Dept. of Information Systems, University Technology, Malaysia

Computer Self-Efficacy, Anxiety and Attitudes Towards use of Technology Among University Academicians: A Case Study of University of Port Harcourt Nigeria 1 Oye, N. D., 2 Dr. A.Iahad, N., 3 Dr. Ab.Rahim, N. 1,2,3 Dept. of Information Systems, University Technology, Malaysia Abstract The 21st century also called ICT literacy includes not only the traditional concept of literacy, but it also encompasses the ability to incorporate new technologies into teaching and learning. The paper focus on computer, self-efficacy, anxiety and attitudes towards use of technology, as it influence the behavioral intention of the university academicians to accept and use ICT for teaching and learning. The University of Port Harcourt Nigeria was use as a case study, and 100 questionnaires were administered and collected. The technology usage by the academic staff shows that 74% are willing to use ICT once or more a day. 51% of the respondents said that the use of ICT is voluntary. Three null hypotheses were stated. The findings shows that the Uniport academic staff had medium computer anxiousness, they have moderate computer self efficacy and high attitudes towards use of technology. Therefore, as attitudes towards use of technology increase, computer self efficacy also increase and this cause a gradual decrease in computer anxiety. The most influential construct is attitudes towards use of technology. This was determined by the regression analysis and the hypotheses. The knowledge gained from this study is beneficial to university administrators, academic staff and the Nigerian ICT policy makers. Keywords Computer Self-Efficacy, Computer Anxiety, Attitudes, ICT I. Introduction Acceptance of mobile phones for learning can be incorporated into university structures of learning. Other studies shows that the use of ICT have positive impact in tertiary institutions, [1-2]. ICT here refers to the application of digital equipments to all aspects of teaching and learning, which encompasses (PC, TV, Radio, Cellular phones, Laptops, overhead projectors, slide projectors, power-point projector, electronic boards, internet, hardware, software, and any technology specific to your teaching area). II. Problem Statement Nigerian university academic staff should be able to compete globally with their colleagues. However the concern is whether university academic staff are prepared to integrate the technology that is feasible to them into effective lessons for their students. [3-5], argue that, the integration of ICT into our classrooms is determined by key factors, such as the contexts in which teachers interact, their beliefs, and their attitudes towards teaching and learning. The stage of enlightenment on which ICT could be use in education is still low. Many lecturers hardly comprehend the benefit of ICT in education. Most of the lecturers acknowledged the fact that internet could be browsed as a point of supply of teaching materials. [6], Investigated the level and depth of use of computers by university staff. From the survey, in Nigeria, 58.5% use computers for word processing, 32.2% use it for spreadsheet and data processing and 20.5% use it for programming. 66.9% use it for e- mail/internet while 9.4% use the computer for other purposes apart from the aforementioned [7], stated that 90% of Nigerian educational institutions are in the emerging phase of ICT, 7% in the applying phase, and 3% in the infusing and transforming phases. ICT is therefore in its infancy in Nigeria. According to the [8], much of our curricula and education systems are still products from a mechanistic past, in which predetermined knowledge was delivered in a linear format to a mass audience. The focus was on transferring information in a controlled sequence without accounting for the contextual settings of the different learners. The Universities in Nigeria need to align its teaching and learning methods with best practices found both nationally and globally. Adopting the use of ICT and IS within higher education seems inevitable as digital communication and information models become the preferred means of storing, accessing and disseminating information. The question of why university academicians decide to accept or reject a particular technology continues to be an important issue. The research problem revolves around ICT acceptance and use by Nigerian University academicians. [9-10] comment that HEIs with good network service have the problem of adoption [11], said that, lack of interest, limited access to ICT facilities and lack of training opportunities constitute the major problem of ICT usage by University academicians [12], opined that