integrating user interface and personal innovativeness into the tam for mobile learning in cyber...

Integrating user interface and personal innovativenessinto the TAM for mobile learning in Cyber University

Young Ju Joo • Hyeon Woo Lee • Yookyoung Ham

� Springer Science+Business Media New York 2014

Abstract This study aims to add new variables, namely user interface, personal

innovativeness, and satisfaction in learning, to Davis’s technology acceptance

model and also examine whether learners are willing to adopt mobile learning.

Thus, this study attempted to explain the structural causal relationships among user

interface, personal innovativeness, perceived ease of use, usefulness, intention to

use, and satisfaction in learning. A total of 350 students who enrolled in major

courses of W Cyber University that provided mobile services responded to the

survey. The results of the Structural Equation Modeling revealed that (1) user

interface and perceived ease of use had significant effects on perceived usefulness;

(2) user interface and personal innovativeness have significant effects on perceived

ease of use; (3) perceived usefulness and perceived ease of use significantly affect

satisfaction in learning; (4) perceived usefulness does not have significant effects on

intention to use. The study has shown that usefulness and ease of use perceived by

learners increase satisfaction in learning, and usefulness and satisfaction in learning

create a positive intention to use. The findings of the study have highlighted user

interface as an important factor that affects usefulness and ease of use perceived by

learners.

Keywords Mobile learning � User interface � Personal innovativeness �Satisfaction � TAM

Y. J. Joo � Y. Ham

Ewha Womans University, Seoul, Korea

e-mail: [email protected]

Y. Ham


H. W. Lee (&)

Department of Education, Sangmyung University, Hongjimun-2gil, Jongno-gu,

Seoul 110-743, Korea


123

J Comput High Educ

DOI 10.1007/s12528-014-9081-2

Introduction

The recent diffusion of the wireless Internet and mobile technology has caused a

rapid paradigm shift from personal computers that have dominated the digital

domain for more than three decades, to smartphones and tablet PCs. The paradigm

shift facilitates the advancement of mobile learning systems in the realm of

e-learning business. Improvements in data processing speed and device performance

in the mobile environment allows mobile learning to provide additional services to

learners in a wide range of areas compared to conventional e-learning. For example,

universities are implementing smart learning systems that enable students to engage

in academic activities using mobile devices whenever and wherever.

However, simply transferring Internet screens that had been used in the PC

environment onto the mobile environment causes inconvenience to users due to

small screen size, slow download speed, and difficult input mechanisms (Kim and

Park 2010). Such inconveniences may affect the usefulness and ease of use

perceived by mobile learning learners as well as their intention to use mobile

learning. In this context, it is necessary to design interfaces compatible with the

mobile devices different from the traditional PC-based environments.

Previous studies on user interface have focused on interface design (Baldonado

2000; McFarland 1995; Phillips 2012) and the interaction between specific types of

interface and users (Cheon and Grant 2012; Gatsou et al. 2011; Hong et al. 2011).

Although some studies have examined the relationships among user interface, ease

of use, usefulness, and intention to use (Cho et al. 2009; Hong et al. 2011; Thong

et al. 2002; Yang and Shin 2010), there has been insufficient research to empirically

explain the structural causal relationships between interface, usefulness, ease of use,

and intention to use, especially in mobile learning. Hence, this study examines the

influence of the user interface on usefulness, ease of use, and intention to use mobile

learning.

Furthermore, personal innovativeness, which is the level of willingness to accept

new technology, has been regarded as an important element that affects the intention

to use informational technology (Venkatesh and Davis 2000). Rogers (1995) studied

individuals with and without an innovative disposition and claimed that innovative

individuals displayed adventurous spirit and leadership skills as along with attitudes

for accepting new unverified things. Liu et al. (2010a) found that learners with

innovative tendencies were likely to use mobile learning. Agarwal and Prasad

(1998) reported that individuals with high levels of innovativeness positively

recognized the relative advantages and ease of use; accordingly, they demonstrated

higher intent to use new informational technology. As personal innovativeness has

been discussed as an important factor for predicting the acceptance of technology

(Lewis et al. 2003), it is also meaningful to empirically examine the effects of

personal innovativeness on ease of use, usefulness, and intention to use mobile

learning.

