Behavioural E-Learning Adoption among Higher Education Institution

Students: A Possibility for Mentawaian Students Living in

Contemporary Culture

Elﬁondri

, Yetty Morelent

, Desi Ilona

, and Zaitul

Universitas Bung Hatta, Padang, Indonesia

UPI YPTK Padang, Padang, Indonesia

Keywords:

Personalization, Responsiveness, Controllability, Two-Way Communication, Perceived Ease Of Use,

Perceived Usefulness And Intention To Participate In E-Learning.

Abstract:

This study aims to investigate the inﬂuence of personalization, responsiveness, controllability, and two-way

communication on perceived ease of use and perceived usefulness. besides, this study also determines the

inﬂuence of perceived ease of use and perceived usefulness on students’ intention to participate in e-learning.

Besides that the study also glances at a possibility of development of e-learning for Mentawaian students living

in contemporary culture. Forty-four students were participating in this study and SEM-PLS is used to analyse

the primary data collected through on-line survey. Ten hypotheses were developed. The study results in

ﬁve hypotheses are supported and the rest rejected. The result show that personalization signiﬁcantly effects

on perceived ease of use and perceived usefulness. In addition, the controllability is positively related to

perceived usefulness. Further, two-way communication is also positively associated with perceived ease of

use. Finally, the perceived ease of use positively determines the students’ intention to participate in e-learning.

This study provide contribution to Technology acceptance model by extending this theory. Practically, this

study highlights some ﬁndings which are contribute to the university management and they discuss in detail.

1 RESEARCH BACKGROUND

World is currently marked by new economy. (Cidral

et al. 2018) argue that the new economy

is characterised by the revolution of information

technology (IT), reinventing of the classroom and etc.

In fact, the great availability of devices to access

the internet (e.g. computers, tablets, laptop and

smartphones) and the population and access to the

World Wide Web (www) learning utilizing e-learning

practices has broadened quickly (Cidral et al. 2018).

In addition, (Al-gahtani 2014) add that technology

(the internet and network) transformed our world into

ubiquitous connectivity. Thus, development on IT has

brought about e-applications. (Alsabawy et al., 2016).

System of E-learning is a system which enables the

21st-century education and it has a big effect on

the educational environment (Aparicio et al., 2016).

There is increasing dependents on e-learning usage to

accommodate the knowledge transfer and generation

in workplace (Fleming, Becker, and Newton 2017).

An e-learning can be deﬁned as an information

system in which various teaching-materials like text,

video and audio media can be integrated are delivered

in online learning (discussion, email and assignment

(Lee et al., 2011). In addition, (Y. Cheng 2011)

also deﬁnes that e-learning is a device employing the

computer and instrument network such as extranets,

internets and internet to convey the learning materials

to learners. Such learning may return to the beginning

of 1980’s in which subjects on television were

offered. The online learnings so-called virtual or

distance learning were developed because technology

and information develop fast. (Fleming, Becker, and

Newton 2017). E-learning systems give chance to

students to study regardless of place and time, and

support them with new teaching methods (Alhabeeb

and Rowley, 2018). Through E-learning, students

also gain new techniques of learning, and lecturers

can convey a learning guidance using audio, video,

animation, text, and pictures, and can give online

feedback and spaces on learning. (Abdullah and

Ward, 2016). (Clay et al., 2008) argue that in

order to be successful applying the devices and

procesures, it depends on intention of students to

Elﬁondri, ., Morelent, Y., Ilona, D. and Zaitul, .

Behavioural E-Learning Adoption among Higher Education Institution Students: A Possibility for Mentawaian Students Living in Contemporary Culture.

DOI: 10.5220/0009100001570164

In Proceedings of the Second International Conference on Social, Economy, Education and Humanity (ICoSEEH 2019) - Sustainable Development in Developing Country for Facing Industrial

Revolution 4.0, pages 157-164

ISBN: 978-989-758-464-0

157

receive or refuse the tool and systems. There have

been many universities in the world implementing

e-learning (Garrison, 2011), one of which is Bung

Hatta University, Indonesia (Khairuddin et al. 2018).

Unfortunately, even though e-learning gives beneﬁts,

the students and lecturers still use it in low level

(Bhuasiri et al., 2012) included its use at Bung Hatta

University (Khairuddin et al., 2018).

