yusufdauda2015

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Contents lists available at ScienceDirect
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Information Systems
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journal homepage: www.elsevier.com/locate/infosys
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Technology adoption: A conjoint analysis of consumers'
preference on future online banking services
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Q1
Samson Yusuf Dauda n,1, Jongsu Lee
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Seoul National University, Technology Management, Economics and Policy Program (TEMEP), Administrative Office (International Energy/
IT Policy Program), Bldg #37-307, 1 Gwanak-ro, Gwanak-gu, 151-742 Seoul, Republic of Korea
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a r t i c l e i n f o
abstract
Article history:
Received 3 March 2015
Received in revised form
24 April 2015
Accepted 25 April 2015
Recommended by: Prof. D. Shasha
The importance of service delivery technology and online service adoption and usage in
the banking industry has received an increased discussion in the literature in recent years.
Owing to the fact that Strong online banking services are important drivers for bank
performance and customer service delivery; several studies have been carried out on
online banking service adoption or acceptance where services are already deployed and
on the factors that influence customers' adoption and use or intention to use those
services. However, despite the increasing discussion in the literatures, no attempt has
been made to look at consumers' preference in terms of future online banking service
adoption. This study used conjoint analysis and stated preference methods with discrete
choice model to analyze the technology adoption pattern regarding consumers' preference for potential future online banking services in the Nigerian banking industry. The
result revealed that to increase efficiency and strengthen competitiveness, banks need to
promote smart and practical branded services especially self-services at the same time
promote a universal adoption of e-banking system services that add entertainment or
extra convenience to customers such as ease of usage including digital wallet, real-time
interaction (video banking), ATMs integrated with smart phones, website customization,
biometric services, and digital currency. These services can contribute to an increasing
adoption of online services.
& 2015 Elsevier Ltd. All rights reserved.
Keywords:
Technology adoption
Conjoint analysis
Stated preference
Discrete choice
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1. Introduction
In recent years, technology has increasingly been employed in the delivery of services. Service delivery technology
has become the vital operating elements of today's organizations that help to reduce costs and improvements in the
overall efficiency of operations [1–5]. Technology adoption is
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Corresponding author.
E-mail addresses: [email protected] (S. Yusuf Dauda),
[email protected] (J. Lee).
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Department of Science Nasarawa State Polytechnic, P.M.B. 109 Lafia,
Nasarawa State, Nigeria.
the choice to acquire and use a new invention or innovation
[6]. According to Saleem and Higuchi [7], technology adoption
is the main reason why developing countries are yet to
develop; old technology produces less quantity, low quality
product, and short life span. They import old technology due
to less investment in technology, risk aversion, additional cost
of assessment, and lack of appropriate consultant for selection
of appropriate technology. Innovation is not based on the R &
D alone but in some firms, it is also by the use of innovative
product and process from external source [8]. The paradigm
shift in the way companies interact with their customers, this
has been led by the proliferation of technology-based systems
mostly in the service industry and the banking sector are the
http://dx.doi.org/10.1016/j.is.2015.04.006
0306-4379/& 2015 Elsevier Ltd. All rights reserved.
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Please cite this article as: S. Yusuf Dauda, J. Lee, Technology adoption: A conjoint analysis of consumers' preference on
future online banking services, Information Systems (2015), http://dx.doi.org/10.1016/j.is.2015.04.006i
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S. Yusuf Dauda, J. Lee / Information Systems ] (]]]]) ]]]–]]]
most beneficial. They are developing a number of alternate
delivery channels with a view of attracting tech-savvy customers, improving customers' expectations and ensuring customer loyalty [9]. The need to explain the user acceptance of
new technologies and the factors influencing the acceptance
of such technologies is increasing [10]. According to Martins
et al. [11], the banking sector has been using information
services not only to run internal business activities and to
promote products, but also to provide main services to
their customers. Thus, the better use of the numerous new
information services available in the market is a challenge
facing this sector. Zhu and Chang [12] argued that Information
technology is the basis of technology-based services; consumers may be concerned with the usefulness and ease of use of
a technology-based service before they decide to avail themselves to try it. The effective service delivery on behavioral
factors may result in customer loyalty (which impacts their
future utilization of online banking patterns) in relation to
other factors just like it has on customer satisfaction [13].
According to Martins et al. [11] adopters of internet and online
banking have a lower propensity to leave the bank and have
increased banking activity, acquire more products, and maintain higher asset and liability balances [5]. Information and
communication technology, competition, deregulation and
globalization have forced banks to balance the goals of outreach and sustainability, characterized based on the services
the banks offer to customers across the globe and in numerous channels [13,14]. Banks have largely implemented service
delivery technology as a way of augmenting the services
traditionally provided by bank personnel, but technologybased services could only improve corporate performance
through consumer acceptance or adoption [15,16,12]. Thus,
online banking may be the instigator of this new environment
and the prime mover in terms of providing the potential
solution for bank's survival in the near future [17–20,4,21].
The level of online banking adoption will directly impact on
the degree to which the customers are satisfied, in terms of
the behavioral factors [19]. Several studies analyze online
banking service adoption or acceptance when the services are
already deployed and they look at factors that influence
customers' adoption and use or intention to use those
services. However, despite the increasing discussion about
online banking service adoption in the literature, no attempt
has been made to look at consumers' preference with regards
to future online banking service adoption. Based on the
aforementioned, the following questions have been raised
by the current study: what is the preference structure and
willingness of the customers to pay for the future online
banking services? Which future online banking service promotion policy can be applied base on the heterogeneous
population in Nigeria that can best promote an increasing
online banking service adoption?
According to Miltgen et al. [22], the isolated impacts of
technical, social, and risk factors on intention to accept IT
have limit the ample view of different factors that organizations trying to succeed with IT implementation have to
carefully address in order for the target users to accept the
IT under investigation. Jayawardhena and Foley [23] identified the benefit of increasing the customer base, because
using multiple distribution channels (branch networks, Internet banking, mobile banking, etc.) amplifies market coverage
by enabling different products to be targeted at different
demographic segments. Thus, with a larger customer base,
banks can profit from marketing and communication, with
the possibility of mass customization for each group of
clients, offering innovative products [11]. This is an important
issue because many organizations today are saturated with
mass automation and homogenized products and services. In
the customer view, there is an increase in the autonomy with
less dependency on the branch banking and consequently
less time and effort. According to Miltgen et al. [22], IT is
becoming increasingly complex and crucial for business
operations, thus making the issue of acceptance an important
challenge in IT implementation. Despite impressive advances
in technology capabilities, the problem of underutilization of
IT, especially for more radical technologies, is still present
[24].This study uses a conjoint based discrete choice modeling with stated preference data to construct the banking
customers' behavior corresponding to the future online banking service preference of Nigerian banking customers. The
objective of this research is to use the conjoint and discrete
choice analysis to investigate and test the variance of
consumers' behavior in relation to future online banking
products/service preferences and willingness to pay – that
will reveal a basic background for potential pricing themes
and funding directions for banks in the developing countries
with specific empirical analysis of the Nigeria banking
industry. This study seek to help banks to understand the
type and nature of future online banking services that can be
well accepted by the customers and to create the right
policies and actions to attract customers to use these services.
In addition, it is in the banks' and clients' interest to direct
their communication from bank branches to online channels
in order to be more productive and cost-effective.
This paper is organized as follows: the next section
presents the theoretical background and justification of
attributes selection, followed by the research methodology. Then, we present the experimental design and data
collection, followed by the empirical analysis and findings.
Finally, the interpretation of the findings and both theoretical and practical implications are described. This paper
concludes by presenting the research limitations and
proposing avenues for future research.
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2. Literature review
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2.1. Adoption models
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The growth of information services and IT acceptance and
use has been studied using various models and varied
conclusions have been drawn from such studies that offer
new insights at both the individual and organizational levels
with focus on a country or a set of countries [15,25,10]. Each
of the several models that have been proposed in the
literature has the same dependent variable, use or intention
to use [11], but with various antecedents to understand
acceptance of technology. Technology acceptances are information service theories that model how users come to accept
and use a specific technology. These theories suggest that
when users are presented with a new technology, a number
of factors influence their decision about how and when they
will use it. Many authors have studied different aspects of
Please cite this article as: S. Yusuf Dauda, J. Lee, Technology adoption: A conjoint analysis of consumers' preference on
future online banking services, Information Systems (2015), http://dx.doi.org/10.1016/j.is.2015.04.006i
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new technology acceptance from a variety of theoretical
perspectives explaining the relationship between user beliefs,
attitudes, and intentions, including Theory of Reasoned
Action (TRA – [26]), Technology Acceptance Model (TAM –
[27]), Theory of Planned Behavior (TPB – [28]), Model of PC
Utilization (MPCU – [29]), Motivational Model (MM – [30]),
TPB, a hybrid model combining constructs from TAM and
TPB (C-TAM-TPB – [31]), Innovation Diffusion Theory (IDT –
[32–35]), Social Cognitive Theory (SCT – [36]), and Unified
Theory of Acceptance and Use of Technology (UTAUT – [15]).