Although previous studies have examined user interface and personal innova-

tiveness as factors for accepting mobile learning (Liu et al. 2010b; Smet et al. 2012),

there has not been a study that incorporated the two variables into a single model for

Y. J. Joo et al.

123

comprehensive verification. This study intends to verify the effects of the two

variables on the intention to use mobile learning in causal relationships.

The technology acceptance model (TAM) developed by Davis has been used in

various studies to explain the process of accepting new technology. According to

TAM, external variables affect perceived usefulness and perceived ease of use,

determining the attitude toward and the willingness to accept new technology. TAM

suggests that individuals undergo these processes before arriving at acceptance of

new technology (Davis and Venkatesh 1996). This study adopts TAM as the basis

for examining the effects of user interface and personal innovativeness on ease of

use, usefulness, and intention to use mobile learning.

According to Davis et al. (1989), usefulness and ease of use significantly affect

user satisfaction; further, a number of previous studies have reported similar results

(Bhattacherjee 2001; Chiu et al. 2005; Devaraj et al. 2002; Joo et al. 2011; Koo et al.

2006; Rai et al. 2002). Accordingly, this study predicts that ease of use and

usefulness influence satisfaction in learning.

Theoretical background

Technology acceptance model

Davis (1989) developed the TAM based on Fishbein and Ajzen’s (1975) theory of

reasoned action (TRA) to explain what kind of causal relationships user’s

acceptance of technology and actual usage had with external variables. According

to TAM (Davis et al. 1989), it is understood that external variables, such as system

design characteristics, training, implementation process, and self-efficacy on

computer, influence perceived usefulness and perceived ease of use of the media.

Also, intention to use in TAM is determined by perceived usefulness and perceived

ease of use (see Fig. 1).

According to Davis (1989), intention to use is a factor that determines the

individual’s use of a system, precursors of which include perceived usefulness and

perceived ease of use. In this study, perceived usefulness refers to the level to which

a learner recognizes a mobile device to be helpful toward academic achievement,

and perceived ease of use is the degree to which a learner believes he will be able to

use a mobile device without particular difficulty (Davis 1989).

User interface

User interface in informational technology refers to the symbols or command

structures used in human–computer interaction. Phillips (2012) defined user

interface as an interactive system that acts as a bridge between the user and the

system. More specifically, Cheon and Grant (2012) characterized user interface as

the text and graphic layouts presented on the computer screen. In this context, user

interface in mobile learning implies the user environment that includes the menus

and various functions for controlling the mobile devices (Hiltunen et al. 2007).

Integrating user interface and personal innovativeness

123

To explain the factors that determine the acceptance of the digital archive based

on TAM, Yang and Shin (2010) analyzed the causal relationships between mobile

phone user interface and consumer’s service usage behavior, and found that the

mobile phone’s display method and output interface have significant effects on

perceived ease of use. Also, Hong et al. (2011) introduced perceived enjoyment and

user interface as external variables and categorized user interface into interface

design, human factor interface, and human–computer interface. As a result, the three

types of interface all had significant effects on perceived ease of use. Moreover,

Thong et al. (2002) attempted to explain user’s intention to accept digital libraries

and elicited variables concerning individual differences, organizational context, and

user interface as factors that affect intention to use. Their study results indicated that

elements of user interface, such as terminology, navigation, and screen design had

significant influences on ease of use, which in turn significantly affected the

intention to use digital libraries.

Similarly, Cho et al. (2009) expanded TAM by adding user interface,

functionality, system resources, and user satisfaction. The results of their study

found that user interface significantly influences perceived usefulness and perceived

ease of use, suggesting that user interface can have significant effects on perceived

usefulness.