There are bundle of studies investigating the

behavioural intention of using the e-learning (Cheng,

2014; Zhang et al., 2012; Roca et al., 2006;

Rui-Hsin and Lin, 2018; Cheung and Vogel, 2013;

Timothy, 2011; Khairuddin et al., 2018; S

anchez

et al., 2013; Fleming et al., 2017; Abdullah and

Ward, 2016; Al-Gahtani, 2016; Alhabeeb and

Rowley, 2018; Alsabawy et al., 2016; Aparicio

et al., 2016; Ching-Ter et al., 2017; Cidral et al.,

2018; Hubalovsky et al., 2019; Khasawneh, 2015;

Kimiloglu et al., 2017; Cheng, 2011; Bhuasiri

et al., 2012). Based on previous studies, there

is very limited studies investigating in Indonesia’s

environment. In addition, most of studies used the

teachers, instructors and lecturers as research object

and there is lack of studies using the students as

object of research (Abdullah et al., 2016; Cheng,

2014; Rui-Hsin and Lin, 2018; Cheung and Vogel,

2013; S

anchez et al., 2013; Ali et al., 2018).

Besides, studies which employ the external factors

to technology acceptance model (TAM) variables are

also lack. In addition, studies using an Indonesia’s

environment have seldom used the students as

research object. Therefore, there is gap in literature.

It needs further investigation in this ﬁeld.

E-learning system participation is considered as

adoption of technology. Several theories discuss

adoption of the technology in organization and

individual level, such as innovation diffusion theory

(Rogers, 1962), and reason action theory (Fishbein

and Ajzen, 1977), theory of plan behaviour (Ajzen,

1991), task-technology ﬁt (Goodhue and Thompson,

1995). This research applies the model of technology

acceptance (Venkatesh and Davis, 2000) and plan

behaviour theory (Ajzen 1991). Theory of plan

behaviour (Ajzen 1991) predicts that intention

determines behaviour to behave. Additionally,

determinants of intention to behave involves attitude

on technology, subjective norm and control of

perceived behaviour (Ajzen 1991). Based on model of

technology acceptance (Venkatesh and Davis 2000),

perceived ease of use and perceived usefulness

determine acceptance of technology. (Abdullah

and Ward 2016) and (Ching-ter, Su, and Hajiyev

2017) conclude that there are external variables

inﬂuencing the variables of TAM. Experience,

subjective norm, enjoyment, computer anxiety and

self-efﬁcacy are used as antecedents of perceived

ease of use (PEU) and perceived usefulness (PU)

variable. (Y. Cheng 2011) classiﬁed the antecedents

of PEU and PU into social, factors, and individual

factors. (Al-Gahtani, 2016) involve the variables

of subjective norm, image, job relevance, output

quality, result demonstrability as determinants of

PU. PEU determinants are computer self-efﬁcacy,

perception of external control, computer anxiety, and

computer playfulness (Al-gahtani 2014). Only (Y.

M. Cheng 2014) proposed a different external factors

to PEU and PU: personality, controllability, two-way

communication and responsiveness as antecedents

of PEU and PU. This model is not yet applied in

Indonesia’s education environment and therefore, this

study’s objectives are to investigate the effect of these

four external factors (personality, controllability,

two-way communication and responsiveness) on PEU

and PU. In addition, this research also determines the

inﬂuence of PEU and PU on students’ intention to

participate in the system of e-learning. This paper

is organised as follow (i) background, (ii) method

and material, (iii) result and discussion, and (iv)

conclusion and recommendation.

In the learning context, personalization deals with

transmitting the learning contents that suit particular

individual through the electronic learning system (Y.

M. Cheng 2014). Therefore, personalised e-learning

system is perceived as more useful (Baylari and

Montazer 2009) and provides student with robust

guidance mechanism, such as adaptive navigation

support, curriculum ordering, tailored presentation

and etc. (Papanikolaou et al. 2002). In addition,

Controllability, in the context of learning, refers to

students’ capability to manage time, content and

communication stream by means of e-learning system

(Y. M. Cheng 2014). If the students think that they

can manage the e-learning system, they believe that

the e-learning is more useful and easy to use (Pituch

and Lee 2006). Further, responsiveness refers to the

extent to which students discern that the reaction from

the learning system is consistent, fast and reasonable

(Pituch and Lee 2006). If the students notice that

the system is so, they are aware of system response

useful and easy to use (Pituch and Lee 2006).

