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Factors Influencing the Behavioural Intention to use the Internet as a Teaching-Learning Tool in Home Economics
Available online at www.sciencedirect.com
Procedia - Social and Behavioral Sciences 59 (2012) 180 – 187
UKM Teaching and Learning Congress 2011
Factors influencing the behavioural intention to use the internet as a teaching-learning tool in home economics Peng Lin Phuaa*, Su Luan Wongb & Rosini Abub b
a Cheras Perdana, Secondary School, Malaysia Department of Science and Technical Education, Faculty of Educational Studies, Universiti Putra Malaysia
Abstract The extended Technology Acceptance Model (TAM) is employed to explore how the behavioural intention (BI) of Home Economics (HE) teachers to use the Internet as a teaching tool is influenced by four factors, viz. Internet attitude (IA), perceived usefulness (PU), perceived ease of use (PEU) and perceived enjoyment (PE). The findings suggest that HE teachers’ BI to use the Internet is positively correlated (n=106, p<0.0005) with IA (r = .67), PU (r = .63), PEU (r = .54) and PE (r = .70). The implications on the teaching-learning of HE based on the findings of this study are discussed. © 2011 2011Published PublishedbybyElsevier Elsevier Ltd. Selection and/or reviewed responsibility of theTeaching UKM Teaching and Learning © Ltd. Selection and/or peer peer reviewed underunder responsibility of the UKM and Congress 2011. Learning Congress 2011 Keywords : Behavioural intention; internet; internet attitude; perceived usefulness; perceived ease of use; perceived enjoyment
1.
Introduction
To achieve the objective of creating an Information and Communication Technology (ICT)-based nation, and in line with Vision 2020, the Ministry of Education (MOE) has drawn up a blueprint to implement an Internet-based education for the future generations (MDC, 2005). ICT development in education, especially in the use of the Internet as a teaching-learning tool, has been growing rapidly. This represents a new paradigm for learning in the 21st century-providing space and opportunities for individuals to access, collect, analyse and transfer unlimited data and knowledge easily, quickly, conveniently and without charge (Yee, Luan, Ayub, & Mahmud, 2009; Luan, Fung, & Atan, 2008; Liaw, Huang, & Chen, 2007; Richards, 2005; Luan, Fung, Nawawi, & Hong, 2005). The Internet and the World Wide Web have opened up unlimited space in the field of communication and interaction, education and other areas without physical and time limitations (Azizan, 2010). The Internet, with its multiple uses, can function, as a teacher, a study mate and a tool to improve the overall teaching-learning process (Azizan, 2010; Yee et al., 2009). As technology has become part and parcel of everyday life in this information age, the use of ICT to facilitate the teaching-learning process should be a routine practice. Indeed, technology has also transformed the way teachers
* Corresponding author. Tel.:+6-012-2773-660; Fax:+6-03-8943-5386 E-mail address: [email protected]
1877-0428 © 2011 Published by Elsevier Ltd. Selection and/or peer reviewed under responsibility of the UKM Teaching and Learning Congress 2011 doi:10.1016/j.sbspro.2012.09.263
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teach in the classrooms (Afshari, Bakar, Luan, Samah, & Fooi, 2008). Teachers are expected to be able to use the Internet in the classroom to teach all subjects (Lokken, Cheek, & Hastings, 2003; McFadden, Croxall, & Wright, 2001). This implies that all teachers, including Home Economics (HE) teachers, should be able to meet the challenges of using ICT effectively in the classroom (Wong, Jalil, Ayub, Bakar, & Tang, 2003; Luan, Atan, & Sabudin, 2010; Moses, Khambari, & Luan, 2008). Teachers are advised to combine ICT elements which will contribute to the improvement of HE teaching quality (Dexter, Doering, & Riedel, 2006). However, whether they actually do so cannot be taken for granted. For this reason, it is important to study the behavioural intention (BI) of HE teachers to use the Internet by employing the extended Technology Acceptance Model (TAM). BI is the probability or a measure of strength of one’s intention to perform a specific behaviour (Fishbein & Ajzen, 1975). Strong BI to use a technology will reflect the individual’s acceptance and use of the techology, and this is the key measure of the success of the technology system in the classroom (Yi, Jackson, Park, & Probst, 2006; DeLone & McLean, 1992; Fishbein & Ajzen, 1975). The present study employed the extended TAM with perceived enjoyment (PE) as another external variable besides perceived usefulness (PU), perceived ease of use (PEU) and Internet attitude (IA) as PE is believed to have a direct influence on the intention to use (Davis, Bagozzi, & Warshaw, 1992). This is supported by Zhang, Zhao and Tan (2008) who have reported the existence of a positive relationship between the PE and BI. The latter acts as a predictor to the desired behaviour. Hence, BI increases when one enjoys interacting with a network-based learning system (Zhang et al., 2008). 2.
Review of Related Literature
2.1 Technology Acceptance Model (TAM) The TAM was introduced by Davis in 1989. TAM is an adaptation and extension of the Theory of Reasoned Action (TRA) proposed by Fishbein and Ajzen in 1975. According to the TRA, an individual’s behaviour is determined by one’s intention to perform the behaviour, and this intention is jointly influenced by one’s attitude and subjective norm related to the specific behaviour (Ajzen & Fishbein, 1980; Fishbein & Ajzen, 1975). Theoretically, the TAM is one of the most influential theories in information system that models the causal relationships of internal belief-attitude-intention-actual behaviour by two most important factors, namely PU and PEU. It explains and states that an individual’s acceptance and adoption of computer-based technology is dependent on their PU and PEU of the technology. Unlike the TRA, the TAM is more specific, and was developed to explore and predict user acceptance and behaviour of an information system (Davis, 1989). Figure 1 presents the original theoretical framework of TAM (Davis, 1989). It shows that the actual use of the system is determined by BI to use the particular system, and in turn, BI is jointly and directly determined by one’s attitude toward using the system and one’s PU. Attitude is also influenced by PU and PEU.
