Exploring the Intention to Use Mobile Learning in Higher Education

Risma Nur Anissa, Tusyanah, Rediana Setiyani, Fentya Dyah Rahmawati, and Ashomatul Fadlilah

The Faculty of Economics, Universitas Negeri Semarang, Sekaran Campus, Semarang, Indonesia

Keywords: Students’ Perception, Personal Innovativeness, Mobile Learning.

Abstract: The transition to mobile devices and ubiquitous computing technology in education provides an

unprecedented opportunity to help instructors influence ively deliver learning materials to learners anytime

and anywhere (Cheng, 2013). The objective of the study is to determine the influence of students 'perception

and personal innovativeness on students' intention to use mobile learning. It is a quantitative research. The

variables of the study are students' perception, personal innovativeness, and intention to use mobile

learning. The population of this research are Accounting students in the sixth semester at Unversitas Negeri

Semarang. The samples of the research are 89 respondents taken by using simple random sampling

technique and data are collected by questionnaires. Then, data are analyzed by descriptive analysis and

multiple regression analysis with SPSS 21 application. The result of multiple regression analysis showed

that simultaneously, social proof (X1) and purchase intention (X2) have significant influence on students'

purchasing decision (Y) for 58.1% . Meanwhile, partially, the influence of students’ perception (X1) and

personal innovativeness (X2) on intention to use mobile learning (Y) are 22% and 25% respectively. Thus,

the model of the study is Y = 2.254 + 0.299X1 + 0.626X2 + e. The personal innovativeness variable shows

a higher influence than the students’ perception. It is understandable that personal innovativeness is more

likely to trigger concrete behavior to try new things about technology, as Lopez-Nicolas et al. (2008) argues

that personal innovativeness in the use of information technology reflects a desire to try new technologies.

1 INTRODUCTION

The rapid development of the digital age today

modifies various human activities including teaching

and learning techniques in education institutions.

Almost everyone today already has a mobile device

in his or her hand. In Indonesia, as per January 2018,

the population of mobile device users reaches 177.9

million users, with penetration rate reaching 67%

(bisnis.co, 2018). It is also used by educators and

students in supporting the learning process. The

transition to mobile devices and ubiquitous

computing technology in education provides an

unprecedented opportunity to help instructors deliver

learning materials to learners anytime and anywhere

(Cheng, 2013).

Then, m-learning is defined as a form of

electronic learning (e-learning) which specifically

uses mobile devices to deliver the learning content

and support (Brown, 2005; Muyinda, 2007; Cheng,

2013). In addition, mobile learning can be seen as a

mobile or wireless device application for learning on

the go (Park, 2011; Chaka and Irene, 2017). Digital

mobile devices such as cell phones, PDAs, and

smartphones are often used for the educational

purposes. The use of mobile digital technology is the

core of a dynamic and growing research flow known

as mobile and ubiquitous learning. The two concepts

are highly interconnected (Pimmer, Magdalena, and

Urs, 2016).

Mobile technology and applications have

grown rapidly and widely developed for m-learning.

Today, there are few studies to ascertain whether m-

learning has the potential to attract more learners or

not. Therefore, a deeper understanding on the factors

which influence the learner’s intention to use m-

learning in a mobile-based interactive learning

environment is essential to be done.

Furthermore; Technology Acceptance Model

(TAM) is one of the most widely applied models in

various domains related to IT acceptance study

(Lindsay et al., 2011; Wu, 2011). ; Maditinos et al.,

2013; Cheng, 2013), and it can be used as a basis for

the research model of this study. To enhance the

power of TAM explanation, it must first include the

perspective of intrinsic motivation to expand its

function (Davis et al., 1992; Lee et al., 1999; Cheng,

2013), and it can then be integrated with innovation

Nur Anissa, R., Tusyanah, ., Setiyani, R., Dyah Rahmawati, F. and Fadlilah, A.

Exploring the Intention to Use Mobile Learning in Higher Education.

