Measuring campus portal effectiveness and the contributing factors

The current issue and full text archive of this journal is available at www.emeraldinsight.com/1065-0741.htm CWIS 24,5 342 Measuring campus portal effectiveness and the contributing factors Mohamad Noorman bin Masrek MARA University of Technology, Shah Alam, Malaysia Abstract Purpose – The purpose of the paper is to evaluate the effectiveness or success of universities’ portal implementation from the perspective of students as users. Adopting the model developed by Delone and McLean, portal effectiveness is defined as being composed of information quality, systems quality and service quality. In addition, the paper also seeks to investigate the influence of individual factors comprising attitudes towards the portal, personal innovativeness and web self-efficacy on the effectiveness of the portal. Design/methodology/approach – The study adopted a survey research design with questionnaires being administered to 600 students as respondents. The cross-sectional strategy for data collection resulted in 405 usable responses that were used for data analysis. Findings – The results show that IS effectiveness dimensions consisting of service quality and systems quality are significantly correlated with user satisfaction. In addition, the study also showed that of the three predictors investigated, only attitudes towards the portal were found to be significantly correlated with IS effectiveness dimensions. Research limitations/implications – The perceptual self-report measures rather than objectives measures adopted in this study contribute to bias, and a cross-sectional design for data collection only provides data at one point in time. Practical implications – The instrument developed in the study could assist the authorities concerned in evaluating the effectiveness of the portal. Originality/value – Despite the growing interest in universities adopting portal technologies, studies addressing the issues of successes and effectiveness are still very limited. Hence, this study provides an impetus for evaluating campus portals from the user’s perspective. Keywords Worldwide web, Portals, Universities, User studies, Attitudes Paper type Research paper Campus-Wide Information Systems Vol. 24 No. 5, 2007 pp. 342-354 q Emerald Group Publishing Limited 1065-0741 DOI 10.1108/10650740710835760 Introduction When the web was first introduced to colleges and academic communities in the mid-1990s, among the initial applications was the development of campus homepages as gateways to the institution’s limited and disparate databases (Jafari, 2003). Since then, universities’ websites have undergone major change, becoming more advanced and sophisticated. Thus, at present, universities’ websites have significantly matured, and to reflect these advances and complexities, more sophisticated terms such as “portal”, “intranet portal”, “enterprise portal”, and “enterprise information portal” have been coined. Even though there is no general consensus regarding the definition of the term “portal”, many would agree that a portal could be described as a single, personalized interface through which users access all information resources and services in a secure, consistent and customizable manner (Bajec, 2005). Enormous benefits and advantages are associated with the implementation of a university portal. Karim and Masrek (2005) noted that portal implementations are helpful in helping enterprises achieve organizational effectiveness. According to Eisler (2003), other than providing a personalized and customizable user interface for accessing both internal and external information, a campus portal also provides the opportunity to create gateways to information and points of access for constituent groups. Bajec (2005) noted that today, almost all universities are either developing or purchasing portal solutions for their needs. Despite the growing interest in universities adopting portal technologies, studies addressing the issues of success and effectiveness are still very limited. The bulk of the studies that were found in the literature were mainly concerned with reporting the experiences of developing a university portal or setting plans and strategies for its development (see Jafari, 2003; Eisler, 2003; Thomas, 2003; Campbell and Aucoin, 2001; Frazee et al., 2003; Bajec, 2005; Bishop, 2003). Hence, this study was undertaken with the purpose of evaluating the effectiveness or success of university portal implementation from the perspective of students as users. In essence, this study seeks to investigate the influence of individual factors comprising attitudes towards the portal, personal innovativeness and web self-efficacy on the effectiveness