International Review of Research in Open and Distributed Learning Exploring the Moderating Role of Perceived Flexibility Advantages in Mobile Learning Continuance Intention (MLCI)

The primary purpose of this study was to explore the key factors that could affect mobile learning continuance intention (MLCI), and examine the moderating effect of perceived flexibility advantages (PFA) on the relationship between key mobile learning elements and continuance intention. Five hundred undergraduate students who had previously adopted mobile devices to learn English took part in this study. Partial least squares (PLS) analysis was utilized to test the hypotheses in this study. It has been found that the perceived usefulness of mobile technology, subjective norm, and self-management of learning could be closely linked to mobile learning continuance intention. With particular respect to the moderating role of perceived flexibility advantages, it has been demonstrated that PFA could moderate the relationship between perceived usefulness of mobile technology and mobile learning continuance intention, as well as the association between subjective norm and mobile learning continuance intention, whereas PFA did not moderate the link between self-management of learning and mobile learning continuance intention.This report has further added to the body of knowledge in the field of mobile learning through empirical examination.


advantages on mobile
learning outcome, it has been found that there is still a dearth of studies probing into the moderating effect of perceived flexibility advantages on mobile learning continuance intention, which refers to students' continuance intention to use mobile technology to acquire new knowledge.

Accordingly, this issue should be worthy of further investigations, and the primary purpose of this study was to explore the key factors that could affect mobile learning continuance intention, and examine the moderating effect of perceived flexibility advantages on the relationship between key mobile learning elements and continuance intention.Intention (MLCI)


Perceived Usefulness of Mobile Technology (PUMT)

In prior information technology (IT) research, it has been shown that a person's perception of usefulness toward IT could be closely connected with his or her technology acceptance (Davis, 1989;Roca & Gagné, 2008).According to the technology acceptance model (TAM) proposed by Davis (1989), a person's perceived usefulness of certain IT is described as "the degree to which a person believes that using a particular system would enhance his or her job performance" (Davis, 1989, p. 320).Several online learning studies have indicated a positive lin

between learn
rs' perceived usefulness and continuance intention (Lee, 2006;Lee, 2010;Roca & Gagné, 2008;Saade & Bahli, 2005).In the mobile learning context, a learner's perceived usefulness of mobile technology (PUMT) is described as the degree to which a learner believes that using mobile devices would enhance his or her English learning performance.It is possible that a learner with higher perceived usefulness of mobile technology would have a more positive MLCI.Hence, this study proposes the following hypothesis.

H1: Perceived usefulness of mobile technology could have a positive influence on mobile learning continuance intention.


Self-Management of Learning (SML)

The self-management of learning (SML), which refers to "the extent to which an individual feels he or she is self-disciplined and can engage in autonomous learning" (Wang et al., 2009, p. 101), has several synonyms such as autonomous, self-directed, self-regulated, and independent learning (Regan, 2003).SML has received much attention in prior research mainly because it could have a positive impact on learning

Vol 15 | No 3 July/14 143 outcomes (Ommundsen, Haugen, & Lund, 2005).An early review by Chen (2002) has revealed t at learners' self-management of learning could be closely associated with their academic performance.Another English learning study by Weschler and Pitts (2000) showed that it could be suitable and beneficial to suggest that English learners with higher levels of self-directed-learning capabilities use electronic dictionaries.This could be because such learners would like to have more autonomous and independent learning opportunities.Wang et al. (2009) indicated that SML could be positively associated with learners' behavioral intention to engage in mobile learning.In mobile learning domains, it is likely that learners with higher SML could have more positive MLCI.Consequently, this study proposes the following hypothesis.

H2: Self-management of learning could have a positive influence on mobile learning continuance intention.


Subjective Norm (SN)

I has been found that advice, suggestions, and viewpoints from critical people such as supervisors, intimate friends, or family members could have a pivotal influence on our decision making process and outcomes (Aggelidis & Chatzoglou, 2009).According to Ajzen (1991), the subjective norm (SN), which is described as "the perceived social pressure to

erform or not to perform the behavior" (p.188), c
uld play a key role in determining people's acceptance and usage of new IT (Aggelidis & Chatzoglou, 2009;Schepers & Wetzels, 2007;Venkatesh & Davis, 2000;Wang et al., 2009).Nevertheless, some IT studies have revealed that SN has no impact on people's behavioral intention (Hsu & Lin, 2008;Yuen & Ma, 2008) and IT system usage (Van Raaij & Schepers, 2008).With specific regard to the effect of SN on continuance intention, it is shown that the positive relationship between SN and continuance intention has been well documented in previous reports (Chen et al., 2012;Lee, 2010).Similarly, in the context of mobile learning, it is possible that mobile learning continuance intention could fall under the sway of subjective norm.Thus, this study proposes the following hypothesis.

H3: Subjective norm could have a positive influence on mobile learning continuance intention.