Personal innovativeness

Personal innovativeness conveys the willingness to accept new technology

(Agarwal and Prasad 1998). Rogers (1995) defined personal innovativeness as the

level of intention to accept new technology quicker than other constituents of the

social structure, and Van Raaji and Schepers (2008) defined it as an open attitude

toward change. From the previous studies, personal innovativeness is the level of

individual’s openness toward mobile learning in this study.

Agarwal and Prasad (1998) wanted to examine the factors that affected users in

accepting new technologies and reported that personal innovativeness has signif-

icant effects on perceived usefulness, perceived ease of use, and ultimately,

acceptance of new technology. Based on TAM, Lewis et al. (2003) examined the

effects of self-efficacy and personal innovativeness on perceived ease of use and

perceived usefulness with 161 university staff members. Their results indicated that

personal innovativeness significantly affects ease of use and usefulness. Using TAM

Fig. 1 Technology acceptance model (TAM). Source: Davis and Venkatesh (1996)

Y. J. Joo et al.

123

and DeLone and McLean’s (1992) information system success model, Kim and Ha

(2012) categorized corporate mobile social networking service characteristic factors

into system, user, and social factors to analyze the effects of the three factors on user

satisfaction and intention of continued usage. Their study results indicated that

personal innovativeness, which is one of the user characteristics, has significant

influences on user satisfaction and intention of continued usage with ease of use as a

medium.

Furthermore, Liu et al. (2010b) applied TAM to examine the effects of personal

innovativeness on the intention to use mobile learning of Chinese university

students and found that personal innovativeness positively influences perceived

usefulness in the long term. After observing the factors that affect user’s acceptance

of the mobile Internet, Lu et al. (2003) reported that personal innovativeness

determines user’s perceived usefulness and perceived ease of use in the short and

long term, and further affects the attitude toward accepting and the intention to

accept new technology. The results of these previous studies suggest that personal

innovativeness can have significant effects on perceived ease of use and perceived

usefulness.

Satisfaction in learning

Hui et al. (2008) understood satisfaction in learning as the positive emotion felt by a

learner when he experiences success. Erdogan et al. (2008) viewed satisfaction in

learning as an object or circumstance that satisfies an attitude regarding the

individual’s desire or specific situation. In this study, satisfaction in learning is

defined as the level of satisfaction experienced by learners in mobile learning.

Koo et al. (2006) used TAM to examine the effects of internal and external

motivation factors on the satisfaction of mobile commerce users. They concluded

that internal and external motivation factors influence user satisfaction, and satisfied

users engage in positive word-of-mouth promotion. In e-learning context, Sun et al.

(2008) reported that perceived usefulness and perceived ease of use significantly

influence satisfaction in learning. Roca et al. (2008) suggested that perceived

usefulness and perceived ease of use have significant effects on satisfaction in

learning as well as indirect effects on intention to continuously use e-learning. Joo

et al. (2011) conducted a study with students of online universities to examine the

structural relationships among the level of perceived sense of reality, perceived

usefulness, perceived ease of use, satisfaction in learning, and intention of continued

usage. They reported that the sense of reality in teaching, perceived sense of reality,

perceived usefulness, and perceived ease of use affect the level of satisfaction in

learning.

Relationship between intention to use and related variables

Chung and Kwon (2009) reported that perceived usefulness and perceived ease of

use of mobile technology had significant influences on intention to use. Similarly,

Thong et al. (2006) added perceived ease of use and perceived usefulness to the

expectation–confirmation model to analyze the intention to continuously use mobile


123

Internet services and suggested that perceived ease of use, perceived usefulness, and

level of satisfaction have significant effects on the intention of continuous usage,

with the level of satisfaction having the greatest influence. Based on expectation–

confirmation model, theory of planned behavior, Davis’s TAM, and Csikszentmih-

alyi’s concept of flow, Lee (2010) tried to explain the factors that affect users in

continued usage of e-learning, and demonstrated that the level of satisfaction is the

strongest predicting indicator of continued usage.