In each of these theories, behavior is viewed as the result of a
set of beliefs about technology and a set of affective responses
to the behavior. The most popular theoretical models are TRA,
TPB, TAM, IDT, and UTAUT.
The most employed model in the study of information
technology adoption is the TAM [12]. This model examines
the causal linkages between two key beliefs and the behavioral attitudes and intentions of users. TAM was designed to
predict information technology acceptance and use on the job,
in which perceived usefulness and perceived ease of use are
the main determinants of the attitudes [27]. Miltgen et al. [22]
argued that, despite the simplicity and contentiousness of
TAM, it has proven particularly useful in studying the intent to
accept new IT in a wide variety of contexts, such as across US
companies [15], among college students shopping online [37],
in regards to internet banking [38], e-procurement [39], or
electronic toll collection service [40]. TPB focuses more on the
perceived behavioral control, which is viewed in terms of the
perceived ease or difficulty of performing the behavior [28].
Both the TAM and TPB models were based on TRA. The TRA
model is one of the most important theories of human
behavior drawn from social psychology where attitudes and
subjective norms are considered as the determinants of
behavior. The theory proposes that beliefs influence attitudes
which in turn lead to intentions and then consequently
generate behaviors [26]. The IDM separates the adopters into
categorizes as: innovators, early adopters, early majority, late
majority, and laggards. Innovation must be widely adopted in
order to self-sustain. “Individuals are seen as possessing
different degrees of willingness to adopt innovations, and
thus it is generally observed that the portion of the population
adopting an innovation is approximately normally distributed
over time” [33]. In addition, he proposes that four main
elements influence the spread of a new idea: the innovation
itself, communication channels, time, and a social system. In
this regard, an organization decision to adopt an innovation or
not relies on organizational and market forces. The UTAUT
model which can explain as much as 70% of the variance in
intention [15], postulates that four constructs act as determinants of behavioral intentions and use behavior: performance
expectancy, effort expectancy, social influence, and facilitating
conditions. In addition, the model also posits the role of four
key moderator variables: gender, age, experience, and voluntariness of use. According to Miltgen et al. [22], while the TAM
and IDT perspectives focus almost exclusively on beliefs about
the technology and the outcomes of using it, UTAUT includes
other beliefs that might influence behavior, independent of
perceived outcomes. Recently, researchers have increasingly
turned to testing and applying UTAUT to explain technology
adoption. Examples are: online bulletin boards [41], Webbased learning [42] and instant messengers [43].
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2.2. Bank service product portfolio
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Banking service product quality has been found by
previous studies to plays an important role in determining
customers' perceptions of overall banking service quality. Jun
and Cai [44] explained that banking product quality is
primarily associated with product variety and diverse features. According to Yang, Jun, and Peterson [45], online
customers are more inclined to patronize firms which offer
a substantial variety of services. The primary reason for this
choice is that it is more likely that their diverse needs can be
fulfilled. This is especially the case for desired services that
are not widely distributed or unavailable at physical outlets
[46]. Thus, a key to gaining customer satisfaction and loyalty
is providing a mix of offerings preferred by target customers.
Strieter et al. [47] observed that one of the most important
developments in banking is the increased emphasis on
marketing a wide variety of financial services. In terms of
internet shopper satisfaction, Cho and Park [48] have identified “variety of products” as one of the seven key dimensions
that influence internet shopper satisfaction. In the same vein,
in terms of the banking, Dixon [49] argues that the key to
getting more customers from the banks through the online
service is not the attraction of the internet itself but the
products offered to the customers. Lepkowska-White and
Page [50] also pointed out that one of the important
ingredients for developing consumer value in online companies is a suitable selection of products/services.
Another rationale for customer use of the internet is
convenience. Latimore et al. [51] discovered that when
possible, 87% of internet banking customers prefers to complete their transactions at one site. Aliyu [13] considered
convenience to be an influential factor for the use of online
banking and that there is a direct relationship between
technology and behavioral (external) factors in the adoption
of online banking. For instance, numerous online banking
customers wish to pay their bills electronically and automatically, view and print their monthly bank statements, and
purchase stocks, insurance, and other financial offerings. For
this reason, companies with wide product lines may be able
to attract a large number of customers to their sites. Also,
introducing new forms of products/services to the marketplace appeals to customers whose needs are unfulfilled by
existing offerings. Malarvizhi [52] argued that the relationship
of convenience and service delivery via online banking is the
ability of online banking to meet users' needs using the
different feature availability of the services. Therefore, a key
to gaining customer satisfaction is providing a wide range of
products/services and diverse features in the format required
by customers. In line with this, results of Aliyu [13] shows that
convenience and security have strong evidence of customer
satisfaction via online banking as the mediator linking the
relationship between online banking and customer service
delivery. Therefore, with the advent of internet technology,
present banking customers can have unlimited access to
financial information and enjoy a wider range of choices in
selecting financial institutions and competitive products. The
key driving force in attracting new customers and enhancing
customers' satisfaction is subtly differentiating quality levels in
terms of diverse features of bank products and their timely
introduction to the marketplace [53].
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Please cite this article as: S. Yusuf Dauda, J. Lee, Technology adoption: A conjoint analysis of consumers' preference on
future online banking services, Information Systems (2015), http://dx.doi.org/10.1016/j.is.2015.04.006i
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Table 1
Q7 Variable definition for the future bank services.
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Variable name
Services
Rank
Variable
ATM services
ATMCCT
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11
Video banking
RTINT
Mobile Banking
MOBW
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15
17
Security Services
BIOS
ATMISP
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OCERT
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Internet Banking
DCUR
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WEBCUS
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COSTa
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Description
Definition
Rankings of the cards given
by the respondents
Cardless ATMs (Dummy
variable for ATMs with
capacity to perform
cardless transaction)
Real time Interaction
(Dummy variable for Video
technology integrated with
most banking channels)
Mobile wallet (Dummy
variable for mobile wallet
services)
1–3, repeated
Distribution of part-worth
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ATMs smartphone
integration (Dummy
variable for mobile-based
pre-staging of ATM
transactions)
Occupational certification
(Dummy variable for
flexible, authentication and
transaction security
solutions)
Digital currency (Dummy
variable for Bitcoin
services)
Website Customization
(Dummy variable for
customers chance to
personalize each page of
the website)
Transaction cost (cost per
online or wire transfers)
‘1’ if it is with capacity; ‘0’ otherwise
Normal
‘1’ if it is Available; ‘0’ otherwise
Normal
‘1’ if it is Available; ‘0’ otherwise
Normal
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‘1’ if it is Available; ‘0’ otherwise
‘1’ if it is Integrated; ‘0’ otherwise
Normal
Normal
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‘1’ if it is Available; ‘0’ otherwise
Normal
‘1’ if it is Available; ‘0’ otherwise
Normal
‘1’ if it is Possible; ‘0’ otherwise
Normal
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50; 100; 150 (Nigerian Naira ¼
N/online or wire transfer)
Log-normal
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Nine future online services have been defined as attributes
Q3 to establish the choice experiments for the conjoint survey in
the current study. All the attributes are treated as dummies
with the values of one if available and zero otherwise. The
characteristics of those attributes and their levels are presented in Table 1. First, the price is a very important factor in
determining the WTP of the consumer. According to Aliyu
et al. [54], for consumers to use new technologies, it must be
reasonably priced relative to alternatives. Otherwise, the
acceptance of the new technology may not be viable from
the standpoint of the customer. In contrast, results of Aliyu
[13] indicated that cost has no direct effect of customer service
delivery via online banking. However, most researchers
believe that the price level is always one of the most
important factors that will influence the evaluation result by
customers [55,56]. In this research, the levels of price are
constructed hypothetically, but based on real prices of the CBN
of Nigeria 2013 revised guide to bank charges.2
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2.3. Attributes and attribute levels
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The detail of revised price guide of CBN can be obtained at http://
www.cenbank.org/out/2013/fprd/circular%20to%20all%20banks%20and%
20discount%20houses.%20the%20rrvised%20guide%20to%20bank%
20charges.pdf.
