E-learning acceptance based on technology acceptance model (TAM)

Ibrahim, Roslina; Leng, N. S.; Yusoff, Rasimah Che Mohd; Samy, Ganthan Narayana; Masrom, Suraya; Rizman, Zairi Ismael

doi:10.4314/jfas.v9i4s.50

Cited by 116 publications

(115 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The prior literature has proven the existence of a significant direct relationship between the PEOU and a user's intentional behavior to adopt the system in various fields of study [54,93,94]; this relationship was also discussed by Esteves, Shin, and Weerakkody [21,23,39] when studying user intentional behaviors regarding the adoption of big data, although these studies were less focused on this study context. For example, measuring the relationship of PEOU with the intention to use BDA is essential for healthcare organizations.…”

Section: Perceived Ease Of Usementioning

confidence: 96%

Investigating the adoption of big data analytics in healthcare: the moderating role of resistance to change

et al. 2019

View full text Add to dashboard Cite

IntroductionBig data analytics (BDA) is a course of action to examine large and complex data sets (i.e., big data) and select veiled information that can help organizations with efficient decision making [1]. The volume of data related to healthcare organizations has grown dramatically in past years and is expected to increase in coming years due to the use of innovative technologies [2]. Meanwhile, healthcare reimbursement methods are changing, and pay for performance is an emerging factor in the current healthcare environment. Recently, healthcare organizations have only focused on profit and have neglected to acquire the essential tools, infrastructure, and technologies for effective control of big data to ensure citizens' health care [3,4]. Big data incorporates features such as variety, velocity, and veracity. BDA techniques can be applied to the massive amount of prevailing patient-related medical information to analyze outcomes for improvement of the healthcare sector [5,6]. Using BDA in the healthcare sector will help inform each physician of the medical histories of individuals and the population and enable appropriate Abstract Big data analytics is gaining substantial attention due to its innovative contribution to decision making and strategic development across the healthcare field. Therefore, this study explored the adoption mechanism of big data analytics in healthcare organizations to inspect elements correlated to behavioral intention using the technology acceptance model and task-technology fit paradigm. Using a survey questionnaire, we analyzed 224 valid responses in AMOS v21 to test the hypotheses. Our results posit that the credentials of the technology acceptance model together with task-technology fit contribute substantially to the enhancement of behavioral intentions to use the big data analytics system in healthcare, ultimately leading towards actual use. Meanwhile, trust in and security of the information system also positively influenced the behavioral intention for use. Employee resistance to change is a key factor underlying failure of the innovative system in organizations and has been proven in this study to negatively moderate the relationship between intention to use and actual use of big data analytics in healthcare. Our results can be implemented by healthcare organizations to develop an understanding of the implementation of big data analytics and to promote psychological empowerment of employees to accept this innovative system. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

show abstract

Section: Perceived Ease Of Usementioning

confidence: 96%

Investigating the adoption of big data analytics in healthcare: the moderating role of resistance to change

et al. 2019

View full text Add to dashboard Cite

show abstract

“…While, for instance, [86] have experienced the application of the original TAM in the educational field, [87] and [88] have utilized the extended TAM model. Diverse acceptance researches in the field have been examining TAM's suitability for various learning technologies, similar to mobile learning [89], Personal Learning Environments (PLEs) [90], Learning Management Systems (LMSs) in general [91] in addition to open-source LMS Moodle [71] and commercial LMS Blackboard [92] in particular.…”

Section: Literature Review Technology Acceptance and Adoption In Educmentioning

confidence: 99%

Technology Acceptance Model' Concepts, Contribution, Limitation, and Adoption in Education

Zaineldeen¹,

Li²,

Koffi³

et al. 2020

ujer

View full text Add to dashboard Cite

“…The few existing studies used micro data to support the given argument, while at a macro level; there is no direct study available to comprehend the statement (see Ibrahim et al, 2017;Biswas and Roy, 2018;Sheen, 2018;Samad et al, 2020, etc. ).…”

Section: Advancement In the Cleaner Productionmentioning

confidence: 99%

Green Technology Acceptance Model and Green Logistics Operations: “To See Which Way the Wind Is Blowing”

2020

View full text Add to dashboard Cite

The study provides a new insight to extend traditional technology acceptance model (TAM) in to energy-augmented TAM (EA-TAM) model in order to analyze the micro and macro perspectives of green enterprise technology to ensure eco-friendly products. The critical review would enhance analytical abilities to understand the main determinants of the TAM model at both firms' and industry perspective. The study proposed micro and macro enterprise energy-augmented TAM models and found that microenterprise EA-TAM associated with green investment decision that increases the use of renewable energy demand to improve logistics performance, export capabilities, technology advancement, resource inputs, and competitiveness. These factors would largely support to green product innovation, which is fueled by green R&D expenditures. The green supply chain process would develop an attitude and behavioral intention toward the use of technology and green products, which ultimately would bond with green system use. The macro perspective of EA-TAM shows that green factors including green supplier selection, industrial green engineering, green procurement, green consumerism, green product recovery, and green innovation would increase green purchase decisions that convert into perceived ease of green products usage. The eco-product choice faced three compelling issues that could negatively affect the use of green products attitude and intentions, i.e., political constraints, carbon pricing, and consumer willingness-to-pay for public goods. The study concludes that the need for green policies is imperative to regulate firms and the industrial decision to go-for eco-friendly production. The use of renewable energy, carbon pricing and willingness-to-pay for public goods would be recommended for long-term sustainable development.

show abstract

E-learning acceptance based on technology acceptance model (TAM)

Cited by 116 publications

References 17 publications

Investigating the adoption of big data analytics in healthcare: the moderating role of resistance to change

Investigating the adoption of big data analytics in healthcare: the moderating role of resistance to change

Technology Acceptance Model' Concepts, Contribution, Limitation, and Adoption in Education

Green Technology Acceptance Model and Green Logistics Operations: “To See Which Way the Wind Is Blowing”

Contact Info

Product

Resources

About