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«Journal of Information Technology Education: Research Volume 13, 2014 Cite as: Mac Callum, K., Jeffrey, L., & Kinshuk. (2014). Factors impacting ...»

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Due to the limited empirical research into lecturers’ adoption of mobile learning, this paper proposes and tests a new model of adoption. This model measures the impact of digital literacy, ICT anxiety, and ICT teaching self-efficacy, in addition to the well established factors of perceived usefulness and perceived ease of use, on lecturer’ acceptance of mobile learning.

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Perceived Usefulness Perceived usefulness is the degree to which a person believes that a particular technology will be beneficial to their lives (Chang & Tung, 2008). Research has shown that if a person believes a new technology will be of benefit to them, they will more likely adopt this new technology (Chin & Todd, 1995).

Therefore it is hypothesized that:

H1: Perceived usefulness will have a positive effect on the behaviour intention to use mobile learning.

Perceived Ease of Use Perceived ease of use is the measure of the degree an individual believes a particular technology is free from effort. Previous research has found a positive effect this perception has on the behaviour intention and perceived usefulness of the new technology (Chin & Todd, 1995, Chang &

Tung, 2008). Therefore the following are hypothesized:

H2: Perceived ease of use will have a positive effect on the behaviour intention to use mobile learning.

H3: Perceived ease of use will have a positive effect on perceived usefulness.

Digital Literacy Digital literacy is the measure of an individual’s ability to use digital technology, communication tools, and/or networks to access, manage, and integrate digital resources (Markauskaite, 2007). A user’s perceived digital literacy has been consistently reported in the literature as having a positive relationship with the adoption of new technology (Hasan, 2003; Hasan & Ahmed, 2010; Potosky, 2002). Therefore, it can be surmised that a lecturer with high digital literacy will be more confident about integrating technology into the classroom and therefore more likely to adopt new

technology, such as mobile learning. Therefore the following are hypothesized:

H4: Digital literacy will have positive effect on the behaviour intention to use mobile learning.

Teachers’ Adoption of Mobile Learning H5: Digital literacy will have positive effect on perceived ease of use and usefulness.

ICT Anxiety ICT anxiety has been defined as “the feeling of discomfort, apprehension and fear of coping with ICT tools or uneasiness in the expectation of negative outcomes from computer-related operations” (Rahimi, Yadollahi, 2011, p. 204). These negative feelings have been shown to have negative effect on lecturers’ adoption of new technology and perception of how easy new technology will be to use (Agarwal et al., 2000; Beckers et al., 2007; Imhof et al., 2007; Parayitam et al., 2010; Saadé & Kira, 2007; Smith & Caputi, 2007). Anxiety has also been shown to have a negative influence on lecturers’ digital literacy, making them more likely to resist learning new ICT skills (Barbeite & Weiss, 2004; Sun, Tsai, Finger, Chen, & Yeh, 2008; van Raaij & Schepers, 2008).

Therefore the following are hypothesized:

H6: ICT anxiety will have a negative effect on the behaviour intention to use mobile learning.

H7: ICT anxiety will have a negative effect on the perceived ease of use of mobile learning.

H8: ICT anxiety will have a negative effect on a lecturers’ digital literacy.

ICT Teaching Self-Efficacy Teaching self-efficacy is the belief of a lecturer that they are able to effectively teach their students. According to Gibbs (2003, p. 3), educators who exhibit high levels of teaching selfefficacy tend to “persist in failure situations, take more risks with the curriculum, use new teaching approaches, make better gains in students’ achievement and have more motivated students.” When this form of self-efficacy is extended to the context of integrating ICT into teaching, it describes teachers who view technology as an effective way to enable student learning and perceive it as a useful medium to support their learning. Research has shown that a positive attitude to technology and having the skill to use the technology in the classroom are important and measurable factors in the level of integration of technology into their teaching (Zhao & Cziko, 2011).

Therefore the following are hypothesized:

H9: ICT teaching self-efficacy will have a positive effect on the behaviour intention to use mobile learning.

H10: ICT teaching self-efficacy will have a positive effect on the perceived ease of use and usefulness.

