Abstract/Summary

E-learning has been emerging for more than a decade, and institutions are increasingly adopting it to provide a better learning experience to their students. E-learning is the use of electronic means to deliver and receive education. E-learning offers a wide range of benefits (flexibility of time and space, cost effectiveness etc.), it also overcomes the shortcomings of traditional learning which has resulted in its vast adoption by the institutes. Despite its vast growth i.e. 17% per annum, the failures of e-learning are still at large. Whilst reviewing the literature concerning e-learning failures, it was identified that numerous barriers, which are hindering the promised benefits of elearning, are openly discussed in the literature. To understand these factors, the TIPEC framework, which structures e-learning barriers, was developed; to consolidate literature from the past 26 years (1990-2016). 259 papers concerning e-learning barriers, was included in the framework, to better understand the barriers that hinder e-learning implementation. TIPEC framework comprises of 68 unique e-learning implementation barriers, which were grouped into 4 main categories, i.e., Technology, Individual, Pedagogy and Enabling Conditions. This thesis focuses on understanding the impact of the e-learning student’s individual culture orientation on technology related barriers within the Individual Category. The TIPEC framework highlighted e-learning failures and motivated this thesis to provide explanations and recommendations to support more successful elearning implementation and technology adoption, i.e. by accommodating student’s individual preferences. The objective of this thesis is to identify the role of individual cultural orientation in determining student’s expectation of services being offered in an e-learning setup and his/her preference and acceptance of technological component concerning which device he/she prefers to receive specific e-learning services. For that reason, data was captured from 560 higher education students of Pakistan; where there have been a lot of initiatives taken up by the government of Pakistan in past years to improve the state of education in the country. A study was carried out using a mono method approach and quantitative methodology, using structured questionnaire, to answer three research questions. Research question 1 explains the role of education as a service and assessment of students’ perception about the quality of higher education on the basis of services being offered by the institutions. After a detail review of literature, 8 Higher Education Service (HES) quality indicators (i.e. Course content, Lecturer’s Concern for Students, Facilities, Assessment, Social Activities, Communication with University, Counselling Services and People), proposed by Kwan and Ng (1999), were selected to serve as the basis of my research experiment for question 1. These higher education services are checked for students’ preference, i.e. whether they prefer to receive these services through traditional/face to face education or via one of the six identified e-learning devices i.e. TV, Radio, Desktop, Laptop, Mobile and Tablet. Overall preference results showed that for 5 out of 8 higher education service indicators, students preferred two devices i.e. Laptop or Mobile. This suggests that students may be willing, for some services, to use e-learning devices instead of traditional face-to-face interaction. Literature suggested that attitudes towards adoption and preference of technological devices are influenced by cultural orientation. After the review of different concepts of culture i.e. national, organisation and individual culture, the phenomena of technology preference and acceptance was explored with reference to the culture at the individual level. This led to the development of second research question, i.e. does culture at the individual level play a significant role in device preference? An experiment was performed to analyse technology preference of students against the HES quality indicators proposed by Kwan and Ng, based on the cultural setting of the respondents at an individual level. Culture at the individual level was investigated by applying the Cultural Value Scale (CVSCALE), which is based on the Hofstede’s five cultural dimensions (Power Distance, Uncertainty Avoidance, Masculinity/Femininity, Individualism/Collectivism and Long term/Short term Orientation) enhanced for measurement at the individual level. Three significant clusters of culture at the individual level were found. Cluster 1 was highest in Power Distance and highest in Masculinity, and they preferred face to face learning. Cluster 2 is the highest in Uncertainty Avoidance and lowest in Power Distance preferred Mobile for learning activities. Cluster 3 students were lowest in Uncertainty Avoidance, highest in both Collectivism and Long-term Orientation, they preferred Laptop for most of the higher education service quality indicators. This answered the second research question i.e. to improve student satisfaction with his university experience, we have to keep in view their culture orientation, as their preference varies across the multiple HES quality indicators and the devices available to receive them. If we do not accommodate their individual cultural preferences, we risk reducing the student satisfaction towards the e-learning experience. Second research question led to the formulation of third research question which investigates the role of culture at the individual level in determining the factors predicting technology acceptance. The extended model of Unified Theory of Acceptance and Use of Technology (UTAUT2) was developed by Venkatesh, Thong and Xu (2012) using 8 previous technology acceptance models. This model was adapted for this study. Based on individual culture based cluster segmentation, acceptance of Laptop and mobile (the two preferred devices) for 3 significant clusters were checked. Results showed that acceptance for Laptop and Mobile significantly varied across the three cluster segments. For Cluster 2 and Cluster 3, which preferred Mobile and Laptop respectively, different combinations of variables were found to be statistically significant determinants of the student’s behavioral intention towards the use of their preferred device. Conclusion is drawn on the basis of results of three research questions and future recommendations and limitations are then mentioned in detail.