Secondly, cardless ATM access is defined due to the fact
that ATM service is the most popularly used channel of
transaction mostly in areas where there is low internet
penetration. Cardless ATM access will allow customers to
securely perform transactions at the ATM channel without
the use of a card using one-time SMS PIN or biometric
authentication [57–60]. The system includes an ATM and a
net Work each configured to communicate with a mobile
user device and the provider system, either a proxy-based
transaction completion (where mobile numbers, one-time
PINs or voucher numbers are entered in place of card
swiping), the use of newer quick response codes(QR codes),
or near-field communication (NFC) technology, through
which a bank's customers have access to a full range of
ATM services including cash withdrawal, money transfers,
debt payments, bill transactions, and counter top-ups. Also,
non-customers can have access to bill payment, electronic
check purchase, and redemption and mobile top-up without the use of a card.
Third, real-time interaction service was included as one of
the future service attributes (e.g. video-enabled mobile
phones, web conferencing, online chat, TV banking, 3D video
banking, and video tellers). According to D'Angelo and Little
[61], factors such as navigational and visual characteristics
such as video are critical features of a website. Valentine [62]
reported that First Capital Bank of Texas launched ten
Please cite this article as: S. Yusuf Dauda, J. Lee, Technology adoption: A conjoint analysis of consumers' preference on
future online banking services, Information Systems (2015), http://dx.doi.org/10.1016/j.is.2015.04.006i
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interactive video teller Kiosks that provides customers with
live, remote teller services at four of the banks seven branches
located throughout western Texas. Other examples include
the National Australia Bank, which provides online loan
processing through video chat; the SNS Bank Netherlands,
which provides video web conferencing for instructing customers through multimedia presentations; and Bank Sabadell
Spain, which provides video-enabled phone banking for
addressing customer queries.
Fourth, mobile wallet is defined due to the fact that
mobile phones, especially smartphones, are becoming one
of the most important channels for banking due to easy
accessibility and great importance for mobile and social
customers. The expanded use of smartphones has increased
demand for m-banking services [63,64] and is likely to have
significant effects on the market [65]. Mobile wallet is a
system that securely stores users' payment information and
passwords for numerous payment methods and websites.
The technology can be provided as a turnkey, or a hosted
solution enabling financial institutions, retailers, payment
service providers, merchants, banks, telecom operators, utility
providers, or other businesses to offer their customers smart
and practical branded services. It can be used in conjunction
with mobile payment systems that allow customers to pay
for purchases with their smartphones, and can also be used
to store loyalty card information, tokens, and digital coupons.
Unlike debit or credit cards, transactions using an electronic
wallet are carried out off-line without the direct involvement
of financial intermediaries and the burden of these institutions' high fixed costs [66,67]. In addition, Van Hove [68]
argues that even though electronic wallets, are frequently
compared to debit cards, it should rather be compared to
cash since the aim is to provide consumers and merchants
with an electronic payment instrument that could handle
small transactions cost effectively. Different mobile wallets
use different technologies and offer unique rewards to
customers. Some mobile wallets use NFC to transfer payment
information to a vendor's POS terminal, e.g., Google Wallet,
PayPal, or Square Wallet.
The fifth, sixth and seventh attributes will be clarified
by the security and privacy of online transaction. Many
researchers have emphasized the importance of security in
online banking [44,69–73,45], no customer wants to reveal
their personal information, such as credit card numbers,
due to the growing rate of online fraud. In addition, Aliyu
et al. [74] identified security as an important characteristic
from a customer's perspective on the adoption of innovation. The current study defines three security measures as
future service attributes: biometrics sensors, ATMs integrated with smartphones, and occupation certification.
Biometrics sensor-related services include: ATM/e-banking face recognition systems, voice recognition services,
optical sensor, finger print recognition, and name recognition
services. According to McGarr [75], the fact that biometric
systems require two forms of input for identity verification,
including biometric input along with a personal identification
number (PIN), makes the systems more powerful. In fact, the
augmented interest in biometric technology is led by anticipated decrease of technology costs, improved technical
quality of the systems and socio-political pressures for better
security-related controls [76]. However, user resistance to
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utilize such pervasive technology is still an issue [76].
According to Bankston [77], while the technology goes back
years and has been used in highly sensitive institutions such
as defense and nuclear facilities, the proliferation of electronic
data exchange has generated new demand for biometric
applications that can secure electronically stored data and
online transactions. Various researchers have identified the
crucial need to inform biometric technologies implementation with various factors affecting its acceptance [78,76].
However, only a few authors have discussed biometric
systems from a consumer acceptance perspective [78,22].
The integration of ATMs with smartphones involves a
mobile-based pre-staging of transactions (where the mobile
device replaces the ATM screen and keyboard); transactions
are started on a smartphone, where a one-time code is
received and typed into the ATM, releasing the cash. According to Dabholkar [79], there is greater control when the
customer is in direct contact with technology such as internet
banking and m-banking. Joseph and Stone [80] added that
empowering customers by providing them with the option of
using technology-based service delivery systems may be a
relatively inexpensive way to maintain customer loyalty. For
example, at the 2013 International Consumer Electronics
Show, Diebold, Inc., a software and technology firm in North
Canton, Ohio, unveiled an ATM that is designed to work with
smartphones.
Occupational certification is a comprehensive and flexible authentication and transaction security solution that
allows financial organizations to meet the needs of an
escalating threat landscape and tailor mitigation methods
to different customers' risk profiles based on their online
banking activity [81] today and as threats and opportunities evolve in the future. Even though the perception and
behavioral response of end users are important considerations when designing systems that employ digital identities [82] as issues of privacy, security and online identity
management are frequently a source of concern to consumers [83]. Solutions may range from easy-to-deploy,
software-based out-of-band (OOB) strong authentication
solutions for low-risk online banking activities to digital
transaction signing solutions for validating online transactions and use of digital certificates.3 In addition to this, an
SMS notification for designated online transactions is
provided along with the digital certification.
Finally, the eighth and ninth attributes will be clarified
by the internet banking service of which digital currency
and website customization are incorporated as future
banking service attributes. The attraction of banks to
online banking is fairly obvious: diminished transaction
costs, easier integration of services, improvements in the
overall efficiency of operations, interactive marketing
capabilities, and other benefits that boost customer lists
and profit margins [79,44,1,80]. Additionally, web banking
services allow institutions to bundle more services into
single packages, thereby luring customers and minimizing
overhead. Banks look to the web as a way of maintaining
their customers and building loyalty [80].
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See more on digital certificates at http://www.netpnb.com/web/
L001/webpages/digicertuserguide.pdf.
Please cite this article as: S. Yusuf Dauda, J. Lee, Technology adoption: A conjoint analysis of consumers' preference on
future online banking services, Information Systems (2015), http://dx.doi.org/10.1016/j.is.2015.04.006i
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Digital currency (bitcoin) was introduced as open-source
software in 2009 as an innovative payment network and a
new kind of money. Bitcoin uses peer-to-peer payment
system technology to operate with no central authority or
banks; managing transactions and the issuing of bitcoins is
carried out collectively by the network and everyone can
take part. Steadman [84] explains it as crypto-currency,
because it uses cryptography to control the creation and
transfer of money. Conventionally, the capitalized word
Bitcoin refers to the technology and network, whereas
lowercase bitcoin refers to the currency itself.
Website customization service allows banks to give
customers a chance to personalize each page of the
website, with the customers deciding which features will
show up on the screen and where. Many researchers
believe that banks need to customize their websites to
meet customer preferences [61,85,86,44]. According to
Dabholkar [79], there should be flexibility in the design
of the technology to allow customers to make changes
during the transaction. Joseph and Stone [80] added that
this also raises design issues with the technology itself,
including ease of access, ease of use, and provision of
sufficient menu options on ATM and internet banking
services. Personalized experience can also help customers
understand their needs and get recommendations based
on their age, income group etc., or create tag-based
interactions that help users retrieve information about
their past online banking activities. Although, customization cannot be priced explicitly, it does help in bolstering
customer retention and improving loyalty, thus adding to
the bank's profitability from individual accounts.
33
35
3.1. Random utility model
37
41
43
45
47
The random utility model is used as a theoretical basis
for analyzing consumer preferences using discrete choice
models [87]. Regarding the banking services, the customers
have their own valuation on the future banking service
attribute and their own characteristics. Thus, we assume
that each customer perceives the utility associated with
each attribute of the future banking services and chooses
the service with the greatest possible perceived utility
[88,89]. This utility is decomposed into two parts in random
utility model: deterministic (observable by the researcher)
and stochastic (unobservable by the researcher).
49
U nj ¼ V nj þ εnj
ð1Þ
51
U nj ¼ V nj þ εnj ¼ V X nj ; Sn þ εnj
ð2Þ
53
where U nj ¼utility that decision maker n obtains from
alternative j; V nj ¼representative utility/deterministic part
of consumers utility, Sn ¼attributes of decision maker;
εnj ¼error term.