of a portal. Theoretical framework As IS are being improved and developed, discussions on their effectiveness and evaluation of their success have been continuously debated by researchers, scholars and practitioners (Hussein et al., 2005). To this effect, Torkzadeh et al. (2005) argued that academics and practitioners have sought – and continue to seek – reliable and valid measures of IS effectiveness. The authors further elaborate that ideally, one would like to measure effectiveness through objective means such as monitoring user behavior or measuring decision outcome, but these measures of effectiveness are unfortunately not often feasible. Hence, perceived measures have long been appropriate and have been adopted by many IS researchers, and for the last two decades various models and frameworks for measuring IS effectiveness have been proposed. In particular, one of the most cited models for evaluating IS effectiveness is the IS success model developed by Delone and McLean (1992). The proponents of this model claim that it offers a comprehensive view of IS effectiveness. Since its inception in 1992, more than 200 studies have been reported to cite or test this model (Delone and McLean, 2002). However, very few studies have attempted to focus on the antecedents or determinants of IS success. As noted by Delone and McLean (2002), IS studies need to concentrate on the predictors or antecedents to IS success. They further elaborate that these factors might include various contextual and demographic factors affected by environmental, organizational, technological and individual traits. Figure 1 depicts the research model for studying the effect of individual factors on portal effectiveness. The framework is conceptualized based on the work of Delone and McLean (2002), Hussein (2004) and Mohamed et al. (2006). The dependent variables are the dimensions of portal effectiveness (i.e. information quality, systems quality, service quality and user satisfaction). The independent variable consists of attitudes towards the portal, personal innovativeness and web self-efficacy. Campus portal effectiveness 343 CWIS 24,5 344 Portal effectiveness In an attempt to evaluate or measure the effectiveness of IS, various models and frameworks have been proposed and validated in diverse IS implementation settings. The IS effectiveness or success model developed by Delone and McLean (1992) was considered to be the most successful model in measuring IS effectiveness. The model consists of six inter-relationship dimensions and posits that “information quality” and “system quality” singularly and jointly affect both “use” and “satisfaction”. In addition, the amount of use can positively or negatively affect the degree of satisfaction, and vice versa. Both use and satisfaction are direct antecedents of individual impact, which in turn may influence or affect organizational impact. Ten years after its inception, the original IS effectiveness model was reformulated based on research contributions adopting the original IS effectiveness model. Three distinct changes were made in the updated model: (1) the inclusion of service quality; (2) the collapsing of individual impact and organizational impact; and (3) adopting intention to use as an alternative measure for use. While the original service quality construct is described as “the overall support delivered by the service provider”, rendered either by the IS department or outsourced organizations, many had successfully modified its measures to suit the internet or a web computing environment such as e-commerce or e-learning (Ahn et al., 2004; Roca et al., 2005). Considering the mandatory nature of portal use, and following the work of Mohamed et al. (2006), this study will only adopt four dimensions of the IS effectiveness model: (1) service quality; (2) systems quality; (3) information quality; and (4) user satisfaction. Thus, information quality is defined as a function of the value of the output produced by a system as perceived by the user (Negash et al., 2003). Measures associated with information quality include content variety, complete information, detailed information, accurate information, timely information, reliable information, and appropriate format (Ahn et al., 2004). Systems quality is the measure of the portal itself and focuses on the outcome of the interaction between the user and the portal system. Figure 1. Research model Items measuring system quality would include design, navigation, response time, system security, system availability and functionality (Ahn et al., 2004). Service quality is defined as the users’ subjective assessment that the service they are receiving from the portal is the service they expect. Aspects covering