DOI: 10.5220/0009015600002297

In Proceedings of the Borneo International Conference on Education and Social Sciences (BICESS 2018), pages 11-15

ISBN: 978-989-758-470-1

diffusion theory to overcome compatibility (Chen et

al., 2002; Wu and Wang, 2005; Tan and Chou;

2008; Tung and Chang; 2008; Ryu et al., 2009;

Cheng , 2013). Thus, a hybrid model is developed to

explore the learners’ intentions for using m-learning.

Based on the above explanation, the main purpose of

this study is to examine students’ perceptions and

personal innovativeness on students’ intention to

use mobile learning.

Most studies on mobile learning show the

positive influence. Students begin to accept mobile

technology as a new learning tool, the consequences

of this acceptance affecting their learning

achievements both directly and indirectly (Shin and

Minseok, 2015). It is supported by the research

results of Nassuora (2013) which shows that the rate

of students’ enrolment towards mobile learning in

Saudi Arabia is quite high. However, empirical

evidence supporting the widespread application of

learning with mobile learning in higher education

settings is limited. (Anissa et al., (2017) showed

there is a significant difference of students’ skills in

producing text between classical class and blended

learning (combination of face-to-face and online

delivery methods). Hwang and Tsai (2011) report

that high-education students are the most frequently

researched targets for mobile learning, particularly

in meta-analysis. Most of the studies included

reported positive learning outcomes. Cheng (2013)

reports that Perceived usefulness (PU), perceived

ease of use (PEOU), perceived pleasure (PE), and

compatibility can play an important role in

influencing the learner's intent to use m-learning.

The student's perception seems to be quite

instrumental in choosing the use of mobile learning.

It is supported by research Wong et.all., (2015)

which reveals that students prefer to use mobile

devices than desktops to access the internet. So the

hypothesis is:

H1 Student's perception has a significant

influence on students’ intention to use mobile

learning.

Besides perception, personal innovativeness

also takes part in the use of mobile learning.

Personal innovativeness is reported to moderate the

influence s of PU, PEOU, and compatibility on the

intent to use m-learning (Cheng, 2013). Individuals

with higher levels of personal innovation tend to be

more confident in new technologies (Lewis,

Agarwal, & Sambamurthy, 2003). Personal

innovativeness stifles the impact of one’s decision

on the perception of mobile adoption. Thus;

individuals with innovations are more anticipated to

generate more positive thinking for new IT (Lopez-

Nicolas et al., 2008). A different story is reported by

Tan, et.al, (2014). They find that personal

innovativeness does not affect behavioural intention

to use mobile learning. Related to this, the

hypothesis is:

H2 Personal innovativeness has a significant

influence on intention to use mobile learning.

Then, the antecedent of attitude construction is

the belief of attitude. Confidence in attitudes comes

from TAM. TAM argues that there is a causal

relationship between perceived ease of use, benefit

perception, the desire to use the new system, and the

intention of using the system (Davis, 1989; Teo,

2009; Cheon et.al., 2012). The study included two

such perceptions (i.e. ease of use and usefulness) as

a perception of mobile learning usage. Personal

innovativeness in the domain of information

technology as an individual tendency that, in

general, is associated with positive beliefs about the

use of technology (Lewis, Agarwal, &

Sambamurthy, 2003). Rogers's theory of the

diffusion of innovation holds that individual beliefs

are increasing about new technologies by

synthesizing information from multiple channels,

including mass media and interpersonal channels.

Individuals with higher personal innovation are

expected to develop a more positive belief of target

technology (Rogers, 1995). The study also wants to

examine the simultaneous influence of students’

perception and personal innovativeness on intention

to use mobile learning.

H3 Students' perception and personal

innovativeness simultaneously influence the

intention to use mobile learning.

Based on above description, the study proposed

the theoretical framework as presented in Figure 1.

Figure 1. Theoretical Framework

2 METHODS

This research is conducted at the Faculty of

Economics, Universitas Negeri Semarang. In the

learning, the combination of learning in the

classroom and the use of mobile learning began to

BICESS 2018 - Borneo International Conference On Education And Social

be combined in several courses. The population of

the study are Accounting students; they are 118

students. It used the simple random sampling which

took 89 students as the samples.