inadequate ICT facilities and excess workload are major challenges to ICT usage among academic staff in Nigerian universities. Given that the academicians are the key to effective usage of information technologies in the university education system. It is important to understand the academicians behavioral intention towards IT as related to computer self efficacy, anxiety and attitudes towards use of technology. III. Computer Anxiety The use of technology sometimes has unpleasant sides effect, which may include strong, negative emotional states that arises not only during interaction but even before, when the idea of having to interact with the computer (ICTs) begins. Frustration, confusion, anger, anxiety and similar emotional states can affect not only the interaction itself, but also productivity, learning, social relationships, these are all determinant of the states and type of anxiety the individual is experiencing [13-14]. Mostly such factors as self-efficacy and attitudes towards computer usage are posited and influencing the computer anxiety [15-18], opined that there are three types of anxieties thus: Trait anxiety which is related to pervasive anxiety that is experienced by a person over the entire range of life experience. State anxiety which is related to experience as anxiety that fluctuates over time and arises to a responsive situation. Then we have the concept-specific anxiety which is the range between the trait and state anxieties, which is an anxiety that is associated with a specific situation. Therefore, computer anxiety is concept-specific anxiety because it is a feeling that is associated with a persons interaction with ICT [19]. Computer anxiety is also considered as the tendency of a person to experience a level of uneasiness over his/her impending use of a computer. The study of IS has been viewed as a personality variable that influences system use [20-22]. Therefore anxiety can be viewed as a result www.ijcst.com International Journal of Computer Science And Technology 213

of the belief an individual has, rather than as an antecedent to them. For example, an individual who has a belief that he/she will be embarrassed by delivering his/her lectures using power point, thus he has ICT anxiety (that is ICT Phobia). A number of studies have provided evidence supporting a direct relationship between computer anxiety and computer use [23-25]. Computer anxiety is a common emotional response to computers characterized by the fear that many adult exhibits. Fear and anxiety towards ICT or any subject matter are situation that tends to support negative learners attitudes. Anxiety usually occurs when something new is being learned. This causes resistance to change and has negative effects on cognitive performance. Computer anxiety is temporary conditions that can be reduced through a comfortable learning environment. IV. Self-Efficacy [26-33], stated self-efficacy as a construct often used to explain ones ability to judge how well he/she can execute a task to achieve a desired goal. This was initially defined as an individuals belief about his/her ability to successfully execute a behavior required to produce a desired outcome. Self efficacy has been shown to influence choices of whether to engage in a task, the effort expended in performing it, and the persistence shown in accomplishing it. The greater people perceived their self efficacy to be, the more active and longer they persist in their efforts [23, 29, 32, 34-36]. Computer anxiety has been defined as a fear of computers (ICT) when using one, or fearing the possibility of using ICT [24, 37-38] opined that attitudes towards computer are very critical issues. Monitoring the users attitudes towards computers (ICT) should be a continuous process if ICT is to be used for effective training and learning [39-40]. V. Attitude Towards use of Technology According to [41], attitudes is an individuals overall affective recation to using a system. Several studies reveals that individual attitude towards technology have a strong effect on use intention [42-43]. Attitudes of University academic staff toward technology use within the institution are important and often overlooked component of successful curriculum integration of technology. Much of the research done on technology integration assumes that once appropriate technological tools are in place in the classroom, lecturers, and students will overwhelmingly support the change toward a technologically based curriculum. The success of any initiatives to implement technology in an educational program depends strongly upon the support and attitudes of lecturers involved. It has been suggested that if lecturers believed or perceived proposed