Perceived Usefulness
External
Attitude towards Behaviour
Variables
Perceived Ease of Use
Figure 1. Original Technology Acceptance Model (TAM) source: (Davis, 1989)
Behaviour Intention to Use
Actual System Use
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2.2 Factors of the Technology Use in the Teaching-Learning Process A number of studies have been conducted to predict user acceptance of an information systems (Davis, 1989; Selim, 2003; Ong & Lai, 2006; Maslin, 2007; Huang, Lin, & Chuang, 2007; Teo, Luan, & Sing, 2008; Sun, Tsai, Finger, Chen, & Yeh, 2008; Yahaya, Hashim, & Chia, 2009). Factors affecting the use of ICT mainly in the field of education have also been reviewed (Moses et al., 2008; Yee et al., 2009). Thus, in this study, four factors or predictor variables will be highlighted as significant in influencing the BI, acceptance and use of this particular technology in the teaching and learning processes. Fishbein and Ajzen (1975) and Ajzen and Fishbein (1980) defined attitude as a learned predisposition to respond in a consistently favourable or unfavourable manner with respect to a given object. According to Selwyn (1997), attitude will affect the preparedness, acceptance and individual behaviour towards computer technology, therefore attitude is important to be studied in the use of computer technology. If one’s attitude is not positive towards computer technology, an effective integration of computer technology into the teaching-learning process will not be realised even though sophisticated ICT infrastructures is provided (Selwyn, 1997). One will not use anything related to ICT and this can lead to low intention to use the particular ICT. Sang, Valcke, van Braak and Tondeur (2010), Teo, Lee, Chai and Wong (2009), Huang and Liaw (2005) and van Braak, Tondeur and Valcke (2004) also agreed that attitude is one of the contributing factors that influences the use and integration of computers in the teachinglearning process. PU is one of the main factors that impact the BI directly. Davis, Bagozzi and Warshaw (1989) defined PU as the degree to which an individual believes that using a particular system would enhance one’s performance. Masrom (2007) and Venkatesh and Morris (2000) asserted that PU has a positive and significant influence on individual behaviour governing one's intention to use a technology. Sun et al. (2008), Ong and Lai (2006) and Selim (2003) also highlighted that individual’s acceptance and intention to use a course web site will most likely be increased when one perceived the web site as useful. The review on ninety articles by Li, Qi and Shu (2008) supports the findings of many studies. They found that PU showed forty five significant relationships with dependent variables such as intention and actual usage behaviour. Davis et al. (1989) defined PEU as the degree to which an individual believes that using a particular system would be free of effort. Users who perceive a system as easy to use will assume that the system is simple and if the level of PEU towards online learning is high, then the acceptance and usage of online learning will also be high (Ong & Lai, 2006). This is because when the users perceive that a technology is easy to use, it is probable that they will assume that the system is simple and will be satisfied with the system (Sun et al., 2008; Ramayah, Muhamad Jantan, & Noraini Ismail, 2003). The researchers further mentioned that the users will then eventually sign up to join and use the system and remain longer in the system. The finding from Zhang et al. (2008) also asserted that student’s acceptance towards the use of web-based learning system was influenced by one’s PEU. Thus, website designers play an important role in developing suitable and easy to use education oriented websites for students and teachers as PEU is important in influencing the user’s decision whether to accept or reject the websites. PE refers to the extent to which the activity of using the computer is perceived as enjoyable in its own right, apart from any performance consequences that may be anticipated (Davis et al., 1992). Studies had identified PE to have direct influences on the intention to use, especially to the hedonic system which brings joy or happiness to users (Venkatesh, Speier, & Morris, 2002; Koufaris, 2002; Davis et al., 1992). PE as the internal motivation has been studied to discuss how it affects one’s behaviour towards technology adoption (Heijden, 2003; Venkatesh et al., 2002; Venkatesh & Speier, 1999; Davis et al., 1992). As the user is having fun with the process, he or she is willing to put in more effort, hence PE is believed to be able to reduce one‘s cognitive load (Agarwal & Karahanna, 2000; Deci, 1975) and significantly affected the attitude and BI to use a personal website (Heijden, 2003; Moon & Kim, 2001; Al-Gahtani & King, 1999). 3.
Objective of the study
This study aimed to explore HE teachers’ BI to use the Internet as a teaching-learning tool. The study also sought to explore the relationships of HE teachers’ BI to use the Internet as a teaching tool with four factors, viz. IA, PU, PEU and PE.
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4.
183
Methodology
A descriptive, correlational research design, using a cross-sectional survey technique, was used for this study. The population of this study comprised teachers (N= 799) who taught the HE option for the Living Skills subject in daily secondary schools within 10 districts in the state of Selangor. Teachers from this state were chosen for this study mainly because the Selangor State Education Department with the cooperation of the Educational Technology Department had developed the educational web portal, MyERT.net specifically for the use of HE teachers. All teachers were exposed to this portal in the hope that they will utilise and integrate the Internet technology in their teaching and learning. There are 203 daily secondary schools that offer the HE option in the Living Skills subject. The cluster sampling technique was used to select schools for this study where all teachers in the selected schools were then chosen for this study. Through this method, a total of 106 HE teachers participated in this survey study. All the participants were females and were within the age range of 23 to 55 years. The participants had an average of 5.44 years of Internet experience at home (SD=3.63) and an average of 3.45 years of Internet experience in school (SD=2.98). The survey method was employed where a questionnaire was administered to obtain information from the participants. The questionnaire consisted of seven sections: Section A - Demographic Profile; Section B - Internet Experience; Section C – PU; Section D - PEU; Section E – PE; Section F – IA; Section G – BI. A total of 67 items were developed by the researchers based on the literature review and past studies from the work of Davis (1989), Al-Ghatani and King (1999), Duggan, Hess, Morgan, Kim and Wilson (2001), Lee, Cheung and Chen (2005) and Saade and Bhali (2005). The researchers were given permission to translate and modify their works before the construction of the instrument. The questionnaire used a five-point Likert-type scale ranging from (1) strongly disagree to (5) strongly agree. The instrument was content validated by two HE experts and one ICT expert. It was pilot tested on 30 HE teachers who were not involved in the actual data collection process. Face validity was also established through five participants when they agreed that the instrument did measure what it was supposed to measure. From the reliability tests, the overall Cronbach’s alpha values for both pilot test and actual study were .90 and .95. These values indicated good reliability. 5.
Results
Results of the survey on the BI of HE teachers with regard to the use of the Internet as a teaching-learning tool and the correlation coefficients of BI and IA, PU, PEU and PE respectively are presented in this section. 5.1 Descriptive Analysis The findings of this study provided some baseline information about the HE teachers’ BI to use the Internet. The overall mean score was 3.94 (SD=3.68). Two of the nine items were in negative form and they were reversed scored prior to further analysis. Table 1 shows that more than three quarters of HE teachers agreed that they had intentions to use the Internet in HE instructional planning process (78.3%), particularly in planning suitable instructional pedagogies (81.1%), set inductions (77.4%) and access the Internet to acquire more information relating to HE (77.4%). These items were above the overall mean score (M=3.94, SD=3.68). Item 4, “In future, I wish to access the Internet to acquire more information concerning HE for the purpose of preparing lesson plans” had the highest mean score of 4.13 (S.D.=0.46), followed by item 2, “I will use the Internet to plan instructional pedagogy for HE if given the opportunity to do so” (M=4.02, S.D.=0.44) and item 3, “I will increase the use of the Internet in planning set induction for HE classes” (M=4.02, S.D.=0.48). Item 1, “In future, I intend to use the Internet in HE instructional planning process” showed the next highest mean score (M=4.00, S.D=0.498).
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Table 1. Percentage, Means & Standard deviations for Items on Behavioural Intention (n=106)
Frequency & Percentage No
1
2
Item
In future, I intend to use the Internet in Home Economics instructional planning process. I will use the Internet to plan instructional pedagogy for Home Economics if given the opportunity to do so.
Mean
SD
12 (11.3)
4.00
0.498
86 (81.1)
11 (10.4)
4.02
0.436
SD
D
NS
A
SA
f (%)
f (%)
f (%)
f (%)
f (%)
0 (0)
1 (0.9)
10 (9.4)
83 (78.3)
0 (0)
0 (0)
9 (8.5)
I will increase the use of the Internet in planning set induction for Home Economics classes.
0 (0)
0 (0)
11 (10.4)
82 (77.4)
13 (12.3)
4.02
0.478
3
0 (0)
0 (0)
5 (4.7)
82 (77.4)
19 (17.9)
4.13
0.459
4
In future, I wish to access the Internet to acquire more information concerning Home Economics for the purpose of preparing lesson plans.