It used the primary data using questionnaires. In

this questionnaire, the students’ perception, personal

innovativeness and intention to use mobile learning

variables are measured by the likert items (1=

"strongly disagree" - 4= "strongly agree"). The items

chosen to measure variables adapted from previous

researches. Data are then analyzed by descriptive

statistics and multiple linear regression tests. In the

statistical analysis is supported by the Statistical

Package for Social Sciences (SPSS) version 21.0.

before being analyzed, the data confirmed its

validity and reliability. Tables 1 and 2 show the

validity. Furthermore, the data are tested to ensure

there is no classical assumptions that accompany it.

After that, data are analyzed by using multiple linear

regression which will be described below.

3 RESULT AND DISCUSSION

3.1 Reliability Testing

Reliability relates to the consistency of a

measure (Heale & Twycross, 2015). A study is said

to be reliable if it has Cronbach’s alpha> 0.70

(Nunnally, 1994).

Table 1. Realibility Analysis

Variable No. of

Items

No. of

the

Delete

Items

Cronba

ch's

Students’

Perception

9 0 0.872

Personal

Innovativeness

4 0 0.854

Intention to Use

Mobile Learining

6 0 0.849

Based on the table above, it shows that the

students’ perception (X1) has a Cronbach's by 0.872.

Personal innovativensess has Cronbach's 0.854. And

intention to use mobile learning has Cronbach's for

0.849. It means that all the instruments of the study

are reliable.

3.2 Construct Validity

Validity is defined as the extent to which a

concept is accurately measured in a quantitative

study (Heale & Twycross, 2015). Validity is tested

by comparing item scores to total score with

correlation analysis. The research instrument is said

to be valid if it has significance <0.05. Table 3.2.

shows the results of validity testing.

Table 2. Correlation

Items Pearson

Correlati

Sig. (2-

tailed)

Students’ Perception

Using mobile learning

improves the learning process

.747 .000

Using mobile learning make

my study better

.692 .000

Mobile learning is useful for

the learning process

.740 .000

Using mobile learning is

effortless

.585 .000

Using mobile learning is easy

to be understood

.716 .000

Mobile learning is easy to use .754 .000

Using mobile learning is

interesting

.686 .000

The process to use mobile

learning make me study

happily

.637 .000

I often use mobile learning .760 .000

Personal Innovativeness

When I know something new

regarding the technology, I

will learn how to use it

.816 .000

I am interested to the new

technology

.851 .000

I am not worried to try the

new technology

.846 .000

I like making experiments

with technology

.821 .000

Intention to Use Mobile

Learning

I will use mobile devices to

support my study

.809 .000

I will use mobile learning for

my study in the future

.832 .000

I will use mobile learning in

every occasion that I have

.663 .000

I will install aplication which

support my study

.754 .000

I am ready to receive the

learning material from my

lecturer through mobile

devices

.765 .000

I am ready to do the task or

quiz from my lecturer

through mobile learning.

.705 .000

Based on the correlation table above, it shows

that all the instruments in the study are valid,

because they have significance <0.05.

Exploring the Intention to Use Mobile Learning in Higher Education

3.3 Regression Analysis among

Variables

Table 3. Simultaneous Test Result (F Test)

ANOVA

Model Sum of

Squares

df Mean

Square

F Sig.

Regression

516.011 2 258.005 61.996 .000

Residual

357.899 86 4.162

Total

873.910 88

a. Dependent Variable: IUtotal

b. Predictors: (Constant), PItotal, SPtotal

Based on the ANOVA table or F table, they

indicate that the calculated F value of 61,996 with a

significance of 0.000. Because of the significance

0.000 <0.05 then the regression model can be used

to predict students’ perception (X1) and the personal

innovativeness (X2) simultaneously influences

intention to use mobile learning.

3.4 Partial Test (t test)

All the variables of this study are significant.

The probability of significance of independent

variables; students’ perception (X1) and personal

innovativeness (X2) are more than 0.05.

Table 4. The Result Analysis of Multiple Linear

Regression

Model Unstandardized

Coefficients

Standa

rdized

Coeffi

cients

t Sig.

B Std.

