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Acceptance of e-book reading among higher education students in a

developing country: The modified diffusion innovation theory

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DOI: 10.1504/IJBIS.2018.10009810

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222 Int. J. Business Information Systems, Vol. 27, No. 2, 2018

Acceptance of e-book reading among higher

education students in a developing country:
the modified diffusion innovation theory

Wasim Qazi
Department of Education and Learning Sciences,
IQRA University,
Karachi, 75300, Pakistan
Email: whqazi@gmail.com

Syed Ali Raza and Nida Shah*

Department of Management Sciences,
IQRA University,
Karachi, 75300, Pakistan
Email: syed_aliraza@hotmail.com
Email: nidashah_18@hotmail.com
*Corresponding author

Abstract: This study explores the students’ intention towards the e-book
adoption in Pakistan by using the modified diffusion innovation theory. Student
attitude, awareness, perceived innovation characteristics (PIC) are taken as
independent variables; actual usage is taken as a dependent variable, whereas
intention plays the role of the mediator in the relationship. The data is collected
through five-point Likert questionnaire from 620 university students. The
confirmatory factor analysis (CFA), partial least square structure equation
modelling (PLS-SEM) has been applied. The results show that compatibility,
complexity, observability, trialability, attitude and awareness has a significant
positive effect on behavioural intention, whereas, relative advantage has an
insignificant effect on behavioural intention. Moreover, behavioural intention
creates a significant positive effect on actual usage. This study will be useful in
understanding the factors associated with the adoption of e-book reading. This
work will help the managers to understand the role of PIC in adoption of new
product or technology.

Keywords: e-book reading; modified innovation diffusion theory; actual usage;

Pakistan.

Reference to this paper should be made as follows: Qazi, W., Raza, S.A. and
Shah, N. (2018) ‘Acceptance of e-book reading among higher education
students in a developing country: the modified diffusion innovation theory’,
Int. J. Business Information Systems, Vol. 27, No. 2, pp.222–245.

Biographical notes: Wasim Qazi is associated with IQRA University as a

Professor and Vice President. He received his PhD in Educational Management
from Hamdard University, Pakistan and Post-Doctorate from Education
Eastern Kentucky University, USA. He has published numerous papers in
international refereed ISI indexed journals. He is the Editor-in-Chief of Journal
of Management Sciences (JMS), a biannual online double blind peer reviewed

Copyright © 2018 Inderscience Enterprises Ltd.

 Acceptance of e-book reading among higher education students 223

research journal in the area of management sciences. He is also heading the

Editorial Team of Journal of Education and Social Sciences (JESS) a biannual
online double blind peer reviewed research journal in the areas of education
and social sciences.

Syed Ali Raza is associated with IQRA University as an Asst. Professor and
Deputy Director, Research and Publications. His areas of interest include
financial economics, energy economics, tourism economics, corporate finance
and behavioural sciences. He has published numerous papers in international
refereed journals including Tourism Management, Energy Policy, Economic
Modelling, Social Indicators Research, Quality and Quantity, International
Migration, Total Quality Management and Business Excellence, Journal of
Business Economics and Management, Journal of Transnational Management,
Transition Studies Review, Global Business Review and Journal of Chinese
Economic and Foreign Trade Studies.

Nida Shah is associated with IQRA University as a Research Associate. She

received her MBA in Finance from IQRA University. Currently, she is
pursuing his PhD in Finance from IQRA University. Her areas of interest are
corporate finance, energy economics, financial economics, international
economics and behavioural sciences.

1 Introduction

Information technology has been changing the individual lifestyle and their technology
adoption. The innovation in information technology gives the opportunity to produce the
unique products (Waheed et al., 2015). The technological advancement creates a
significant impact on the lives of the peoples, now they are not satisfied with the
traditional means of learning and obtaining knowledge (Xiao et al., 2014). E-book
technology has changed the consumer reading experiences and reading patterns. E-book
makes the reading process convenient and ubiquitous (Folb et al., 2011). In 2000, the first
e-book ‘Riding the Bullet’ by Stephen King becomes the first mass-market e-book, and
give expansion to the e-book industry (Lee et al., 2002). The e-book development
changes the relationship between the publishers and the readers (D’Ambra et al., 2013);
furthermore, it also changes the nature of publishing and behaviours of the readers
(Bhattacharjee et al., 2011).
The e-book adoption also increases the demand for e-readers such as Apple iPad,
Amazon Kindle, etc. (Jung et al., 2012). According to the companies Apple, Amazon and
Barnes and Noble they sell thousands of e-books every year (Rao, 2012). E-book sales
grew 200% in 2010 compare to 2011 which is 169.4% and the sale of printed book
decline by 24.8% (Jung et al., 2012). By 2025, 75% of the books will be in the digital
format (Rao, 2012). Moreover, the demand for e-book will increase because of the
penetration of smartphone in Asia Pacific and the availability of e-readers in the North
America (PriceWaterhouseCoopers, 2010).
Electronic books (e-books) are the books in the digital form that can be accessed on
computers, handheld devices, i.e., personal digital assistants (PDAs), tablets,
smartphones, e-readers (Lam et al., 2009, Rao, 2012, Reitz, 2014). E-reading has become
one of the leading media because of the faster broadband services and relatively smaller
224 W. Qazi et al.