0 (0)
1 (0.9)
25 (23.6)
65 (61.3)
15 (14.2)
3.89
0.637
5
I intend to recommend the use of the Internet to other Home Economics teachers because Internet is useful in planning Home Economics lessons. I have no objection to use the Internet for writing Home Economics instructional objectives.
0 (0)
6 (5.7)
21 (19.8)
69 (65.1)
10 (9.4)
3.78
0.690
6
I do not have any intention to use the Internet to prepare Home Economics teaching and learning materials.
19 (17.9)
60 (56.6)
14 (13.2)
13 (12.3)
0 (0)
3.80
0.877
7*
I will stop using the Internet to plan Home Economics teaching and learning activities because it is not beneficial at all.
21 (19.8)
66 (62.3)
15 (14.2)
4 (3.8)
0 (0)
3.98
0.703
8*
3.86
0.723
I will not stop using the Internet to plan 1 5 15 72 13 assessment activities for Home Economics (0.9) (4.7) (14.2) (67.9) (12.3) teaching and learning process. Note : Scale SD “Strongly Disagree”; D “Disagree”; NS “Not Sure”; A “Agree”; SA “Strongly Agree” * Negative Item 9
5.2 Correlational Analysis The correlation coefficients of BI with IA, PU, PEU and PE respectively were analysed using Pearson productmoment correlation coefficient and the results are presented in Table 2. Preliminary analyses were performed to ensure that there was no violation of the assumption of normality, linearity and homoscedasticity. The coefficient correlation results revealed that HE teachers’ BI to use the Internet as a teaching-learning tool was significantly correlated with their IA (r = .67, n = 106, p < .0005), PU (r = .63, n = 106, p < .0005), PEU (r = .54, n = 106, p < .0005) and PE (r = .70, n = 106, p < .0005). According to Cohen (1988), this means that there were large and significant positive relationships among the variables. There was a strong relationship between HE teachers’ BI to use the Internet as a teaching-learning tool and the aforesaid four factors.
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Table 2. Pearson product-moment correlation between behavioural intention and Internet attitudes, perceived usefulness, perceived ease of use & perceived enjoyment (n=106)
Behavioural intentions
Internet Attitude
Perceived usefulness
Perceived ease of use
Perceived enjoyment
.674**
.625**
.536**
.704**
** Correlation is significant at the .01 level (2-tailed)
6.
Discussion and Conclusion
This study explored HE teachers' BI to use the Internet as a teaching-learning tool. It also further examined the associations between HE teachers' BI and the four selected variables, viz. IA, PU, PEU and PE. The descriptive analysis of BI and the correlation analysis between BI and the aforesaid variables confirmed similar findings in the literature. The results of the study revealed that the majority of HE teachers had a strong BI to use the Internet during the teaching-learning process. This implies that HE teachers are more likely to use the Internet in the classroom once they possess strong intentions to do so. The results from this study are consistent with Ajzen and Fishbein’s theory (1980) and are in congruence with the findings of Davis (1989) which suggested that BI was a good predictor of actual computer usage. The correlation analysis shows that BI has a strong and positive relationship with IA, PU, PEU and PE. This means that when IA, PU, PEU and PE towards the Internet as a teaching-learning tool are enhanced among HE teachers, their BI is likely to increase. Once they have strong BI, their acceptance of ICT as a teaching-learning tool should be reflected in their actual use of the Internet to conduct their lessons. These findings are in congruence with those of Yi, Jackson, Park and Probst (2006) and Fishbein and Ajzen (1975). Suffice to say, past studies have confirmed the importance of TAM and IA, PU, PEU and PE in influencing the user’s decision whether to accept or reject a technology. The findings in this study indicate that IA, PU, PEU and PE relate positively to BI. In other words, the present findings suggest that IA, PU, PEU and PE should be taken into consideration in order to encourage and enhance HE teachers’ acceptance of technology as an educational tool. Hence, it is important to reduce or eliminate all the negative perceptions and attitudes such as dislike or fear of the Internet so that HE teachers will accept the Internet as a teaching-learning tool with an open mind. 7.
Implications of the study
In this information age, the Internet and web technologies are regarded as invaluable tools to supplement and complement traditional instructional methods (Usun, 2003). The Internet is immensely useful to educators and students, particularly in facing the challenges of ICT and globalisation (Ahmad, Tengku Zainal, & Omar, 2006). The MOE should consider introducing more ICT related components, particularly the Internet, into the curriculum as the present study has shown that there is a strong relationship between HE teachers’ BI and the four factors, viz. IA, PU, PEU and PE. For this reason, it is of paramount importance for the MOE to boost BI. There should be more road shows, workshops, intervention programmes, or even motivational seminars for HE teachers to enhance their IA, PU, PEU and PE towards using the Internet as an ICT teaching-learning tool, besides enabling teachers to enhance their computer competency and ICT skills (Phua, Wong, & Abu, 2011). 8.
Recommendations for future studies
We recommend that future studies involve HE teachers from other states in Malaysia. Comparison studies can then be done to investigate their BI to use the Internet for the teaching-learning process. In addition, teachers teaching other similar subjects within the Vocational and Technical domain can also be involved in the future as the Internet technology has been emphasised for such subjects. It is important to explore their BI to use the Internet as a teaching-learning tool as it gives an indication of their likelihood of using such technologies in their classrooms. We
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also recommend that more rigorous statistical analyses be used (ie. Structural Equation Modelling) in place of Pearson's r Correlation in order to identify the causal effects between the variables studied in the present study. References Afshari, M., Bakar, K. A., Luan, W. S., Samah, B. A., & Fooi, F. S. (2008). School leadership and information communication technology. Turkish Online Journal of Educational Technology, 7 (4), 82-91. Agarwal, R., & Karahanna, E. (2000). Time flies when you're having fun : Cognitive absorption and beliefs about information technology usage. MIS Quarterly , 24 (4), 665-694. Ahmad, S., Tengku Zainal, T. Z., & Omar, A. (2006). Isu-isu dalam pendidikan Matematik (2nd ed.). Utusan Publications & Distributions. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs: Prentice-Hall. Al-Gahtani, S. S., & King, M. (1999). Attitudes, satisfaction and usage : Factors contributing to each in the acceptance of information technology. Behaviour & Information Technology , 18 (4), 277-297. Azizan, F. Z. (2010). The effectiveness of using internet as a principal information resource in teaching and learning activity in higher educational institutions in Malaysia. Regional Conference on Knowledge Integration in ICT, (pp. 7-12). Davis, F. D. (1989). Perceived usefulness, perceives ease of use and user acceptance of IT. MIS Quarterly , 319-339. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and instrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology , 22, 1111-1132. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology : A comparison of two theoretical models. Journal of Management Science , 35 (8), 982-1003. Deci, E. L. (1975). Intrinsic motivation. New York: Plenum. DeLone, W. H., & McLean, E. R. (1992). Information system success : The quest for the dependent variable. Information Systems Research , 3 (1), 60-95. Dexter, S., Doering, A. H., & Riedel, E. S. (2006). Content area specific technology integration : A model for educating teachers. Journal of Technology and Teacher Education , 14 (2), 325-345. Duggan, A., Hess, B., Morgan, D., Kim, S., & Wilson, K. (2001). Measuring students' attitude toward educational use of the internet. J. Educational Computing Research , 25 (3), 267-281. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior : An introduction to theory and research. Boston: Addison-Wesley. Heijden, H. V. (2003). Factors influencing the usage of websites: The case of a generic portal in the Netherlands. Information & Management , 40 (6), 541-549. Huang, H. M., & Liaw, S. S. (2005). Exploring users' attitudes and intentions toward the web as a survey tool. Computers in Human Behavior , 21 (5), 729-743. Huang, J. H., Lin, Y. R., & Chuang, S. T. (2007). Elucidating User Behavior of Mobile Learning. Journal of The Electronic Library , 25 (5), 585598. Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online consumer behavior. Information Systems Research , 13 (2), 205-223. Lee, K. O., Cheung, M. K., & Chen, Z. H. (2005). Acceptance of internet-based learning medium : The role of extrinsic and intrinsic motivation. Information & Management , 42 (8), 1095-1104. Li, Y. Q., Qi, J. Y., & Shu, H. Y. (2008). Review of relationships among variables in TAM. Tsinghua Science and Technology , 13 (3), 273-278. Liaw, S., Huang, H., & Chen, G. (2007). Surveying instructor and learner attitudes toward e-learning. Computers & Education , 49 (2007), 10661080. Lokken, S. L., Cheek, W. K., & Hastings, S. W. (2003). The impact of technology training on family and consumer sciences teacher attitudes toward using computers as an instructional medium. Journal of Family and Consumer Sciences Education , 21 (1), 18-32. Luan, W. S., & Teo, T. (2009). Investigating the technology acceptance among student teachers in Malaysia: An application of the technology acceptance model (TAM). Asia-Pacific Education Researcher , 18 (2), 261-272. Luan, W. S., Atan, H., & Sabudin, S. (2010). Exploring teachers perceptions of their pedagogical role with computers: A case study in Malaysia. Procedia-Social and Behavioral Sciences , 2 (2), 388-391. Luan, W. S., Fung, N. S., & Atan, H. (2008). Gender differences in the usage and attitudes toward the Internet among student teachers in a public Malaysian university. American Journal of Applied Sciences , 5 (6), 689-697. Luan, W. S., Fung, N. S., Nawawi, M., & Hong, T. S. (2005). Experienced and inexperienced Internet users among pre-service teachers: Their use and attitudes toward the Internet. Educational Technology and Society , 8 (1), 90-103.
Peng Lin Phua et al. / Procedia - Social and Behavioral Sciences 59 (2012) 180 – 187
187
Masrom, M. (2007). Technology acceptance model and e-learning. 12th International Conference on Education (pp. 1-10). Brunei Darulssalam: Sultan Hassanal Bokiah Institute of Education. McFadden, J. R., Croxall, K. C., & Wright, C. B. (2001). The place of computers in family and consumer sciences classrooms. Journal of Family and Consumer Sciences Education , 19 (2), 11-18. MDC. (2005). Malaysian smart school roadmap 2005-2020 : An educational odyssey. Putrajaya: Government of Malaysia. Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a world-wide web context. Information and Management , 38 (4), 217-230. Moses, P., Khambari, M. N., & Luan, W. S. (2008). Laptop use and its antecedents among educators: A review of literature. European Journal of Social Sciences , 7 (1), 104-114. Ong, C. S., & Lai, J. Y. (2006). Gender differences in perceptions and relationships among determinants of e-learning acceptance. Computers in Human Behavior , 22 (5), 816-829. Phua, P. L., Wong, S. L., & Rosini Abu. (2011). A review of the literature: Predictors of internet use as an ICT teaching and learning tool among home economics teachers. 16th ARAHE International Biennial Congress. Manila: PAHESCU. Ramayah, T., Jantan, M., & Ismail, N. (2003). Impact of intrinsic and extrinsic motivation on internet usage in Malaysia. The 12th International Conference, (pp. 1-10). Richards, C. (2005). The design of effective ICT-supported learning activities : Exemplary models, changing requirements and new possibilities. Language Learning & Technology , 9 (1), 60-79. Saade, R., & Bahli, B. (2005). The impact of cognitive absorption on pu and pue in online learning: An extension of the TAM. Information and Management Amsterdam , 43 (2005), 317-327. Sang, G. Y., Valcke, M., van Braak, J., & Tondeur, J. (2010). Student teachers' thinking processes and ICT integration : Predictors of prospective teaching behaviors with educational technology. Computers & Education , 54 (1), 103-112. Selim, H. M. (2003). An empirical investigation of students acceptance of course website. Computers & Education , 40 (2003), 343-360. Selwyn, N. (1997). Students' attitudes toward computers : Validation of a computer attitude scale for 16-19 education. Computers Education , 28 (1), 35-41. Sun, P. C., Tsai, R. J., Finger, G., Chen, Y. Y., & Yeh, D. (2008). What drives a successful e-learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education , 50 (4), 1183-1202. Teo, T., Lee, C. B., Chai, C. S., & Wong, S. L. (2009). Assessing the intention to use technology among pre-service teachers in Singapore and Malaysia: A multigroup invariance analysis of the technology acceptance model. Computers & Education , 53 (3), 1000-1009. Teo, T., Luan, W.S., & Sing, C. C. (2008). A cross-cultural examination of the intention to use technology between Singaporean and Malaysian pre-service teachers : An application of the Technology Acceptance Model (TAM). Educational Technology & Society , 11 (4), 265-280. Usun, S. (2003). Undergraduate students attitudes towards educational uses of internet. Interactive Educational Multimedia , 7, 46-62. Van Braak, J., Tondeur, J., & Valcke, M. (2004). Explaining different types of computer use among primary school teachers. European Journal of Psychology Education , 19 (4), 407-422. Venkatesh, V., & Morris, M. G. (2000). Why don't men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior : A meta analysis. Computers in Human Behavior , 13 (1), 1-22. Venkatesh, V., & Speier, C. (1999). Computer technology training in the workplace : A longitudinal investigation of the effect of mood. Organizational Behavior amd Human Decision , 79 (1), 1-28. Venkatesh, V., Speier, C., & Morris, M. G. (2002). User acceptance enablers in individual decision making about technology: Toward an integrated model. Decision Sciences , 33 (2), 297-316. Wong, S.L., Jalil, H. A., Ayub, A. F.M., Bakar, K.A. & Tang, S. H. (2003). Teaching a discrete information technology course in a constructivist learning environment: Is it effective for Malaysian pre-service teachers? The Internet and Higher Education , 6 (2), 193-204. Yahaya, A., Hashim, S., & Yee, C. S. (2009). Kesediaan guru teknikal terhadap penggunaan teknologi maklumat dan komunikasi dalam pengajaran dan pembelajaran di sekolah menengah teknik. Jurnal Pendidikan Universiti Teknologi Malaysia , 14, 64-78. Yee, H. T., Luan, W. S., Mohamad Ayub, A. F., & Mahmud, R. (2009). A review of the literature: Determinants of online learning among students. European Journal of Social Sciences , 8 (2), 246-252. Yi, M. Y., Jackson, J. D., Park, J. S., & Probst, J. C. (2006). Understanding information technology acceptance by individual professionals? Toward an integrative view. Information & Management , 43 (3), 350-363. Zhang, S., Zhao, J., & Tan, W. W. (2008). Extending TAM for online learning systems: An intrinsic motivation perspective. Tsinghua Science and Technology , 13 (3), 312-317.