Error

Beta

(Constant) 2.254 1.483 1.520 132

SPtotal .299 061 .413 4.884 .000

PItotal .626 .117 .451 5.331 .000

The model formed from the analysis is Y =

2.254 + 0.299X1 + 0.626X2 + e. It means that:

1. Constant = 2.254, if the independent variables

are constant or 0 then the average of intention

to use mobile learning is 2.254.

2. Coefficient X1 (Students’ Perception) = 0.299,

it means that if students’ perception variable

increased by 1 point while personal

innovativeness is 0, it would lead to an increase

of intention to use mobile learning for 0,299.

3. Coefficient X2 (Personal Innovativeness) =

0.626, it means that if personal innovativeness

increased by 1 point while students’ perception

variable is 0; it would lead to an increase of

intention to use mobile learning for 0,626.

3.5 Coefficient Determination Test

(R2)

Table 5. The Result of Simultaneous Determination

Coefficient

Model Summary

Model R R

Square

Adjusted

Square

Std. Error

of the

Estimate

Durbin-

Watson

1 ,768

,590 ,581 2,04001 2,176

a. Predictors: (Constant), PItotal, SPtotal

b. Dependent Variable: IUtotal

Based the Model Summary, the Adjusted R

Square is 0,581 (58.1%). It means that 658.1%

variable on intention to use mobile learning can be

explained by two independent variables, students’

perception and personal innovativeness. While the

rest (100% -58.1%), i.e. 41.9% are explained by

other variables not examined.

Table 6. The Calculation Result Coefficient of

Determination of Partial

Model t Sig. Correlations Collinearity

Statistics

Zero-

order

Parti

Part Tolera

nce

VIF

(Constant) 1.520 .132

SPtotal 4.884 .000 .675 .466 .337 .665 1.504

PItotal 5.331 .000 .691 .498 .368 .665 1.504

From the table above, it can be seen that the

partial correlation value of Student Perception (X1)

is 0.466, so the influence of Student Perception (X1)

on Intention to Use Mobile Learning (Y) is equal to

(0.4662 x 100%) or 22%. Then, the partial

correlation of Personal Innovativeness (X2) is 0.498;

the influence of Personal Innovativeness (X2) on

Intention to Use Mobile Learning (Y) is (0.4982 x

100%) or 25%.

Then, from the hypotheses, we can state that:

H1: Student's perception has a significant influence

on intention to use mobile learning. The

hypothesis is accepted.

H2: Personal innovativeness has a significant

influence on intention to use mobile learning.

The hypothesis is accepted.

BICESS 2018 - Borneo International Conference On Education And Social

H3: Simultaneously, students' perception and

personal innovativeness have a significant

influence on intention to use mobile learning.

The hypothesis is accepted.

The results show that students' perception and

personal innovativeness can be the determinants of

intention to use mobile learning. This result is in line

with the results of a study from Lopez-Nicolas et al.

(2008) which states that Perceived of usefulness and

perceived ease of use have significant influence on

the behavioral intention to use mobile learning by

45% and 17%. Individuals with higher levels of

personal innovation tend to be more confident in

new technologies (Lewis, Agarwal, &

Sambamurthy, 2003).

Related to the magnitude of the influence given

by each independent variable, the personal

innovativeness variable shows a higher influence

(25%) than the students’ perception (22%). It is

understandable that personal innovativeness is more

likely to trigger concrete behavior to try new things

about technology, as Lopez-Nicolas et al. (2008)

argues that personal innovativeness in the use of

information technology reflects a desire to try new

technologies. Individuals with innovation are more

anticipated to generate more positive thinking for

new IT.

4 CONCLUSIONS

It can be concluded that simultaneously, the variable

of Students' perception (X1) and personal

innovativeness (X2) influence the intention to use

mobile learning (Y) for 58.1%. Partially, there are

two variables affecting intention to use mobile

learning, they are Students' Perception (X1) for 22%

and Personal Innovativeness (X2) for 25%.

Therefore, increasing the personal innovativeness is

needed to increase intention to use mobile learning

which will impact of one’s decision on the

perception of mobile adoption..

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Exploring the Intention to Use Mobile Learning in Higher Education