Perceived Flexibility Advantages (PFA)

Previous studies have revealed that learners' perceptions of the flexibility advantages of online learning could positively affect their intention to adopt online l arning courses in the future (Hamzaee, 2005;Hollis & Madill, 2006;McGorry, 2003).Arbaugh (2000) proposed that online lea

ing, which gives learners "a high
egree of flexibility in when


Research Methodology


Data Collection

The data were collected via a pencil and paper survey.Five hundred undergraduate students who had previously adopted mobile devices to learn English took part in this study.Except for four incomplete surveys, the other 496 surveys were usable.As shown in Table 1, there were 245 and 247 male and female participants respectively.Only 23 participants were English major students.In terms of the academic level of participants, it was found that the sophomore group was the largest group, and the freshman group was the second largest group.Finally, it was revealed that most participants in this study were business majors.


Instrumentation

A 7-point Likert scale was used to measure the level of agreement of each construct.

Items which measured learners'perceived usefulness of mobile technology, subjective norm, and mobile learning continuance intention were adopted from Davis (1989), Venkatesh and Davis (2000), as well as Roca et al. (2006), respectively.Four items which measured perceived flexibility advantages were chosen from Huang et al. (2012) and Marks et al. (2005).In addition, four items which measured self-management of learning were selected from Wang et al. (2009).


Data Analysis and Results

Partial least squares (PLS) analysis was utilized to

est the hypotheses in
this study.The PLS analysis, one of the structural equation modeling (SEM) techniques, was more suitable not only to analyze the relationship between predictors and outcome variables (Fornell & Bookstein, 1982), but also to give readers a clear picture of variances explained by predictor variables in this study (Barclay, Higgins, & Thompson, 1995).

First, with respect to the reliability and internal consistency of measuring scales, according to Table 2, it was found that the factor loading of each variable was all above .70,and the composite reliability (CR) of each construct exceeded .90(Fornell & Larcker, 1981).Hence, it was demonstrated that all scales indicated an adequate internal consistency.

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Moreover, according to Table 3, it was shown that the convergent and discriminant validity were all satisfactory not only because the average variance extracted (AVE) for each construct was higher than the suggested value of 0.5 (Fornell & Larcker, 1981), but also because the square root of AVE values on the diagonal were greater than the offdiagonal correlation values (Fornell & Larcker, 1981).As this study further examined path coefficients of he structural model and hypotheses, it was found that hypothesis 1, 2, and 3 were all supporte

by the study results, which indicated t
at the perceived usefulness of mobile technology, subjective norm, and self-management of learning explained a total of 41.8 % of variance in mobile learning continuance intention (see Figure 2).

Finally, in order to determine the moderating effect of perceived flexibility advantages on the relationship between key mobile learning elements and continuance intention, based on the median score of perceived flexibility advantages = 4.75, 496 participants were divided into two groups: high flexibility advantages group (n = 236), and low flexibility advantages group (n = 260).The PLS analysis was subsequently carried out to explore the path structures for both models (see Figure 3 and 4).Additionally, the analysis of pat

coefficient comp
rison, which was suggested by Keil, Tan, Wei, and Saarinen (2000), was conducted to examine the moderating effect of flexibility advantages in this study.As shown in Table 4, it was demonstrated that except hypothesis 6, hypothesis 4 and 5 were supported by the study findings, which revealed that the perceived flexibility advantages moderated the relationship between perceived usefulness of mobile technology and mobile learning continuance intention, as well as the link between subjective norm and mobile learning continuance intention.


Discussion and Implications

The primary purpose of this study was to explore the key factors that could affect MLCI, and further examine the moderating effect of perceived flexibility advantages on the relationship between key mobile learning elements and continuance intention.First, it has been found that hypothesis 1, 2, and 3 are by study findings.The study results are congruent with previous research which indicates that the perceived usefulness of mobile technology (PUMT), subjective norm (SN), and self-management of learning (SML) could

e closely linked to mobile
learning continuance intention (Lee, 2010;Roca & Gagné, 2008;Wang et al., 2009).It is implied that more attention should be paid to whether the functions and services of mobile devices are suitable for mobile learning, mainly because perceived usefulness of mobile technology could play the most important part in determining learners' mobile learning continuance intention.

Moreover, in order to minimize the possible interrup ion to mobile learning, it is important that more efforts should be made not only to facilitate learners to have better self-management of learning, but also to properly give them recommendations for future mobile learning.

Third, it has been demonstrated that hypothesis 4 and 5 are supported by study results.

The findings are in line with early reviews (Evans, 2008;López-Nicolás et al., 2008), which suggest that perceived flexibility advantages could moderate the relationship between perceived usefulness of mobile technology and mobile learning continuance intention, as well as the association between subjective norm and mobile learning continuance intention.That is, learners with higher perceived flexibility advantages are more likely to have stronger relationship between perceived usefulness of mobile technology and mobile learning continuance intention than those with lower perceived flexibility advantages, whereas learners with higher perceived flexibility advantages are more likely to have weaker relationship between subjective norm and mobile learning continuance intention than those with lower perceived flexibility a vantages.It is hinted that with respect to learners with lower perceived flexibility advantages, subjective norm should deserve more attention, mainly because critical insights from key people could be more important to them in affecting their mobile learning continuance intention.On the contrary, with specific regard to learners with higher perceived flexibility advantages, perceived usefulness of mobile technology should merit first consideration, mainly because it could play a relatively more important role in facilitating their mobile learning continuance intention.