55
57
3.2. Multinomial logit model
59
61
V ni
e
P ni ¼ P
63
65
67
69
ð3Þ
71
Rank-ordered logit model is an extension to multinomial logit model in the sense that an individual n states
their ranking of alternativer n ¼ r n1 ; r n2 ; ……; r nJ , as the
descending order of preference [87]. Then, facing the
maximization problem, the order of choice is made if:
U ðr n1 Þ 4 U ðr n2 Þ 4 … 4U r nJ
ð4Þ
73
V nj
je
J1
eβxðrk Þ
Pr U ðr 1 Þ 4 U ðr 2 Þ 4… 4 U r J ¼ ∏ PJ
eβxðrm Þ
k¼1
m¼k
ð5Þ
where k denotes the order of alternative ranked at kth by
the respondent. In the current study the utility is specified
as in Eq. (6) and the variable definitions and descriptions
are given in Table 1.
U ijt ¼ βATMCCT ATMCCTþ βRTINT RTINTþ βMOBW MOBW þ βBIOS BIOS
þ βATMISP ATMISP þ βOCERT OCERT þ βDCUR DCUR
75
77
79
81
83
85
87
89
ð6Þ
91
The RUM with part-worths interacting with demographics and individual characteristics is also specified in
the current study as follows:
93
þ βWEBCUS WEBCUS þ βCOST COSTþ εijt
95
U ijt ¼ βATMCCT ATMCCTþ ðβRTINT þ βIFMRT IFM þ βIURT IUÞRTINT
3. Methodology
39
characteristics are assumed to be homogeneous, assuming
that each εnj is independently and identically distributed
(IID) type I extreme value distribution [90]. Also assuming
independence of irrelevant alternatives (IIA) [89] i.e. the
relative odds of choosing ‘i’ over ‘k’ are the same no matter
what other alternatives are available or what the attributes
of the other alternatives are.
In the setting of multinomial logit model the degrees of
influence of attributes of product/service and individual
97
þ ðβMOBW þ βIAMW IAþ βIFMMW IFMÞMOBW
þ βBIOS þ βIABS IA BIOS þ βATMISP þ βIFMASP IFM
þ βICASP IC þβBAASP BA ATMISPþ ðβOCERT þβIFMOC IFM
99
þ βIUOC IUÞOCERT þ ðβDCUR þ βIADC IAþ βPIBDC PIB
þ βBCDC BCÞDCUR þ βWEBCUS þβIUWC IU þ βIBWC IB
þ βIOWC IO WEBCUSþ ðβCOST þ βAGECO AGEþ βWKECO WKE
þ βBTCO BTþ βPCHCO PCH þβPMBCO PMBþ βBBCO BB
101
103
105
ð7Þ
107
IFM is the internet familiarity of the respondents, IU is
the internet usage frequency of the customer, IA is the
internet access of respondents, IC is the internet access of
respondents at a cafe, BA is the respondents whose major
bank is Bank A, PIB is percentage of IB of the respondents,
BC is the respondents whose major bank is Bank C, IB is the
respondents who use IB, IO is the internet access of
respondents at the office, AGE is the age of the respondents,
WKE is the working experience of the respondents, BT is
the bank transaction frequency of the respondents, PCH is
the respondents having a PC at home, PMB is the percentage of m-banking of the respondents, BB is the respondents whose major bank is Bank B. According to Savage and
Waldman [91], these interaction terms relax the restriction
of fixed coefficients that are specified in Eq. (6).
109
þ βBACO BAÞCOST þεijt
Please cite this article as: S. Yusuf Dauda, J. Lee, Technology adoption: A conjoint analysis of consumers' preference on
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3.3. Random coefficient model (mixed logit)
3
The random coefficient model gives space for random
variation in taste, relaxation from the IIA property, and
correlation among unobserved factors in repeated choices
avoid biased utility results [92]. The stochastic
term
can be
decomposed into two additive parts εn ¼ ηn þσ n , a stochastic part ðηn Þ that is correlated over alternatives, having
its own distribution that is defined by the researcher
depending on underlying parameters and observed data
relating to the alternative j and the individual n and
another stochastic part ðσn Þ that is independently and
identically distributed over alternatives and individuals.
Accordingly, the utility of a customer n from alternative j
can be defined as follows:
5
7
9
11
13
15
17
U nj ¼ X nj βn þεnj
ð8Þ
23
where unknown parameter βn , which comprises a vector
of coefficients of bank service attributes X nj , allows a
variation in tastes with respect to the customers. In the
current study the random coefficients is defined similar to
Eq. (6). The difference is that all the coefficients βn are
assumed to have their own distribution in the population.
25
Lnj ðβÞ ¼ PK
19
21
eV nj ðβÞ
k¼1
27
eV nk ðβÞ
ð9Þ
V nj ðβÞ is the observed part of the utility.
7
where the parameters estimates β^ k and β^ c are the true
parameters and using the corresponding elements in the
asymptotic variance-covariance matrix [94].
4. Experimental design and data collection
4.1. Conjoint experiment
33
35
37
39
41
43
45
3.4. Willingness to pay
93
Given the consumer income and budget constraints
willingness-to-pay (WTP) is what the consumers are prepared to spend on a certain goods or services. Following
Chaudhuri et al. [93], WTP for banking services in this study
is related to a set of underlying service characteristics that
combine to produce a single, separable index of service
utility to consumers. WTP for future bank services is affected
by existing bank service accessibility, use experience, perception, affordability, and ability to pay, together with customers' level of awareness of bank services as well as internet
use experience. According to Bliemer [94], when a utility
function is linear in parameters and attributes, the WTP for a
one unit improvement in that attribute is the ratio of its
marginal utility to the marginal utility of price:
49
where βk is the parameter for attribute k and βc is the cost
parameter. In the more general case of a nonlinear utility
function, the WTP of attribute k is defined as:
55
57
59
61
69
91
wk ¼ βk =βc
53
67
4.2. Data collection
47
51
65
71
The conjoint questionnaire was developed in English.
There are five choice sets; each choice set has three
73
alternatives. Each respondent answered the choice questionnaire by ranking alternatives in each choice set. The
75
choice sets are orthogonally designed using the SPSS,
therefore avoiding individual effect. A pilot survey of over
77
55 samples in Abuja the Federal Capital and Lagos State
the former Capital was conducted. This was followed by
79
the main survey conducted in five States in Nigeria by
face-to-face interviews led by research assistants. The
81
areas surveyed were evenly spread throughout in all
regions (North, Central, and South regions). These even
83
disparities made sure the responses received represent all
the preferences of citizens in the country. The other Q4
85
aspects of the population such as gender and age are also
distributed evenly to make the survey stratified in terms of
87
the age and sex ratio according to the population of the
4
country. A sample of the conjoint questionnaire is given
89
in Appendix A (Table 2).
29
31
63
wk ¼
∂g j =∂X jk
∂g j =∂X jc
ð10Þ
ð11Þ
Two main methods for determining confidence intervals for WTP measures in the case of fixed coefficients are
the Krinsky and Robb method (K&R) and Delta methods
[94]. For the delta method, in this case used in this study,
has been defined by Bliemer [94] as:
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ffi
^ k Þ ¼ 1 var β 2wk cov β ; β þ w2 var β
seðW
ð12Þ
k
k c
c
k
βc
The data for this study was obtained through a conjoint
survey using the current banking customers. Of 1700 questionnaires that were distributed, a total of 1412 responses
were received corresponding to an initial response rate of
(83.06%). After discarding invalid responses, we had 1291
responses left of which 51.74% respondents are male and
48.26% female. The other questionnaires are invalid because
they were either left “blank”, “not fully filled in” or “only one
number was written” in the choice sets. The age range is
from 18 to 82 of which (39.43%) are in the age range of 18–
29, (35.09%) are between 30 and 39, (19.29%) are between 40
and 49, (4.26%) are between 50 and 59, and (1.94%) are 60 or
older. For analytical purposes the age range of respondents is
divided into – below 30 (39.43%) and at least 30 (60.57%).
The demographics of the respondents can be shown in Fig. 1.
For the entire sample, there is low percentage of those
educated from primary to secondary school (13.71%). Similarly, there was also low percentage of those with master and
doctoral degrees (14.87% master and 3.02% doctorate). The
majority of respondents are those with high school diplomas
or technicians with technical or professional training in
colleges, polytechnic, or technical training centers (35.48%),
followed by those with a university education (32.92%). Two
educational groups was focused: Education 1 refers to those
educated up to diploma level (49.91%), while the rest
Education 2 belongs to the ones having highest intellectual
4
Details of the Nigerian population and sex ratio can be obtained at
https://www.cia.gov/library/publications/the-world-factbook/geos/ni.
htmlThe WorldFactbookwww.cia.gov.