Moreover, the ability of reciprocal communication

between lecturer and students are suggested deﬁnition

of two-way communication in the learning context

(Pituch and Lee 2006) and they add that if this type

of communication occurs, student will feel that the

e-learning is useful and easily used. If it is usefully

perceived and easily used, the students will create an

intention to participate in the system of e-learning

ICoSEEH 2019 - The Second International Conference on Social, Economy, Education, and Humanity

158

(Abdullah and Ward 2016; Cheng 2011; Ching-ter,

Su, and Hajiyev 2017; Al-gahtani 2014). Based

on explanation above, we proposed the research

framework as follow.

Figure 1: Research Framework

2 MATERIAL AND PROPOSED

METHODS

This research uses the students in the semester six

and above who are registered in faculty of business

and economics and education as research object.

Data in this research is primary data that gathered

through online survey. The research uses intention

to participate in e-learning as dependent variable

and personalization, controllability, responsiveness,

two-way communication, perceived ease of use, and

perceived usefulness as independent latent variables.

Intention to participate in e-learning has three items

which developed by (Bhattacherjee, 2001; Roca

et al., 2006). PEU and PU consist of three

items each (Davis, 1989; Ngai et al., 2007). In

addition, personalization has four items which was

developed by (Papanikolaou et al., 2002; Wu and

Guohua, 2006). Further, controllability has three

items (Liu, 2003; Wu and Guohua, 2006). Thus,

responsiveness also has three items (Liu, 2003;

Pituch and kuei Lee, 2006; Song and Zinkhan,

2008). Finally, two-way communication employ

four items (Liu 2003; Song and Zinkhan 2008).

Five-scale Likert is used to measure all constructs.

Structural Equation Modeling is used to run data.

Smart-pls is employed due to beneﬁt of smart-pls

(Chin et al., 1998). It has two assessments:

measurement model and structural model assessments

(Hair Jr et al., 2017). Measurement model

assessment has two criteria’s: convergent validity

and discriminant validity. Convergent validity is

assessed using four properties (Hair et al., 19).

In addition, Fornell-Lacker criterion (Fornell and

Larcker, 1981)(Fornell and Larcker 1981) and

cross-loading (Hair et al. 2017) are applied as base

for discriminant validity evaluation. Further, value of

Q square and R square is used to assess the structural

model. Acceptance or rejection of The hypotheses is

based on the value of path coefﬁcient and p-value.

At last, the hypotheses are connected qualitatively

with learning-culture of Mentawaian students to see a

possibility for developing the e-learning in Mentawai.

3 RESULT AND DISCUSSION

This session would be discussed about result and

discussion: demographic data, measurement model

assessement, and structral model assessment. The

result begins with demographic data. Table 1

provide us with data of gender, age, semester, CGPA

and department of respondents. Based on gender,

respondents are dominated by female respondents

(86.36%). With regard to age, most of respondents

is age of 21 to 22 years old (65.91%). Further,

respondents are dominated by students in fourth and

sixth semester. Based on CGPA, most of students

is with CGPA of 3.00-3.50 (45.45%). Finally,

respondents mostly are from accounting department,

faculty of economic and business.

Table 1: Demographic Variables

Dem var Category Count %

Gender

Female 38 86.36

Male 6 13.64

not answer 0 0

Age

19 to 20 years old 9 20.45

21 to 22 years old 29 65.91

23 to 24 years old 3 6.82

¿ 24 years old 2 4.55

not answer 1 2.27

Semester

4th to 6th 40 90.91

7th to 9th 3 6.82

¿ 9th 1 2.27

not answer 0 0

CGPA

2.50 to 3.00 6 13.64

3.01 to 3.50 20 45.45

3.51 to 4.00 15 34.09

not answer 3 6.82

Department

Accounting 38 86.36

Elementary school teacher training 1 2.27

Management 2 4.55

English training 2 4.55

not answer 1 2.27

Table 2 demonstrates the assessment result gained

from measurement model (convergent validity).

Based on outer loading, all items from all constructs

have outer loading value which is bigger than

0.700. and it can be concluded that these value

support the convergent validity (Hulland, 1999).

