British Journal of Educational Technology doi:10.1111/j.1467-8535.2012.01327.x
Vol 44 No 1 2013
E22–E25
Colloquium Reconsidering the role of attitude in the TAM: An answer to Teo (2009) and Nistor and Heymann (2010), and Lopez-Bonilla and Lopez-Bonilla (2011) _1327
22..25
Ömer Faruk Ursavas¸ Address for correspondence: Dr Ömer Faruk Ursavas¸, Department of Computer Sciences and Instructional Technologies, Recep Tayyip Erdog˘an University, Faculty of Education, Çayeli-Rize 53200, Turkey Rize 53200, Turkey. Email:
[email protected]
Introduction Several models have been developed to explain and predict the technology acceptance. Over the years, the Technology Acceptance Model (TAM) has been widely accepted as a robust, powerful and parsimonious model capable of explaining user’s technology acceptance in a variety of contexts. The model developed by Davis (1989) aims at explaining how users perceive and use technology. Since its development, the TAM has been used as a research framework in many studies under different contexts. Although TAM has been widely used, Dishaw and Strong (1999) recommended that the TAM be studied further to obtain more insights into its validity. In three recent contributions to the British Journal of Educational Technology colloquium, Teo (2009), Nistor and Heymann (2010), and López-Bonilla and López-Bonilla (2011) discuss the role of attitude in the TAM. According to these researches results the role of the attitude in the TAM is not clear yet. While Teo (2009) and Nistor and Heymann (2010) investigated into the effect of attitude on actual usage and determined that it is an unnecessary variable, López-Bonilla and López-Bonilla (2011) investigated its effect on the behavioural intention and concluded that it is a necessary variable. In conclusion, the role of the attitude for the technology acceptance models still has to be clarified. The aim of the present study was to examine the role of attitude for acceptance models in Teo’s (2009) method. Research results were also compared with the results of Teo’s (2009), Nistor and Heymann’s (2010), and López-Bonilla and López-Bonilla’s (2011) researches. Method Participants The sample of this study consisted of n = 1664 participants, 1058 female and 606 male. The mean age was 21.46 years (standard deviation [SD] = 1.27). It was reported that 1146 of preservice teachers have a computer that they can use at home or at school. All participants were volunteers and they were briefed on the purpose of this study and informed about their rights of not participating and withdrawing from completing the questionnaire at any time during or after the data have been collected. Instruments The instrument comprised questions on demographics and 16 questions adapted from a published study (Teo, Ursavas¸ & Bahçekapılı, 2011) on each of the four variables in the TAM for this study. The variables (perceived usefulness [PU], perceived ease of use [PEU], attitude towards © 2012 The Author. British Journal of Educational Technology © 2012 BERA. Published by Blackwell Publishing, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.
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Table 1: Descriptive statistics of the variables in the technology acceptance Variable
Mean
SD
Skewness
Kurtosis
Perceived usefulness Perceived ease of use Attitude towards computer use Behavioural intention to use Usage
4.21 3.78 4.06 4.01 2.04
0.62 0.74 0.73 0.73 0.88
-0.88 -0.42 -0.61 -0.61 0.47
1.04 0.28 0.11 0.24 -0.54
SD, standard deviation.