Procedia - Social and Behavioral Sciences 59 (2012) 180 – 187
UKM Teaching and Learning Congress 2011
Factors influencing the behavioural intention to use the internet as a teaching-learning tool in home economics Peng Lin Phuaa*, Su Luan Wongb & Rosini Abub b
a Cheras Perdana, Secondary School, Malaysia Department of Science and Technical Education, Faculty of Educational Studies, Universiti Putra Malaysia
Abstract The extended Technology Acceptance Model (TAM) is employed to explore how the behavioural intention (BI) of Home Economics (HE) teachers to use the Internet as a teaching tool is influenced by four factors, viz. Internet attitude (IA), perceived usefulness (PU), perceived ease of use (PEU) and perceived enjoyment (PE). The findings suggest that HE teachers’ BI to use the Internet is positively correlated (n=106, p<0.0005) with IA (r = .67), PU (r = .63), PEU (r = .54) and PE (r = .70). The implications on the teaching-learning of HE based on the findings of this study are discussed. © 2011 2011Published PublishedbybyElsevier Elsevier Ltd. Selection and/or reviewed responsibility of theTeaching UKM Teaching and Learning © Ltd. Selection and/or peer peer reviewed underunder responsibility of the UKM and Congress 2011. Learning Congress 2011 Keywords : Behavioural intention; internet; internet attitude; perceived usefulness; perceived ease of use; perceived enjoyment
1.
Introduction
To achieve the objective of creating an Information and Communication Technology (ICT)-based nation, and in line with Vision 2020, the Ministry of Education (MOE) has drawn up a blueprint to implement an Internet-based education for the future generations (MDC, 2005). ICT development in education, especially in the use of the Internet as a teaching-learning tool, has been growing rapidly. This represents a new paradigm for learning in the 21st century-providing space and opportunities for individuals to access, collect, analyse and transfer unlimited data and knowledge easily, quickly, conveniently and without charge (Yee, Luan, Ayub, & Mahmud, 2009; Luan, Fung, & Atan, 2008; Liaw, Huang, & Chen, 2007; Richards, 2005; Luan, Fung, Nawawi, & Hong, 2005). The Internet and the World Wide Web have opened up unlimited space in the field of communication and interaction, education and other areas without physical and time limitations (Azizan, 2010). The Internet, with its multiple uses, can function, as a teacher, a study mate and a tool to improve the overall teaching-learning process (Azizan, 2010; Yee et al., 2009). As technology has become part and parcel of everyday life in this information age, the use of ICT to facilitate the teaching-learning process should be a routine practice. Indeed, technology has also transformed the way teachers
* Corresponding author. Tel.:+6-012-2773-660; Fax:+6-03-8943-5386 E-mail address: [email protected]
1877-0428 © 2011 Published by Elsevier Ltd. Selection and/or peer reviewed under responsibility of the UKM Teaching and Learning Congress 2011 doi:10.1016/j.sbspro.2012.09.263
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teach in the classrooms (Afshari, Bakar, Luan, Samah, & Fooi, 2008). Teachers are expected to be able to use the Internet in the classroom to teach all subjects (Lokken, Cheek, & Hastings, 2003; McFadden, Croxall, & Wright, 2001). This implies that all teachers, including Home Economics (HE) teachers, should be able to meet the challenges of using ICT effectively in the classroom (Wong, Jalil, Ayub, Bakar, & Tang, 2003; Luan, Atan, & Sabudin, 2010; Moses, Khambari, & Luan, 2008). Teachers are advised to combine ICT elements which will contribute to the improvement of HE teaching quality (Dexter, Doering, & Riedel, 2006). However, whether they actually do so cannot be taken for granted. For this reason, it is important to study the behavioural intention (BI) of HE teachers to use the Internet by employing the extended Technology Acceptance Model (TAM). BI is the probability or a measure of strength of one’s intention to perform a specific behaviour (Fishbein & Ajzen, 1975). Strong BI to use a technology will reflect the individual’s acceptance and use of the techology, and this is the key measure of the success of the technology system in the classroom (Yi, Jackson, Park, & Probst, 2006; DeLone & McLean, 1992; Fishbein & Ajzen, 1975). The present study employed the extended TAM with perceived enjoyment (PE) as another external variable besides perceived usefulness (PU), perceived ease of use (PEU) and Internet attitude (IA) as PE is believed to have a direct influence on the intention to use (Davis, Bagozzi, & Warshaw, 1992). This is supported by Zhang, Zhao and Tan (2008) who have reported the existence of a positive relationship between the PE and BI. The latter acts as a predictor to the desired behaviour. Hence, BI increases when one enjoys interacting with a network-based learning system (Zhang et al., 2008). 2.
Review of Related Literature
2.1 Technology Acceptance Model (TAM) The TAM was introduced by Davis in 1989. TAM is an adaptation and extension of the Theory of Reasoned Action (TRA) proposed by Fishbein and Ajzen in 1975. According to the TRA, an individual’s behaviour is determined by one’s intention to perform the behaviour, and this intention is jointly influenced by one’s attitude and subjective norm related to the specific behaviour (Ajzen & Fishbein, 1980; Fishbein & Ajzen, 1975). Theoretically, the TAM is one of the most influential theories in information system that models the causal relationships of internal belief-attitude-intention-actual behaviour by two most important factors, namely PU and PEU. It explains and states that an individual’s acceptance and adoption of computer-based technology is dependent on their PU and PEU of the technology. Unlike the TRA, the TAM is more specific, and was developed to explore and predict user acceptance and behaviour of an information system (Davis, 1989). Figure 1 presents the original theoretical framework of TAM (Davis, 1989). It shows that the actual use of the system is determined by BI to use the particular system, and in turn, BI is jointly and directly determined by one’s attitude toward using the system and one’s PU. Attitude is also influenced by PU and PEU.