computer devices (Brown, 2001). The electronic books (e-book) demand has been
increasing drastically, which slow down the printed book sales. E-book is preferred over
textbooks because of the flexibility, accessibility, searchability sustainability, visual
appeals (Shelburne, 2009; Woody et al., 2010).
Despite the advantages the usage of e-book has some obstacles attached to it which
includes incompatibility, navigation issues, digital right management issues (DRM), on
screen reading problems and internet service issues (Gibbons, 2001; Chu, 2003;
Shelburne, 2009; Chong et al., 2009). All these factors hinder the use of e-book
extensively (Chong, et al., 2009). One of the utmost barriers in the acceptance and use of
e-book is customer apathy (Lam et al., 2009) which means customers are not comfortable
with the idea of the reading through e-book, however, they admit the benefits of using the
e-book technology, but still like some features of the paper books (Worlock, 2009).
Previously, many studies examined the factors affecting the adoption of e-book in the
context of higher education (Simon, 2002; Wilson, 2003; Hernon et al., 2007; Nelson,
2008; Lam et al., 2009), in the context of library (Tedd, 2005; Just, 2007; Renner, 2007),
medical contexts (Morton et al., 2007), e-book as learning aids (Larson, 2010; Halliday,
et al., 2010; Smeets and Bus, 2012), the role of user preference on e-book (Chang and
Tung, 2008), the ease and difficulty in using an e-book (Rao, 2001; Sottong, 2008)
barriers in acceptance of e-book (Gibbons, 2001; van der Velde and Ernst, 2009),
e-book in place of printed books (Bredding, 2000; Rojeski, 2012). Some empirical
studies also investigate the role of cloud computing applications on e-book (Lu et al.,
2005). Despite the concerns on the usage of e-book (Kang et al., 2009) very few studies
have been conducted which analyse the factor which affect the e-book adoption (Vernon,
2006; Aharony, 2014).
As e-book is an innovative product several factors affect its adoption, which includes
individual innovation characteristics (Moore and Benbasat 1991), individual innovation
adoption, self-efficacy level (Thomson et al., 2005; Eiamkanchanalai and Assarut, 2012;
Duane et al., 2014), attitude (Cviko et al., 2012; Aldunate and Nussbaum 2013) and
awareness (Jung et al., 2012). All the above factors play a vital role in the adoption of the
technology.
E-book is considered as a novel product for the young students (Terpend et al., 2014)
and higher education universities are spending a substantial amount to promote e-book
usage among students and teachers (Muir and Hawes 2013). For the acceptance of novel
products intention plays an important role and according to Venkatesh et al. (2003)
behavioural intention is the significant factor in the technology usage. To explore the
student’s acceptance towards e-book most of the studies have been conducted in the
developed countries (Walton, 2013; Aharony, 2014) and no study in the context of
Pakistan has been conducted to examine the factors that affect student’s intention to
adopt e-book in higher education.
According to the global formation report the technology acceptance in Pakistan is
quite fluctuating. In 2012–2013 it was in a position of 102nd then in 2013–2014 it
reached up to 111th position, however, in 2014–2015 the ranking got improved and
currently has 97th position. Since, the acceptance of technology is quite uncertain in
Pakistan this research is conducted to figure out whether the technical innovative
products like e-book is accepted in this country or not. Hence, this study is carried out to
examine the adoption of e-book technology among university students.
 Acceptance of e-book reading among higher education students 225

The present paper is arranged in five sections. Section 2 presents the literature review.
Section 3 discusses the methodology, Section 4 presents the empirical results, Section 5
presents the discussion on the results and Section 6 shows the conclusion and
recommendations.

2 Literature review

2.1 Theoretical background

In the past, numerous theoretical models have been introduced regarding the innovation
adoption (Jung et al., 2012). Among them the diffusion innovation theory (DIT) has been
used frequently and has been considered as a reliable framework in investigating the
diffusion and acceptance of the technology (Moore and Benbasat, 1991; Venkatesh et al.,
2003).
The theory was established in 1962 by Rogers and further modified by Rogers in
1995. Diffusion Innovation theory (DIT) was developed to identify the acceptance of
innovating products. (Waheed et .al, 2015). The theory emphasis how, why and to what
extend the innovate technologies and ideas are spread throughout a society with the
passage of time (Rogers, 1962). According to Rogers (1983) innovation is an object, idea
or practice that can be supposed as novel by a person in any other group of adoption.
Whereas, the diffusion is the process by which the innovation is transmitted over time
with the members of a social system by using different communication networks (Rogers,
2003).
Rogers (2003) described that the decision related to innovation involves five steps,
i.e.,
1 knowledge
2 persuasion
3 decision
4 implementation
5 confirmation which follows each other in a chronological manner.
The first step is known as knowledge stage in which an individual came to know about
the innovation and collect information regarding it. In this phase, the individual explores
what is the innovation and how it works (Rogers, 2003). The second step is the
persuasion in which, the individual develops a positive or negative attitude towards the
innovation, and however, this does not mean adoption or rejection of the innovation. In
this stage the individual is more sensitively involved with the innovation and the reviews
from peers, colleagues, etc. influence its beliefs or opinion related to innovation (Sahin,
2006). In the third step, the individual adopts or reject the innovation. According to
Rogers (2003), in this step the adoption means full use of innovation and rejection means
no adoption of the innovation. At the implementation stage, the innovation has practically
come into practice. In the confirmation stage the individual reconsidered his decision and
may change its decision if he finds anything conflicting about the innovation.
226 W. Qazi et al.

