Factors Affecting Lifelong Learners’ Intention to Continue Using E-Learning Website: An Empirical Study

Liao, Hsiu-Li; Liu, Su-Houn; Pi, Shih-Ming; Chou, You-Jie

doi:10.1007/978-3-642-20539-2_13

Cited by 10 publications

(11 citation statements)

References 10 publications

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“…14.Perceived usefulness and ease of use perceptions Venkatesh,2000;Wong, Nguyen, Chang, & Jayaratna,2003;Cantoni et al,2004;Lu & Chen,2007;Liao, Liu et al,2011;Digión & Sosa,2012;Tao et al,2012 Students' intentions to carry on elearning lifelong and his/her usage behaviour of ICTs. Galusha,1998;Elango et al,2008;Lewis & Chen,2009;Chen,2009;Stansfield, et al,2009;Yaghoubi et al,2008;Anohina-Naumeca & Grundspenkis,2012 Support provided by students in successful implementation of elearning system.…”

Section: Computer Literacymentioning

confidence: 99%

“…Computer literacy Eisenberg and Johnson (1996), Fyfe (2000), Sharma (2003), Andersson and Grönlund (2009), Kwofie and Henten (2011), Nor and Mohamad (2013), Karaman et al (2014) Lack of computer literacy in students 14. Perceived usefulness and ease of use perceptions Venkatesh (2000), Wong et al (2003), Cantoni et al (2004), Lu and Chen (2007), Liao et al (2011), Digión andSosa (2011), Tao et al (2012) Students' intentions to carry on e-learning lifelong and his/her usage behaviour of ICTs Teachers lacking grip on course content while delivering an e-learning session Table III.…”

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confidence: 99%

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A conceptual framework highlighting e-learning implementation barriers

Ali

Uppal

Gulliver

2018

ITP

147

173

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Purpose E-learning has gained much focus from educators and researchers, with many extolling e-learning over traditional learning. Despite this focus, implementation of e-learning systems often fails. The purpose of this paper is to consider a range of barriers, impacting the success of e-learning implementations, yet to the best of the authors’ knowledge no conceptual framework is able to consolidate existing research. Design/methodology/approach This paper undertook an in-depth review of literature concerning e-learning implementation barriers. Papers were extracted from established peer-reviewed journals and open sources. Articles not related to e-learning implementation barriers were discarded. A total of 259 papers were identified, published between 1990 and 2016. Hermeneutics and data-driven qualitative content analysis was used to define 68 unique barriers. Findings The 68 unique barriers were thematically grouped into four conceptual categories, i.e. Technology (T), Individual (I), Pedagogy (P), and Enabling Conditions (EC). These four categories led to the conceptualization of “TIPEC” framework, which highlights the key concepts hindering e-learning implementation and delivery. Results show that most articles only consider a narrow range of success barriers. Practical implications The proposed TIPEC framework acts as a guide for education practitioners, system developers, policy makers, and researchers. It provides stakeholders with a summary of e-learning barriers. Originality/value This paper fulfils an identified need for a conceptual framework that consolidates all current research related to e-learning implementation barriers.

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Section: Computer Literacymentioning

confidence: 99%

mentioning

confidence: 99%

A conceptual framework highlighting e-learning implementation barriers

Ali

Uppal

Gulliver

2018

ITP

147

173

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“…Yang, Shao, Liu, and Liu (2017) in context of MOOCs found that course quality has a convincing influence on retention intention. Liao et al (2011) found that course quality and course flexibility has a significant impact on continuance intention. Dağhan and Akkoyunlu (2016) stated that information quality acts upon customer's intention to use online platforms.…”