computer programs as fulfilling neither their own or their students needs, they are not likely to attempt to introduce technology into their teaching and learning. Among the factors that affect the successful use of computers in the classroom are lecturers attitudes towards computers [44]. Attitude, in turn, constitutes various dimensions. Some examples of these are perceived usefulness, computer confidence [45], training [46], gender [47], knowledge about computers [48], anxiety, confidence, and liking [49]. VI. Methodology The influence of self efficacy, anxiety and attitudes towards use of technology on behavioral intention of the university academic staff to accept and use ICT will be considered. The University of Portharcourt was chosen for the case study. One hundred questionnaires were administered to the academic staff and 214 In t e r n a t i o n a l Jo u r n a l o f Co m p u t e r Sc i e n c e An d Te c h n o l o g y ISSN : 0976-8491 (Online) ISSN : 2229-4333 (Print) collected. The data was runned using SPSS version 17. Using the demographic statement, we want to answer three major questions: (i) Is ICT Mandatory or Voluntary at your institution? (ii)how often do you use ICT? (iii ) What are the greatest barriers to using ICT to you as an academicians? Self-Efficacy Anxiety Attitudes Towards Use of Technology Behavioral Intention Fig. 1: The Influence of SE, ANX and ATUT on BI Using regression analysis, the study want to verify the influence of the three constructs (SE, ANX and ATUT) on the behavioral intention of the academic staff to use the technology and which of them is the most influential. The findings will be use to accept or reject the three null hypotheses: H0 1 : Computer self-efficacy does have impact on Uniport academic staff to accept and use ICT. H0 2 : Uniport academicians attitudes towards ICT influence their acceptance and use of technology. H0 3 : Anxiety about computer use does have an impact on Uniport academic staffs acceptance and use of ICT. VII. Summary of the Demographic Statements The question, how often do you use ICT? The summary of the technology usage are as follows: (74%) use technology once or more in a day, (7%) use it once a week, while (9%) claimed that they have never use technology. The majority of the academic staff (52%) responded that ICT is voluntary. On the other hand (48%) responded that ICT is Mandatory. What are the greatest barriers to using ICT to you as an academic staff? The majority of the respondents (26%) said that their problem is technical; on the other hand (17%) said that the problem is cost. Others respondents (21%) said that training are their problem, another group (21%) said that they need time and the final group (1%) said that, it does not fit my programe and (14%) claim that they need incentive. ICT development programe among academic staff of educational institutions especially at the tertiary level is faced by number of obstacles. Prominent among them is the lack of training opportunities for staff. The same problem is recurring in this study again. In a study by [11] lack of interest, limited access to ICT facilities and lack of training opportunities were among the obstacles to ICT usage among academic staff. [12], opined that inadequate ICT facilities, excess workload and funding were identified as major challenges to ICT usage among academic staff in Nigerian universities. VIII. Regression Analysis The Influence of Self-Efficacy (SE1-5) on Behavioral Intention (BI 1-5) of the university academic staff to accept and use ICT. A. Discussion (1) [Tables 5-9] From the model summary Table 5, SE1-5 contributed only 9.6% of the total variation observed in BI, which is the behavioral intention to accept and use ICT for teaching and learning by the University www.ijcst.com

academicians. Since R 2 (.096) is low, the correlation (.310) is also low. Therefore, the independent variables have contributed less to the variation in the dependent variable (BI). The regression equation Y = 3.495 + 0.213SE 1 + 0.009SE 2 +0.084SE 3 + 0.018SE 4 0.126SE 5 is not significant with P-value (.085) and only SE1 is significant with P-value (.005). Therefore we can infer that only SE1 have positive influence on the behavioral intention to accept and use ICT by the university academicians. From the model summary Table 6, SE1-5 contributed only 11.3% intention to accept and use ICT for teaching and learning by the University academicians. The correlation is (.337). The regression equation is significant with P-value (.042) and only SE1 is significant with p-value (.013). Therefore we deduce that SE1 have positive influence on the behavioral intention to accept and use ICT by the University academicians. From