Internal consistency of indicator is second criteria

of convergent validity (CV). The consistency is

determined by using Cronbach’s Alpha (CA) and

composite reliability (CR). Thus, the result show

that value of CA and CR for all constructs is

above 0.700 and it support the convergent validity

(Bagozzi and Yi, 1988). Dealing with the average

variance extracted (AVE), the result indicates that

all constructs have the value of AVE exceed the

Behavioural E-Learning Adoption among Higher Education Institution Students: A Possibility for Mentawaian Students Living in

Contemporary Culture

159

cut-off value, 0.500 (Bagozzi and Yi 1988). Based on

four SEM properties above, it can be concluded that

convergent validity requirement has been reached.

Table 2: Convergent Validity

Construct items outer loading CA CR AVE

Intention to itp1 0.935

0.899 0.936 0.831participate in itp2 0.923

e-learning itp3 0.875

Controllability

con1 0.835

0.772 0.866 0.683con2 0.779

con3 0.863

Personalization

per1 0.818

0.852 0.9 0.693

per2 0.82

per3 0.887

per4 0.803

perceived ease of use

peu1 0.86

0.875 0.923 0.801peu2 0.926

peu3 0.898

perceived usefulness

pu1 0.898

0.873 0.922 0.798pu2 0.893

pu3 0.888

Responsiveeness

res1 0.862

0.882 0.927 0.81res2 0.896

res3 0.941

twc1 0.827

0.861 0.905 0.704

two way twc2 0.909

communication twc3 0.777

twc4 0.838

The result of second assessment (discriminant

validity) of measurement model is presented in

Table 3. In this case, we use the Fornell-Lacker

criterion. Fornell-Lacker criterion must be reached

by comparing the square root of a construct (Fornell

and Larcker 1981). For example, the square root

of controllability is 0.826 (bold) and this value is

greater than correlation between controllability with

other construct, such as intention to participate in

e-learning (0.800), perceived ease of use (0.671) and

so on. Thus, it can be concluded that the discriminant

validity requirement is reached.

Table 3: Fornell-Lacker Criterion

Const 1 2 3 4 5 6 7

CON(1) 0.83

ITP (2) 0.8 0.91

PEU (3) 0.67 0.78 0.9

PU (4) 0.79 0.73 0.84 0.89

PER(5) 0.79 0.78 0.85 0.92 0.83

RES (6) 0.82 0.74 0.68 0.82 0.86 0.9

TWC(7) 0.79 0.82 0.81 0.76 0.84 0.78 0.84

Second criteria for discriminant validity is

cross-loading. Table 4 provide us with result

of discriminant validity assessment (cross-loading).

Cross-loading is assessed by determining the loading

of an indicator (item) to its assigned construct is

higher compared to its loading to other construct (

Hair et al., 2017). For example, loading of item of

itp1, itp2, and itp 3 (ITP is its assigned construct)

are higher to ITP (bold, 0.935, 0.923, and 0.875)

compared to other constructs, such as PEU (0.627,

0.637, and 0.839). These values show that the

discriminant validity is reached.