H11: Digital literacy will have a positive effect on teaching self-efficacy.

H12: ICT anxiety will have a negative effect on a lecturers’ teaching self-efficacy.

Research Method A survey was used to measure the major variables in this study. A multi-stage stratified convenience sampling method was used to survey the lecturers. Two strategies were used to recruit lecturers: staff emails lists and presentations at conferences. These two methods where used to encourage eligible teaching staff to take part. Lecturers were also encouraged to distribute the invitation to participate to other lecturers. Although the sampling method in this research is a form of convenience sampling, the representativeness of the sample was checked against population characteristics and found to be within acceptable limits. However, the sampling approach used has made it difficult to determine the response rate. This therefore indicates an important limitation of this study, which may influence the generalizability of these findings.





Mac Callum, Jeffrey, & Kinshuk

A total of 196 responses were received. Of these, 21 surveys were removed because they were incomplete or had significant outliers, giving a total of 175 eligible responses. The number of suitable responses received was not particularly large, but it is close to Hoelter’s (1983) recommended ‘critical sample size’ of 200. While this sample size is considered adequate, caution is still needed when interpreting the results. Of the total responses 61% (n=107) were female. The average age fell within the 40-49 age group (x¯=4.38, s =8.21). The vast majority of respondents were of European decent (90%, n=157). The remainder of the respondents were of Polynesian, Asian, or African descent.

Instrument To ensure the content validity of the scales adopted in this study, the items were derived from existing instruments used to measure the concepts of interest in this study. This approach helped ensure content validity (Chang & Tung, 2008). All items were measured using a 7-point Likert scale where 1 represented “Strongly disagree” and 7” Strongly agree”.

The questionnaire included 5 parts. The first part was used to measure the digital literacy of the respondents. Respondents were asked to rate their own skill in carrying out a range of tasks using either a computer or mobile device. These tasks used in this study were taken from Kennedy, Dalgarno, Bennett, Judd, Gray, and Chang (2008). Computer based activities required a range of skills from using word processing software to searching and downloading files from the Internet.

Mobile device usage included items relating to activities such as sending and receiving texts and uploading programs onto their phone.

Part 2 of the questionnaire measured the construct of ICT anxiety. This measure was adapted from Wilfong (2006). Examples of statements include, “I feel apprehensive when using a computer” and “I have a lot of confidence when it comes to working with information and communication technology”.

Part 3 of the questionnaire measured the respondents ICT self-efficacy for teaching. The items for this construct were derived from Mueller et al. (2008). In their study, they developed a comprehensive summary of teacher characteristics and variables that best discriminated between teachers who integrated computers into their teaching and those that did not. Mueller et al. (2008) did not formally define these characteristics nor coin a label. The scale used in this study assessed the attitudes of educators towards computers and their opinion of computers as an important instructional tool. The statements focused on ICT in general and included the following statements “I see ICT as tools that can complement my teaching,” “ICT allows me to bring current information to the class”, and “I feel frustrated more often when I use ICT in my classes than when I don’t use them.” Part 4 of the questionnaire measured the constructs of the TAM, namely, perceived usefulness, perceived ease of use, and behavioral intention to use. This was adapted from Venkatesh et al.

(2003). The items are slightly modified to fit the mobile learning context of this study. The last part collected demographic information and general comments about mobile learning. Questions included “Mobile technology will enable me to access learning content more often” for perceived usefulness and “I think it might take me awhile to get comfortable with using a mobile device for learning” for ease of use. One question was used to capture the future intention to adopt mobile learning, “Overall, I think mobile learning would be beneficial to my learning and I would be willing to adopt it, if I had the opportunity, in the future.” In part 2 and 3 the focus was placed on assessing anxiety and teaching self-efficacy of ICT in general rather than mobile technology specifically. The reason for this was it was considered that mobile anxiety and teaching self-efficacy and ICT anxiety and teaching self-efficacy were not disparate concepts. Furthermore, it could not be assumed that teachers would have used mobile