In a study conducted with university students, Shin et al. (2011) integrated TAM

and expectation–confirmation model to analyze the intention to continuously use the

smartphone as a tool for ubiquitous learning. The study reported that expectation–

confirmation model had significant effects on perceived usefulness, which in turn

significantly influenced the level of satisfaction. It was also shown that the level of

satisfaction and previous usage experience have significant effects on the intention

of continuous usage. Similarly, Lin and Chen (2012) incorporated DeLone and

McLean’s (1992) information systems success model to TAM and the results

highlighted that system quality, platform data quality, and lecture quality had

significant effects on user’s level of satisfaction and intention to use e-learning, with

perceived usefulness and perceived ease of use as mediators. They also portrayed

that user’s level of satisfaction directly influenced intention to use e-learning. Based

on this previously discussed research, satisfaction in learning in the mobile

environment has significant effects on the intention to use mobile learning.

Accordingly, this study proposes the following hypothetical research model (see

Fig. 2).

The purpose of this study is to explain the structural–causal relationships among

user interface, personal innovativeness, perceived ease of use, usefulness, intention

to use, and satisfaction in learning based on Davis’s TAM in mobile learning

environments. It is hypothesized that higher ratings of user interface and personal

innovativeness will result in higher levels of perceived ease of use in mobile

learning. Second, it is also predicted that higher ratings of user interface, personal

innovativeness, and perceived ease of use will result in higher ratings of perceived

usefulness in mobile. Third, it is hypothesized that higher levels of perceived

usefulness will result in higher ratings of satisfaction in learning in mobile learning.

Fourth, it is predicted that higher ratings of perceived ease of use, perceived

usefulness, and satisfaction in learning will result in higher ratings of intention of

use in mobile learning.

Method

Participants and procedure

The participants of this study were Korea W Cyber University students enrolled in

major required courses that provided mobile services in the fall semester of 2012.

Other than test taking, the university provides academic and administrative affairs,

such as attendance checking and Q&A sessions, through mobile devices.

Y. J. Joo et al.

123

The survey was conducted on the Web, and the students responded based on their

experience of using mobile devices. To assess intention to use and level of

satisfaction, the survey was administered 2 weeks prior to the final exams of the

semester. A total of 385 students responded to the survey; the study analyzed 350

students, excluding 35 incomplete responders.

Instrument

The study modified and adapted the instruments that had been used in the previous

studies to measure user interface, personal innovativeness, perceived ease of use,

perceived usefulness, satisfaction in learning, and intention to use. All of the items

were constructed using a five-point Likert-type scale (1: strongly disagree; 2:

disagree; 3: neutral; 4: agree; 5: strongly agree). First, to evaluate the user interface,

the instrument developed by Liu et al. (2010a) was used, including three items such

as ‘‘The screen design of the mobile device made it easy to read’’. For the internal

consistency of the instrument, Cronbach’s a was .91. Second, the personal

innovativeness questionnaire (Liu et al. 2010b) was used to evaluate personal

innovativeness, including three items such as ‘‘I like experiencing mobile services’’.

Cronbach’s a was .87. Third, for perceived ease of use, an instrument developed by

Davis (1989) was used. There were six items, such as ‘‘It is easy to learn how to use

the mobile device’’. Cronbach’s a was .94. Fourth, the instrument developed by

Davis (1989) was used to evaluate perceived usefulness, including six items such as

‘‘Using mobile devices makes learning faster’’. Cronbach’s a was .94. Fifth, eight

statements developed by Shin (2003) were used to evaluate the satisfaction of

learning at Cyber University. The statements were modified to accommodate the

mobile learning environment including ‘‘Studying the mobile course was a valuable

experience’’. Cronbach’s a was .96. Sixth, in order to evaluate the intention to use

mobile devices, the instrument developed by Taylor and Todd (1995) was adapted.

The instrument contained three items, such as ‘‘I intend to use a mobile device on a

continuous basis for my study this semester’’. Cronbach’s a was .95.