Please cite this article as: S. Yusuf Dauda, J. Lee, Technology adoption: A conjoint analysis of consumers' preference on
future online banking services, Information Systems (2015), http://dx.doi.org/10.1016/j.is.2015.04.006i
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1
3
Characteristics
7
Age range (18–64 yrs)
Age1 ( o30 yrs)
Age2 ( Z30 yrs)
Gender
Female
Male
Education
Edu1 (r Diploma)
Edu2 (Z Bachelor)
Occupation
Unemployed
Civil Servant
Businessman
Academic
Others
Working experience
Exp1 (0–5 yrs)
Exp2 (6–10 yrs)
Exp3(Z 11 yrs)
11
13
15
17
19
21
65
Characteristics of study subjects N¼1291
5
9
63
Table 2
Demographic statistics.
Percentage
39.43
60.57
48.26
51.74
49.19
50.81
16.81
51.43
14.33
9.84
7.59
52.83
23.00
24.17
Characteristics
Percentage
Monthly income(USD$)
Low (0–315)
49.57
High ( Z 316)
50.43
Marital status
Single
47.71
Married
47.10
Others
5.19
Family size
Small (1–5)
52.05
Big ( Z6)
47.95
Major bank
Bank A
24.86
Bank B
22.54
Bank C
23.16
Other banks
29.43
Deposit size
Low (0–315)
81.87
High ( Z 316)
18.13
Characteristics
Percentage
Transaction frequency
1–2/week
76.76
Z3
23.24
No. of credit cards
0
48.95
1
34.55
Z2
16.60
No. of debit cards
0
10.07
1
62.97
Z2
26.96
Credit card usage
0/week
49.81
1/week
27.03
Z 2/week
23.16
Debit card usage
0/week
10.38
1/week
48.72
Z 2/week
40.90
Characteristics
Percentage
Bank cheque usage
0/week
59.72
1/week
29.59
Z 2/week
10.69
Internet access
No
19.67
Yes
80.33
Internet familiarity
Not at all
19.44
Fairly
7.36
Averagely
24.48
Well
24.86
Very well
23.86
Internet usage
Never use
18.59
1/week
9.45
2–3/week
14.25
4–5/week
17.04
Every day
40.67
27
29
90
80
70
60
50
40
30
20
10
0
31
33
37
39
41
43
45
47
49
51
53
55
57
73
75
77
79
81
83
87
5.1. Rank-ordered logit model
89
Age
Gen
Edu
group B
Inc
group C
Dep
Major Bank
group D
Fig. 1. Demographics of respondents.
level of education from bachelor to doctorate (50.81%). The
monthly income level of respondents was also categorized
into 2 groups: the low income group earning up to $315 US
dollars per month (49.57%) and high income group, above
$315 US dollars per month (50.43%). In the same way, the
monthly deposit of the respondents was also categorized
into 2 groups and majority of the respondents were found to
have very low deposits (81.87%) ranging from $0 to 315. As
for the major banks, majority of the respondents use other
banks (29.43%), while the smallest percentage used Bank B
(22.54%), with Bank A (24.86%) and Bank C (23.16%) in the
middle. Customers' use debit cards more (40.90%) as compared to credit cards (23.16%) and bank cheque (10.69%).
51.43% of the respondents were civil servants of which
52.83% of them have very low experience (0–5years) and
almost 52.05% have a small family size (1–5 people). 80.33%
of the respondents have internet access. About 19.44% of the
respondents are not familiar with the internet at all, while
23.86% are very well familiar with the internet. 18.59% have
never used internet, while almost 40.67% of the respondents
use the internet every day. This is reasonable because the
internet penetration rate for Nigeria as at 2012 is 32.8%
according to the World Bank 2012 estimate.5
59
61
71
5. Results and discussion
group A
35
69
85
23
25
67
5
http://databank.worldbank.org/data/views/reports/tableview.aspx.
The total observations for ranked-ordered logit estimation
are 6455. This study uses the LIMDEP program to estimate the
rank-ordered logit model. The results of rank-ordered logit
estimation are shown in Table 3 that includes the parameter
estimates, asymptotic t-statistics, willingness-to-pay of consumers for each attribute of the future banking services and
relative importance of the attributes. This study interprets the
parameters as marginal utility that is as a partial derivative,
which means the change in utility for a unit increase in the
variable [91]. In addition, the significant or confidence interval
of the WTP has been calculated using the delta mesa method
from Eq. (12) and their significant level with the asymptotic
t-statistics is also presented in Table 3.
From Table 3, consumers have high utility with increase in
access to ATMs with capacity for cardless transaction, realtime-interaction services, mobile wallets, ATMs integrated
with smartphones, occupational certification, digital currency
and website customization services. On the other hand,
customers have less utility with increase in service fees. This
finding is in line with other researchers [55,56,54], and in
contrast with results of Aliyu [13] that indicated that cost has
no direct effect of customer service delivery via online banking. The biometric service was not significant in this model
showing that; even though the biometric services are introduced their utility does not change. This finding is similar to
[76,78] who pointed out the issue of user resistance to utilize
such pervasive technology and crucial need to inform biometric technologies implementation with various factors
affecting its acceptance. Also from Table 3, the result of the
relative importance reveals that as a whole, the consumers
rate the service fee and real-time-interaction services as the
most important future banking services (20.33%) and
(18.65%) respectively. The least important is biometric service
(5.58%). If we translate it into willingness to pay (WTP), based
Please cite this article as: S. Yusuf Dauda, J. Lee, Technology adoption: A conjoint analysis of consumers' preference on
future online banking services, Information Systems (2015), http://dx.doi.org/10.1016/j.is.2015.04.006i
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Table 3
Q8 Rank-ordered logit estimation results.
3
5
7
9
11
13
Variable matching
Variable
Coeff. β
Std. Err.
t-value
WTPa
WTP t-value
Rel. imp.
Cardless ATMs
Real time Interaction
Mobile wallet
Biometrics sensors
ATMs smartphone integration
Occupational certification
Digital currency
Website Customization
Service cost
ATMCCT
RTINT
MOBW
BIOS
ATMISP
OCERT
DCUR
WEBCUS
COSTa
0.3259nnn
0.4890nnn
0.2046n
0.1464
0.1759nnn
0.2015nnn
0.2209nnn
0.3248nnn
0.0053nnn
0.0876
0.0982
0.0944
0.1012
0.0419
0.0529
0.0433
0.0695
0.0015
3.722
4.982
2.168
1.448
4.201
3.809
5.097
4.673
3.571
61.14nnn
91.73nnn
38.38n
N/A
32.99nnn
37.79nnn
41.43nnn
60.92nnn
N/A
2.577
2.902
1.853
0.000
2.721
2.605
2.925
2.837
N/A
12.43
18.65
7.80
5.58
6.71
7.68
8.42
12.39
20.33
N 1 the exchange rate was $1 ¼ ¼
N 160.
N ¼6455. Asterisk nnn indicates significant level at 99.9% (p-value o 0.001). LRI: ρ ¼0.431. Unit of WTP is ¼
a
Willingness to pay (WTP).
19
21
23
25
27
29
on this model, customers are willing to pay a high premium
price of about ¼
N¼
N 90.73 per month for real-time-interaction
services. Similarly, they are willing to pay high price for
cardless ATMs and website customization ¼
N 61.14 per month
and ¼
N 60.92 per month respectively. For the other services,
their willingness to pay is reasonably high ¼
N 41.43 per month
for digital currency service, ¼
N 38.38 per month for mobile
wallet service, ¼
N 37.79 per month for occupational certification service, and ¼
N 32.99 per month for ATMs integrated with
smartphones. Based on WTPs, policy makers and bank
management can establish their general priorities on which
future online banking service to promote that will bring
about increase in adoption of online services and subsequently lead to customer's satisfaction.
31
5.2. Model with interaction SDCs
33
35
37
39
41
43
45
47
49
51
53
55
57
59
61
67
69
71
73
75
77
15
17
65
The result of the interaction of demographics and other
individual's characteristics (income, deposit, education,
etc.), which allows a systematic test variation and enables
the heterogeneity to be incorporated into the part-worth is
as presented in Table 4. Accordingly, the model made its
interaction with eight future online banking service attributes. The sign of the mean values of the coefficients are
the same as those in Table 3.The order of the importance of
attributes is also similar except for their magnitudes,
which were slightly higher for the interaction model.
From Table 4, customers still rate the service fee as the
most important attribute (20.23%) followed by the real-time
interaction services (19.02%). The least-preferred future
online service is still the use of biometric sensors (5.08%).
