the ways
in which mobile banking services should develop in order to meet customers’
needs. Thus, for example, the study provides data on the service attributes
that will be most valued in the future, and on the optimal modes and styles of
information dissemination.
5.1.3 Implications for practitioners
According to Lievens and Moenaert (2000) the
study of innovation in the financial service industry is a relatively new area
of business research; they date the research on this topic as having got under
way around the mid-1980s. Admittedly, this could be seen as a factor that may
increase the interest of the findings of the present study. Given the “newness”
of the phenomena under study, the present research is itself fairly unique, providing
results which, for the most part, have not been obtained before. Academic research on the factors affecting
the adoption of mobile banking has been sparse, and has tended to be limited in
scope (see e.g. Deutsche Bank Research 2002). It was for this reason that, in
attempting to close the knowledge gap, the initial approach largely involved
extrapolating from knowledge acquired about other modes of electronic banking.
Identification of the demographic and
psychographic profiles of adopters provides a number of implications for
services marketing managers (see Research Article. While such information is
important in planning each element of the marketing mix, it is especially
important in developing the promotional activities and media plan.
When innovations are introduced, the
most desirable outcome from a financial institution’s point of view might well
be to make the innovation a routine part of the adopter’s life. Rogers (1995)
argues that the final phase in the adoption of an innovation is that of routinisation, which occurs when the innovation has become
incorporated to regular activities of the adopter. Novelty and curiosity
regarding the use of mobile banking services was mentioned in the survey as one
trigger for adoption. The present results reflect the fact that mobile banking
services are at a relatively early stage in the path of diffusion (see Research
Articles II and IV). It is often the case that the first adopters of an
innovation are motivated simply by the desire to get their hands on the latest
and greatest innovation; the stimulus is curiosity regarding anything that is
truly brand-new. Mobile banking has not yet gone beyond this phase, indicating
clearly that mobile banking services are not yet fully institutionalised; they
have not entirely become part of the ongoing practice and way of life of the
adopters.
Mobility-specific factors were shown to
be the most significant triggers for mobile banking adoption,
meaning accessibility and availability of services regardless of time and place
(see Research Article III). From the point of view of mobile banking adoption,
the accelerating pace of development entails both encouraging and discouraging
aspects. On the one hand, customers like the idea of being technologically
up-to-date; on the other hand, being an early adopter means having to tolerate
possible initial glitches and investing time and effort in learning. One
negative effect of the accelerating pace of development is manifested in
services that are launched at too early a stage of development, due to
pressures of competition and cost.
It is encouraging that even though the
security of the services was found to be a significant factor in channel
choice, the wireless channel was viewed as being trustworthy. This would predict
a positive future for mobile banking. Moreover, as mentioned above, enthusiasm
for technological development itself is obviously a
driver for the adoption of mobile banking. And there are advantages in mobile
banking also in the savings made in time and effort. The main impediments to
the adoption of mobile banking are functional-specific in nature. Examples of
these include problems regarding the supplier side of the services: too slow
data transmission, a complicated user interface, malfunctions in the service,
and insufficient guidance.
The research findings provide some
indications as to who could be the next mobile banking customers, and what
their characteristics might be. These aspects emerge from the questions asked
of all three users groups regarding their intentions. The results are discussed
in more detail in Research Article IV. The examination of intention has met
with some criticism among researchers on consumer behaviour; however, research
on technology-based products has employed questions on intention as a useful
empirical procedure. For example, Lu et al. (2003) studied intention to use wireless internet via
mobile devices rather than the actual use of these devices, or Molesworth and Sourtti (2002) had
the focus on initial trial-adoption of online car buying. This was due to the novelty and the fact that
the services were (and are) still at an early stage of the diffusion process.
It was nevertheless argued that an investigation into the intention to use
would enhance the predictive power of the model in question.
5.1.4 The revised
model
As part of its
primary academic goal, this chapter sets out a model which responds to the
initial question raised in the study: What are the dimensions affecting the
adoption of mobile banking services in Finland? In so doing, it draws on the theoretical as
well as the practical contributions of the study; it also makes use of the
answers to the research questions that were discussed in the previous chapters
and in the research articles.
The framework
of the dissertation was first outlined in Section 3.4. The revised model
highlights and summarises the findings and the effects of the dimensions on
adoption of mobile banking. The reader will have observed that the dimensions
presented were validated and justified as the dissertation progressed.
Rogers’ model and the five (six)
innovation attributes were further validated and are given an in-depth
treatment in Research Article II
At this point it may be of interest to
explain certain changes that occurred in the framework as the research
progressed. These changes involved the dimensions time and demographics.
Initially, time as a diffusion element was not included in the study as a core
construct. However, the empirical findings, discussed in
detail in Research Article III, indicated that time has a significant effect on the adoption of mobile
banking. In fact, it was found that time (which was defined herein as the rate
of technological development of the surrounding social system, and the constituting
of a variably accelerating pace of development) has both a positive and a
negative effect on adoption behaviour.
In addition, following a more precise
review of the theoretical base underlying the research, together with support
given by the empirical survey, the researcher was led to incorporate the
dimension of demographics
within consumer characteristics. Re-labelling the dimension this way was found
to increase the comprehensiveness of the model. In addition to the demographics,
such variables as the beliefs and perceptions of consumers, personality traits,
and intentions to use can be now included in this dimension. Thus, a new, more
general label is appropriate better describing the nature of the dimension (see
'Consumer Characteristics' in Figure 23 below).