Data analysis

Using SPSS, the researchers analyzed descriptive statistics including the variable’s

mean, standard deviation, skewness, and kurtosis, in order to examine the normal

Fig. 2 Hypothetical research model


123

distribution of the variables as well as the correlation among them. Next, using

AMOS, whether the indices adequately measured the latent variables was checked,

and the measurement model to assess the discrimination ability among the latent

variables was measured. The structural equation model to examine the structural

causal relationships among the variables was examined. The results of the

exploratory factor analysis indicated that all of the variables were single factors

(Sass and Smith 2006).

In order to determine the method for estimating the statistical model, multivariate

normality was tested using SPSS and AMOS, and applied maximum likelihood

estimation was suggested to estimate the model’s goodness of fit as well as its

parameters. As for the criteria for determining the model’s goodness of fit, absolute

fit indices (CMIN and RMSEA) and incremental fit indices (TLI and CFI) were

referenced. The statistical significances of the effects among variables were tested at

the significance level of .05.

Results

Correlation matrix and descriptive statistics of observed variables

Mean, standard deviation, skewness, and kurtosis were examined to verify the

multivariate normality. The mean, standard deviation, skewness, and absolute value

of kurtosis of the variables ranged from 3.39 to 3.88, .75–.89, .02–.64, and .04–.55,

respectively and the multivariate normality was satisfied (Kline 2005). Due to the

concern of multicollinearity caused by the high correlation among variables, the

variance inflation factor (VIF) was measured, and the results were \10 (from 1 to

1.55), indicating that multicollinearity was not violated. All of the variables showed

significant correlations at the significance level of .05 (see Table 1).

Measurement model

Before testing the structural regression model’s estimation ability and goodness of

fit, the measurement model’s goodness of fit was measured according to the two-

step process for checking the model’s estimation ability and maximum likelihood

estimation (Kline 2005). As shown in Table 2, both TLI and CFI are above .90,

satisfying the acceptance criteria. Moreover, RMSEA is .052, indicating that the

measurement model has a goodness of fit.

Examining the relationships between latent and observed variables, the

standardized factor loading of the observed variables in terms of all of the latent

variables ranged from .85 to .98, and the variables were statistically significant at

the significance level of .05. These results imply that the observed variables selected

to measure the theoretical variables of the study model have sufficient convergent

adequacy. As the measurement model was determined to be capable of estimating

all of the latent variables with statistical accuracy and adequacy, goodness of fit and

parameters of the structural regression model that established the causal relation-

ships among the measured theoretical variables were measured.

Y. J. Joo et al.

123

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123

Structural model

As the goodness of fit index of the measurement model constructing the statistical

model satisfied the criterion, and the structural regression model’s estimation ability

was theoretically confirmed, the goodness of fit of the structural regression model

used in this study was measured, as presented in Table 3.

The goodness of fit indices of the initial structural model indicated that the model

is adequate. According to the results, personal innovativeness to perceived

usefulness (b = -.066, t = -.691, p [ .05) and perceived usefulness to intention

to use (b = .046, t = .850, p [ .05) paths were not significant; hence, these two

paths were removed in order to set a simpler modified model. Because the initial

structural model and the modified simplified model form a hierarchical model, v2

test was performed to see if there was a statistically significant difference between

the two models. The test results were Dv2 = 1.207 and p = .547, indicating that

there is no statistically significant difference; thus, the modified model was used as

the final model for the study.

The modified model’s indices of goodness-to-fit measured using the maximum

likelihood method are shown in Table 4. The results indicate that the model is

adequate. The causal relationships among user interface, personal innovation,

perceived ease of use, perceived usefulness, satisfaction in learning, and intention to

use are provided in Fig. 3.

The direct and indirect effects were estimated and the results are shown in

Table 5

Conclusions

This study attempted to add new variables, including user interface, personal

innovativeness, and satisfaction in learning, to Davis’s TAM and also examine

whether learners are willing to adopt mobile learning. This empirical study

validated the proposed research model and demonstrated that the hypotheses were

supported. The results of the study suggest the following implications.