service quality include responsiveness, reliability, confidence, empathy, follow-up service and competence (Ahn et al., 2004). User satisfaction is defined as the degree to which users believe that the portal at their disposal fulfils their needs (Ives et al., 1983). Antecedents of portal effectiveness Web self-efficacy Computer self-efficacy is an individual’s belief in their ability to use technology in order to solve problems, make decisions, and to gather and disseminate information. Johnson (2001) writes that individuals having a high level of computer self-efficacy should be more likely to engage in computer tasks and to show persistence in completing computer tasks despite possible difficulties. In contrast, individuals with a low level of computer self-efficacy should be more likely to avoid computer tasks or to give up on a computer task in the face of performance obstacles. Many researchers have proved that a high level of computer self-efficacy contributes towards a high degree of IT acceptance and usage (Cheung, 2001; Brown, 2002; Hwang and Yi, 2003; Thong et al., 2004; Keenan and Lee, 2004; Boyle and Ruppel, 2004). The influence of computer self-efficacy is also significant in the intranet computing environment. A study by Tang (2000) discovered that managers perceived user ability to be one of the strong determinants for successful intranet adoption. In another study, Young (2001) found that computer self-efficacy was among the most critical factors affecting user satisfaction in using an intranet. However, considering that the object of the study was a portal in a web environment, we argue that web self-efficacy is more relevant in the context of the study. Web self-efficacy is one’s belief in one’s capabilities to organize and execute the courses of internet actions required to achieve one’s usage goals. Numerous studies have shown the contributing effect of web self-efficacy on internet usage and satisfaction (Kurniawan et al., 2002; Roca et al., 2005). Against this background, this study hypothesizes that web self-efficacy will be significantly related to portal effectiveness dimension (i.e. information quality, system quality, service quality and user satisfaction). Personal innovativeness Personal innovativeness is the domain-specific individual trait that reflects the willingness of a person to try out a new information technology. Past studies on IT adoption reveal that personal innovativeness has been diversely used as either an antecedent or a moderator. Agarwal and Prasad’s (1998) study hypothesized and proved empirically that personal innovativeness serves as a key moderator for both antecedents and consequences of utilization behavior. In another study, Agarwal and Karahanna (2000) hypothesized, tested, and empirically confirmed that the degree of personal innovativeness in IT, mediated by the level of cognitive absorption of an individual, has a substantial positive influence on both perceived usefulness and perceived ease of use of the system. Limayem et al. (2000) found support for the link between personal innovativeness and intention to purchase over the internet. Lee et al. (2002) discovered that personal innovativeness has a positive direct impact on the degree of perceived usefulness of mobile internet. Based on the aforementioned Campus portal effectiveness 345 CWIS 24,5 346 discussion, we argue that personal innovativeness will be significantly related with the portal effectiveness dimension (i.e. information quality, system quality, service quality and user satisfaction). Attitude towards portal The Theory of Reasoned Action (TRA; Fishbein and Ajzen, 1975) suggests that individual behaviour is determined by behavioral intentions, where behavioural intentions are a function of an individual’s attitude towards a behaviour. Attitude towards a behaviour is defined as the individual’s positive or negative feelings about performing a behaviour. If a person perceives that the outcome from performing a behaviour is positive, he will have a positive attitude towards performing that behaviour. Various studies have found that attitudes towards an IS (in this case a portal) are influential in determining the eventual success of an IS (Mahmood et al., 2001). Hussein (2004), for example, discovered that attitude towards an IS was the strongest predictor of four dimensions of IS effectiveness (i.e. information quality, system quality, user satisfaction and perceived usefulness). To this effect, we also argue that attitudes towards a portal will be significantly related to the portal effectiveness dimension (i.e. information quality, system quality, service quality and user satisfaction). Research methodology The research methodology of this study was specifically