Towards Understanding the Determinants of Employees' E -Learning Adoption in Workplace

Sarabadani, Jalal; Jafarzadeh, Hamed; ShamiZanjani, Mahdi

doi:10.4018/ijeis.2017010103

Cited by 37 publications

(27 citation statements)

References 54 publications

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“…EE was created from the perceived ease of use, complexity and ease of use constructs found in the TAM (Davis, 1989), MPCU (Thompson, et al, 1991) and IDT (Moore and Benbasat, 1991) models respectively. Previous studies have shown that EE positively affects the behavioural intentions to use as well as actual usage of a system or technology (El-Masri and Tarhini, 2017;Wang, 2016;Sarabadani, et al, 2017). For this study we define EE as the ease of using the Canvas application and it is viewed that EE will influence students' behavioural intention to use Canvas, thus: Hypothesis H2: EE will positively influence the student's behavioural intention (BI) to use Canvas.…”

Section: Effort Expectancy (Ee)mentioning

confidence: 99%

Factors affecting the adoption of e-learning technologies among higher education students in Nigeria

Yakubu

Dasuki

2018

Information Development

124

101

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The aim of the study is to investigate factors that influence the adoption and use of educational technology by students of a higher education institution in developing countries. The study employed the unified theory of acceptance and use of technology (UTAUT). The online survey method was used to collect data from 286 students of a higher education institution in Nigeria. The maximum likelihood method based on structural equation modelling (SEM) using IBM Amos 22.0 application was used to analyse the data. The study determined that performance expectancy and effort expectancy (p < 0.001) were significant factors in influencing the behavioural intention to use Canvas. Social Influence was deemed to be a statistically non-significant factor in influencing behavioural intentions as (p > 0.05). Facilitating conditions and behavioural intentions were determined to be salient factors that positively influence the actual usage of Canvas by the students. The results from the data obtained partially support the UTAUT’s ability to explain the factors responsible for the acceptance of educational technology in developing countries, in Nigeria to be specific. Furthermore the study contributes to the formulation of approaches and guidelines to enhance the adoption of educational technologies in developing countries.

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Section: Effort Expectancy (Ee)mentioning

confidence: 99%

Factors affecting the adoption of e-learning technologies among higher education students in Nigeria

Yakubu

Dasuki

2018

Information Development

124

101

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“…However, advances in information and communication technology (ICT) are changing all industries and sectors (Jun & Cai, 2001); higher education is no exception (Chow & Shi, 2014). E-learning is becoming increasingly popular in high education (Tsai, Shen, & Chiang, 2013;Wu, 2016) as the applications of ICT continue to provide a variety of teaching and learning options for faculty and students (Sarabadani, Jafarzadeh, & ShamiZanjani, 2017). E-learning can be seen as an innovative approach to the delivery of educational services through electronic forms of information that enhance knowledge, skills, and other outcomes of learners (Fazlollahtabar & Muhammadzadeh, 2012).…”

Section: Introductionmentioning

confidence: 99%

Does e-learning service quality influence e-learning student satisfaction and loyalty? Evidence from Vietnam

Pham

Limbu

Bui

et al. 2019

Int J Educ Technol High Educ

354

262

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Prior studies on e-learning service quality were conducted mainly in developed countries; however, little effort has been made in emerging countries. This study examines the relationships among e-learning service quality attributes, overall elearning service quality, e-learning student satisfaction, and e-learning student loyalty in the context of Vietnam, an emerging country. Survey data collected from 1232 college students were analyzed by means of exploratory factor analysis, confirmatory factor analysis, and structural equation modeling using SPSS 25 and SmartPLS 3.0. The results indicated that e-learning service quality was a second-order construct comprising of three factors, namely, e-learning system quality, e-learning instructor and course materials quality, and e-learning administrative and support service quality. The e-learning system quality was the most important dimension of overall e-learning service quality, followed by e-learning instructor and course materials quality, and e-learning administrative and support service quality. In addition, the overall e-learning service quality was positively related to e-learning student satisfaction, which in turn positively influences e-learning student loyalty. Also, overall e-learning service quality has a direct effect on e-learning student loyalty. Implications for colleges and universities are discussed.

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“…In several cases ( Moses et al, 2014 ; Sarabadani et al, 2017 ) the perceived ease of use and the perceived enjoyment are the variables most effective in predicting the intention of use and the informational power is not considerate in past studies.…”

Section: Discussionmentioning

confidence: 99%

Informational Power and Perceived Collective Benefit Affecting the Users’ Preference for a Mobile Technology: Evidences From a Survey Study

Fantinelli

Cortini

2018

Front. Psychol.

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This study takes place from the idea that the personal usage of mobile technologies can bring positive outcomes to the user and to their society in an indirect way. Technologies studied in this work are defined as persuasive technologies (Fogg, 1997) because they are intentionally designed to modify the users’ attitude or behavior. This research is aimed to evaluate if the intention to use the application can be influenced by positive attitudes toward technology, by the persuasive power of the application and by the perceived fun. Participants (N = 118; M = 55; F = 63; mean age = 27.4; range age = 15–69) filled in an online questionnaire that was partly based on the Media and Technology Usage and Attitude Scale (MTUAS – Rosen et al., 2013). An additional eight items were added to the scale, aimed at evaluating participants’ technophobia, technophilia, perceived technology pervasiveness and perceived persuasive power of technology. By using linear regression analysis, it was found that the application’s informational power and the perceived entertainment positively influenced the usage intention. Another interesting result, obtained through ANOVA, concerns a generational difference: baby boomers tended to trust more the fact that the single individual action through the application can have an effective impact on the environment. These results represent a basis for future in-depth investigations about socially relevant use of the ICT.

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Towards Understanding the Determinants of Employees' E -Learning Adoption in Workplace

Cited by 37 publications

References 54 publications

Factors affecting the adoption of e-learning technologies among higher education students in Nigeria

Factors affecting the adoption of e-learning technologies among higher education students in Nigeria

Does e-learning service quality influence e-learning student satisfaction and loyalty? Evidence from Vietnam

Informational Power and Perceived Collective Benefit Affecting the Users’ Preference for a Mobile Technology: Evidences From a Survey Study

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