the model summary Table 7, SE1-5 contributed only 5.9% University academicians. Since the correlation (.243) is very low, this implies that the independent variables (SE1-5) will contribute less to the variation in the dependent variable BI. The regression equation is not significant with p-value (.324) and only SE3 is significant with P-value (.045). Here we see that only SE3 have positive influence on the behavioral intention to accept and use ICT by the University academicians. From the model summary Table 8, SE1-5 contributed only 13% university academicians. The correlation is (.360). The regression equation is significant with P-value (.021) and E2 is significant with P-value (.009). Therefore we deduce that SE2 have positive influence on the behavioral intention to accept and use ICT by the University academicians. From the model summary Table 9, SE1-5 contributed only 15.4% university academic staff. The correlation is (.393) and R 2 is (.154). The regression equation is significant with P-value (.007) and SE4 is also significant with P-value (.001). We can infer that only SE4 have positive influence on the behavioral intention to accept and use ICT by the University academicians. The Influence of Anxiety (AX 1-4) on Behavioral Intention (BI 1-5) of the academic staff to accept and use ICT for teaching and learning by University academic staff. B. Discussion (2) [Tables 10-14] From the model summary Table 10, the correlation is (.304) and the R 2 is (0.93). The regression equation Y =4.483-0.070AX 1 + 0.064AX 2 0.137AX 3 + 0.016AX 4 is significant with P-value (.053) and AX3 is also significant with P-value (.012). Therefore AX3 have positive influence on the behavioral intention to accept and use ICT by the University academicians. From the model summary Table 11, AX1-4 contributed 189% of the total variation observed in behavioral intention to accept and use ICT by the University academicians. R 2 is (.189) and the correlation is (.434). The regression equation is significant with P-value (.000). Both AX1, and AX3 are significant with P-value (.003 and.000) respectively. Therefore we deduce that both AX1 and AX3 have positive influence on the behavioral intention to accept and use ICT by the university academicians. From the model summary table 12, Ax1-4 contributed only 4.7% of the total variation observed in behavioral intention to accept and use ICT by the University academicians. Since R 2 (.047) is very low, the correlation (.217) is also very low. Therefore the independent variables (Ax1-4) have contributed less to the variation in the dependent variable (BI). The regression equation is not significant with P-value (.327) and the entire variables (AX1-5) are not significant. Therefore the variables have no positive influence on the behavioral intention to accept and use ICT by the university academicians. From the model summary Table 13, AX1-4 contributed only 1.2% of the total variation observed in the behavioral intention to accept and use ICT by the University academicians. Since R 2 (.012) is very low, the correlation (.109) is also very low. Therefore the independent variables (AX1-4) have contributed less to the variation in the dependent variable (BI). The regression equation is not significant with P-value (.886) and all the independent variables (AX1-4) are not significant. Therefore we deduce that the variables have no positive influence on the behavioral intention to accept and use ICT by the university academicians. From the model summary Table 14, Ax1-4 contributed only 7.8% of the total variation observed in the behavioral intention to accept and use ICT by the University academicians. Since R 2 (.078) is low, and the correlation (.279) is also low. The independent variable (AX 1-4) has contributed less to the variation in the dependent variable (BI). The regression equation is not significant with P-value (.099) and all the independent variables are not significant. Therefore we infer that the variables have no positive influence on the behavioral intention to accept and use ICT by the University academicians. The Influence of Attitudes Towards using Technology (ATUT 1-5) on Behavioral Intention (BI 1-5) of the university academic staff to accept and use ICT for teaching and learning. C. Discussion (3) [Tables 15-19] From the model summary Table 15, ATUT1-6, contributed only 11.5% of the total variation observed in behavioral intention to accept and use ICT by the university academicians. R 2 is (.115),and the correlation is (.339). The regression equation Y = 2.739 + 0.015AT 1 + 0.011AT 2 + 0.090AT 3 + 0.007AT 4 + 0.018AT 5 + 0.217AT 6 is not significant with P-value (.071) only ATUT1 is significant with P-value (.010). Here