designed to achieve the research objectives of the study. MARA University of Technology of Malaysia (UiTM) was chosen to participate in the study as it is the largest university in Malaysia, with a total of 100,000 students enrolled, dispersed in all 13 states in Malaysia. The other justification for choosing the UiTM student portal was because the portal was considered very comprehensive as it provides various categories of information for students, including the student academic calendar, events, new intake, convocations and others. It also acts as a gateway to other systems such as e-learning systems, the course registration system, student billing, hostel registration, student affairs information and a few other related systems. However, the population of the study is the main campus, which is located in Shah Alam, Selangor, with a total of 46,000 students enrolled at 24 different faculties. These faculties were grouped into six constellations: (1) Sciences; (2) Medical Sciences; (3) Engineering; (4) Social; (5) Humanities; and (6) Business and Management. A stratified sampling technique was adopted as to ensure that the respondents were well represented by various constellations. Data was collected using a survey research design. Self-administered questionnaires were sent out to 600 participants and 442 were returned but only 405 were usable, hence making a response rate of 75 per cent. The high response rate could be attributed to the fact that lecturers were engaged to disseminate and collect the questionnaires. The questionnaire of the study consists of 49 closed and open-ended questions divided into five sections preceded by a covering letter explaining the purpose of the questionnaire and the definition of a portal. The first section captures demographic information such as age, gender, faculty, qualification pursued, and semester. The second section captures information on attitudes towards the portal, personal innovativeness and web self-efficacy. The fourth section captures information on the four dimensions of portal effectiveness. The last section is an open-ended question asking respondents to add additional comments on the quality aspects of the student portal. Other than the questions on demographic information, all other questions used perceptual measures with a corresponding five-point Likert scale ranging from 1 ¼ strongly disagree to 5 ¼ strongly disagree. As noted by Torkzadeh et al. (2005), perceptual measures are acceptable measures and are used extensively in IS studies. Nine items adapted from Roca et al. (2005) were used to measure web self-efficacy. Four items adapted from Schillewaert et al. (2000) were used to measure personal innovativeness. Six items adapted from Hartwick and Barki (1994) were used to measure attitudes towards the portal. Eighteen items adapted from Ahn et al. (2004) were used to measure service quality, system quality and information quality. Three items adapted from Wixom and Todd (2005) and Roca et al. (2005) were used to measure satisfaction. Findings Based on the 405 usable responses, data were analyzed using SPSS Version 14.0. Factor analysis was executed on the three dimensions of individual factors (i.e. attitudes towards the portal, personal innovativeness and web self-efficacy) and the four dimensions of portal effectiveness (i.e. service quality, information quality, service quality and user satisfaction). In interpreting factors to determine which factor loadings are worth considering, this study adopted loadings of 0.5. All the measures were entered into principal axis factoring with Varimax rotation. The results of the factor analysis revealed that six items from the web self-efficacy loaded onto factor 1, all six items measuring attitudes loaded cleanly onto factor 2, and all four items measuring personal innovativeness loaded cleanly onto factor 3. Hence, all three antecedent factors are retained, but two items from the web-efficacy measure had to be removed as they did not meet the cut-off point. As for the portal effectiveness dimensions, the results of the factor analysis indicated that six items from service quality measures plus two items from information quality measures loaded onto factor 1. Four items from the system quality measures and three items from the information quality measures loaded onto factor 2. The three items from user satisfaction measures loaded cleanly onto factor 3. Two items from the system quality measures did not meet the cut-off point and were removed. Following the results, the portal effectiveness dimension was redefined as consisting of only three dimensions (i.e. service quality, systems quality and user satisfaction). The information quality dimension was removed as the items loaded onto both service quality and systems quality dimensions. This is not considered an uncommon finding, since similar situations were also reported by Almutairi (2001) and Mohamed et al. (2006) when measuring IS effectiveness. Accordingly, reliability analyses were performed on the portal Campus portal effectiveness 347 CWIS 24,5 effectiveness measures and all three individual factor measures. The result of this procedure is shown in Table I. Figure 2 demonstrates the revised research model. Based on the preceding discussion and the revised research model shown in Figure 2, the study at hand would test the following hypotheses: H1. Service quality is significantly related with user satisfaction. 