DIT discusses the five Perceived characteristics of innovation (PIC) namely relative
advantage, compatibility, complexity, and trialability and observability as a key predictor
which affects the individual attitude towards the adoption of innovation (Rogers 1983).
IDT theory argues that the potential users accept or reject the innovation on the basis of
their beliefs regarding innovation (Agarwal, 2000).
Therefore, we use the modified version of the Roger’s innovation theory by using the
all the five DIT characteristics and added the role of attitude and awareness of e-book
reading to increase the effectiveness of the study.

2.2 Empirical evidences and hypotheses

In this study, we use the Rogers (1983) perceived characteristics of innovation
(i.e., relative advantage, compatibility, complexity, trialability, and observability),
attitude and awareness to predict the intention towards the actual usage of the e-book
reading. The empirical evidences of each variable are explained separately in the
following sections.

2.2.1 Relative advantage

Relative advantage is the user perception related to the level of advantage gained by the
innovation. If the user believes that the new innovation will bring in more advantage
compared to the preceding one, the new innovation has a greater relative advantage
(Rogers, 1983). The more is the relative advantage of the innovation the more is the
changes of the adoption (Ooi et al., 2011).
The previous researchers show that the perceived relative advantages have a positive
relationship with the users’ intention to use the technology (Shih, 2007; Lee, 2006).
Likewise, the relative advantage of mobile commerce (Chung, 2014), a mobile payment
(Duane et al., 2014) changes the consumer attitude towards its acceptance. The
advantages of e-book reading includes convenient to use, easy access to relevant material,
time saving, economical. All these factors influence the individual intention to adopt the
e-reading technology. The following hypothesis is used to explain the relative advantage:
H1 The relative advantage of e-book reading has a significant effect on behavioural
intention.

2.2.2 Compatibility
According to Rogers (1995) compatibility is a user’s belief that how the innovation fits
with their current needs, values and past experiences. The more compatible the
innovation is with the user needs and values, the more is the changes of its adoption
(Tornatzky and Klein, 1982; Shih and Fang, 2004; Antón et al., 2013; Chung, 2014).
Previous studies show that the compatibility has a direct effect on the behavioural
intention (Wu and Wang, 2005; Chang and Tung, 2008). The e-book reading gives the
same personalise feeling as reading through normal books, the option to add bookmarks
and comments make it compatible and easy to use for the users. The proposed hypothesis
for compatibility is:
H2 The compatibility of e-book reading has a significant effect on behavioural
intention.
 Acceptance of e-book reading among higher education students 227

2.2.3 Complexity
It is the user feeling related to the level of difficulty in learning, operating and
understanding the innovation (Rogers, 1983). The innovation that is less complex and
user friendly are easily accepted by the users (Chung, 2014). Some studies show that the
significant negative relationship exists between the complexity and the intention to use.
(Shih, 2007; Lee, 2006) In other words, the more complex the technology is, the less
intention the user have to use the technology (Lin, 2006). The e-book reading interface is
easy to understand and user friendly which influence the users to adopt this technology.
The hypothesis used for complexity is:
H3 The complexity of e-book reading has a significant effect on behavioural intention.

2.2.4 Trial-ability
Trialability is the possibility of trying the innovation before its actual use by the
individual. It increases the changes of the adoption of the innovation (Rogers, 1983). The
innovation which can be tried or tested by the users increases the users’ attitude to adopt
it (Chung, 2014). The users prefer to try an innovation to increase their comfort level
(Waheed et al., 2015). Previous studies show that the positive association exists between
the trialability and the intention of users towards the technology (Lee, 2006; Yang, 2007).
We use the following hypothesis:
H4 The trialability of e-book reading has a significant effect on behavioural intention.

2.2.5 Observability
Rogers (1983) explains observability as the extent to which the innovation results are
visible to others. The innovation visibility encourages the individual to discuss it with
their friends and neighbours and creates positive intention to adopt the technology
(Duan et al., 2010). Several studies reported that the positive relationship exists between
the observability and the intention to use (Lee, 2006; Yang, 2007). Chung (2014) found
that the facility of observing mobile commerce increases its adoption among the users.
The hypothesis used is:
Rogers (2003) proposed that the innovation that has the greater relative advantage,
compatibility, trialability, and observability and less complexity are rapidly adopted by
the individual compare to the other innovations. These PIC identified by Rogers are
considered as important predictors that explains the innovation adoption (Hsu et al.,
2007).
H5 The observability of e-book reading has a significant effect on behavioural
intention.

2.2.6 Attitude
According to Venkatesh et al. (2003) attitude is the individual reaction towards the usage
of the system. The successfulness of the technology is highly dependent on the users’
attitude towards it (Waheed et al., 2015). In past studies several models have been used to
investigate the role of attitude as a mediator between intentions and beliefs (Fishbein and
Ajzen, 1975; Davis et al., 1989; Taylor and Todd, 1995). Many studies show that the
228 W. Qazi et al.

users’ attitude plays a vital role in the adoption of the new technology or innovation
(Cviko et al., 2012; Aldunate and Nussbaum, 2013). The hypothesis used to represent the
attitude variable is:
H6 Attitude has a significant effect on behavioural intention.

2.2.7 Awareness
The adoption of the innovation starts with its awareness (Rogers, 1995). Innovation
awareness is the key variable for the technology adoption (Jung et al., 2012). Lack of
awareness has a negative association with the adoption process (Feldstein and Glasgow
2008; Solomons and Spross, 2011). In the context of e-book, Gunter (2005) conducted
survey on the awareness of e-book in UK and reported that respondents are aware of e-
book. On the contrary, the survey conducted by Abdullah and Gibb (2006) on e-book
awareness and usage in British academic library indicates that the e-book awareness and
its usage both are low among the students. The hypothesis used for awareness is:
H7 Awareness has a significant effect on behavioural intention.