Perceived Usefulness
External
Attitude towards Behaviour
Variables
Perceived Ease of Use
Figure 1. Original Technology Acceptance Model (TAM) source: (Davis, 1989)
Behaviour Intention to Use
Actual System Use
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2.2 Factors of the Technology Use in the Teaching-Learning Process A number of studies have been conducted to predict user acceptance of an information systems (Davis, 1989; Selim, 2003; Ong & Lai, 2006; Maslin, 2007; Huang, Lin, & Chuang, 2007; Teo, Luan, & Sing, 2008; Sun, Tsai, Finger, Chen, & Yeh, 2008; Yahaya, Hashim, & Chia, 2009). Factors affecting the use of ICT mainly in the field of education have also been reviewed (Moses et al., 2008; Yee et al., 2009). Thus, in this study, four factors or predictor variables will be highlighted as significant in influencing the BI, acceptance and use of this particular technology in the teaching and learning processes. Fishbein and Ajzen (1975) and Ajzen and Fishbein (1980) defined attitude as a learned predisposition to respond in a consistently favourable or unfavourable manner with respect to a given object. According to Selwyn (1997), attitude will affect the preparedness, acceptance and individual behaviour towards computer technology, therefore attitude is important to be studied in the use of computer technology. If one’s attitude is not positive towards computer technology, an effective integration of computer technology into the teaching-learning process will not be realised even though sophisticated ICT infrastructures is provided (Selwyn, 1997). One will not use anything related to ICT and this can lead to low intention to use the particular ICT. Sang, Valcke, van Braak and Tondeur (2010), Teo, Lee, Chai and Wong (2009), Huang and Liaw (2005) and van Braak, Tondeur and Valcke (2004) also agreed that attitude is one of the contributing factors that influences the use and integration of computers in the teachinglearning process. PU is one of the main factors that impact the BI directly. Davis, Bagozzi and Warshaw (1989) defined PU as the degree to which an individual believes that using a particular system would enhance one’s performance. Masrom (2007) and Venkatesh and Morris (2000) asserted that PU has a positive and significant influence on individual behaviour governing one's intention to use a technology. Sun et al. (2008), Ong and Lai (2006) and Selim (2003) also highlighted that individual’s acceptance and intention to use a course web site will most likely be increased when one perceived the web site as useful. The review on ninety articles by Li, Qi and Shu (2008) supports the findings of many studies. They found that PU showed forty five significant relationships with dependent variables such as intention and actual usage behaviour. Davis et al. (1989) defined PEU as the degree to which an individual believes that using a particular system would be free of effort. Users who perceive a system as easy to use will assume that the system is simple and if the level of PEU towards online learning is high, then the acceptance and usage of online learning will also be high (Ong & Lai, 2006). This is because when the users perceive that a technology is easy to use, it is probable that they will assume that the system is simple and will be satisfied with the system (Sun et al., 2008; Ramayah, Muhamad Jantan, & Noraini Ismail, 2003). The researchers further mentioned that the users will then eventually sign up to join and use the system and remain longer in the system. The finding from Zhang et al. (2008) also asserted that student’s acceptance towards the use of web-based learning system was influenced by one’s PEU. Thus, website designers play an important role in developing suitable and easy to use education oriented websites for students and teachers as PEU is important in influencing the user’s decision whether to accept or reject the websites. PE refers to the extent to which the activity of using the computer is perceived as enjoyable in its own right, apart from any performance consequences that may be anticipated (Davis et al., 1992). Studies had identified PE to have direct influences on the intention to use, especially to the hedonic system which brings joy or happiness to users (Venkatesh, Speier, & Morris, 2002; Koufaris, 2002; Davis et al., 1992). PE as the internal motivation has been studied to discuss how it affects one’s behaviour towards technology adoption (Heijden, 2003; Venkatesh et al., 2002; Venkatesh & Speier, 1999; Davis et al., 1992). As the user is having fun with the process, he or she is willing to put in more effort, hence PE is believed to be able to reduce one‘s cognitive load (Agarwal & Karahanna, 2000; Deci, 1975) and significantly affected the attitude and BI to use a personal website (Heijden, 2003; Moon & Kim, 2001; Al-Gahtani & King, 1999). 3.
Objective of the study
This study aimed to explore HE teachers’ BI to use the Internet as a teaching-learning tool. The study also sought to explore the relationships of HE teachers’ BI to use the Internet as a teaching tool with four factors, viz. IA, PU, PEU and PE.
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4.
183
Methodology
A descriptive, correlational research design, using a cross-sectional survey technique, was used for this study. The population of this study comprised teachers (N= 799) who taught the HE option for the Living Skills subject in daily secondary schools within 10 districts in the state of Selangor. Teachers from this state were chosen for this study mainly because the Selangor State Education Department with the cooperation of the Educational Technology Department had developed the educational web portal, MyERT.net specifically for the use of HE teachers. All teachers were exposed to this portal in the hope that they will utilise and integrate the Internet technology in their teaching and learning. There are 203 daily secondary schools that offer the HE option in the Living Skills subject. The cluster sampling technique was used to select schools for this study where all teachers in the selected schools were then chosen for this study. Through this method, a total of 106 HE teachers participated in this survey study. All the participants were females and were within the age range of 23 to 55 years. The participants had an average of 5.44 years of Internet experience at home (SD=3.63) and an average of 3.45 years of Internet experience in school (SD=2.98). The survey method was employed where a questionnaire was administered to obtain information from the participants. The questionnaire consisted of seven sections: Section A - Demographic Profile; Section B - Internet Experience; Section C – PU; Section D - PEU; Section E – PE; Section F – IA; Section G – BI. A total of 67 items were developed by the researchers based on the literature review and past studies from the work of Davis (1989), Al-Ghatani and King (1999), Duggan, Hess, Morgan, Kim and Wilson (2001), Lee, Cheung and Chen (2005) and Saade and Bhali (2005). The researchers were given permission to translate and modify their works before the construction of the instrument. The questionnaire used a five-point Likert-type scale ranging from (1) strongly disagree to (5) strongly agree. The instrument was content validated by two HE experts and one ICT expert. It was pilot tested on 30 HE teachers who were not involved in the actual data collection process. Face validity was also established through five participants when they agreed that the instrument did measure what it was supposed to measure. From the reliability tests, the overall Cronbach’s alpha values for both pilot test and actual study were .90 and .95. These values indicated good reliability. 5.
Results
Results of the survey on the BI of HE teachers with regard to the use of the Internet as a teaching-learning tool and the correlation coefficients of BI and IA, PU, PEU and PE respectively are presented in this section. 5.1 Descriptive Analysis The findings of this study provided some baseline information about the HE teachers’ BI to use the Internet. The overall mean score was 3.94 (SD=3.68). Two of the nine items were in negative form and they were reversed scored prior to further analysis. Table 1 shows that more than three quarters of HE teachers agreed that they had intentions to use the Internet in HE instructional planning process (78.3%), particularly in planning suitable instructional pedagogies (81.1%), set inductions (77.4%) and access the Internet to acquire more information relating to HE (77.4%). These items were above the overall mean score (M=3.94, SD=3.68). Item 4, “In future, I wish to access the Internet to acquire more information concerning HE for the purpose of preparing lesson plans” had the highest mean score of 4.13 (S.D.=0.46), followed by item 2, “I will use the Internet to plan instructional pedagogy for HE if given the opportunity to do so” (M=4.02, S.D.=0.44) and item 3, “I will increase the use of the Internet in planning set induction for HE classes” (M=4.02, S.D.=0.48). Item 1, “In future, I intend to use the Internet in HE instructional planning process” showed the next highest mean score (M=4.00, S.D=0.498).
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Table 1. Percentage, Means & Standard deviations for Items on Behavioural Intention (n=106)
Frequency & Percentage No
1
2
Item
In future, I intend to use the Internet in Home Economics instructional planning process. I will use the Internet to plan instructional pedagogy for Home Economics if given the opportunity to do so.
Mean
SD
12 (11.3)
4.00
0.498
86 (81.1)
11 (10.4)
4.02
0.436
SD
D
NS
A
SA
f (%)
f (%)
f (%)
f (%)
f (%)
0 (0)
1 (0.9)
10 (9.4)
83 (78.3)
0 (0)
0 (0)
9 (8.5)
I will increase the use of the Internet in planning set induction for Home Economics classes.
0 (0)
0 (0)
11 (10.4)
82 (77.4)
13 (12.3)
4.02
0.478
3
0 (0)
0 (0)
5 (4.7)
82 (77.4)
19 (17.9)
4.13
0.459
4
In future, I wish to access the Internet to acquire more information concerning Home Economics for the purpose of preparing lesson plans.