Last but not least, it has been shown that hypothesis 6 is not supported by study findings.The study result is not consistent with previous research (Gardner & Miller, 2011), which reveals that perceived flexibility advantages did not moderate the relationship between self-management of learning and mobile learning continuance


Limitations and Conclusions

There are some limitations and restrictions in this study that should be further addressed.First, findings and implications drawn from this study should be appli

with caution, mainly due to
the limited data available.Moreover, it is necessary that suppliers of mobile technology, one of the important stakeholders, should be further incorporated into future mobile learning studies in order to gain insights from different stakeholders.Third, due to age differences, it is possible that mobile learning effectiveness and efficiency could be subject to change.Accordingly, more studies should be conducted to investigate the role of the age variable in subsequent mobile learning studies.

In conclusion, this report has further added to the body of knowledge in the field of mobile learning through empirical examination.As mobile learning has gradually become a key learning channel in our lives, it is critical that the researchers and practitioners should concentrate not only on the use of mobile technology, but also on the continued use of mobile technology in learning.

Figure 1 .
1
Figure 1.Theoretical framework of th study.


Figure 2 .Figure 3 .
23
Figure 2. PLS solution for full data set.


Figure 4 .
4
Figure 4. PLS solution for low perceived flexibility advantages group.




intention.In other words, learners with different levels of perceived flexibility advantages could still have a similar relationship between self-management of learning and mobile learning continuance intention.Nevertheless, it is suggested that more Vol 15 | No 3July/14 152 studies are needed in order to verify the role of perceived flexibility advantages in selfmanagement of learning and mobile learning studies.







Huang, Hsiao, Tang, and Lien
Literature Review and Hypothesis DevelopmentThe Definition of Mobile LearningIt has been shown that although there is no universal agreement as to the definition ofmobile learning (m-learning), relevant discussions on m-learning are mainly centeredon learning flexibility and educational applications of mobile technology (El-Hussein &Cronje, 2010; Park, 2011; Wang et al., 2009). For instance, an early report by Wang etal. (2009) has revealed that "M-learning refers to the delivery of learning to studentsanytime and anywhere through the use of wireless Internet and mobile devices,Vol 15 | No 3July/14142
including mobile phones, personal digital assistants (PDAs), smart phones and digital audio players" (p.93).Another recent review by Park (2011) has suggested that "mobile learning refers to the use of mobile or wireless devices for the purpose of learning while on the move" (p.79).Consequently, based on previous suggestions, mobile learning, in this study, could be broadly described as learning activities through the use of mobile technology.


Table 2
2
Confirmatory Factor Analysis of Each Model
ItemPUMTSMLSNMLCIFMHLFMHLFMHLFMHLPUMT1.89 .86 88PUMT2.92 .90 .92PUMT3.88 .85 .87PUMT4.88 .85 .87SML1.88.86.91SML2.90.90.89SML3.86.87.77SML4.87.90.80SN1.88 .90 .84SN2.93 .93 .93SN3.82 .92 .90MLCI1.91.91.88MLCI2.91.90 .88MLCI3.88 .86 .86CR.94 .92 .93 .93.93.90.93 .93 .92 .92 .92 .90AVE.80 .75.78 .77.77.71.83 .83 .79.81.79.76Cronbach's.91.88 .90 .90.90.86.90 .90 .87 .88 .87 .84AlphaNote. PFA, perceived flexibility advantages; FM, full model; H, high perceived flexibilityadvantage group; L, low perceived flexibility advantage group; PUMT, perceived usefulness ofmobile technology; SML, self-management of learning; SN, subjective norm; MLCI, mobilelearning continuance intention; CR, Composite Reliability; AVE, Average Variance Extracted

Table 3
3
The Correlations of Each Latent Variable amon

Different Models
Full Model
ol 15 | No 3July/14 153
Using a modified technology acceptance model in hospitals. V P Aggelidis, P D Chatzoglou, 10.1016/j.ijmedinf.2008.06.006International Journal of Medical Informatics. 782009

The theory of planned behavior. I Ajzen, 10.1016/0749-5978(91)90020-TOrganizational Behavior and Human Decision Processes. 501991

Virtual classroom characteristics and student satisfaction with internet-based MBA courses. J B Arbaugh, 10.1177/105256290002400104Journal of Management Education. 242000

The partial least squares approach to causal modeling: Personal computer adoption and use as an illustration. D Barclay, C Higgins, R Thompson, Technology Studies. 21995

Ubiquitous learning we site: Scaffold learners by mobile devices with information-aware techniques. G D Chen, C K Chang, C Y Wang, 10.1016/j.compedu.2006.03.004Computers & Education. 502008

Personalized mobile English vocabulary learning system based on item response theory and learning memory cycle. C-M Chen, C-J Chung, 10.1016/j.compedu.2007.06.011Computers & Education. 512008

Self-regulated learning stra