we can conclude that the independent variable ATUT1 have influenced positive change in the behavioral intention to accept and use ICT by the university academicians. From the model summary Table 16, ATUT1-6, contributed only 21.2% university academic staff. R 2 is (.212), and correlation is (.461). The regression equation is significant with P-value (.001). Both ATUT2 and ATUT5 are significant with P-values (.001 and.018) respectively. Here we deduce that, the independent variables (ATUT2 and ATUT5) have positive influence on BI that is the behavioral intention to accept and use ICT by the university academicians. From the model summary Table 17, ATUT1-6, contributed only 10.6% of the total variation observed in BI which is the behavioral University academic staff. The correlation is (.326) and R 2 is (.106). The regression equation is not significant with p-value (.099) and only ATUT6 is significant with P-value (.004). Therefore only ATUT6 have positive influence on the behavioral intention to accept and use ICT by the university academicians. www.ijcst.com International Journal of Computer Science And Technology 215

From the model summary Table 18, ATUT1-6, contributed only 19% of the total variation observed in BI which is the behavioral university academic staff. R 2 is (.190) and the correlation is (.436). The regression equation is significant with p-value (.003) and only ATUT6 is significant with p-value (.033). Hence we deduce that ATUT6 have positive influence on the behavioral intention to accept and use ICT by the university academicians. From the model summary Table 19, ATUT1-6 contributed 32.8% of the total variation observed in behavioral intention to accept and use ICT by the University academicians. Since R 2 is high (.328) the independent variables (ATUT1-6) have contributed highly to the variation because the correlation (.572) is also high. The regression equation is significant with P-value (.000) and ATUT1, ATUT3 and ATUT6 all significant with p-values (.003;.003 and.004) respectively. Therefore we infer that (ATUT1,3 and 6) have positive influence on the behavioral intention to accept and use ICT by the university academic staff. IX. Discussion on the Hypotheses Technology usage by the academic staff shows that (74%) are willing to use ICT once or more in a day. Although the majority of the academic staff (52%) claimed that, use of ICT is voluntary. This may not be unconnected to the fact that the University has no guideline or policy on the use of ICT for teaching and learning. The Uniport academic staff stated that the greatest barriers to using ICT for teaching and learning are technical problems, which is related to lack of ICT facilities and infrastructure. No programme and time schedule for training the academic staff and lack of incentives. The management is only paying lip service to the issues without financial commitment. 1. H0 1 Computer self efficacy does have impact on Uniport academic staff to accept and use ICT. From the regression analysis summary outcome, (9), shows that SE 1-5 have positive influence on BI(5), which states that I would use ICT in my class frequently. This is significant with p-value.007. Therefore self efficacy which is related to an individuals confidence to perform the behavior required, have positive influence on the Uniport academicians. Hence we accept the null hypothesis (H0 1 ). Computer self efficacy does have an impact on the university academicians acceptance and use of the technology. 2. H0 2 Uniport academicians attitudes towards ICT influence their acceptance and use of the technology. From Table 19, AT 1-6 is highly significant to BI(5) which is the behavioral intention to accept and use ICT. Precisely BI(5) talks on the intention to use ICT in my class frequently. The p-value is.000. This shows that attitudes towards the use of ICT by the academic staff have positive influence on their behavioral intention to accept and use the technology. Therefore we accept the null hypothesis (H0 2 ). 