2.2.8 Behavioural intention

Behavioural intention (BI) is the individual willingness to adopt new technology
(Tsai, 2012). Actual behaviour can be gauged through intentions (Jackson et al., 1997;
Webb and Sheeran, 2006) and considered as an important variable in acceptance of the
technology (Irani et al., 2009). In the past, many studies have considered behavioural
intention as an important factor in determining the acceptance of the technology
(Teo, 2011, 2010; Park, 2009). Aharony (2014) identifies that the personal characteristics
play an important role in the adoption of the technology. Ngafeeson and Sun (2015) by
using the data of 158 undergraduate students reported that only high innovative
individuals converted their intentions to the actual adoption of the technology. The
hypothesis used in this study for behavioural intention is:
H8 Behavioural intention has a significant effect on adoption of e-book.

2.2.9 Adoption
Adoption is the individual willingness to accept or reject the technology or innovation
(Straub, 2009). There are numerous factors that change the individual decision to use
technology (Venkatesh et al., 2003). According to Ajzen and Fishbein (1980) the
acceptance of technology can be determined by the intention of the individual. Lee et al.
(2011) reported that the DIT characteristics have a significant effect on the individual’s
intention to use systems. The DIT characteristics have a direct effect on innovation
adoption (Rogers, 2003). According to Rogers (1995) even if the innovation seems to be
useful cannot be adopted by the individual due to the influence of the contextual factors.
Therefore, it is extremely important to understand those factors which create obstacles in
user willingness towards e-book.
 Acceptance of e-book reading among higher education students 229

3 Methodology

The conceptual model of our study is illustrated in Figure 1. The model demonstrates the
effect of the Rogers perceived innovation characteristics (PIC) that are relative
advantage, compatibility, complexity, trialability, observability, multi-dimensional
attitude and awareness on the actual usage of e-book whereas, intention plays a role of a
mediator in this framework.

Figure 1 Modelling framework

Perceived characteristics
of innovation

Relative advantage
Behavioural Actual use
Compatibility intention
Complexity

Trialability

Observability

Attitude Awareness

Source: Authors’ construction

3.1 Measurement instrument

The PIC items are taken from Waheed et al. (2015) who adapted the Moore and Benbasat
(1991) PIC scales. This study uses the 20 items of the PIC. Awareness items are taken
from Meng et al. (2015) Attitude, intention and actual usage items are also adapted from
the Waheed et al. (2015). All the items in the questionnaire were stated in English while
the content validity was endorsed by independent market and academic expert. A pilot
study was also performed by distributing the questionnaire to the university students who
use the e-book reading. A five-point Likert scale which ranges from (1) ‘strongly
disagree’, (2) ‘disagree’, (3) ‘neutral’, (4) ‘agree’ and (5) ‘strongly agree’ was used to
evaluate the student adoption of e-book reading. Moreover, the demographic data is also
collected from the respondents of the study. The sample data is collected from survey
method and the convenience sampling technique was used. The sample size was based on
the guidelines given by Comrey and Lee (1992) and Ali and Raza (2015) which consider
the sample size of 50 as poor, 300 as good, 500 as very good and 1,000 is considered as
excellent for the purpose of factor analysis. The same guidelines are also adopted by us in
this study and total sample of 620 is collected. The total questionnaire items are 42 which
satisfy the minimum questionnaire requirement given by Hair et al. (2006).
230 W. Qazi et al.

Throughout the data collection process, all respondents are requested to participate
voluntary and assurance was given that their information will be kept confidential. The
impact of independent variables (PIC, attitude, awareness, intention) is analysed on
dependent variable (actual usage of e-book) through this questionnaire. The basic
regression models of the study are:
yn = a + bxn + 1n, (1)

yn = a + bxn + 1n, (2)

In equation (1) y represents a dependent variable (intention) and a denotes the intercept
term. X represents explanatory variables (relative advantage, compatibility, complexity,
trialability, observability, awareness, attitude) while b represents the regression
coefficient.
In equation (2) y represents a dependent variable (actual usage) and a denotes the
intercept term. X represents the independent variable (intention) while b represents the
regression coefficient.
The basic functional form of the above equations is:
Intention = f (relative advantage, compatibility, complexity, trialability,
(3)
observability, awareness, attitude)

Actual usage = f (intention) (4)

The following regression models are used for the purpose of the study:
BI = α 0 + β1 RA + β 2 CPT + β 3CPX + β 4TR
(5)
+ β5OB + β 6 ATT + β 7 AWR + εt

AU = α o + β1 BI + εt (6)

In equation (5) BI is the behavioural intention, RA is the relative advantage, CPT is the
compatibility, CPX is the complexity, TR is the trialability, OB is the observability, ATT
is the attitude, AWR represents the awareness, and εt is the error term.
In equation (6) AU shows the actual usage where BI represents the behavioural
intention and εt is the error term.