However, the results are not homogenous for all respondents; with the interaction terms we can see an improvement in the significance level of the biometric sensor service
since it was not significant in the first model. First, an
individual's relative utility increases with increasing access
to biometrics services (βBIOS ¼0.4368) but it is likely that the
more a customer has access to the internet the lower their
preference for the use of biometric sensors βIABS (0.3873).
Meaning that, the consumer preference for the use of
biometric sensors is changing from those that have internet
access to those that do not have access to the internet.
Instead of providing general information about the population's lack of preference to the biometric sensor services as
in rank-ordered logit, these flexible results enable us to
categorize the preference levels depending on demographic
and individual characteristic groups. As a result, consumers
are willing to pay ¼
N 25.12/month for biometrics services if
made available to them. Secondly, an individual's relative
utility decreases when the price increases (βCOST ¼ 0:0143),
but it decreases more among the individuals who have more
working experience (βWKECO ¼ 0:0001) or have a higher
transaction frequency per week (βBTCO ¼ 0:0005). It may
even decrease much more among those individuals whose
major banks are Bank A (βBACO ¼ 0:0024) and Bank B
(βBBCO ¼ 0:0026), while it decreases less in the case of
those who are of the higher age group (βAGECO ¼ 0:0003),
have a PC at home (βPCHCO ¼ 0:0040), or have a higher
percentage of m-banking use (βPMBCO ¼ 0:286E 4). Clearly,
such categories of individuals do not care much about the
cost. Meaning that, younger people with higher working
experience are more sensitive to price and also those who do
not have PCs and access the internet at public centers. Thus,
consumers' choice is based on their utility level. It is likely
that at certain pricing levels, those that are of a higher age
group, have a PC at home, and are already using m-banking
will chose to use the future online services while those that
have more frequent bank transactions, have more work
experience, and use Bank A or Bank B will choose not to.
Thirdly, an individual's relative utility increases with an
increase in access to real-time interaction services
(βRTINT ¼ 0:7832), but it is more steady for those who have
lower frequency of internet usage (βIURT ¼ 0:0226) and
even much more for those that are less or not very familiar
with the internet (βIFMRT ¼ 0:0715). Meaning that, realtime interaction services are more effective when they target
those who have a lower frequency of internet usage and
those in the rural and remote areas that are less familiar with
the internet. As a result, consumers are willing to pay as
much as ¼
N 94.03/month for real-time interaction if made
available to them. Fourthly, an individual's relative utility
increases with an increase in access to mobile wallet services
(βMOBW ¼ 0:5757), but decreases among individuals that are
more familiar with the internet (βIFMMW ¼ 0:0681) and
even much more among individuals that already have access
to the internet (βIAMW ¼ 0:2218). However, consumers are
willing to pay ¼
N 37.35/month for the mobile wallet service if
made available to them. Fifth, an individual's relative utility
increases with an increase in access to ATM smartphone
integration services (βATMISP ¼ 0:3635). However, the relative
Please cite this article as: S. Yusuf Dauda, J. Lee, Technology adoption: A conjoint analysis of consumers' preference on
future online banking services, Information Systems (2015), http://dx.doi.org/10.1016/j.is.2015.04.006i
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81
83
85
87
89
91
93
95
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10
1
7
9
11
13
15
17
19
21
23
25
27
29
31
33
35
37
39
41
43
45
47
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51
53
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59
61
63
Table 4
Model with interaction.
3
5
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Cardless ATMs
Real time Interaction
Mobile wallet
Biometrics sensors
ATMs S. phone integration
Occupational. Cert.
Digital currency
Web Customize
Service cost
Age and Cost
Work Exp. & Cost
Bank Trans. & Cost
PC at Home & Cost
Inter. Acc. & Mob. Wallet
Inter. Acc. & Bio. Sensor
Inter. Acc. & Dig. Currency
Inter. Fam. & Real Time Inter.
Inter. Fam. & Mob. Wallet
Inter. Fam. & ATM S. phone Integration
Inter. Fam. & Occ. Cert.
Internet Usage and Real Time Interaction
Inter. Use & Occ. Cert.
Inter. Use & Web Customize
Inter. Banking & Web Customize
Per. of Inter. Banking & Digital Currency
Per. of Mob. Banking & Cost
Inter. at Office & Web Customize
Inter. at Café & ATM S. phone Integration
Bank A & ATM S. phone Integration
Bank C & Digital Currency
Bank B & Cost
Bank A & Cost
Variable
Coefficient
ATMCCT
RTINT
MOBW
BIOS
ATMISP
OCERT
DCUR
WEBCUS
COSTa
AGECO
WKECO
BTCO
PCHCO
IAMW
IABS
IADC
IFMRT
IFMMW
IFMASP
IFMOC
IURT
IUOC
IUWC
IBWC
PIBDC
PMBCO
IOWC
ICASP
BAASP
BCDC
BBCO
BACO
βATMCCT
βRTINT
βMOBW
βBIOS
βATMISP
βOCERT
βDCUR
βWEBCUS
βCOST
βAGECO
βWKECO
βBTCO
βPCHCO
βIAMW
βIABS
βIADC
βIFMRT
βIFMMW
βIFMASP
βIFMOC
βIURT
βIUOC
βIUWC
βIBWC
βPIBDC
βPMBCO
βIOWC
βICASP
βZBASP
βGTBDC
βFBCO
βZBCO
0.3017nnn
0.7832nnn
0.5757nnn
0.4368nnn
0.3635nnn
0.3334nnn
0.4382nnn
0.5314nnn
0.0143nnn
0.0003nnn
0.0001n
0.0005nn
0.0040nnn
0.2218nn
0.3873nnn
0.1770nn
0.0715nnn
0.0681nnn
0.0505nnn
0.0761nnn
0.0226n
0.0227n
0.0368nnn
0.0579n
0.0022nnn
0.286E 4nnn
0.1828nnn
0.2201nn
0.1179n
0.1067n
0.0026nnn
0.0024nnn
Std. Err.
t-Value
MWTPa
Rel. Imp.
0.0884
0.1081
0.1071
0.1125
0.0602
0.0702
0.0646
0.0821
0.0019
0.400E 4
0.0001
0.0002
0.0006
0.0896
0.0578
0.0591
0.0172
0.0210
0.0134
0.0170
0.0106
0.0113
0.0101
0.0271
0.0007
0.749E 5
0.0485
0.0729
0.0582
0.0483
0.0006
0.0007
3.414
7.244
5.373
3.882
6.037
4.748
6.779
6.469
7.502
6.253
2.002
2.606
6.493
2.475
6.696
2.997
4.164
3.240
3.769
4.475
2.124
2.002
3.661
2.135
3.425
3.817
3.773
3.020
2.025
2.207
4.398
3.504
60.27
94.03
37.35
25.12
33.80
38.01
43.70
62.05
N/A
12.19
19.02
7.56
5.08
6.84
7.69
8.84
12.55
20.23
NB: N¼ 6455, Asterisk *** indicates significant level at 99.9% (p-value o 0.001). LRI: ρ ¼0.439. At the time of survey, the exchange rate was $1 ¼ ¼
N 160.
a
Currency unit of WTP is ¼
N 1 (Nigerian naira).
utility decreases among customers that are more familiar
with the internet (βIFMASP ¼ 0:0505) and even much more
among those who access the internet at public centers such
as internet cafes (βICASP ¼ 0:2201) and among those individual whose major bank is Bank A (βBAASP ¼ 0:1179). This
implies that, ATM smartphone integration services are more
effective when they target those in the rural and remote
areas that are less familiar with the internet; those that
access the internet at centers other than internet cafes, such
as at home or at the office or using mobile phones or
smartphones; and those customers whose major bank is
not Bank A. As a result, consumers are willing to pay ¼
N 33.80/
month for ATM smartphone integration services if made
available to them. Sixth, an individual's relative utility
increases with an increase in access to occupational certification service (βOCERT ¼ 0:3334) but the more familiar an
individual is with the internet, the lower their preference
for this service βIFMOC (0.0761). However, as a customer use
the internet frequently it is more likely that they will have a
higher utility with access to occupational certification services βIUOC (0.0227). This reveals that familiarity with a future
online service alone does not guarantee a customer's preference for adoption of that service when deployed and if he
does not use internet frequently, which shows that experience of internet use has a great impact on a customer's
preference for future online services. Specifically, in the case
of occupational certification, it implies that the more
customers use the internet the more they understand the
risks of not having good security due to the increase in
internet fraud, which makes them want to have improved
and better security. This finding is in line with the findings of
Sathye's [85] study on the adoption of IB by Australian
consumers, which found that difficult in use and security
were the two most important reasons that customers did not
want to use the service. Liu and Arnett [86] also added that
system use is one of the four factors when considering the
major ingredients for success of a website. As a result,
customers are willing to pay ¼
N 38.01/month for the occupational certification service if made available to them. Seventh,
an individual's relative utility increases with an increase in
access to digital currency services (βDCUR ¼ 0:4382). However, the relative utility of a customer decreases among those
who have access to the internet (βIADC ¼ 0:1770), who
have a high percentage of IB (βPIBDC ¼ 0:0022), and whose
major bank is Bank C (βBCDC ¼ 0:1067). Meaning that,
digital currency services are more effective when they
targets those who have no access to the internet, who have
a low percentage of IB; and whose major bank is not Bank C.