computer use [ATCU] and behavioural intention to use [BIU]) were measured using a 5-point scale, with 1 (strongly disagree) and 5 (strongly agree). Actual usage was measured by the question, “How many hours do you use computer in a day?” Results Analysis of the data was performed using structural equation modelling. Relevancy of the structural model that was used in the study was tested by using AMOS 18 program (IBM SPSS® Amos™ 18). Table 1 shows mean, SD, skewness and kurtosis points related to each variable (PU, PEU, ATCU, BIU and U) in the model. The average points belonging to all variables, except for usage, are more than 3.00 which is the mid breakpoint of the scale and at least 3.78 and at most 4.21. When the SD value obtained from all dimensions is handled, it was calculated that all SDs are lower than 1.00. In other words, measurement scores related to groups are around the average scores. According to Kline (2005) the limit values of skewness and kurtosis should not be more than 3.0 and 10.0 respectively. The skewness and kurtosis from all scores obtained from the groups were calculated. The variation of skewness values varied from -0.88 to 0.47 and the variation of kurtosis values varied from -0.54 and 1.04. It can be adduced from the obtained values that the hypothesis of normality was satisfied in this sense. Table 2 shows the results from two different TAM models which were compared in the study, and also shows the results of a previous research which were compared with the results of this study. In this study, for both models (with ATCU and without ATCU), the total amount of variance accounted by the independent variables in usage was assessed by examining the squared multiple correlation (SMC). Results showed that there was no significant difference in the SMC for usage of the two models. In addition ATCU did contribute to the overall variance in BIU. Discussion and conclusion This research focuses on a comparison of the two versions of models (with attitude and without attitude). To test for the role of attitude in the TAM, the research model was tested with and without attitude as a construct in the model. Although ATCU has a significant correlation with other variables, when it was taken from the model, accounted total variance ratio on the actual usage did not show any difference. This is in contrast to previous research results (Nistor & Heymann, 2010; Teo, 2009). The study also showed that attitude was a significant predictor of BIU and this inference shows similarity with López-Bonilla and López-Bonilla (2011). In conclusion, the study supported the view that the attitude towards use did not contribute to the overall variance in usage, but it played a significant role as a predictor of the intention to use technology in TAM, especially while using technology voluntarily. © 2012 The Author. British Journal of Educational Technology © 2012 BERA.
© 2012 The Author. British Journal of Educational Technology © 2012 BERA.
22.2 51.8 70.2 2.1
Variable
PU ATCU BIU U
22.2 – 47.5 2.1
TAM without ATCU (%)
31.7 33.3 44.0 –
31.8 – 40.7 –
TAM-I without ATCU (López-Bonilla & López-Bonilla, 2011) (%) 20.9 34.9 44.8 –
TAM-K with ATCU (López-Bonilla & López-Bonilla, 2011) (%) 20.9 – 38.1 –
TAM-K without ATCU (López-Bonilla & López-Bonilla, 2011) (%) 28.4 26.7 46.6 –
TAM-M with ATCU (López-Bonilla & López-Bonilla, 2011) (%) 28.4 – 35.9 –
TAM-M without ATCU (López-Bonilla & López-Bonilla, 2011) (%)
40.8 55.6 33.9 4.7
TAM with ATCU (Nistor & Heymann, 2010) (%)
40.8 – 34.5 4.7
TAM without ATCU (Nistor & Heymann, 2010) (%)
25.0 36.7 20.3 4.8
TAM with ATCU (Teo, 2009) (%)
25.0 – 19.0 4.8
TAM without ATCU (Teo, 2009) (%)
TAM, Technology Acceptance Model; TAM-I, Technology Acceptance Model for Internet; TAM-I, Technology Acceptance Model for Kiosk; TAM-I, Technology Acceptance Model for Mobile; PU, Perceived Usefulness; PEU, Perceived Ease of Use; ATCU, Attitude Towards Computer Use; BIU, Behavioural Intention to Use; U, Usage.
TAM with ATCU (%)
TAM-I with ATCU (López-Bonilla & López-Bonilla, 2011) (%)
Table 2: Explained variance of actual usage in the 12 technology acceptance models
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References Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319–340. Dishaw, M. T. & Strong, D. M. (1999). Extending the technology acceptance model with task-technology fit constructs. Information and Management, 36, 1, 9–21. Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). New York: Guilford Press. López-Bonilla, L. M. & López-Bonilla, J. M. (2011). The role of attitudes in the TAM: a theoretically unnecessary construct? British Journal of Educational Technology, 42, 6, E160–E162. Nistor, N. & Heymann, J. O. (2010). Reconsidering the role of attitude in the TAM: an answer to Teo (2009). British Journal of Educational Technology, 41, 142–145. Teo, T. (2009). Is there an attitude problem? Reconsidering the role of attitude in the TAM. British Journal of Educational Technology, 40, 1139–1141. Teo, T., Ursavas¸, Ö. F. & Bahçekapılı, E. (2011). Efficiency of the Technology Acceptance Model (TAM) to explain pre-service teachers’ intention to use technology: a Turkish study. Campus-Wide Information Systems, 28, 2, 93–101.
© 2012 The Author. British Journal of Educational Technology © 2012 BERA.