3.2 Demographics
The sample represented the responses of the students from different universities and in
total 671 questionnaires was filled and returned. After deletion of outliers and erroneous
responses 620 responses were found useable. The details of demographic profiles are
presented in Table 1. As seen from demographic characteristics the 77% respondents
were the students of private universities, 22% were the students of public universities and
1% was student of semi-private universities. In terms of gender, the 51% of respondents
were male, while 49% were female hence equally distributed. The majority of the
respondents were undergraduates (53%) whereas, 37% were graduates and 10% were
postgraduate. The respondent age group category showed that 78% were falling in the
age bracket of 18–25 whereas 17% were in the age bracket of 26–30 and the rest 5%
were in the age bracket of 31–35. The field of study shows that 53% respondents were
 Acceptance of e-book reading among higher education students 231

studying business studies, 17% were studying engineering, 13% were studying computer
science, and the rest 15% were studying medical and other study program.
Table 1 Profile of respondents (N = 620)

Demographic items Frequency Percentile

Student of university
Public sector 136 22%
Private sector 478 77%
Semi-private sector 6 1%
Gender
Male 316 51%
Female 304 49%
Education level
Under graduate 329 53%
Graduate 229 37%
Post graduate 62 10%
Age
18–25 484 78%
26–30 105 17%
31–35 31 5%
Field of study
Business 328 53%
Engineering 108 17%
Computers 83 13%
Medical 57 9%
Other 44 8%
Source: Author estimations

4 Data analysis

4.1 Common method biasness

To examine the potential existence of common method variance bias among the
dependent and independent variables, we have applied the Harmon’s one factor test
(Chuang and Lin, 2013). In this test, all the proposed variables under the varimax rotation
were loaded in a single exploratory analysis (Karahanna et al., 1999). According to the
Harmon’s test assumption, if a single or general construct shows more than 50% of the
variance among the variables, then there exists a sufficient evidence of common method
variance (Kim and Park, 2012). In this study, we have extracted the variables that
represent our dependent and independent variables with the division of different factors,
while each factor account for less than 50% of the total variance. Therefore, the findings
revealed that the common bias is not a serious problem for our sample data.
232 W. Qazi et al.

4.2 PLS-SEM analysis

In this study, the PLS-SEM is used to test the developed model shown in Figure 1
through Smart PLS 3.2.3 software (Ringle et al., 2014), by using a method of bootstrap
resampling of 5,000 subsamples (Hair et al., 2011). PLS is a structural path estimation
method (Chin, 1998) preferred and considered as a multivariate method in social sciences
for experimental and non-experimental data (McIntosh et al., 1996). PLS-SEM was
developed by Joreskog and Wold (1982) and Wold (1975, 1980). PLS-SEM examines the
associations among multiple latent variables (LV). LV is an unseen construct that is
accountable for the association among the measured variables (Waheed et al., 2015).
PLS-SEM is competent enough to work with unobservable LV and can identify the errors
in the development of LV (Chin, 1998). In this study, the items are perception-based
assess through a Likert scale, normality cannot be verified, and the model is complex as
well, therefore, PLS-SEM is preferred over other structural equation models (Hair et al.,
2012). The competency of the model is evaluated by the individual item reliability
analysis, convergent validity and the discriminant validity.
For the assessment of individual item reliability, the standardised loadings (or simple
correlation) are examined. As proposed by Tabachnick and Fidell (2007) and Raza and
Hanif (2013) 0.55 is a cut-off point. All the items in this study have loadings above 0.55
as shown in Table 2. In PLS, the convergent validity of the measured items is validated
by two tests
1 Cronbach’s alpha and composite reliability results
2 average variance extracted (AVE).
Table 2 shows that Cronbach’s α is greater than 0.70 which means that all the variables
have good reliability and meets the suggested benchmark of 0.60 by Churchill’s (1979)
and Raza and Jawaid (2015). The composite reliability is measured through the Nunnally
et al.’s (1976) benchmark of 0.7 and Table 2 shows that composite reliability is greater
than 0.7. The average extracted (AVE) is above 0.5 which is according to the Fornell and
Larcker (1981). As all the constructs exceed the benchmark, the convergent validity is
established for the constructs.
After evaluating the individual item reliability and convergent validity, the
discriminant validity is analysed by using two tests
1 cross loading analysis
2 AVE analysis.
The cross loading analysis presented in Table 4 shows that the individual items of each
construct are loaded higher in their relevant constructs compare to the other constructs
and the cross loading difference is also higher than the suggested threshold of 0.1
(Gefen and Straub, 2000). In Table 3 the diagonal of the matrix represents the square root
of the AVE and shows that the correlation matrix for each construct and the absolute
value of their correlation is below than the AVE square root of each construct (Fornell
and Larcker, 1981). Thus, explains the adequacy of discriminant validity. Furthermore,
Table 5 shows that the heterotrait-monotrait ratio of correlations (HTMT) shows that
none of the HTMT criteria are higher than the criteria of 0.85 (Henseler et al., 2014).
 Acceptance of e-book reading among higher education students 233

Table 2 Measurement model results

Composite Average variance

Constructs Items Loadings Cronbach’s α reliability extracted
Behavioural BI1 0.7790 0.7710 0.8540 0.5960
intention
BI2 0.8380
BI3 0.7220
BI4 0.7350
Attitude ATT1 0.8220 0.8120 0.8880 0.7250
ATT2 0.8600
ATT3 0.8710
Actual usage AU1 0.8380 0.8000 0.8830 0.7160
AU2 0.8740
AU3 0.8200
Awareness AWR1 0.6960 0.7380 0.8340 0.5580
AWR2 0.7680
AWR3 0.7270
AWR4 0.7920
Compatibility CPT1 0.8350 0.8280 0.8860 0.6610
CPT2 0.8560
CPT3 0.7830
CPT4 0.7810
Complexity CPX1 0.8280 0.7690 0.8660 0.6840
CPX2 0.8380
CPX3 0.8180
Observability OB1 0.7180 0.7470 0.7690 0.5320
OB2 0.8440
OB3 0.6150
Relative RA1 0.7620 0.8240 0.8710 0.5330
advantage
RA2 0.6580
RA3 0.7510
RA4 0.8020
RA5 0.7600
RA6 0.6460
Trialability TR1 0.6730 0.7000 0.8050 0.5130
TR2 0.6980
TR3 0.7430
TR4 0.7680
234 W. Qazi et al.