As a result, consumers are willing to pay ¼
N 43.70/month for
digital currency services if made available to them. Finally,
though in general people have a high preference for website
customization (βWEBCUS ¼ 0:5314), individuals that have a
high frequency of internet usage, those that are already
doing IB, and those that access the internet at the office have
Please cite this article as: S. Yusuf Dauda, J. Lee, Technology adoption: A conjoint analysis of consumers' preference on
future online banking services, Information Systems (2015), http://dx.doi.org/10.1016/j.is.2015.04.006i
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Table 5
Estimation results of the random coefficient model.
Variables
Mean (b) of B
5
7
11
Mean
ATMCCT
RTINT
MOBW
BIOS
ATMISP
OCERT
DCUR
WEBCUS
COSTa
Std. Err.
nnn
1.0138
1.1243nnn
1.2161nnn
1.0957nnn
0.4539nnn
0.5242nnn
0.0413
0.7108nnn
0.6715nnn
65
Variance (w) of B
0.0994
0.1141
0.1052
0.1156
0.0498
0.0613
0.0562
0.0936
0.1563
t-value
10.2
9.85
11.56
9.48
9.12
8.56
0.74
7.59
4.3
Std. Div.
0.4782
0.0039
0.2808
0.0103
0.0106
0.3680
0.5827
0.0453
1.0822
Variance
nnn
0.2287
1.5E 05
0.0788nn
0.0001
0.0001
0.1354nnn
0.3395nnn
0.0021
1.1712nnn
Std. Err.
t-value
67
0.0651
0.0723
0.1149
0.0826
0.0820
0.0800
0.0652
0.0673
0.1108
7.35
0.05
2.44
0.13
0.13
4.6
8.94
0.67
9.77
69
15
N ¼19,365; Asterisk (n) marks the significance at 5% (|t| 41.65); LR chi2 (10) ¼ 806.80, Log likelihood ¼ 6592.9407, LRI: ρ ¼0.672.
17
a lower marginal utility regarding website customization
services (βIUWC ¼ 0.0368, βIBWC ¼ 0.0579, and βIOWC ¼
0.1828). This implies that website customization services
are more effective when they target those that have a lower
frequency of internet usage, those that are not doing IB, and
those that access the internet at their homes or internet cafes
or using mobile phones or smartphones. As a result, consumers are willing to pay a very good amount for this
service: up to ¼
N 62.05/month for website customization
services if made available to them.
The factor of high frequency in the interaction terms is
internet familiarity, showing its importance as a strong
influence on future online banking service adoption. The
more familiar a person is with the internet the more they
want to have access to it, and subsequently will use the
internet more frequently. In this way, they can be motivated to adopt the future online services based on their
experience and habit of frequently using the internet.
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25
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31
33
35
5.3. Random coefficients model
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The random coefficient model that relaxes the restrictions of the ML model is analyzed by assigning the
distribution for the part-worth in the RUM [95]. For the
estimation, maximum-likelihood estimation with a sample
size of 19,365 was used. This research follows the estimation method used by Train [89], Kim [96], and Lee et al.
[97]: StataSE 12 (64-bit) was used to do the estimate and
the estimated means and their variance are shown in
Table 5, in which almost all the results are significant at
5% except the mean estimate of the parameter DCUR.
The estimation assumes a log-normal distribution for
the transaction cost. The means of coefficient estimates in
Table 5 have the same signs and same order of significance
as those of Table 3, except for MOBW, BIOS and DCUR
where the significant levels changed. In general, the
variances in random coefficient estimates are relatively
smaller compared to their means except in the cost and
digital currency attributes. Hence it shows that the variation in the consumer price and service attributes is clearly
captured, showing either extreme heterogeneity or heterogeneous preferences of the consumers depending on
type of future banking service. In addition, not all estimates of variance are statistically significant, showing that
customers are homogeneous with regards to some future
online services. The WTP for each attribute was also
calculated along with the significant or 95% confidence
interval, the result along with the asymptotic t-statistics is
also presented in Table 6.
First from Table 5, the mean coefficient of digital
currency service is small (0.0413) and not significant in
this model showing that there are some few minorities
who actually do not care, so even though the digital
currency service is introduced, their utility level does not
change. This finding is in contrast to the finding of the
rank-ordered logit model, which shows that the whole
population has a positive preference for digital currency.
The large variance (0.3395) reveals the heterogeneity in
preference. Second, the mean coefficient of website customization services is large (0.7108) showing that a
majority of customers like the website customization
services which supports the finding of the rank-ordered
logit model. The insignificantly small variance (0.0021)
reveals that they are highly homogeneous in their preference to this service. Third, the mean coefficient of ATMs
integrated with smartphones services is large (0.4539)
showing that quite a reasonable amount of customers like
the ATMs integrated with smartphones services which
supports the finding of rank-ordered logit result. Similarly,
the insignificant variance (0.0001) shows that they are
extremely homogeneous regarding this service. Fourth, the
mean coefficient for occupational certification service is
also high (0.5242). This supports the findings of rankordered logit result showing that a majority of customers
like the occupational certification service, but the high
variance (0.1354) reveals the heterogeneous in their preference. Fifth, the mean coefficient for ATMs with capacity
for cardless transactions is very large (1.0138). This also
supports the findings of rank-ordered logit result showing
that a majority of customers like the ATMs with capacity
for cardless transactions, while the high variance (0.2287)
shows that they are highly heterogeneous in their preference. Sixth, the mean coefficients for real-time interaction services are also very large (1.0138), which supports
the findings of rank-ordered logit result showing that a
majority of customers like the real-time interaction services. However, the small insignificant variance (1.5E 05)
reveals that they are highly homogeneous with regards to
this service. Seventh, the mean coefficient for biome
tric sensor services is very large (1.0957) showing that a
Please cite this article as: S. Yusuf Dauda, J. Lee, Technology adoption: A conjoint analysis of consumers' preference on
future online banking services, Information Systems (2015), http://dx.doi.org/10.1016/j.is.2015.04.006i
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Table 6
Willingness to pay for random coefficient estimates.
Variables
5
Mean
7
9
11
13
15
17
19
Cardless ATMs
Real time interaction
Mobile wallet
Biometrics sensors
ATMs smartphone integration
Occupational certification
Digital currency
Website customization
Service cost
ATMCCT
RTINT
MOBW
BIOS
ATMISP
OCERT
DCUR
WEBCUS
COSTa
1.5099
1.6743
1.8111
1.6318
0.6760
0.7807
0.0615
1.0585
N/A
49
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6. Managerial implications
53
Based on the above results, several implications have
been drawn for management. First, the critical need for
managers to focus on promoting these future online services as they have strong impact on customer's satisfaction
or dissatisfaction depending on service features or demographic and characteristics groups has been emphasized.
However, relative to other services, the service fee and realtime interaction are the most important services. First, in
line with other researchers, Koenig-Lewis et al. [98], Luo
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[95% Conf. Interval]
Lower
Upper
1.0425
1.1675
1.2526
1.1635
0.4512
0.5173
0.0861
0.6940
N/A
1.9772
2.1811
2.3697
2.1000
0.9007
1.0441
0.2092
1.4231
N/A
Mean
241.584
267.888
289.776
261.088
108.16
124.912
9.84
169.36
N/A
[95% Conf. Interval]
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Lower
Upper
69
166.8
186.8
200.416
186.16
72.192
82.768
13.776
111.04
N/A
316.352
348.976
379.152
336
144.112
167.056
33.472
227.696
N/A
At the time of estimation $1 ¼ ¼
N 160.
majority of customers like the biometric sensor services.
This finding reveals the shortcoming of the rank-ordered
logit model, which shows that the utility of the consumers
does not change even with the introduction of biometric
sensor services as it was not significant in the rankordered logit model. However the small insignificant
variance (0.0001) shows that preferences are extremely
homogeneous. Finally, the mean coefficient for mobile
wallet services is also very large (1.2161) showing that a
majority of customers like mobile wallet services which
supports the finding of the rank-ordered logit model. The
small variance (0.0788) shows that they are not too
different in their opinion about the mobile wallet services.