348 H2. System quality is significantly related with user satisfaction. H3. Web self-efficacy is significantly related with service quality. H4. Web self-efficacy is significantly related with system quality. H5. Web self-efficacy is significantly related with user satisfaction. H6. Personal innovativeness is significantly related with service quality. H7. Personal innovativeness is significantly related with systems quality. H8. Personal innovativeness is significantly related with user satisfaction. H9. Attitudes towards the portal are significantly related with service quality. H10. Attitudes towards the portal are significantly related with system quality. H11. Attitudes towards the portal are significantly related with user satisfaction. Demographics Table II presents the demographic profile of the respondents according to gender, qualification pursued, age group, semester and constellation where the students belong. In terms of gender, 43.2 per cent of the respondents were male and 56.8 per cent were female. A higher percentage of female respondents could be attributed to the fact Variable Table I. Reliability analysis of research variables Figure 2. Revised research model Attitude towards portal Personal innovativeness Web self-efficacy Service quality System quality User satisfaction Number of items Reliability (Cronbach’s a) 6 4 6 8 7 3 0.908 0.774 0.854 0.916 0.887 0.905 Characteristics Items Gender Male Female Diploma First degree 19-21 22-24 25-27 .27 2 3 4 5 6 7 8 Sciences Medical Sciences Engineering Social Sciences Humanities Business and Management Qualification pursued Age group Semester Constellation of faculties Frequency Percentage 175 230 67 338 218 168 15 4 99 71 81 12 93 24 25 70 80 78 52 39 86 43.2 56.8 16.5 83.5 53.8 41.5 3.7 1.0 24.4 17.5 20.0 3.0 23.0 5.9 6.2 17.3 19.8 19.3 12.8 9.6 21.2 that there were more female students (60 per cent) compared to male students (40 per cent) in the university. The majority of respondents were pursuing Bachelor’s degrees (83.5 per cent), while 16.5 per cent were doing diploma programs. This composition is justified considering that the study was conducted at the main campus, where the majority of students (80 per cent) were doing degree programs. With regard to respondents’ semester level, the figures suggest that the majority of respondents were in semester 2 (24.4 per cent), followed by semester 6 (23.0 per cent) and semester 4 (20.0 per cent). Students from semester 1 were purposely excluded from the sample frame because their engagement and experience with the portal was still very limited. In terms of constellation breakdown, the Humanities constellation had the least responses (9.6 per cent), followed by Social Sciences (12.8 per cent). The low responses from these two constellations could be explained by the high percentage of unusable responses. Correlation analysis Table III shows the results of the correlation analysis between the three IS effectiveness dimensions employed in the study (i.e. service quality, system quality and user satisfaction). Adopting the cut-off value for highly correlated factors of 0.7, as suggested by Bryman and Cramer (2001), the results indicate that the three variables are moderately correlated with one another, hence suggesting the existence of a substantial relationship. The correlation value between system quality and user satisfaction is 0.603, while the correlation value between service quality and user satisfaction is 0.686. These values suggest that both H1 and H2 are supported, and imply that the three IS effectiveness dimensions are significantly related with each other. The findings are consistent with Seddon and Kiew (1994), Rai et al. (2002), Hussein et al. (2003, 2005) and Mohamed et al. (2006). The findings also suggest that Campus portal effectiveness 349 Table II. Profile of respondents CWIS 24,5 350 Table III. Correlation analysis among research variables Items Service quality System quality User satisfaction Web self-efficacy Personal innovativeness Attitude