The first hypothesis, higher ratings of user interface and personal innovativeness

will results in higher levels of perceived ease of use in mobile learning, was

supported. Finding that user interface significantly influences ease of use is

consistent with the results from prior studies (Hong et al. 2011; Hubona and Blanton

1996; Smet et al. 2012; Thong et al. 2002), which indicated that user interface has

positive effects on allowing learners to easily use the technology. The relationship

between personal innovativeness and ease of use corresponds with the results of a

Table 2 Statistics of model fit measures of the measurement model

CMIN p df CMIN/df TLI CFI RMSEA (90 % reliability)

Measurement model 96.740 .000 50 1.935 .986 .991 .052 (.036–.067)

Recommended [.90 [.90 \.08

Y. J. Joo et al.

123

prior TAM-based study that examined the effects of personal innovativeness on the

intention to use a virtual reality simulation (Fagan et al. 2012). The results suggest

that the openness of learners toward using mobile devices have positive influences

on enabling them to have easy control of the devices.

The second hypothesis, higher ratings of user interface, personal innovativeness,

and perceived ease of use will result in higher ratings of perceived usefulness in

mobile learning was partially supported. The result revealed that user interface and

perceived ease of use had significant effects on perceived usefulness in mobile

learning; however, personal innovativeness did not affect perceived usefulness. The

result that user interface had significant effects on perceived usefulness corresponds

with a number of prior studies (Cho et al. 2009; Hubona and Blanton 1996),

suggesting that user interface elements, such as screen design and font, size, and

color of text, have positive effects on the usefulness perceived by the learners.

The relationship between ease of use and usefulness found in the study was also

consistent with a previous study that examined the intentions of those using mobile

device for mobile learning (Huang et al. 2007). That is, students who can readily use

mobile devices perceive them as a useful study tool. However, personal

innovativeness did not have significant influences on perceived usefulness. The

result was consistent with a prior study conducted with middle school students to

Fig. 3 Standardized path coefficients of the modified model

Table 3 Statistics of model fit measures of the initial structural model


Initial model 148.916 .000 54 2.758 .974 .981 .071 (.058–.085)


Table 4 Statistics of model fit measures of the modified model


Modified model 150.123 .000 56 2.681 .974 .981 .069 (.056–.083)

Initial model 148.916 .000 54 2.758 .974 .981 .071 (.058–.085)



123

examine the factors that affect LMS utilization (Smet et al. 2012). This finding

supports the claim that students who are open to using mobile devices do not

necessarily perceive as being useful when using them in academic activities. Such

study results suggest the possibility that other factors might be involved in the

relationship between personal innovativeness and usefulness, indicating a need for

further research.

The third hypothesis, higher ratings of perceived ease of use, and perceived

usefulness will result in higher ratings of satisfaction in learning in mobile learning,

was also supported. The findings suggest that when learners believe that it is easy to

use a specific technology that helps improve academic achievement, it enhances

satisfaction in learning (Carlisir and Carlisir 2004; Chiu et al. 2005; Roca et al.

2008).

The fourth hypothesis, higher ratings of perceived ease of use, perceived

usefulness, and satisfaction in learning will result in higher ratings of intention of

use in mobile learning, was partially supported. While perceived ease of use and

Table 5 Direct, indirect, and total effects of each construct

Related variables Unstandardized

coefficients (b)

Standardized coefficients

(b)

Overall Direct Indirect Overall Direct Indirect

User interface ? Perceived ease of

use

.134 .134 – .113 .113 –

Personal

innovativeness

? .831 .831 – .740 .740 –

User interface ? Perceived

usefulness

.346 .292 .055* .333 .280 .052

Personal

innovativeness

? .339 – .339* .343 – .343*

Perceived ease of

use

? .407 .407 – .463 .463 –

User interface ? Satisfaction in

learning

.235 – .235* .231 – .231

Personal

innovativeness

? .429 – .429* .446 – . 446*

Perceived ease of

use

? .516 .285 .231* .603 .333 .270*

Perceived

usefulness

? .567 .567 – .583 .583 –

User interface ? Intention to use .220 – .220 .195 – .195

Personal

innovativeness

? .497 – .497* .464 – .464*

Perceived ease of

use

? .597 .162 .436* .628 .170 .458*

Perceived

usefulness

? .479 – .479* .443 – .443*

Satisfaction in

learning

? .845 .845 – .760 .760 –

* p \ .05

Y. J. Joo et al.

123

satisfaction in learning significantly influence intention to use, perceived usefulness