From Table 6, the result of the WTPs for the random
coefficient model shows that majority of consumers are
willing to pay only ¼
N 9.84/month for digital currency service,
which is quite true since it was not significant in this model
compared to ¼
N 108.16/month for ATMs with smartphone
integration, ¼
N 124.91/month for occupational certification,
and ¼
N 169.36/month for website customization. On the other
hand, customers are willing to pay very high amount for
other services (approximately ¼
N 289.78 for mobile wallet
services, ¼
N 267.89/month for real-time interaction services,
and ¼
N 241.58/month for cardless ATMs). In addition, customers are willing to pay ¼
N 261.09/month for biometric service,
which is in contrast to the rank-ordered logit model, as it
was not significant in that model. These amounts are quite
high compared to the current payment levels of ¼
N 150/
transaction for online transfers, ¼
N 200/transaction for interbank transfers using an ATM and online bill payment, ¼
N 5 for
commission on turn over (COT), and ¼
N 4 for alert charges.
21
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¼)
WTP (N
WTP (US$)
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[99], and Laukkanen et al. [100] young people who use
online banking regularly are early adopters of innovative
technologies that will eventually filter through to older age
groups and with increased word of mouth, it can lead to a
network effect that will reach critical mass adoption.
Managers can take advantage of this and introduce services
that add entertainment for youths or extra convenience in
terms of ease of usage [101,102], e.g., digital wallets, realtime interaction (video banking), ATMs integrated with
smartphones, website customization, and digital currency.
Second, since the ATM is the most common channel used
by most customers for transactions, ATM integrated services have a better chance of increasing adoption, especially
in developing countries where there is low access to the
internet, low internet banking and m-banking adoption.
Third, even though website customization services cannot
be priced explicitly, they can help in customer retention and
improve satisfaction. Since majority are interested in this
service, Banks can concentrate on developing a customized
website with tag-based interactions that help users retrieve
information about their past online banking activities for
the majority while providing a standard website for other
groups. Fourth, promoting digital currency may not be an
effective policy. Meanwhile, Bank A, Bank C, and other
banks can be the chief promoters of digital currency
targeted at those with less access to the internet and lower
percentage of IB use. Business models based on P2P NFC
should be promoted to help create new payment-driven
revenue streams. Fifth, there should be a policy for promoting comprehensive, flexible, and additional authentication
transaction security solutions that will allow the banks to
meet the needs of an escalating threat landscape and tailor
mitigation methods to different risk profiles. Sixth, even
though the biometric service is preferred less, Bank managers can introduce this by providing various options in
terms of differentiated services since majority that like the
service are not very heterogeneous in their preference.
Clearly, Bank B, Bank C, and other banks can be the chief
promoters of these services, which should be targeted at
customers who are less familiar with the internet and
access the internet at home or the office or using mobile
phones or smartphones. In addition, other bank strategies
to encourage potential adopters like offering incentives for
Please cite this article as: S. Yusuf Dauda, J. Lee, Technology adoption: A conjoint analysis of consumers' preference on
future online banking services, Information Systems (2015), http://dx.doi.org/10.1016/j.is.2015.04.006i
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adoption, providing customers valued-added promotion
programs, and aggressive marketing could lead to increasing adoption.
5
7. Conclusions
7
In order to attain global networking and increased
efficiency of the banking industry, especially in developing
countries, one of the most important tasks of management
is promoting the adoption and use of e-banking systems
for fast and efficient delivery of services that will lead to
increased sales and market share at the same time meet
customer satisfaction, attract new customers, and retain
existing ones. The aim of this paper therefore is to
construct the banking customer's behavior corresponding
to future online service preference. This study has used
conjoint analysis and stated preference methods using
discrete choice modeling to study the technology adoption
pattern regarding consumers' preference for potential
future online banking services in the Nigerian banking
industry. As a foundation for policy making, this research
has incorporated heterogeneity into the models using a
random coefficient model as well as interaction between
the primary bank services attributes and individual demographics and characteristics and the results are consistent
with the conventional rank-ordered logit model. They are
also consistent with previous studies and have proved to
be suitable under the conditions of low market penetration and in areas where there is no market information, as
in the case of new products that have not yet entered the
market which reveals future technologies that can foster
an increased adoption of online banking services. Policies
to promote smart and practical branded services especially
self-services and promoting a universal adoption of
e-banking systems will contribute to increasing adoption
of online services, as customers can enjoy making in-store
payments quickly and securely without having to locate
their credit or debit card. Findings from the study are
expected to have a profound impact on and important
implications for product and service selection, marketing
campaigns, and operating strategies for Nigerian banks
and other financial organizations. Empirically, the affecting
vectors reflecting both exogenous and endogenous factors
that have an impact on a bank customer's choice of service
can be referred to in future banking studies. More significantly, the level of importance of each factor has also
been revealed, and the preference structure is likely to
provide a well-established background for reference in
future studies on banking service marketing as well as in
policy making areas.
Some limitations from the current study offer opportunity for additional research. First, the current study focused
on consumers' preference on future online services, but
online services are vast in scope, comprising detailed
analysis from the supply side such as cost investment,
infrastructure, technology, innovation, and efficiency. Incorporating all these aspects of electronic banking into future
studies will be useful for a more in-depth analysis. Second,
the core of the current study is acceptance or adoption of
future online banking services, so it excluded some behavior intensions of customers based on experience and use of
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the current online services that may impact on their
perception to adopt the future online services. Similarly,
studies specifically discussing factors that might prevent
the adoption of future online banking services were also
considered beyond the scope of this study. Finally, our data
is limited to potential banking customers only. However,
there are several areas that have the potential to deliver
additional and relevant Insights; an example is microfinance institutions that serve low-income markets. For
further studies, studies on consumers' preference for future
online services that undertake a comparative analysis of
developed and developing economies using different models and approaches could produce meaningful insights into
the behavior and attitudes of customers. According to
Crabbe et al. [103] as cited in Shaikh and Karjaluoto [64],
“cross-cultural and transnational studies would enable
researchers to determine how specific social and cultural
characteristics of a society influence the adoption of technologies and services among its members”. In addition, a
research that will investigate the behavior of smartphone or
tablet users in relation to the adoption of future online
services will be interesting. Following Shaikh and Karjaluoto [64], since this subset of consumers that have smartphone or tablet have convenient access to the internet; the
basic computer skills required for conducting various online
activities such as online shopping and online account management; and the basic computer skills and the necessary
technology infrastructure to conduct m-banking, relies on
the integration of wireless and internet technologies [104],
they may be ready for the adoption of the future online
services.
63
Appendix Sample conjoint experiment card
95
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85
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89
91
93
97
Part 1. Assuming that you can accrue possible benefits from future
online Banking services including ATMs with capacity to perform
cardless transaction, real time interaction services, mobile wallets,
biometrics sensors, ATMs integration with smartphones, occupation
certification, digital currency, and website customization/
personalization by paying some cost as transaction cost. Please rank
the types of future online Banking Services from 1 to 3 (1¼ most
preferred service, 3¼ least preferred service) from the list of 5
hypothetical options provided below.
Please rank from most preferred to least preferred future Banking
services from the four options given here.
99
101
103
105
107
109
Questionnaire 1: Future Online Banking Services
111
Attributes of banking services Service A
Service B
Service C
113
1. ATMs with capacity to
perform cardless transaction
2.Real time interaction
services
3. Mobile wallet
No
capacity
Available
No
capacity
Not
available
Available
115
4. Biometrics sensors
5.ATMs integration with
smartphones
6. Occupation certification
With
capacity
Available
Not
Available
available
Not
Not
Available
available
available
Not
Not
Integrated
integrated integrated
Available
Please cite this article as: S. Yusuf Dauda, J. Lee, Technology adoption: A conjoint analysis of consumers' preference on
future online banking services, Information Systems (2015), http://dx.doi.org/10.1016/j.is.2015.04.006i
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1
7. Digital currency
3
8. Website customization/
personalization
9. Cost (transaction cost)
Rank from 1 to 3 (1¼ most
preferred service & 3¼ least
preferred service)
5
7
Not
available
Available
Not
possible
¼
N 50
()
Not
available
Not
available
Possible
Available
¼
N 50
()
¼
N 150
()
Possible
9
Note: Assume that all the other attributes, besides the nine
proposed here, remain the same.
11
13
15
Appendix A. Supporting information
17
Supplementary data associated with this article can be
found in the online version at http://dx.doi.org/10.1016/j.is.
2015.04.006.
19
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References
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Q5
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