FIGURE
23 Dimensions affecting mobile
banking adoption
5.2 Limitations of the
study
Notwithstanding the contributions discussed every
piece of research has its limitations, and the limitations of this study arise
from its relatively narrow research focus. Even though the dissertation
highlights various research areas within consumer behaviour research, it does
not attempt to propose a model that would be fully comprehensive or universally
applicable. Rather, it should be viewed to some extent as a preliminary insight
into the relatively unexamined and unknown territory of mobile banking. The
research focus was almost entirely on the consumer, with scrutiny of a certain
limited number of adopter characteristics and perceived attributes of
innovation. These elements narrow the scope of generalisations from the findings.
It should be noted, however, that one of the research articles (see Research
Article I) examines the usage of mobile services more generally.
It should also be noted that the study
examined mobile banking only in Finland. Clearly, Finland can be regarded as
one of the most technologically advanced countries, and this technological
advancement extends also to banking services within the country. Overall
caution must be considered in regard to the generalisability
of this study to the application of results across countries. The majority of
the respondents in the study were technologically oriented. It may be that the
stratified sampling technique that was used led to the sample being too
homogeneous, and the statistical efficiency might have been reduced as a
result. This could have had an effect on the validity and reliability of the
results.
Methodologically speaking, the present
research was in the main a descriptive study: it defined and explained the
variables and their relationship within a given situation,
and in the end aimed at forming a model of consumer behaviour patterns in
relation to the phenomenon. Using a more rigorous statistical analysis to
investigate the causal relationships, such as structural equation modelling,
would have added on our understanding calculations of the strength of association
between the variables measured. Be that as it may, whether a study is
descriptive or causal, it should be noted that the model arrived at cannot be
more than a condensed and inadequate representation of the complex real world (Bagozzi 1980). Even though the “hypothesed” world of the
scientists is not an analogue of our world, Kerlinger
(1973, 26) aptly laid stress on the importance of hypotheses in investigations.
“Even when hypotheses are not confirmed, they
have power. Even when y does not covariate with x, knowledge is advanced.
Negative findings are as important as the positive ones, since they cut down
the total universe of ignorance and sometimes point op fruitful further hypotheses
and lines of investigation. But the scientist cannot tell positive from
negative evidence unless he uses hypotheses.”
5.3 Future research
Considering
simply the geographical scope of the present study, there is clearly a need for
further search. In the present survey the sampling frame was limited to
Finland. One would wish to do more than investigate the diffusion of mobile
banking services within a single “pioneering” land of mobile communications; in
so doing, one would probably gain valuable new insights into consumer behaviour
patterns. Replicating this kind of research in a multicultural, cross-border
context would reveal which adoption factors are culturally bound.
Additionally, this research was
cross-sectional. It would be interesting to discover the kind of information a
longitudinal study might produce regarding the real rate of adoption of mobile
banking. In most studies of adoption, both the technology and the consumer are
seen as static entities, i.e. they are not perceived as undergoing change
during the adoption process. However, some studies (e.g. Carlell
2001) indicate that both the consumer and technology change as time passes, and
a longitudinal study would provide evidence on this point. The findings of this
kind of study investigating technological innovations will need to be updated
regularly to keep pace with the changes occurring within the financial services
industry.
Based on the findings of this study,
certain assumptions were made regarding future mobile banking users. It would
obviously be interesting to see whether these assumptions proved to be true. A
further point to note is that this research concentrated on the adoption of
mobile banking purely from the consumer perspective. However (as discussed
briefly in Research Article III) there are a number of what might be called
supplier-side factors boosting the adoption of mobile services. This
organisational perspective has been studied to some extent within Internet
banking research, but not extensively in the mobile banking context.
Bissola
(2003) argues that in the near future banks should be able to offer each
customer a set of products best suited to her personal situation, in order to
cope with and stimulate her financial needs.
Clearly, to prevail over competitors, banks should be aware of this
strategic option and be pro-active: empirical research is, in fact, a means of
providing tools for practitioners. One interesting area of research would be to
compare within the same study the use of various electronic channels and
services: one would hope to discover the reasons that led to a preference for
one channel over another, or for using several channels in parallel. Even though it seemed in light of
the results from this survey that multi-channel distribution management should
play already today a large role in banking. This is
something that one would wish to investigate further.
One possible way to approach the mobile
banking phenomenon could be to determine how quality-value-loyalty chains are
built up in mobile banking services, possibly in comparison to other modes of
electronic banking. The questions raised in Research
Article IV (concerning whether a critical mass in mobile banking will be
reached, and whether a mobile banking mass market will be developed) would provide
a useful avenue for further research.
One particularly interesting finding
from the present survey was that current regular users were not really keen on
increasing their usage of banking services via a wireless channel. This raises
the question of whether it is worthwhile to increase the number of different
services offered through this channel, or whether it would be more valuable to
pursue a more concentrated strategy. However, many banks have continued to
announce investments in mobile banking projects. No doubt the programmes the
banks are undertaking will lead to plenty of challenges for research in the
future.
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