0 (0)
1 (0.9)
25 (23.6)
65 (61.3)
15 (14.2)
3.89
0.637
5
I intend to recommend the use of the Internet to other Home Economics teachers because Internet is useful in planning Home Economics lessons. I have no objection to use the Internet for writing Home Economics instructional objectives.
0 (0)
6 (5.7)
21 (19.8)
69 (65.1)
10 (9.4)
3.78
0.690
6
I do not have any intention to use the Internet to prepare Home Economics teaching and learning materials.
19 (17.9)
60 (56.6)
14 (13.2)
13 (12.3)
0 (0)
3.80
0.877
7*
I will stop using the Internet to plan Home Economics teaching and learning activities because it is not beneficial at all.
21 (19.8)
66 (62.3)
15 (14.2)
4 (3.8)
0 (0)
3.98
0.703
8*
3.86
0.723
I will not stop using the Internet to plan 1 5 15 72 13 assessment activities for Home Economics (0.9) (4.7) (14.2) (67.9) (12.3) teaching and learning process. Note : Scale SD “Strongly Disagree”; D “Disagree”; NS “Not Sure”; A “Agree”; SA “Strongly Agree” * Negative Item 9
5.2 Correlational Analysis The correlation coefficients of BI with IA, PU, PEU and PE respectively were analysed using Pearson productmoment correlation coefficient and the results are presented in Table 2. Preliminary analyses were performed to ensure that there was no violation of the assumption of normality, linearity and homoscedasticity. The coefficient correlation results revealed that HE teachers’ BI to use the Internet as a teaching-learning tool was significantly correlated with their IA (r = .67, n = 106, p < .0005), PU (r = .63, n = 106, p < .0005), PEU (r = .54, n = 106, p < .0005) and PE (r = .70, n = 106, p < .0005). According to Cohen (1988), this means that there were large and significant positive relationships among the variables. There was a strong relationship between HE teachers’ BI to use the Internet as a teaching-learning tool and the aforesaid four factors.
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Table 2. Pearson product-moment correlation between behavioural intention and Internet attitudes, perceived usefulness, perceived ease of use & perceived enjoyment (n=106)
Behavioural intentions
Internet Attitude
Perceived usefulness
Perceived ease of use
Perceived enjoyment
.674**
.625**
.536**
.704**
** Correlation is significant at the .01 level (2-tailed)
6.
Discussion and Conclusion
This study explored HE teachers' BI to use the Internet as a teaching-learning tool. It also further examined the associations between HE teachers' BI and the four selected variables, viz. IA, PU, PEU and PE. The descriptive analysis of BI and the correlation analysis between BI and the aforesaid variables confirmed similar findings in the literature. The results of the study revealed that the majority of HE teachers had a strong BI to use the Internet during the teaching-learning process. This implies that HE teachers are more likely to use the Internet in the classroom once they possess strong intentions to do so. The results from this study are consistent with Ajzen and Fishbein’s theory (1980) and are in congruence with the findings of Davis (1989) which suggested that BI was a good predictor of actual computer usage. The correlation analysis shows that BI has a strong and positive relationship with IA, PU, PEU and PE. This means that when IA, PU, PEU and PE towards the Internet as a teaching-learning tool are enhanced among HE teachers, their BI is likely to increase. Once they have strong BI, their acceptance of ICT as a teaching-learning tool should be reflected in their actual use of the Internet to conduct their lessons. These findings are in congruence with those of Yi, Jackson, Park and Probst (2006) and Fishbein and Ajzen (1975). Suffice to say, past studies have confirmed the importance of TAM and IA, PU, PEU and PE in influencing the user’s decision whether to accept or reject a technology. The findings in this study indicate that IA, PU, PEU and PE relate positively to BI. In other words, the present findings suggest that IA, PU, PEU and PE should be taken into consideration in order to encourage and enhance HE teachers’ acceptance of technology as an educational tool. Hence, it is important to reduce or eliminate all the negative perceptions and attitudes such as dislike or fear of the Internet so that HE teachers will accept the Internet as a teaching-learning tool with an open mind. 7.
Implications of the study
In this information age, the Internet and web technologies are regarded as invaluable tools to supplement and complement traditional instructional methods (Usun, 2003). The Internet is immensely useful to educators and students, particularly in facing the challenges of ICT and globalisation (Ahmad, Tengku Zainal, & Omar, 2006). The MOE should consider introducing more ICT related components, particularly the Internet, into the curriculum as the present study has shown that there is a strong relationship between HE teachers’ BI and the four factors, viz. IA, PU, PEU and PE. For this reason, it is of paramount importance for the MOE to boost BI. There should be more road shows, workshops, intervention programmes, or even motivational seminars for HE teachers to enhance their IA, PU, PEU and PE towards using the Internet as an ICT teaching-learning tool, besides enabling teachers to enhance their computer competency and ICT skills (Phua, Wong, & Abu, 2011). 8.
Recommendations for future studies
We recommend that future studies involve HE teachers from other states in Malaysia. Comparison studies can then be done to investigate their BI to use the Internet for the teaching-learning process. In addition, teachers teaching other similar subjects within the Vocational and Technical domain can also be involved in the future as the Internet technology has been emphasised for such subjects. It is important to explore their BI to use the Internet as a teaching-learning tool as it gives an indication of their likelihood of using such technologies in their classrooms. We
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also recommend that more rigorous statistical analyses be used (ie. Structural Equation Modelling) in place of Pearson's r Correlation in order to identify the causal effects between the variables studied in the present study. References Afshari, M., Bakar, K. A., Luan, W. S., Samah, B. A., & Fooi, F. S. (2008). School leadership and information communication technology. Turkish Online Journal of Educational Technology, 7 (4), 82-91. Agarwal, R., & Karahanna, E. (2000). Time flies when you're having fun : Cognitive absorption and beliefs about information technology usage. MIS Quarterly , 24 (4), 665-694. Ahmad, S., Tengku Zainal, T. Z., & Omar, A. (2006). Isu-isu dalam pendidikan Matematik (2nd ed.). Utusan Publications & Distributions. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs: Prentice-Hall. Al-Gahtani, S. S., & King, M. (1999). Attitudes, satisfaction and usage : Factors contributing to each in the acceptance of information technology. Behaviour & Information Technology , 18 (4), 277-297. Azizan, F. Z. (2010). The effectiveness of using internet as a principal information resource in teaching and learning activity in higher educational institutions in Malaysia. Regional Conference on Knowledge Integration in ICT, (pp. 7-12). Davis, F. D. (1989). Perceived usefulness, perceives ease of use and user acceptance of IT. MIS Quarterly , 319-339. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and instrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology , 22, 1111-1132. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology : A comparison of two theoretical models. Journal of Management Science , 35 (8), 982-1003. Deci, E. L. (1975). Intrinsic motivation. New York: Plenum. DeLone, W. H., & McLean, E. R. (1992). Information system success : The quest for the dependent variable. Information Systems Research , 3 (1), 60-95. Dexter, S., Doering, A. H., & Riedel, E. S. (2006). Content area specific technology integration : A model for educating teachers. Journal of Technology and Teacher Education , 14 (2), 325-345. Duggan, A., Hess, B., Morgan, D., Kim, S., & Wilson, K. (2001). Measuring students' attitude toward educational use of the internet. J. Educational Computing Research , 25 (3), 267-281. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior : An introduction to theory and research. Boston: Addison-Wesley. Heijden, H. V. (2003). Factors influencing the usage of websites: The case of a generic portal in the Netherlands. Information & Management , 40 (6), 541-549. Huang, H. M., & Liaw, S. S. (2005). Exploring users' attitudes and intentions toward the web as a survey tool. Computers in Human Behavior , 21 (5), 729-743. Huang, J. H., Lin, Y. R., & Chuang, S. T. (2007). Elucidating User Behavior of Mobile Learning. Journal of The Electronic Library , 25 (5), 585598. Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online consumer behavior. Information Systems Research , 13 (2), 205-223. Lee, K. O., Cheung, M. K., & Chen, Z. H. (2005). Acceptance of internet-based learning medium : The role of extrinsic and intrinsic motivation. Information & Management , 42 (8), 1095-1104. Li, Y. Q., Qi, J. Y., & Shu, H. Y. (2008). Review of relationships among variables in TAM. Tsinghua Science and Technology , 13 (3), 273-278. Liaw, S., Huang, H., & Chen, G. (2007). Surveying instructor and learner attitudes toward e-learning. Computers & Education , 49 (2007), 10661080. Lokken, S. L., Cheek, W. K., & Hastings, S. W. (2003). The impact of technology training on family and consumer sciences teacher attitudes toward using computers as an instructional medium. Journal of Family and Consumer Sciences Education , 21 (1), 18-32. Luan, W. S., & Teo, T. (2009). Investigating the technology acceptance among student teachers in Malaysia: An application of the technology acceptance model (TAM). Asia-Pacific Education Researcher , 18 (2), 261-272. Luan, W. S., Atan, H., & Sabudin, S. (2010). Exploring teachers perceptions of their pedagogical role with computers: A case study in Malaysia. Procedia-Social and Behavioral Sciences , 2 (2), 388-391. Luan, W. S., Fung, N. S., & Atan, H. (2008). Gender differences in the usage and attitudes toward the Internet among student teachers in a public Malaysian university. American Journal of Applied Sciences , 5 (6), 689-697. Luan, W. S., Fung, N. S., Nawawi, M., & Hong, T. S. (2005). Experienced and inexperienced Internet users among pre-service teachers: Their use and attitudes toward the Internet. Educational Technology and Society , 8 (1), 90-103.
Peng Lin Phua et al. / Procedia - Social and Behavioral Sciences 59 (2012) 180 – 187
187
Masrom, M. (2007). Technology acceptance model and e-learning. 12th International Conference on Education (pp. 1-10). Brunei Darulssalam: Sultan Hassanal Bokiah Institute of Education. McFadden, J. R., Croxall, K. C., & Wright, C. B. (2001). The place of computers in family and consumer sciences classrooms. Journal of Family and Consumer Sciences Education , 19 (2), 11-18. MDC. (2005). Malaysian smart school roadmap 2005-2020 : An educational odyssey. Putrajaya: Government of Malaysia. Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a world-wide web context. Information and Management , 38 (4), 217-230. Moses, P., Khambari, M. N., & Luan, W. S. (2008). Laptop use and its antecedents among educators: A review of literature. European Journal of Social Sciences , 7 (1), 104-114. Ong, C. S., & Lai, J. Y. (2006). Gender differences in perceptions and relationships among determinants of e-learning acceptance. Computers in Human Behavior , 22 (5), 816-829. Phua, P. L., Wong, S. L., & Rosini Abu. (2011). A review of the literature: Predictors of internet use as an ICT teaching and learning tool among home economics teachers. 16th ARAHE International Biennial Congress. Manila: PAHESCU. Ramayah, T., Jantan, M., & Ismail, N. (2003). Impact of intrinsic and extrinsic motivation on internet usage in Malaysia. The 12th International Conference, (pp. 1-10). Richards, C. (2005). The design of effective ICT-supported learning activities : Exemplary models, changing requirements and new possibilities. Language Learning & Technology , 9 (1), 60-79. Saade, R., & Bahli, B. (2005). The impact of cognitive absorption on pu and pue in online learning: An extension of the TAM. Information and Management Amsterdam , 43 (2005), 317-327. Sang, G. Y., Valcke, M., van Braak, J., & Tondeur, J. (2010). Student teachers' thinking processes and ICT integration : Predictors of prospective teaching behaviors with educational technology. Computers & Education , 54 (1), 103-112. Selim, H. M. (2003). An empirical investigation of students acceptance of course website. Computers & Education , 40 (2003), 343-360. Selwyn, N. (1997). Students' attitudes toward computers : Validation of a computer attitude scale for 16-19 education. Computers Education , 28 (1), 35-41. Sun, P. C., Tsai, R. J., Finger, G., Chen, Y. Y., & Yeh, D. (2008). What drives a successful e-learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education , 50 (4), 1183-1202. Teo, T., Lee, C. B., Chai, C. S., & Wong, S. L. (2009). Assessing the intention to use technology among pre-service teachers in Singapore and Malaysia: A multigroup invariance analysis of the technology acceptance model. Computers & Education , 53 (3), 1000-1009. Teo, T., Luan, W.S., & Sing, C. C. (2008). A cross-cultural examination of the intention to use technology between Singaporean and Malaysian pre-service teachers : An application of the Technology Acceptance Model (TAM). Educational Technology & Society , 11 (4), 265-280. Usun, S. (2003). Undergraduate students attitudes towards educational uses of internet. Interactive Educational Multimedia , 7, 46-62. Van Braak, J., Tondeur, J., & Valcke, M. (2004). Explaining different types of computer use among primary school teachers. European Journal of Psychology Education , 19 (4), 407-422. Venkatesh, V., & Morris, M. G. (2000). Why don't men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior : A meta analysis. Computers in Human Behavior , 13 (1), 1-22. Venkatesh, V., & Speier, C. (1999). Computer technology training in the workplace : A longitudinal investigation of the effect of mood. Organizational Behavior amd Human Decision , 79 (1), 1-28. Venkatesh, V., Speier, C., & Morris, M. G. (2002). User acceptance enablers in individual decision making about technology: Toward an integrated model. Decision Sciences , 33 (2), 297-316. Wong, S.L., Jalil, H. A., Ayub, A. F.M., Bakar, K.A. & Tang, S. H. (2003). Teaching a discrete information technology course in a constructivist learning environment: Is it effective for Malaysian pre-service teachers? The Internet and Higher Education , 6 (2), 193-204. Yahaya, A., Hashim, S., & Yee, C. S. (2009). Kesediaan guru teknikal terhadap penggunaan teknologi maklumat dan komunikasi dalam pengajaran dan pembelajaran di sekolah menengah teknik. Jurnal Pendidikan Universiti Teknologi Malaysia , 14, 64-78. Yee, H. T., Luan, W. S., Mohamad Ayub, A. F., & Mahmud, R. (2009). A review of the literature: Determinants of online learning among students. European Journal of Social Sciences , 8 (2), 246-252. Yi, M. Y., Jackson, J. D., Park, J. S., & Probst, J. C. (2006). Understanding information technology acceptance by individual professionals? Toward an integrative view. Information & Management , 43 (3), 350-363. Zhang, S., Zhao, J., & Tan, W. W. (2008). Extending TAM for online learning systems: An intrinsic motivation perspective. Tsinghua Science and Technology , 13 (3), 312-317.