3. H0 3 Anxiety about computer use does have an impact on Uniport academic staff acceptance and use of ICT. From the regression summary outcome, (11) shows that AX 1-4 have positive influence on BI(2) which is the behavioral intention to accept and use ICT. Anxiety which is related to fear of computer (ICT) when using one is highly significant to BI(2) with p-value 216 In t e r n a t i o n a l Jo u r n a l o f Co m p u t e r Sc i e n c e An d Te c h n o l o g y ISSN : 0976-8491 (Online) ISSN : 2229-4333 (Print).000. Therefore we accept the null hypothesis (H0 3 ). This shows that Uniport academicians are still having fear of using ICT for their teaching and learning. Therefore the university academicians need to be aware of the possibility of using ICT for teaching and learning without too much difficulty. X. Conclusion The 21st century also called ICT literacy includes not only the traditional concept of literacy, but it also encompasses the ability to incorporate new technologies into teaching and learning. The study is on computer self efficacy, anxiety and attitudes towards use of technology among Nigerian University academicians. The paper focus on how these affect the behavioral intention of the academicians with respect to acceptance and use of ICT for teaching and learning. The University of Portharcourt Nigeria was used as a case study. The study measured the most influential factor (SE, ANX, and ATUT) on the behavioral intention (BI) to accept and use ICT by the Uniport academic staff. From the regression analysis outcome, Table 11, shows that AX 1-4 on BI(2) has correlation (.434), R 2 (.189) and is significant with p-value.000. Therefore computer anxiety which is related to fear of computer (ICT) when using one has positive influence on BI. Thus Uniport academicians are still having fear of using ICT for their teaching and learning. Table 19, shows that ATUT 1-6 on BI(5) has correlation (.572). R 2 (.328) and is significant with p-value.000. This shows that the attitudes towards use of technology (ICT) by the Uniport academic staff are highly influencing their behavioral intention to accept and use ICT for teaching and learning. Computer self efficacy, which is related to an individual confidence to perform the behavior required to produce specific outcome, from table 9, has low correlation (.393) although it is significant with p-value.007. From our findings we conclude that the most influential construct is Attitudes Towards use of Technology (ATUT). The findings have important implication for teaching and learning using ICT. They need to learn the basics of the technologies that will be most useful in their teaching and learning. The knowledge achieved from the research is beneficial to both the university academic staff and the Nigerian ICT policy makers. Due to the limited sample size of the study, further research is needed. The TAM constructs and the UTAUT four constructs may be combined together, to investigate their influence on the behavioral intention of the academicians to accept and use of new technology. A. Recommendation Recommendations made were that, all employed teachers in Federal, State and private universities in Nigeria, should undertake mandatory training and retraining on ICT programmes. This is to provide them with practical and functional knowledge of computer, internet and associated areas of ICT for improved effectiveness and efficiency. The government should develop ICT policies and guidelines that would support lecturers in their academic work and students in their learning. ICT tools should be made more accessible to both academicians and the students. XI. Acknowledgment The authors would like to thank Prof. V. Venkatesh for allowing the use of UTAUT for this research. In addition, the authors gratefully acknowledge UTM, Research Universiti Teknologi Malaysia for their support and encouragement. www.ijcst.com

Table 20: Regression Analysis Summary outcome Table Independent Variables Dependent Variables R R 2 Significant 5 SE 1-5 BI(1).310.096.089 6 SE 1-5 BI(2).337.113.042 7 SE 1-5 BI(3).243.059.324 8 SE 1-5 BI(4).360.130.021 9 SE 1-5 BI(5).393.154.007 10 AX 1-4 BI(1).304.093.053 11 AX 1-4 BI(2).434.189.000 12 AX 1-4 BI(3).217.047.327 13 AX 1-4 BI(4).109.012.886 14 AX 1-4 BI(5).279.078.099 15 ATUT 1-6 BI(1).339.115.071 16 ATUT 1-6 BI(2).461.212.001 17 ATUT 1-6 BI(3).326.106.009 18 ATUT 1-6 BI(4).436.190.003 19 ATUT 1-6 BI(5).572.328.000 p<0.05 Reference [1] Kennedy, Krause, Churchward, T. Judd, k. Gray, First year students Experiences with Technology: Are they really Digital Natives?, Book First year students, Experiences with Technology: Are they really Digital Natives?, Series First year students, Experiences with Technology: Are they really Digital Natives?, ed., Editor ed.