Enhancing the use of e-learning systems in the public sector: a behavioural intention perspective

Hung, Shin‐Yuan; Chen, Charlie C.; Yeh, Ralph Keng-Jung; Huang, Liying

doi:10.1504/eg.2016.074237

Cited by 6 publications

(11 citation statements)

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“…Sánchez-Torres et al [50] argued that government support is indispensable for the successful implementation of e-learning projects. In addition, previous studies have confirmed that government support has a significant impact on the adoption of innovative information technologies [50][51][52].…”

Section: Government Supportmentioning

confidence: 82%

Drivers of Mobile Payment Acceptance in China: An Empirical Investigation

Chen

2019

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With the rapid development of mobile technologies in contemporary society, China has seen increased usage of the Internet and mobile devices. Thus, mobile payment is constantly being innovated and is highly valued in China. Although there have been many reports on the consumer adoption of mobile payments, there are few studies providing guidelines on examining mobile payment adoption in China. This study intends to explore the impact of the facilitating factors (perceived transaction convenience, compatibility, relative advantage, social influence), environmental factors (government support, additional value), inhibiting factors (perceived risk), and personal factors (absorptive capacity, affinity, personal innovation in IT (PIIT)) on adoption intention in China. A research model that reflects the characteristics of mobile payment services was developed and empirically tested by using structural equation modeling (SEM) on datasets consisting of 257 users through an online survey questionnaire in China. Our findings show that perceived transaction convenience, compatibility, relative advantage, government support, additional value, absorptive capacity, affinity, and PIIT all have a positive impact on adoption intention, while social influence has no significant impact on adoption intention, and perceived risk has a negative impact on adoption intention. In addition, the top three factors that influence adoption intentions are absorptive capacity, perceived transaction convenience, and additional value.

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Section: Government Supportmentioning

confidence: 82%

Drivers of Mobile Payment Acceptance in China: An Empirical Investigation

Chen

2019

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“…Past literature involving adoption of e-Learning technology in educational institutions has shown that SE, and both RFC and TFC, significantly influence PBC (Ahmed & Ward, 2016;Hung et al, 2016;Shiue, 2007).…”

Section: Self-efficacy Resource Facilitating Conditions and Technolog...mentioning

confidence: 99%

“…F I G U R E 2 Adapted model of working adults' microlearning usage intentions based on DTPB (Taylor & Todd, 1995) Hung et al (2016) studied Taiwanese civil servants' intentions to use e-Learning in their jobs using an extended DTPB model. They found all core and decomposed constructs positively influenced their intentions to adopt e-Learning, except for FC.…”

Section: Behavioural Intentionsmentioning

confidence: 99%

Investigating working adults' intentions to participate in microlearning using the decomposed theory of planned behaviour

Puah

Khalid

Looi

et al. 2021

Brit J Educational Tech

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The current work validated the decomposed theory of planned behaviour (DTPB) with working adults to assess its ability to predict intentions to participate in microlearning and also identify the significant factors that drive microlearning usage decisions. We found that positive attitudes towards microlearning (Attitude), stronger beliefs in others' opinions regarding microlearning use (SN), and stronger perceptions about one's capability to engage in microlearning (PBC), are associated with stronger intentions to participate in microlearning. All decomposed constructs were found to be significant predictors of the respective factors, except superior influence (SI) and resource facilitating conditions. We discuss potential targeted interventions focused on what works best to encourage microlearning adoption. For instance, while reporting superiors have no significant influence over one's microlearning use decisions, the opinions of peers and colleagues positively influence microlearning use. Hence, focusing on embedding community-related aspects into a microlearning design may effectively encourage the use of microlearning.

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“…as individual differences may have profound influences on one's beliefs and intentions. Although studies have found that Attitude, SN, and PBC directly and positively affect intentions about using e-Learning (e.g., Hung et al, 2016), the extent to which the relationships between various constructs, and the influence of these constructs on one's intentions to participate in microlearning varies, due to age, gender, and proficiency in technology, remains unclear.…”

Section: Moderating Role Of Individual Differencesmentioning

confidence: 99%

“…As opposed to the use of microlearning with homogeneous populations, such as undergraduates who typically present similar demographics, studying the use of microlearning with the broader workforce requires a more cautious approach as individual differences may have profound influences on one's beliefs and intentions. Although studies have found that Attitude, SN, and PBC directly and positively affect intentions about using e-Learning (e.g., Hung et al, 2016 ), the extent to which the relationships between various constructs, and the influence of these constructs on one's intentions to participate in microlearning varies, due to age, gender, and proficiency in technology, remains unclear.…”

Section: Introductionmentioning

confidence: 99%

Working Adults' Intentions to Participate in Microlearning: Assessing for Measurement Invariance and Structural Invariance

et al. 2021

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The current study set out to understand the factors that explain working adults' microlearning usage intentions using the Decomposed Theory of Planned Behaviour (DTPB). Specifically, the authors were interested in differences, if any, in the factors that explained microlearning acceptance across gender, age and proficiency in technology. 628 working adults gave their responses to a 46-item, self-rated, 5-point Likert scale developed to measure 12 constructs of the DTPB model. Results of this study revealed that a 12-factor model was valid in explaining microlearning usage intentions of all working adults, regardless of demographic differences. Tests for measurement invariance showed support for invariance in model structure (configural invariance), factor loadings (metric invariance), item intercepts (scalar invariance), and item residuals (strict invariance) between males and females, between working adults below 40 years and above 40 years, and between working adults with lower technology proficiency and higher technology proficiency levels. While measurement invariance existed in the data, structural invariance was only found across gender, not age and technology proficiency. We then assessed latent mean differences and structural path differences across groups. Our findings suggest that a tailored approach to encourage the use of microlearning is needed to suit different demographics of working adults. The current study discusses the implications of the findings on the use and adoption of microlearning and proposes future research possibilities.

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Enhancing the use of e-learning systems in the public sector: a behavioural intention perspective

Cited by 6 publications

References 0 publications

Drivers of Mobile Payment Acceptance in China: An Empirical Investigation

Drivers of Mobile Payment Acceptance in China: An Empirical Investigation

Investigating working adults' intentions to participate in microlearning using the decomposed theory of planned behaviour

Working Adults' Intentions to Participate in Microlearning: Assessing for Measurement Invariance and Structural Invariance

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