A Confirmatory Factor Analysis onTask-Technology Fit for a Student Portal Norshidah Mohamed1 email@example.com http://www.ibs.utm.my Muna Azuddin2 firstname.lastname@example.org Nor Shahriza Abdul Karim1 email@example.com Ramlah Hussein3 firstname.lastname@example.org 1International Business School, Universiti Teknologi Malaysia 2Department of Information Systems, International Islamic University Malaysia 3School of Computer Technology, Sunway University Symposium on Information & Computer Sciences (ICS 2011) 28 June 2011 Sunway Pyramid
OUTLINE• Introduction• Literature review• Methodology• Findings• Conclusion
INTRODUCTION• Portal derives from the Latin word porta i.e. something that will be passed in order to get to another place.• Web-based portal assists Web users by leading them to the ultimate location of their choice (Clarke and Flaherty, 2003).• A system that gathers a variety of useful information resources into a single one-stop Web page (Krishnamurthya and Chan, 2005)
INTRODUCTIONResearch Question: What are the predictors ofTask-Technology Fit for a student portal?
LITERATURE REVIEWDefinition: Task-Technology Fit measures the degree to whicha technology assists users in performing their tasks (Goodhueand Thompson, 1995) . Context of prior Findings Authors studiesOnline shopping Task-technology fit was strongly Klopping and associated with perceived usefulness McKinney (2004)Learning Task-technology fit was an influencing McGill and KlobasManagement System factor in expected (2009)(LMS) consequences of LMS use, attitudes towards LMS use, perceived impacts on students’ learning and students’ grades
LITERATURE REVIEW ..CONT’ Context of prior Findings Authors studiesHotel guest • Task and technology characteristics Schrier et al.empowerment are positively and significantly related (2010)technology (GET) to fit • Users’ experiential characteristics are negatively and significantly associated to fit.
METHODOLOGY Context of Study• Undergraduate students as research participants• Public institution of higher learning in Malaysia• Web-portal manages the information among students, academic personnel• Been in implementation since 2006• Developed and currently managed by the university’s Information Technology Division
METHODOLOGY .. CONT’ Population and Sample• Population – approximately 8,000 students at three faculties• Respondents – 570 students• Cluster sampling based on courses offered in a particular semester
METHODOLOGY .. CONT’ InstrumentAdapted from Klopping and McKinney (2004) Code Item DescriptionsTask1 Sufficiently detailed student information is maintained in the Student Portal.Task2 Information about services for students is obvious in the Student Portal.Task3 I can get information about services for students quickly and easily from the Student Portal when I need it.Task4 The information about services for students that I need is displayed in a readable and understandable form in the Student Portal.Task5 The information about services for students maintained in the Student Portal is what I need to carry out my tasks as a student.Task6 The information about services for students is stored in so many forms in the Student Portal that it is hard to know how to use it effectively.
DATA ANALYSIS• AMOS Version 16• Convergent validity is established if the loadings of the measures to their respective constructs are at least 0.60 (Bagozzi and Yi)• All squared multiple correlations (R- square) must be at least 0.40 (Bollen 1989)
FINDINGS .. CONT’ • Top three predictors come from Task2, Task3, Task4 Code Item DescriptionsTask2 Information about services for students is obvious in the Student Portal.Task3 I can get information about services for students quickly and easily from the Student Portal when I need it.Task4 The information about services for students that I need is displayed in a readable and understandable form in the Student Portal. • Answer to research question: All items except Task6 are predictors.
FINDINGS .. CONT’ GOODNESS OF FIT INDICES Fit measure Recommended value Value for the research model Before MI After MI 2 56.01 15.22Degrees of freedom 9 4(df) 2 /df <3 6.22 3.81p-value >.05 .00 .00GFI >.90 .96 .99Adjusted GFI >.90 .91 .96Normed fit index >.90 .95 .99(NFI)Relative fit index >.90 .92 .97(RFI)Incremental fit index >.90 .96 .99(IFI)
FINDINGS .. CONT’ GOODNESS OF FIT INDICES Fit measure Recommended value Value for the research model Before MI After MITucker Lewis index >.90 .93 .97(TLI)Comparative fit index >.90 .96 .99(CFI)Root mean square <.08 .11 .07error ofapproximation(RMSEA)Closeness of fit >.05 .00 .12(PCLOSE)Root mean square <.08 .02 .01residual (RMR)
CONCLUSION CONTRIBUTIONS• Support prior researches in other contexts of studies & in other countries• Added new knowledge that model is valid & reliable in Malaysian higher education environment for a student portal• Application of instrument for practice
CONCLUSION LIMITATIONS• Cross-sectional approach• Only undergraduate students at three faculties as research participants