Second smart-pls assessment is structural model

evaluation. It is to test hypothesis and about the

Table 4: Cross Loading

Items CON ITP PEU PU PER RES TWC

itp1 0.779 0.935 0.627 0.622 0.683 0.695 0.687

itp2 0.74 0.923 0.637 0.643 0.706 0.698 0.741

itp3 0.674 0.875 0.839 0.713 0.738 0.64 0.791

con1 0.835 0.722 0.606 0.68 0.628 0.563 0.658

con2 0.779 0.618 0.405 0.464 0.513 0.685 0.559

con3 0.863 0.643 0.611 0.75 0.773 0.784 0.716

per1 0.668 0.687 0.609 0.738 0.818 0.816 0.694

per2 0.678 0.721 0.795 0.76 0.82 0.679 0.767

per3 0.717 0.644 0.773 0.845 0.887 0.692 0.698

per4 0.553 0.555 0.648 0.699 0.803 0.691 0.625

peu1 0.524 0.695 0.86 0.613 0.695 0.516 0.788

peu2 0.596 0.666 0.926 0.826 0.813 0.675 0.651

peu3 0.678 0.739 0.898 0.82 0.778 0.625 0.73

pu1 0.763 0.705 0.664 0.898 0.864 0.866 0.673

pu2 0.69 0.597 0.7 0.893 0.73 0.652 0.591

pu3 0.647 0.647 0.89 0.888 0.851 0.665 0.775

res1 0.675 0.612 0.575 0.647 0.731 0.862 0.604

res2 0.719 0.635 0.58 0.805 0.808 0.896 0.725

res3 0.804 0.753 0.666 0.756 0.785 0.941 0.767

twc1 0.579 0.587 0.79 0.655 0.718 0.572 0.827

twc2 0.65 0.724 0.823 0.66 0.759 0.604 0.909

twc3 0.663 0.675 0.485 0.558 0.636 0.691 0.777

twc4 0.786 0.786 0.561 0.683 0.682 0.795 0.838

relationship among latent variables as indicated by

Table 5. Before presenting the hypotheses result,

the Q square and R square should be interpreted

ﬁrstly. Q square shows the predictive relevance of

the model. In this study, the value of Q square

is ranging from 0.460 to 0.590 and they fall into

large predictive relevance categories (Henseler et al.,

2009). It means that the structural model is very

much relevance. In addition, R square is predictive

power of structural model. Maximising of R square

of endogenous variables is objective of the SEM-PLS

(Chin 1998). R square of intention to join e-learning

and perceived ease of use endogenous constructs is

0.633 and 0.663 respectively, and they are classiﬁed

into moderate predictive power of structural model

(Hair et al., 2014). R square of other endogenous

construct (perceived usefulness) is 0.854 and it fall

into substantial predictive power (J. Hair et al. 2014).

Table 5 presents hypotheses testing result..

P-value and path coefﬁcient is used to see whether

the hypothesis is supported or not supported. The

signiﬁcant level (1%, 5%, or 10%) or two tail

of t-statistic (2.58, 1.96 or 1.645 respectively)

(J. Hair et al. 2014). Relationship; positive

or negative relationship is known through path

coefﬁcient. The result indicates that ﬁve hypotheses

are supported. First, the personalization has

signiﬁcantly effect on PEU (β=0.811, p-value=0.000)

as well and it can be concluded that H1 is supported.

Second, the association between personalization

and perceived usefulness is positively signiﬁcant

(β=0.813, p-value=0.000) and H2, therefore, is

supported. Third, the effect of controllability on

PU (β=0.180, p-value=0.036) is positively signiﬁcant

(H4 supported). Fourth, two-way communication

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Table 5: Assessment of Structural Model

Endogenous construct Q2 Dec. R2 Dec.

intention to participate in

e-learning

0.46 large 0.633 moderate

perceived ease of use 0.468 large 0.663 moderate

perceived usefulness 0.59 large 0.854 substantial

relationship path coef t stat p value concl

controllability − > perceived

ease of use

-0.031 0.184 0.854 not supported

controllability − > perceived

usefulness

0.18 2.107 0.036** supported

perceived ease of use − >

intention to participate in

e-learning

0.577 2.332 0.020** supported

perceived usefulness − >

intention to participate in

e-learning

0.245 0.836 0.404 not supported

personalization − > perceived

ease of use

0.811 4.422 0.000*** supported

personalization − > perceived

usefulness

0.813 3.58 0.000*** supported

responsiveness − > perceived

ease of use

-0.295 1.322 0.187 not supported

responsiveness − > perceived

usefulness

0.056 0.264 0.792 not supported

two-way communication − >

perceived ease of use

0.385 2.683 0.008*** supported

two-way communication − >

perceived usefulness

-0.103 0.665 0.507 not supported

Note: ***, ** and * (signiﬁcant level at 1%, 5% and 10% respectively)

and perceived ease of use is positively signiﬁcant

(β=0.385, p-value=0.008) and H7 is supported.

Finally, PEU and intention to participate in e-learning

have positive related (β=0.577, p-value=0.020) and

the ninth hypothesis is supported. Structural model

is indicated in the following Figure 2.

Figure 2: Structural Model

The positively signiﬁcant effect of personalization

on PEU and PU is consistent with ﬁnding of (Y.

M. Cheng 2014). PEU and PE are achieved if the

content of learning-system content being conveyed to

the student is customised with the students’ tastes.

