Assessing e-learning 2.0 system success

Wang, Hei-Chia; Chiu, Yi Fang

doi:10.1016/j.compedu.2011.03.009

Cited by 138 publications

(154 citation statements)

References 63 publications

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“…User satisfaction and intention to use affected each other. Some studies also confirmed that information quality, service quality, and system quality affected user satisfaction, and user satisfaction affected e-learning intention (Chang, 2013;DeLone & McLean, 2003;Roca et al, 2006;Wang & Chiu, 2011). …”

Section: Theoretical Framework and Research Hypotheses Revised Informmentioning

confidence: 83%

“…Cho et al (2009), Elsayed (2005, and Liaw (2008) all stated that online interaction affected elearning performance. A study by Bharati and Chandhury (2004) showed that online interaction mechanisms positively affected online user satisfaction, while a study done by Wang and Chiu (2011) revealed that online communication quality positively affected user satisfaction and elearning continuance intention. Li et al (2012) confirmed that system interaction positively affected intention to reuse e-learning through usefulness and ease of use.…”

Section: Theoretical Framework and Research Hypotheses Revised Informmentioning

confidence: 99%

“…Li et al (2012) proved that service quality positively affected reuse intention toward e-learning. A study done by Wang and Chiu (2011) revealed that service quality of an e-learning system positively affected user satisfaction, and user satisfaction also positively affected e-learning continuance intention. Therefore, the hypothesis of H1.1 was established: 37 H1.1: Course flexibility can positively affect learners' e-learning continuance intention.…”

Section: Theoretical Framework and Research Hypotheses Revised Informmentioning

confidence: 99%

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Alteration of Influencing Factors of Continued Intentions to Use e-Learning for Different Degrees of Adult Online Participation

Chang

2015

IRRODL

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The purpose of the present study was to investigate the variation of influencing factors of elearning continuance intention for different degrees of participation and to examine moderating effects of degrees of participation on influencing factors of e-learning continuance intention. Adoption Theory of Rogers were adopted in the present study. A research model including two constructs (curriculum and system as well as innovation adaptation) and eight influencing factors were proposed in the present study based on features of e-learning. The results revealed that the factors in the construct of curriculum and system could be varied for different degrees of learner participation. Among those factors, system quality and online interaction were the factors for the differences between low and high groups of participation. Furthermore, the factors in the construct of innovation adaptation could be varied for different degrees of participation. Among 34those factors, compatibility was the factor for the differences between low and high groups of participation. Degree of online participation demonstrated moderating effects on the influences of online interaction, relative advantage and compatibility in e-learning continuance intention.

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Section: Theoretical Framework and Research Hypotheses Revised Informmentioning

confidence: 83%

Section: Theoretical Framework and Research Hypotheses Revised Informmentioning

confidence: 99%

Section: Theoretical Framework and Research Hypotheses Revised Informmentioning

confidence: 99%

See 1 more Smart Citation

Alteration of Influencing Factors of Continued Intentions to Use e-Learning for Different Degrees of Adult Online Participation

Chang

2015

IRRODL

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“…DeLone and McLean (2003) proposed that, because of the system use and user satisfaction, a particular net benefit will occur from the viewpoint of the stakeholder system. Moreover, based on the IS/educational technology success literature (Chen, 2010;DeLone & McLean, 2003;Lin & Wang, 2012;H.-C. Wang & Chiu, 2011;Y.-S. Wang, 2008) and the theory of planned behaviour (Ajzen, 1991), satisfaction and intention to use are usually modelled as mediating factors in determining information system acceptance by users in IS fields (Ajzen, 1991;Brady & Robertson, 2001;Dabholkar, Shepherd, & Thorpe, 2000;Rai et al, 2002;Zviran & Erlich, 2003). Therefore, we anticipate the intention to use and user satisfaction mediate the effects of predictor variables on student performance as follows: H4: The intention to use and user satisfaction mediates the relationship between predictor variables and student academic performance in a statistics class.…”

Section: The Mediating Effects Of Intention To Use and User Satisfactionmentioning

confidence: 99%

A Model of Information Systems Success for Assessing the Effectiveness of Statistical Learning Tool on University Students Performance in Statistics

Zaremohzzabieh¹,

Samah²,

Bolong³

et al. 2016

MJSS

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“…Evaluating the success of systems within businesses is certainly recognized as the single most critical issue of IS management discipline [7]. IS Scholars use various techniques to assess systems' success such as investigating success of a system via the system usage, user satisfaction and other categories of performance [5], [8], [9]. Interestingly, many prior literature note user satisfaction to be a surrogate measure for system success, and have use user satisfaction to assess the success implementation of a IS [10]- [13].…”

Section: Introductionmentioning

confidence: 99%

End User Satisfaction and Individual Performance Assessments in e-Procurement Systems

Sharabati¹

2015

IJCTE

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Abstract-End-user satisfaction and individual performance have been identified by many researchers as critical determinants of the success of information systems. As an escalating number of organizations now utilize e-procurement systems, there is a desire to understand their effect on individual end-user's performance. Therefore, this research attempts to empirically examine a framework identifying the relationships between end-user satisfaction, and individual end-user performance, in addition to assessing the impact of three proposed antecedents of end-user satisfaction: processing, content and usability. Data gathered from 432 end-users of ePerolehan system in the Malaysian government agencies were utilized to examine the relationships proposed in the framework using the Partial least square (PLS) approach. The findings provide strong support for our model. Our results indicate three factors processing, content and usability significantly affect end-user satisfaction, while the higher levels of end-user satisfaction leads to improved individual performance.Index Terms-E-procurement, user satisfaction, individual performance. I. INTRODUCTIONMost organizations regardless whether they are private or public sector are now using information system (IS), particularly the Internet. As such both these sectors have become IT-enabled. One of the IT enabled system is the e-Procurement system. Many organizations are using e-Procurement. The same can be said for government e-Procurement. Goldfinch [1] points out that the chance to succeed in e-Government projects is only 30%. The same percentage is applicable to the government e-Procurement system as part of the integral component of e-Government project [2]. As such, Government needs to evaluate the success factors that can assist them to successfully perform government projects. Most of government projects are highly scaled and costly, thus successful implementation of the government e-procurement systems is essential.The IS literature considers a system to be effective or successful when it encompasses return on investment, elevates organizational productivity, improves outcome Manuscript received July 22, 2014; revised September 10, 2014. The authors are with the Department of Operations and Management Information System, Faculty of Business and Accountancy, University of Malaya, 50603 Kuala Lumpur, Malaysia (e-mail: manals@gmail.com, ainins@um.edu.my, akmasalleh@um.edu.my) quality, increases user satisfaction, and sustains use by organizational employees [3]. DeLone and McLean [4] propose an IS success model by distinguishing six dimensions of IS success, that include system quality, information quality, information use, user satisfaction, individual impact and organizational impact. For example, user satisfaction is found to be a crucial determinant of system success and effectiveness [4], [5].Therefore, success of a system is considered to be the extent to which a presented IS essentially makes a contribution to achieving business objectives [6]. Evaluating the ...

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Assessing e-learning 2.0 system success

Cited by 138 publications

References 63 publications

Alteration of Influencing Factors of Continued Intentions to Use e-Learning for Different Degrees of Adult Online Participation

Alteration of Influencing Factors of Continued Intentions to Use e-Learning for Different Degrees of Adult Online Participation

A Model of Information Systems Success for Assessing the Effectiveness of Statistical Learning Tool on University Students Performance in Statistics

End User Satisfaction and Individual Performance Assessments in e-Procurement Systems

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