Service quality System quality User satisfaction Web self-efficacy 1 0.756 * * 0.686 * * 0.289 * * 1 0.603 * * 0.217 * * 1 0.246 * * 1 0.260 * * 0.495 * * 0.247 * * 0.443 * * 0.234 * * 0.430 * * 0.431 * * 0.473 * * Personal innovativeness Attitude 1 0.393 * * 1 Note: * *Correlation is significant at the 0.01 level (two-tailed) students tend to perceive the portal to be effective when the portal is capable of fulfilling the systems quality and service quality needs that would eventually relate to their usage satisfaction. As discussed in the preceding section, apart from measuring the effectiveness of the portal, the study seeks to investigate the effect of web self-efficacy, personal innovativeness and attitudes towards the portal on portal effectiveness. Based on the correlation analysis result shown in Table III, it can be concluded that H3, H4, H5, H6, H7 and H8 are not supported, while H9, H10 and H11 are supported. This is implied by the low correlation value, which ranges between 0.217 and 0.289. The findings do signify to some extent that the degree of personal innovativeness and web self-efficacy do not relate to a higher degree of portal effectiveness. Considering the mandatory nature of the portal usage coupled with the fact that most university students are highly internet-literate, these low correlation values could be justified. Nevertheless, the correlation values between attitudes towards the portal and portal effectiveness demonstrate a relatively moderate scoring, hence suggesting that higher positive attitudes towards the portal would relate to higher degree of perceived portal effectiveness. This finding is also consistent with Hussein (2004). Regression analysis To further explore the relationship between the predictors and the portal effectiveness dimensions, a stepwise multiple regression test was conducted. The results of the analysis are shown in Table IV. Apparently, the findings suggest that attitude towards portal was the only significant predictor to all the three portal effectiveness dimensions. The results suggest that 24.5 per cent, 19.7 per cent and 18.5 per cent of the variation in portal service quality, portal system quality and user satisfaction, respectively, can be explained by the students’ attitudes towards the portal. Diverse studies, especially those adopting the Technology Acceptance Model (TAM; Davis, Table IV. Stepwise regression analysis for individual factors and portal effectiveness dimension Dependent variable Independent variable Service quality System quality User satisfaction Intercept/attitude Intercept/attitude Intercept/attitude Note: *Significant at p , 0:05 Beta values ( p values) R 2/adjusted R 2 F statistics 1.711 (0.495) 1.707 (0.443) 1.784 (0.430) 0.245 (0.244) 0.197 (0.195) 0.185 (0.183) 131.045 * 98.612 * 91.587 * 1989) have demonstrated the contributing effect of attitudes towards technology acceptance. Hence, positive attitudes towards technology deployment are seen as being imperative in ensuring the successful adoption and utilization of a technology. Conclusion The main contribution of this study relates to the establishment of an empirically based framework by integrating the individual contextual framework. The results of the analysis have demonstrated that attitudes towards the portal do have some bearing on perceptions of the portal’s effectiveness. Interestingly, web self-efficacy and personal innovativeness were not found to be related to portal effectiveness. From the practical perspective, the instrument developed in the study could assist the authorities concerned with evaluating portal effectiveness. The findings also suggest the importance of acknowledging students’ competency level as well as their attitude towards technology usage. Thus, the need for a training and attitude molding workshop is seen as desirable. Also, the need to involve students in the development of the portal is seen as crucial. Studies such as those of Hartwick and Barki (1994) and Hunton and Beeler (1997) have shown the importance of user participation in ensuring IS success or effectiveness. While this study has successfully accomplished its objectives, it has several limitations. The perceptual self-report measures rather than objective measures adopted in this study could contribute to bias. In addition, this study adopted a cross-sectional design and hence data was captured only at one point in time. Future research should consider adopting an experimental design study or even a longitudinal study to capture data over a given time frame, i.e. from the commencement of new semester until the end. Another possible approach could be mixing a qualitative and quantitative design involving in-depth interviews with students and