Table 3 Correlation matrix

Correlation matrix
Constructs
BI ATT AU AWR CPT CPX OB RA TR
BI 0.772
ATT 0.668 0.851
AU 0.622 0.486 0.846
AWR 0.527 0.600 0.459 0.747
CPT 0.510 0.575 0.344 0.395 0.813
CPX –0.502 –0.537 –0.343 –0.461 –0.649 0.827
OB 0.419 0.391 0.285 0.394 0.205 –0.320 0.729
RA 0.447 0.507 0.341 0.459 0.690 –0.647 0.238 0.730
TR –0.312 –0.422 –0.299 –0.451 –0.307 0.517 –0.459 –0.403 0.716
Notes: BI = behavioural intention; ATT = attitude; AU = actual usage;
AWR = awareness; CPT = compatibility; CPX = complexity; OB = observability;
RA = relative advantage; TR = trialability. The diagonal elements (italics)
represent the square root of AVE
The structural model is depicted in Figure 1 whereas the path of the structural model is
presented in Figure 2 and Table 6. Each path corresponds to a hypothesis. The hypothesis
is tested on the basis of sign, size, and statistical significance of the co-efficient between
each LV and dependent variable. The higher the co-efficient value, the stronger is the
impact of the LV of the dependent variable. The hypotheses are considered on the
significance level of 0.1. Result shows that 8 out of 7 paths are significant. Moreover, the
path co-efficient linking relative advantage to behavioural intention is positive but
insignificant and does not support the (H1). The path linking compatibility, trailability,
observability, attitude, awareness to behavioural intention is positive and significant,
hence supported the H2, H4, H5, H6 respectively. The path linking complexity to
behavioural intention is negative and significant (H3). The path co-efficient linking
behavioural intention to actual usage is positive and significant (H8).

Figure 2 Path analysis

Perceived characteristics
of innovation
R2= 0.5320 R2 = 0.3910
Relative advantage 0.008

0.130 Behavioural Actual use

Compatibility intention 0.625
-0.127
Complexity
0.115

Trialability
0.182 0.406 0.154

Observability

Attitude Awareness
 Acceptance of e-book reading among higher education students 235

Table 4 Loadings and cross loadings

BI ATT AU AWR CPT CPX OB RA TR

BI1 0.7790 0.6460 0.4820 0.4350 0.4810 –0.4540 0.3440 0.3710 –0.3260
BI2 0.8380 0.5650 0.4950 0.4780 0.4590 –0.3870 0.3410 0.3710 –0.2260
BI3 0.7220 0.4530 0.4150 0.4060 0.2860 –0.3920 0.2850 0.3350 –0.2680
BI4 0.7350 0.3610 0.5260 0.2930 0.3170 –0.3060 0.3140 0.2960 –0.1320
ATT1 0.4740 0.8220 0.3860 0.5090 0.4620 –0.4160 0.2490 0.4200 –0.2800
ATT2 0.5830 0.8600 0.3760 0.5170 0.5310 –0.4820 0.3330 0.4870 –0.3950
ATT3 0.6290 0.8710 0.4730 0.5110 0.4740 –0.4690 0.3980 0.3910 –0.3870
AU1 0.5360 0.4250 0.8380 0.4020 0.2870 –0.3210 0.2320 0.2970 –0.3070
AU2 0.5500 0.4230 0.8740 0.3900 0.3270 –0.3190 0.2580 0.3290 –0.2620
AU3 0.4890 0.3830 0.8200 0.3700 0.2530 –0.2220 0.2320 0.2310 –0.1820
AWR1 0.3290 0.4030 0.3320 0.6960 0.3080 –0.3170 0.3180 0.3050 –0.3160
AWR2 0.3560 0.3880 0.3600 0.7680 0.3220 –0.3040 0.2100 0.3510 –0.2250
AWR3 0.3550 0.4190 0.3130 0.7270 0.2720 –0.3860 0.2820 0.3600 –0.4630
AWR4 0.4980 0.5510 0.3650 0.7920 0.2890 –0.3680 0.3530 0.3560 –0.3460
CPT1 0.4190 0.4910 0.2440 0.3370 0.8350 –0.5410 0.1430 0.6190 –0.2400
CPT2 0.4580 0.4840 0.3190 0.3080 0.8560 –0.5240 0.1300 0.5790 –0.1810
CPT3 0.3980 0.4770 0.3080 0.3000 0.7830 –0.4880 0.2190 0.4980 –0.2400
CPT4 0.3820 0.4180 0.2460 0.3470 0.7810 –0.5660 0.1860 0.5520 –0.3540
CPX1 –0.4290 –0.4890 –0.2400 –0.4180 –0.5800 0.8280 –0.2910 –0.6240 0.4220
CPX2 –0.4180 –0.4500 –0.3390 –0.4230 –0.5290 0.8380 –0.3100 –0.5010 0.4570
CPX3 –0.3990 –0.3930 –0.2720 –0.3000 –0.5000 0.8180 –0.1900 –0.4770 0.4040
OB1 0.2880 0.2430 0.1920 0.3200 0.1550 –0.2870 0.7180 0.1910 –0.4080
OB2 0.3400 0.3860 0.2810 0.3870 0.1430 –0.2460 0.8440 0.2330 –0.4080
OB3 0.2860 0.2120 0.1410 0.1410 0.1550 –0.1690 0.6150 0.0870 –0.1790
RA1 0.3500 0.4280 0.2640 0.4250 0.5460 –0.5160 0.1560 0.7620 –0.3240
RA2 0.2540 0.2850 0.1630 0.3690 0.3420 –0.3910 0.1430 0.6580 –0.3560
RA3 0.3800 0.3710 0.3150 0.2650 0.5390 –0.5590 0.1840 0.7510 –0.2260
RA4 0.3520 0.4000 0.3070 0.3700 0.5190 –0.4770 0.2250 0.8020 –0.3260
RA5 0.3470 0.3570 0.2250 0.3160 0.5620 –0.4650 0.1940 0.7600 –0.2720
RA6 0.2500 0.3810 0.1850 0.2890 0.4990 –0.4030 0.1290 0.6460 –0.3050
TR1 –0.1990 –0.2680 –0.1270 –0.2750 –0.2230 0.4000 –0.2600 –0.3010 0.6730
TR2 –0.1850 –0.3980 –0.1800 –0.3180 –0.3050 0.3860 –0.3340 –0.3620 0.6980
TR3 –0.1830 –0.2860 –0.2070 –0.3750 –0.2580 0.3410 –0.3630 –0.3060 0.7430
TR4 –0.2980 –0.2860 –0.3060 –0.3380 –0.1490 0.3730 –0.3640 –0.2360 0.7680
Notes: BI = behavioural intention; ATT = attitude; AU = actual usage;
AWR = awareness; CPT = compatibility; CPX = complexity; OB = observability;
RA = relative advantage; TR = trialability. All self-loadings are significant
(italics).
236 W. Qazi et al.

