Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61636
Title: Digital Learning Analytics Recommender System for Universities
Contributor(s): Alharbi, Hadeel (author); Sandhu, Kamaljeet  (author)orcid 
Publication Date: 2020
DOI: 10.4018/978-1-7998-5171-4.ch010
Handle Link: https://hdl.handle.net/1959.11/61636
Abstract: 

The aim of this chapter is to present the multivariate analyses results of the factors that influence students' acceptance and the continuance usage intention of digital learning analytics recommender systems at higher education institutions in Saudi Arabia. Data was collected from 353 Saudi Arabian university students via an online digital survey questionnaire. The research model was then used to examine the hypothesized relationships between user experiences of the digital 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.

Publication Type: Book Chapter
Source of Publication: Digital Innovations for Customer Engagement, Management, and Organizational Improvement, p. 184-199
Publisher: IGI Global
Place of Publication: United State of America
ISBN: 9781799851714
9781799851738
9781799851721
Socio-Economic Objective (SEO) 2020: 220408 Information systems
HERDC Category Description: B1 Chapter in a Scholarly Book
Series Name: Advances in Business Strategy and Competitive Advantage (ABSCA)
Appears in Collections:Book Chapter
UNE Business School

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