Explaining and Predicting Continuance Usage intention of E-learning Recommender Systems: An Empirical Evidence from Saudi Arabia

2018 ◽  
Vol 29 (1) ◽  
pp. 1
Author(s):  
Kamaljeet Sandhu ◽  
Hadeel Alharbi
Keyword(s):  
Saudi Arabia ◽  
Empirical Evidence ◽  
Recommender Systems ◽  
Continuance Usage ◽  
E Learning ◽  
Continuance Usage Intention

Factors Influencing the Acceptance, Use, and Continued Use of the E-Learning Recommender Systems

2018 ◽  
Vol 10 (1) ◽  
pp. 31-50
Author(s):  
Hadeel Alharbi ◽  
Kamaljeet Sandhu
Keyword(s):  
Saudi Arabia ◽  
Recommender Systems ◽  
Total Sample ◽  
Descriptive Statistics ◽  
Survey Questionnaire ◽  
Quality Factors ◽  
E Learning ◽  
Continued Use ◽  
Main Instrument ◽  
Structured Questionnaire

The purpose of this article is to report the descriptive statistics for the responses obtained from the survey of Saudi Arabia students about their experience of using e-learning recommender system during their study. This article utilizes a survey questionnaire as the main instrument for data collection. Hence, a self-completion, well-structured questionnaire was developed based on previous literature and was then distributed to a random sample and participation was completely voluntary. A total sample of 353 university students from various universities in Saudi Arabia participated in this article. Results showed that user experience and service quality factors are significant predictors of students' adoption and post-adoption of e-learning recommender systems.

PLS Model Performance for Factors Influencing Student Acceptance of E-Learning Analytics Recommender

2020 ◽  
Vol 10 (2) ◽  
pp. 1-14
Author(s):  
Kamaljeet Sandhu ◽  
Hadeel Alharbi
Keyword(s):  
Technology Acceptance ◽  
Learning Analytics ◽  
Online Survey ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Research Model ◽  
Perceived Ease Of Use ◽  
Continuance Usage ◽  
E Learning ◽  
Continuance Usage Intention

The aim of this article is to present the multivariate analyses results of the factors that influence students' acceptance and the continuance usage intention of e-learning analytics recommender systems in higher education institutions in Saudi Arabia. Data was collected from 353 Saudi Arabian university students via an online survey questionnaire. The research model was then used to examine the hypothesised relationships between user experiences of an e-learning analytics recommender system and their intentions for long-term adoption of the system. The research model was primarily based on the Technology Acceptance Model (TAM) developed by Davis (1989) – the variables ‘perceived usefulness,' ‘perceived ease of use,' and ‘acceptance,' particularly – with ‘continuance usage intention' added as an endogenous construct, and with ‘service quality' and ‘user experience' added as external variables.

Robust Data Findings of E-Learning Analytics Recommender Systems and Their Impact on System Adoption for Student Experiences

2018 ◽  
Vol 8 (3) ◽  
pp. 1-12 ◽  
Author(s):  
Hadeel Alharbi ◽  
Kamaljeet Sandhu
Keyword(s):  
Saudi Arabia ◽  
Recommender Systems ◽  
Recommender System ◽  
Learning Analytics ◽  
Cultural Practices ◽  
Student Experiences ◽  
Educational Equality ◽  
E Learning ◽  
Gender Based ◽  
Support Students

E-learning recommender systems have an import role in Saudi Arabia to facilitate the education empowerment of women. The understanding of the key factors that affect adoption is critical to achieving educational equality in outcomes in countries with gender-based cultural practices. Therefore, this study examined Saudi Arabian students' experiences of using an e-learning analytics recommender system during their study and the extent to which their experiences were predictors of their adoption and post-adoption of the system. A sample of 353 students from various universities in Saudi Arabia completed a survey questionnaire for data collection. Results showed that user experience is a significant predictor of student adoption and post-adoption of an e-learning recommender system. This study determined that adoption is significantly linked to the ability to effectively navigate and utilise the e-learning systems. Therefore, based on these findings, this study concluded that universities must support students to develop their awareness of, and skills in using an e-learning recommender system to support students' long-term acceptance and use of the system.

scholarly journals The Role of HRM Policy on Academic Innovativeness Case of Business School on Saudi Arabia

2017 ◽  
Vol 9 (2) ◽  
pp. 187
Author(s):  
Wided Ragmoun ◽  
Abdullah Alwehabie
Keyword(s):  
Saudi Arabia ◽  
Social Development ◽  
Empirical Evidence ◽  
Business School ◽  
Theory Model ◽  
Academic Field ◽  
Five Dimensions ◽  
The One ◽  
Variance Explained

This study aims to understand the role of HRM on academic innovativeness. We will try to identify the adequate policy of HRM for the development of academic innovativeness.The most important is the way in which HRM will be treated as a composite construct of many practices, working in synergistic way, to generate a capacity of innovation or innovativeness according to our definition presented here.Our interest is oriented to the academic field due to importance of its role on social development. And our interest is oriented to the ability to innovate rather than innovation which still difficult to identify and measure. Academic innovativeness here is represented by five dimensions: behavior, product, process, market and strategic innovativeness.The analyze of the variance explained for our variables, provides empirical evidence that the academic innovativeness depends in majority on behavioral dimension and process. The HRM policy in this case is a distinct construction which depends on training and promotion. Added to this, some relations here must be revisited specially the link between behavior dimension of academic innovativeness and the policy of HRM as a construct.At the end of this research, we propose what we have called `the one best way` of academic innovativeness. Our theory model can be considered as user`s guide for academics to innovate based on HRM policy. 

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