observations in a natural usage setting. References Agarwal, R. and Karahanna, E. (2000), “Time flies when you’re having fun: cognitive absorption and beliefs about information technology usage”, MIS Quarterly, Vol. 24 No. 4, pp. 665-94. Agarwal, R. and Prasad, J. (1998), “The antecedents and consequences of user perceptions in information technology adoption”, Decision Support Systems, Vol. 22, pp. 15-29. Ahn, T., Ryu, S. and Han, I. (2004), “The impact of the online and offline features on the user acceptance of internet shopping malls”, Electronic Commerce: Research and Applications, Vol. 3, pp. 405-20. Almutairi, H. (2001), “Evaluating information systems success in public organizations: a theoretical model and empirical validation”, PhD thesis, The Pennsylvania State University, University Park, PA. Bajec, M. (2005), “Educational portals: a way to get integrated, user-centric university information systems”, in Tatnall, A. (Ed.), Web Portals: The New Gateway to Internet Information Services, Idea Group Publishing, Hershey, PA, pp. 252-69. Bishop, A.Y. (2003), “Building a campus portal – a strategy that succeeded”, in Jafari, A. and Sheehan, M. (Eds), Designing Portals: Opportunities and Challenges, Information Science Publishing, Hershey, PA, pp. 186-201. Boyle, R. and Ruppel, C. (2004), “On-line purchasing intent: the effect of personal innovativeness, perceived risk, and computer self-efficacy”, Proceedings of the 7th Annual Conference of Campus portal effectiveness 351 CWIS 24,5 352 the Southern Association for Information Systems (SAIS), Savannah, GA, 27-28 February, pp. 131-7. Brown, I.T.J. (2002), “Individual and technological factors affecting perceived ease of use of web-based learning technologies in a developing country”, The Electronic Journal on Information Systems in Developing Countries, Vol. 9 No. 5, pp. 1-15. Bryman, A. and Cramer, D. (2001), Quantitative Data Analysis with SPSS Release 10 for Windows: A Guide for Social Scientists, Routledge, London. Campbell, K. and Aucoin, R. (2001), “Values-based design of learning portals as new academic spaces”, in Jafari, A. and Sheehan, M. (Eds), Designing Portals: Opportunities and Challenges, Information Science Publishing, Hershey, PA, pp. 162-85. Cheung, C. (2001), “Understanding adoption and continual usage behavior towards internet banking services in Hong Kong”, unpublished Master’s dissertation, Lignan University, Hong Kong. Davis, F. (1989), “Perceived usefulness, perceived ease of use, and user acceptance of information technology”, MIS Quarterly, Vol. 13 No. 3, pp. 319-40. Delone, W.H. and McLean, E.R. (1992), “Information systems success: the quest for the dependent variable”, Information Systems Research, Vol. 3 No. 1, pp. 60-95. Delone, W.H. and McLean, E.R. (2002), “The Delone and McLean model of information systems success: a ten-year review”, Journal of Management Information Systems, Vol. 19 No. 4, pp. 9-30. Eisler, D.L. (2003), “Campus portals strategies”, in Jafari, A. and Sheehan, M. (Eds), Designing Portals: Opportunities and Challenges, Information Science Publishing, Hershey, PA, pp. 68-88. Fishbein, N. and Ajzen, I. (1975), Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research, Addison-Wesley, Reading, MA. Frazee, J.P., Frazee, R.V. and Sharpe, D. (2003), “Begin the end user in mind: planning for the San Diego State University Campus portal”, in Jafari, A. and Sheehan, M. (Eds), Designing Portals: Opportunities and Challenges, Information Science Publishing, Hershey, PA, pp. 127-61. Hartwick, J. and Barki, J. (1994), “Explaining the role of user participation in information systems use”, Management Science, Vol. 40 No. 4, pp. 460-5. Hunton, J.E. and Beeler, J.D. (1997), “Effects of user participation in systems development: a longitudinal field experiment”, MIS Quarterly, Vol. 21 No. 4, pp. 359-88. Hussein, R. (2004), “The contribution of organizational, technological and individual factors in information systems success in Malaysian public sector”, unpublished doctoral dissertation, University Putra Malaysia, Serdang. Hussein, R., Selamat, M.H. and Karim, N.S.A. (2005), “The impact of technological factors on information systems success in the electronic government context”, Proceedings of the 2nd International Conference on Innovations in Information Technology, Dubai, September 26-28. Hussein, R., Selamat, M.H. and Mamat, A. (2003), “The empirical investigation on IS success in the Malaysian electronic government agencies”, in Abd Rahman, M.Z. and Abu Bakar, A.B. (Eds), Building a Knowledge Society: Value Creation Through People, Knowledge and ICT, NCICT 03, Research Centre and Kulliyah of ICT, International Islamic University Malaysia, Kuala Lumpur, pp. 278-94. Hwang, Y. and