Section: Quality Valuesmentioning

confidence: 98%

“…Researchers over the years have analysed the factors that affect usage of e-Learning services from various directions, like, analysing the importance of social interactions on technical elements to improve e-mentoring systems (Headlam-Wells, Gosland, & Craig, 2006), using a task-fit model to understand retention of students in massive online open courses (Huang, Zhang, & Liu, 2017), understanding the influence of factors like, system-quality, perceived ease-ofuse and system functionality on usage-intention (Li, Duan, Fu, & Alford, 2011), and analysing the importance of system response and system functionality of the e-Learning website on customer's continuance intention (Liao, Liu, Pi, & Chou, 2011). Pentina and Neeley (2007) in another comparative study between traditional and online ways of learning found that factors like performance expectancy, financial risks and societal status affects the decisions to choose between traditional and online ways of learning.…”

Section: E-learning Servicesmentioning

confidence: 99%

Exploring values affecting e-Learning adoption from the user-generated-content: A consumption-value-theory perspective

Ray

Bala

Dwivedi

2020

Journal of Strategic Marketing

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The aim of this study is to utilise the user-generated content from social-media platforms and merchandise websites to explore various values affecting behavioural intention in context of e-Learning services from the consumptionvalue-theory perspective. This study has utilised a novel mixed-method approach based on natural language processing (NLP)-techniques for the both the qualitative and quantitative analysis. This study has used user-generated content of Coursera (an e-Learning service) consisting of online reviews from Coursera-100k-dataset and tweets about Coursera. Some of the important themes generated from the thematic based analysis of tweets are "value addition", "course content", "topic cover", "reliability of course", "course quality", "enjoyed course", "recommend the course", "value for money", "facilitator skills", etc. Results of the empirical study reveal that offers and deals, emotional connect, facilitator quality, course reliability, platform innovativeness, and compatibility are important predictors of behavioural intention. This study concludes with the various limitations and future directions.

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“…The data collected from university members was analyzed using the partial small square structural model, and the results showed that course flexibility, course quality, system functionality and interaction with the system affected students' perceptions towards learning. The importance of defining the ability of the learning group to comprehend new knowledge within lifelong learning was also emphasized (Liao & Liu & Pi and Chou, 2011). In this context, during a lifelong learning process that functions with the help of online platforms, the specific characteristics of the learning group should also be taken into consideration.…”

Section: Introductionmentioning

confidence: 99%

An Examination of the Correlation Between Science and Technology Attitudes Scale, Frequency of Smartphone Usage Scale and Lifelong Learning Scale Scores Using the Structural Equation Model

Kör

Erbay

Engin

et al. 2017

JBSE

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Lifelong learning can be defined as all of the activities which aim to develop an individual’s skills, knowledge and abilities, socially, individually and professionally. Previous research on lifelong learning has been about using computers, digital competence and the correlation between demographic characteristics and intelligence. However, only one scale was used in this research, and, in general, only scores for demographic characteristics and lifelong learning were compared. In this research, the correlation between distance-learning students’ attitudes to technology, their frequency of use of smartphones and their attitudes to lifelong learning were examined. Reliability studies were carried out prior to the study and the Turkish adaptations of the scales published in international journals were administered with permission. The study sample consisted of 881 students studying in 12 different units of Hitit University: six Vocational Schools, four Faculties and two Graduate Schools. The data were analysed by creating a structural equation model on the open source R analysis program. According to the research results, there was a significant correlation between the three scales, and the correlation between the ‘lifelong learning’ scores and the ‘frequency of of smartphone usage’ scores was greater than the ‘technology attitudes’ scores. Key words: lifelong learning, technology attitudes, smartphone usage, technology leadership.

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Factors Affecting Lifelong Learners’ Intention to Continue Using E-Learning Website: An Empirical Study

Cited by 10 publications

References 10 publications

A conceptual framework highlighting e-learning implementation barriers

A conceptual framework highlighting e-learning implementation barriers

Exploring values affecting e-Learning adoption from the user-generated-content: A consumption-value-theory perspective

An Examination of the Correlation Between Science and Technology Attitudes Scale, Frequency of Smartphone Usage Scale and Lifelong Learning Scale Scores Using the Structural Equation Model

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