Such system would provide students with strong

guidance mechanisms (Papanikolaou et al., 2002),

such as adaptive navigations support, good material

presentation, and curriculum sequencing. Therefore,

the students feel the system useful and easy to use

(Baylari and Montazer, 2009). (Y. M. Cheng 2014)

also supports that controllability has positive effect

on PU. The e-learning system has been perceived by

student as useful due to the system is manageable,

suitable and interesting content, and facilitating the

stream of communication (Pituch and kuei Lee, 2006)

because the student is able to do so. Therefore,

the e-learning system of Bung Hatta University is

perceived as useful (Pituch and Lee 2006; Y. M.

Cheng 2014). Further, the signiﬁcant association

between two-way communication and perceived ease

of use is also in line with (Y. M. Cheng 2014). Bung

Behavioural E-Learning Adoption among Higher Education Institution Students: A Possibility for Mentawaian Students Living in

Contemporary Culture

161

Hatta university’s e-learning has facilitated reciprocal

communication between lecturers and students and

this type of communication has been happening in the

system. Some students are derived from Mentawai.

Observations qualitatively show that there is no any

complaint with the system. They follow and enjoy

the system which makes them easier to understand

lectures. Even though they are stereotyped as students

from underdeveloped region, they can follow the

higher education system. They have got used to

“playing” with internet in their home region before

they are sent to higher education. Therefore, the

students feel that it is easy to use the e-learning

system easy to use (Pituch and Lee 2006). Finally, the

signiﬁcant effect of perceived ease of use on intention

to participate in e-learning is supported by (Abdullah

and Ward 2016; Al-gahtani 2014; Y. M. Cheng 2014;

Ching-ter, Su, and Hajiyev 2017). Students’ intention

to participate the e-learning class will increase if the

system is easy to use.

4 CONCLUSION AND

RECOMMENDATION

The industry revolution has changed the way

things done, including teaching (e-learning). The

important of learning has been raised by many

experts. The intention to participate in e-learning

very much depends on lecturers, students and

support by prior-to-higher education environment of

internet-development. There are few perspectives

why an intention to use or participate in e-learning

are varying from one lecture or student to another,

such as technology acceptance model. In addition,

technology acceptance model variables is inﬂuenced

by external variables, such as personalization.

However, there is lack of studies in Indonesia’s

learning environment investigating the effect of

external factors on TAM’s variables (perceived ease

of use and perceived usefulness) and their impact on

student’s intention to participate in e-learning. we

ﬁnd that personalization has a positive relationship

with perceived ease of use and perceived usefulness.

In addition, we also documented the signiﬁcant effect

of controllability and perceived usefulness. Further,

two-way communication also has a signiﬁcant

association with perceived ease of use. Finally, the

perceived of ease of use positively determine an

intention of student to participate in e-learning. This

result has practical and theoretical implications.

Theoretically, this study presents the

overwhelming subscription toward the existing

state of the art in e-learning literature, especially

students’ intention to participate in e-learning. This

paper provides with high point of the existing effect

of personalization, controllability and two-way

communication on TAM’s variables. in addition,

this paper also spotlights the relationship between

perceived ease of use and students’ intention to

participate in e-learning. In addition, the implication

of the positively signiﬁcant relationship between

controllability and perceived usefulness is that to

increase the perceived usefulness of e-learning

among students, the university management can

improve the manageability, suitability, interested

e-learning system, and the system provide the

stream communication. Third, the positive effect of

two-way communication and perceived ease of use of

e-learning system implies that to improve perceived

ease of use of the e-learning system by designing

the system with reciprocal communication. Finally,

University management can escalate the students’

intention to participate in e-learning by improve the

perceives ease of use: taking care of the personalized

and two-way communication equipped e-learning

system.

A number of caveats need to be noted regarding

the present study. First, this study uses a limited

sample size and it might not be gaining the rigorous

result. Second, this study sees the students’ intention

to participate in e-learning from external factors of

TAM’s variables. Finally, this study did not test the

mediating role of TAM’s variables between external

factors and the students’ intention to participate in

e-learning. Therefore, it is recommended that further

research be undertaken in the following areas. First,

future study can increase the number of students

involving Mentawaian students in this study. Second,

the students’ intention to participate in e-learning also

can be investigated using other perspective or theory,

such as social cognitive theory. Finally, other future

study also can investigate the role of perceived ease

of use and perceived usefulness as mediator between

external factors and TAM’s variables.

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