Yi, M.Y. (2003), “Predicting the use of web-based information systems: intrinsic motivation and self-efficacy”, International Journal of Human-Computer Studies, Vol. 59 No. 4, pp. 431-49. Ives, B., Margrethe, M. and Baroudi, J.J. (1983), “The measurement of user information satisfaction”, Communications of the ACM, Vol. 26 No. 10, pp. 785-93. Jafari, A. (2003), “Designing campus portals”, in Jafari, A. and Sheehan, M. (Eds), Designing Portals: Opportunities and Challenges, Information Science Publishing, Hershey, PA, pp. 7-27. Johnson, D.M. (2001), “Analysis of the relationships between computer experiences, self-efficacy, and knowledge of undergraduate students entering a land-grant college of agriculture”, Proceedings of the 28th Annual National Agricultural Education Research Conference, New Orleans, LA, December 12, available at: http://aaae.okstate.edu/proceedings/2001/johnson. pdf (accessed 7 September 2005). Karim, N.S.A. and Masrek, M.N. (2005), “Utilizing portal for achieving organizational effectiveness”, Proceedings of the International Conference of Knowledge Management, ICKM2005, Putra World Trade Centre, Kuala Lumpur, 27-28 October. Keenan, A.P. and Lee, Y. (2004), “The influence of system characteristics on e-learning use”, Computers and Education. Kurniawan, S.H., Ellis, R.D. and Allaire, J.C. (2002), “The impact of web self-efficacy, age, and web experience on bookmark manipulation”, Universal Access in the Information Society, Vol. 1 No. 3, pp. 207-16. Lee, W.J., Kim, T.U. and Chung, J.Y. (2002), “User acceptance of the mobile internet”, Proceedings of the First International Conference on Mobile Business, Mobiforum, Athens. Limayem, M., Khalifa, M. and Frini, A. (2000), “What makes consumers buy from internet? A longitudinal study of online shopping”, IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans, Vol. 30 No. 4, pp. 421-32. Mahmood, M.A., Hall, L. and Swanberg, D.L. (2001), “Factors affecting information technology usage: a meta analysis of the empirical literature”, Journal of Organizational Computing and Electronic Commerce, Vol. 11 No. 2, pp. 117-30. Mohamed, N., Hussin, H. and Hussein, R. (2006), “Enabling change factors and IT success in the Malaysian e-government implementation”, Proceedings of the 10th Pacific-Asia Conference on Information Systems, Kuala Lumpur, 6-9 July. Negash, S., Ryan, T. and Igbaria, M. (2003), “Quality and effectiveness in web-based customer support system”, Information and Management, Vol. 40, pp. 757-68. Rai, A., Lang, S.S. and Welker, R.B. (2002), “Assessing the validity of is success models: an empirical test and theoretical analysis”, Information Systems Research, Vol. 13 No. 1, pp. 50-69. Roca, J.C., Chiu, C.M. and Martinez, F.J. (2005), “Understanding e-learning continuance intention: an extension of the Technology Acceptance Model”, International Journal of Human Computer Studies, Vol. 64 No. 8, pp. 683-96. Schillewaert, N., Ahearne, M.J., Frambach, R.T. and Moenaert, R.K. (2000), “The acceptance of information technology in the sales force”, Industrial Marketing Management, Vol. 34, pp. 323-36. Seddon, P.B. and Kiew, M.Y. (1994), “A partial test and development of the DeLone and McLean model of IS success”, Proceedings of the International Conference on Information Systems, ICIS 94, pp. 99-110. Campus portal effectiveness 353 CWIS 24,5 354 Tang, S.M. (2000), “An impact factor model of intranet adoption: an exploratory and empirical research”, The Journal of Systems and Software, Vol. 51, pp. 157-73. Thomas, J. (2003), “Indiana University’s enterprise portal as a service delivery framework”, in Jafari, A. and Sheehan, M. (Eds), Designing Portals: Opportunities and Challenges, Information Science Publishing, Hershey, PA, pp. 102-26. Thong, J.Y.L., Hong, W. and Tam, K.Y. (2004), “What leads to user acceptance of digital library”, Communications of the ACM, Vol. 47 No. 11, pp. 79-83. Torkzadeh, G., Koufteros, X. and Doll, J.W. (2005), “Confirmatory factor analysis and factorial invariance of the impact of information technology instrument”, Omega, Vol. 33 No. 2, pp. 107-18. Wixom, B.H. and Todd, P.A. (2005), “A theoretical integration of user satisfaction and technology acceptance”, Information Systems Research, Vol. 16 No. 1, pp. 85-102. Young, L.-Y. (2001), “Factors affecting user satisfaction on intranet”, unpublished Master’s thesis, National Sun Yat-Sen University, Kaohsiung, available at: www.etd.lib.nsysu.edu. tw/ETD-db/ETD-search/view_etd?URN ¼ etd-0614101-170515 (accessed 18 August 2005). Corresponding author Mohamad Noorman bin Masrek can be contacted at: [email protected] To purchase reprints of this article please e-mail: [email protected] Or visit our web site for further details: www.emeraldinsight.com/reprints