does not have significant effects on intention to use. The results suggest that a

learner capable of easily using a mobile device is likely to develop a positive

attitude toward accepting the technology (Li et al. 2008; Legris et al. 2003). In other

words, user satisfaction has direct influences on potential behavioral intention, such

as intention of continued usage (Bhuatti 2007; Lee and Roh 2004). These results

suggest that the positive emotions that learners develop while using mobile devices

can have positive effects on their intention to use mobile learning.

One of the results of the study is finding that perceived usefulness does not have a

significant influence on the intention to use mobile learning, which counters the

result of a previous study that claims greater perceived usefulness positively

influenced the development of intention to use new technology and also has direct

effects on continued usage (Davis 1989). Although there are learners who

acknowledge the usefulness of mobile devices in academic activities, they are not

yet using mobile devices at a very high rate.

In sum with regard to mobile learning, Davis’ (1989) TAM, these results suggest

that TAM can be extended to include user interfaces and personal innovativeness

and learning and instructional strategies that can enhance user interface and personal

innovativeness should be considered. That is, the findings of the study have

highlighted user interface as an important factor that affects usefulness and ease of

use perceived by learners. Cyber universities should consider mobile interface

design by taking into account the convenience of their students. More specifically,

cyber universities should implement user interface elements, such as screen design

and font, size, and color of text, in ways that are helpful toward student’s academic

activities. Similarly, menus should be presented in a clear and simple manner, and

larger and easily discernible screen designs and texts should also be made available

for senior learners who might not be familiar with mobile devices.

The contribution of this research is that it adds external variables to the original

TAM in the mobile learning context, although previous studies have examined user

interface and personal innovativeness separately as factors that affect the intention

to use mobile learning (Liu et al. 2010b; Smet et al. 2012; Yang and Shin 2010).

While mobile devices such as smartphones and tablets provide increased portability,

they can lead to inconvenience with learners due to the smaller screen size

compared to conventional PCs. This study takes into account such characteristics of

the medium and proposes strategies for improving Cyber university student’s

intention to use mobile learning.

Based on the conclusions drawn from the study, suggestions for future research

are as follows. Because this study was conducted with students enrolled in required

discipline major courses that offer mobile services at W Cyber University, there are

limitations in generalizing the results of the study. First, it is necessary to conduct

further research by expanding the participants to examine whether similar results

can be obtained from students at other cyber education institutes. Second, in

addition to the variables examined in this study, future research needs to consider

other various factors that affect the intention to use mobile learning, such as

interactive effects and perceived enjoyment.


123

Acknowledgments This work was supported by National Research Foundation of Korea Grant funded

by the Korean Government (2012-045331).

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Young Ju Joo is a professor in the Department of Educational Technology at Ewha Womans University

in Seoul, Korea. She received her doctorate in Educational Media & Technology from the Boston

University in 1979. Her research interests include e-Learning, m-Learning and structural equation

modeling.

Hyeon Woo Lee is assistant professor in the Department of Education at Sangmyung University in Seoul,

Korea. He received his doctorate in Instructional Systems Design from the Pennsylvania State University.

His research interests include instructional design, technology-enhanced instruction, human resources

development, and learning sciences.

Yoo Kyoung Ham is a graduate student at Ewha Womans University in Seoul, Korea and studies for a

master’s degree in the department of educational technogy. Her research interests include mobile

learning, e-Learning, learning outcome and structural equation modeling.

Y. J. Joo et al.

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integrating user interface and personal innovativeness into the tam for mobile learning in cyber...

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