Table 5 Heterotrait-monotrait ratio (HTMT) results

BI ATT AU AWR CPT CPX OB RA TR

BI
ATT 0.822
AU 0.792 0.599
AWR 0.675 0.759 0.594
CPT 0.625 0.698 0.419 0.51
CPX 0.648 0.675 0.434 0.605 0.812
OB 0.640 0.565 0.424 0.599 0.311 0.491
RA 0.549 0.620 0.406 0.590 0.825 0.800 0.345
TR 0.400 0.558 0.374 0.625 0.428 0.702 0.727 0.556
Notes: BI = behavioural intention; ATT = attitude; AU = actual usage;
AWR = awareness; CPT = compatibility; CPX = complexity; OB = observability;
RA = relative advantage; TR = trialability.
Table 6 Standardised regression weights for the research model

Hypothesis Regression path Effect type Co-efficient Remarks

H1 RA --> API Direct effect 0.002 Unsupported
H2 CPT --> API Direct effect 0.127* Supported
H3 CPX--> API Direct effect –0.131* Supported
H4 TR --> API Direct effect 0.122* Supported
H5 OB --> API Direct effect 0.186*** Supported
H6 ATT --> API Direct effect 0.411*** Supported
H7 AWR --> API Direct effect 0.150** Supported
H8 API --> AU Direct effect 0.622*** Supported
Notes: SRW = Standardised regression weight
***p < 0.01, **p < 0.05, *p < 0.10
From the above it is concluded that, except for the hypothesis (H1) all the other
hypotheses were accepted, i.e., H2, H3, H4, H5, H6, H7 and H8.

5 Discussion of the results

The major objectives of the study are supported by the results. The result shows the good
measurement and structural fit and seven out of eight hypotheses were supported. The
path between the relative advantage and behavioural intention is insignificant, but
positive, p < 0.1 and β = 0.008. The results are inconsistent with the past studies
(Shih, 2007; Lee, 2006; Ooi et al., 2011). Thus, it implies that relative advantage
(usefulness) is not important when it come to the adoption of the innovation.
The second hypothesis about the effect of compatibility on behavioural intention is
also supported and shows a significant and positive association (P < 0.1, β = 0.130). The
results are consistent with the studies of Wu and Wang (2005) and Chang and Tung
(2008). This indicates that the potential benefits such as searchable readings, personalise
 Acceptance of e-book reading among higher education students 237