^eds., 2006, pp. [2] B. Oliver, V. Goerke, Australian undergraduates use and ownership of emerging technologies: Implications and opportunities for creating engaging learning experiences for the next generations., Australasian Journal of Educational Technology, Vol. 23, No. 2, 2007, pp. 171-186. [3] Brown, Warschauer, From the university to the elementary classroom: students experiences in learning to integrate technology in instruction, Journal of Technology and Teacher Education, Vol. 14, No. 3, 2006, pp. 599-621. [4] A. Ma, R., K.O. Streith, Examining user acceptance of computer technology: An empirical study of student teachers, Journal of Computer Assisted Learning, Vol. 21, No. 6, 2005, pp. 387-395. [5] Keegwe, Onchwari, P. Wachira, The use of computer tools to support meaningful learning, AACE Journal, Vol. 16, No. 1, 2008, pp. 77-92. [6] Oyelaran-Oyeyinka, Adeya, Dynamics of Adoption and usage of ICT in African universities: a study of Kenya and Nigeria, Elsevier, Vol. 24, 2004, pp. 841-851. [7] IIoanusi, Osuagwu, ICT in Education: Achievement so far in Nigeria. Research, reflection and Innovation in Integrating ICT in Education, Book ICT in Education: Achievement so far in Nigeria. Research, reflection and Innovation in Integrating ICT in Education, Series ICT in Education: Achievement so far in Nigeria. Research, reflection and Innovation in Integrating ICT in Education, ed., Editor ed.^eds., 2009, pp. [8] K. Carol, Linear and Non-linear Learning Blog, Book Linear and Non-linear Learning Blog., Series Linear and Non-linear Learning Blog., ed., Editor ed.^eds., 2007, pp. [9] Oyelekan, An Overview of the status of Information and communication Technology in the Nigerian Education system,, Vol. 8, No. 2, 2008. [10] O.S. Oyelekan, An over view of the status of information and communication technology(ict) in the Nigerian Education system, The African Symposium, Vol. 8, No. 2, 2008, pp. 8-14. [11] I.A. Archibong, D.O. Effiom, ICT in University Education: Usage and Challenges among Academic Staff, Africa Research Review, Vol. 3, No. 2, 2009, pp. 404-414. [12] A.A. Ijeoma, E.O. Joseph, A. Franca, ICT Competence among Academic staff in Unversities in Cross Rivers State, Nigeria, Computer and Information Science, Vol. 3, No. 4, 2010, pp. 109-115. [13] L.D. Rosen, M.M. Weil, Computer anxiety: A cross-cultural comparison of university students in ten countries, Computer in Human Behavior, Vol. 11, No. 1, 1995, pp. 45-64. [14] R. Saade, Exploring dimentions to perceived usefulness: Towards an enhanced assessment, Decission Sciences Institute- Decision Sceinces Journal of Innovative Education, Vol. 5, No. 2, 2007. [15] D.J. Ayersman, W.M. Reed, Effect of learning styles programming, and gender on computer anxiety, Journal of Research on Computing in Education., Vol. 28, No. 2, 1995, pp. 148-161. [16] M. Igbaria, A. Chakrabarti, Computer anxiety and attitudes towards microcomputer use, Behavior and Information Technology, Vol. 9, No. 3, 1990, pp. 229-241. [17] W.M. Reed, D.J. Ayersman, M. Liu, The effect of students computer-based prior experiences and instructional exposures on the application of hypermedia-related mental models, Journal of Educational Computing Research, Vol. 14, No. 2, 1996, pp. 175-197. [18] G.S. Howard, D.R. Smith, Computer anxiety in management: Myth or reality?, Communication of the ACM, Vol. 29, No. 7, 1986, pp. 611-615. [19] E.R. Oetting, Oettings computer anxiety scale (COMPAS). Ft Collins, Co: Rocky Moutain Behavioral Science Institute, 1983. www.ijcst.com International Journal of Computer Science And Technology 217

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He is a lecturer in the department of Mathematics and Computer Science in the same University (for the past 15yrs). At the moment he is a Phd student in the department of Information Systems in the Faculty of computer Science and Information systems at the Univeristi Teknologi Malaysia, Skudai, Johor, Malaysia. 218 In t e r n a t i o n a l Jo u r n a l o f Co m p u t e r Sc i e n c e An d Te c h n o l o g y www.ijcst.com

Noorminshah A. Iahad, Ph.D She did her Ph.D at the School of Informatics, The University of Manchester. She worked with Professor Linda Macaulay from the Interactive Systems Design research section in the same school and Dr George Dafoulas from the School of Computing Science, Middlesex University. Her research is on investigating interaction patterns in asynchronous computermediated-communication. Her work includes analysing threaded discussion transcripts from the discussion feature of a well known Leaning Management System: Web CT. FSKSM, UTM 81310 Skudai, Johor, Malaysia. NorZairah Ab. Rahim Ph.D, Faculty of Computer Science and Information systems, Universiti Teknologi Malaysia, 81310 Skudai Johor. I am a lecturer in Department of Information Systems, Faculty of Computer Science & Information Systems. Universiti Teknologi Malaysia (UTM Skudai) Academic Background : PhD (Computer Science), Universiti Teknologi Malaysia (2009) Master in Information Systems, University of Melbourne (2005) BSc. (Hons) in Information Studies (Information Systems Management), Universiti Teknologi Mara (2002) Research Interests : Technology appropriation, Organizational and individual technology adoption and use. www.ijcst.com International Journal of Computer Science And Technology 219