Teachers’ Adoption of Mobile Learning

technology in their teaching, so asking teachers to self-report on this would be limited. Part 1 and 4 focused more specifically on mobile technology. In particular, part 4 did not assume teachers had used mobile technology in their teaching but rather focused on their perceived usefulness and ease of use based on non-teaching experience of mobile technology. This study did not assume that participants had any experience of mobile learning but relied on users’ experience with mobile technology. Participants were expected to project their understanding of mobile technology to a situation of using that technology for learning. This approach of developing a mobile learning adoption model based on limited experience is not new and a number of studies have used this same approach (Akour, 2009; Lu & Viehland, 2008; Theng, 2009). In addition, future usage was calculated from a stated intention to adopt. Extensive empirical research has confirmed the causal link between intention to adopt and actual future adoption therefore giving some credence to using behavioral intention as an indicator of actual future adoption (Davis, 1989; Dillon, 2001).

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Based on the results of the exploratory factor analysis (EFA) of the 16 digital literacy items measured, three latent constructs for were identified. These 16 tasks were categorized into three key groups, namely tasks associated with basic ICT usage, tasks associated with expert/advanced ICT usage, and tasks associated with advanced mobile usage. In each category four items were retained to represent each construct. The items selected all had loadings greater than 0.7 as consistent with Mulaik and Millsap (2000). Basic ICT literacy assessed the competency of users in relation to basic computing tasks, such as using word processing software, searching and emailing on the Internet, and doing basic mobile activities, such as texting and calling. Advanced

Mac Callum, Jeffrey, & Kinshuk

ICT literacy assessed the competency of users in relation to more advanced computing, such as modifying images and sounds and using advanced software (such as Skype). Advanced mobile literacy related to using mobile technology for more complex mobile learning activities, such as accessing the Internet, emailing, and sending photos.

The EFA also indicated two distinct sub-scales for the ICT teaching self-efficacy construct. The first sub-scale related to whether lecturers saw ICT as giving them an advantage in their teaching over traditional methods (r =.85). The second sub-scale related to the ability of lecturers to use ICT in their teaching (referred to as ICT ability) (r =.70).

Correlations between the relationships were assessed to determine the level of multicollinearity.

Multicollinearity exists when factors are highly correlated (Gefen, Straub, & Boudreau, 2000).

High correlation can pose a risk of Type II errors in statistical modelling (Grewal, Cote, & Baumgartner, 2004). The correlations were determined using a bivariate Pearson product-moment coefficient (r). Based on the results of the correlation it was possible to determine that there were a number of significant relationships between the two important relationships in the study. However, these correlations were not sufficiently high for multicollinearity to be a concern. Table 2 presents the correlation matrix.

The composite reliability (internal consistency reliability) approach was estimated using Cronbach’s alpha. Composite reliabilities of constructs ranged between 0.71 and 0.93, exceeding the threshold of 0.7 (Nunnally & Bernstein, 1994).

Table 2: Means, standard deviations, and inter-correlations between latent constructs

ITEM ME SD BICTL AML AICTL ANX SE- SE- PU PEO

AN ATT ABL U Digital Literacy Basic ICT liter- 3.92 1.702 acy (BICTL) Advanced mo- 5.63 1.120.793** bile literacy (AML) Advanced ICT 3.45 2.112.651**.627** literacy (AICTL) Perceived anxiety 3.61 1.505 -.589** -.545** -.377** (Anx) ICT Teaching Self-Efficacy Attitude (SEAtt) 5.55.639.300**.281**.179* -.180* Ability (SEabl) 4.47 1.352.565**.579**.444** -.393**.334**

Mobile learning perceptions:

Perceived use- 5.32.969.199**.206**.101 -.068.207**.086 fulness (PU) Perceived Ease 3.51 1.190.459**.527**.283** -.498**.426**.196**.067 of use (PEOU) Behaviour In- 5.46 1.159.157*.093.156* -.001.006.012.168*.076 tention (BI) Notes: ** p 0.001, *p 0.05 level, highlighted cells refer to non-significant results, p.05. Means for all scales: 1=minimum (low), 7=maximum (high). Educator n = 175

Teachers’ Adoption of Mobile Learning



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