feeling, remote access (Lam et al., 2009) increases the compatibility. High Compatibility
increases the intention of the users to use the technology (Wu and Wang, 2005).
The third hypothesis regarding the effect of complexity on behavioural intention
shows the significant, but a negative relationship (P < 0.1, β = –0.127). The studies which
supported that relationship includes Shih (2007), Lee (2006) and Lee et al. (2011). The
user friendliness should be the basic requirement of the innovation (Waheed et al., 2015)
and if the user find the technology complex it will decrease its intention to use it (Lin,
2006; Aldunate and Nussbaum, 2013).
The other innovation characteristics, i.e., trialability and observability indicates
significant and positive effect on behavioural intention (P < 0.1, β = 0.122; P < 0.1,
β = 0.186). The result is similar to the work done by Hardgrave et al. (2003) Yang (2007)
and Lee et al. (2011). Thus, it is evident that if the users are provided with the
opportunity to try the innovation this would result in creating individuals’ intention to use
it (Chung, 2014). The explanation is plausible for observability, if the users have an
opportunity to observe the use of innovation it will contribute in building its intention to
adopt it (Duan et al., 2010).
The path between the attitude and behavioural intention was significant and positive
and hence supported the hypothesis (P < 0.1, β = 0.406). The results are consistent with
the studies of Stoel and Hye Lee (2003), Cviko et al. (2012) and Aldunate and Nussbaum
(2013). This implies that if the students have a positive attitude toward the e-book it will
creates intention to use it (Letchumanan and Tarmiz, 2011).
The seventh hypothesis related to the role of awareness in creating individual
intention was confirmed. Awareness creates a significant positive effect on the individual
intention (P < 0.1, β = 0.154). Bennett and Landoni (2005) and Abdullah and Gibb
(2008) also reported that awareness of e-book plays an important role in developing
individual intention and usage, hence provide support for our result. If the individual is
well aware about the innovation it increases its adoption rate (Jung et al., 2012).
The last hypothesis is also supported and shows that behavioural intention has a
significant positive effect on the actual usage of the technology (P < 0.1, β = 0.625)
which are supported by the studies of Irani et al. (2009) and Teo (2011). This implies that
behavioural intention actually leads to the usage of the technology (Teo et al., 2008).

6 Conclusions

The Roger’s innovation theory is widely used to predict the individual intention towards
the new technology adoption, but the role of attitude and awareness in creating intention
towards innovation is discussed sparsely. This study tried to cover up this gap by
integrating DIT, attitude, awareness with behavioural intention and its ultimate effect on
actual usage. The empirical result shows the good measurement fit model, and seven out
of eight hypotheses were supported. The three innovation characteristics (compatibility,
observability, trailability), attitude, awareness affect the individual intention significantly
and positively, whereas relative advantage has a positive, but insignificant impact, while
complexity has a negative effect on individual intention. The result showed that if the
students find e-book compatible with their needs, have awareness, positive attitude, and
can try and observe it. This formed the positive intention towards the e-book which
ultimately results in actual usage.
238 W. Qazi et al.

Moreover, the reading through e-book can save both the time and effort.
Convenience, compatibility, trialability and observability create a significant impact on
the intention of the individuals and intensify their intention to adopt it. In order to
promote e-book acceptance, the users should be given the friendly interface and offer
them customised services as this will give the relative advantage to the users over
traditional reading. The e-book interface should be easy and simple as complexity is one
of the hurdles which affecting the consumer attitude to use the product.

6.1 Managerial implications

The findings of the study show many implications. The outcome will be helpful to
develop strategies to capture more consumers to adopt and use e-books. The study will be
useful for the marketers in developing marketing strategies as this study identified the
factors that they should emphasise for the purpose of promoting e-book technology.
Compatibility significantly affects the individual intention to use the e-book
technology, the marketers should enhance the users ‘perception related to compatibility
by notifying the differences between reading through paper books and e-book.
The factors of DIT play a significant role in shaping the consumer attitude to use the
technology. So, the marketers and companies who want to become successful in e-book
business should develop e-books that are affordable and user-friendly as this will increase
the user’s intention to adopt e-book. The findings further suggest that the experts should
develop features that increase the relative advantage and compatibility by providing user-
friendly interface, make resources available as this will encourage students to use
frequently.
The educators should use e-book in their teaching and learning process and the
educational institutions in order to increase the e-book adoption among students should
collaborate with the marketers or other interested parties to give them, enabling
infrastructure and technical support department who ensures accessibility and give
trailability option to the users as this will increase the e-book usage.
The results of the studies are also beneficial for the researchers and managers who are
concerned with the innovation adoption. As seen from the results compatibility,
trialability, observability and complexity significantly affect the behavioural intention.
These results help the managers in the development of new products or technology. To
make the new innovation successful those features should incorporate in new products.

6.2 Limitations of the study

The limitations are also there in the study which needs additional research and further
investigation. First, the study is conducted on the students that use e-book reading so it
cannot be generalised and the intentions of non-users are ignored. Second, the target
population was the higher education students of different domains; therefore, the sample
may have affected the results and showed biases. Third, the actual usage is analysed, but
the usage frequency, digital and social convergence also matters which is ignored in this
study. Fourth, this study has not considered the impact of demographic characteristics
which is considered significant predictor that can affect the technology acceptance among
users in many studies (Morris et al., 2005; Annafari and Bohlin, 2014; Kang et al., 2014).
Lastly, the results of the studies are applicable to the studies conducted in the context of
Pakistan because of the cultural differences.
 Acceptance of e-book reading among higher education students 239

6.3 Future research

Future research should investigate the factors that create resistance to the users to opt
e-book. Furthermore, the same study can be conducted by adding the demographic
factors. The cross-sectional studies with more diverse sample are also suggested to the
researchers. As this study targets the university students of different fields, the future
research can also be done by targeting university students of one specific domain.
Moreover, the impact of factors like subjective norms, facilitating conditions, and
self-efficacy should also be evaluated in upcoming studies.

6.4 Contribution to the study

In this study, we modified the Rogers DIT theory by adding two more variables attitude
and awareness and the mediating role of intention in the model to examine the adoption
of e-book. The combined effect of all these variables is investigated for the first time in
the context of Pakistan. This type of model has not been used before. Moreover, the
PLS-SEM technique is used to analyse the relationship.

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