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According to expectation–confirmation model (ECM) and task-technology fit (TTF) model, the purpose of this paper is to examine the role of TTF in students’ cloud-based e-learning continuance and evaluate whether TTF affects students’ perceived impact on learning of the cloud-based e-learning system within the educational institution.

Sample data for this study were collected from students enrolled in a comprehensive university in Taiwan. A total of 500 questionnaires were distributed in the campus, and 391 (78.2 percent) usable questionnaires were analyzed using structural equation modeling in this study.

This study’s results verified that both task characteristics and technology characteristics affected students’ perceived TTF, which significantly contributed to their perceived usefulness, confirmation and satisfaction with the cloud-based e-learning system, and these in turn directly or indirectly led to their continuance intention of the system and perceived impact on learning; essentially, the results strongly supported the research model integrating ECM and TTF model via positioning key constructs as the drivers with all hypothesized links being significant.

This study contributes to an understanding of the TTF in explaining students’ cloud-based e-learning continuance that is difficult to explain with only their utilitarian perception of the cloud-based e-learning system, and further places considerably more emphasis upon students’ perceived impact on learning greatly driven by their TTF in the system. Thus, this study’s empirical evidence on incorporating ECM and TTF model can shed light on the outcome for cloud-based e-learning continuance and enhance better understanding of a richer post-adoption model.

The purpose of this study is to propose a research model based on expectation-confirmation model (ECM) to examine whether interactivity and course quality factors (i.e. course content quality, course design quality) as antecedents to student beliefs can influence students' satisfaction and continuance intention of the cloud-based electronic learning (e-learning) system within the educational institution.

Sample data were collected from students enrolled in a comprehensive university in Taiwan. A total of 600 questionnaires were distributed in the campus, and 515 (85.8%) useable questionnaires were analyzed using structural equation modeling.

Findings showed that students' perceptions of interactivity, course content quality and course design quality positively significantly contributed to their perceived usefulness, confirmation and satisfaction with the cloud-based e-learning system, which in turn directly or indirectly led to their continuance intention of the system. Thus, the results strongly supported the research model based on ECM via positioning key constructs as the drivers with all hypothesized links being significant.

This study identifies three factors (i.e. interactivity, course content quality, course design quality) as drivers from the learner perspective within the cloud-based e-learning environment, and links these factors to students' satisfaction and continuance intention of the cloud-based e-learning system based on ECM. It is particularly worth mentioning that the three drivers can serve as precursors for recognizing the determinants that are crucial to understand students' satisfaction and continuance intention of the cloud-based e-learning system. Hence, this study may provide new insights in nourishing the cloud-based e-learning continuance literature in the future.

The purpose of this study is to propose an integrated model based on expectation–confirmation model (ECM), flow theory and human–organization–technology fit framework to examine whether human, organizational and technology factors as antecedents to medical professionals' beliefs can affect their continuance intention of the cloud-based e-learning system.

Sample data for this study were collected from medical professionals at five hospitals in Taiwan. A total of 500 questionnaires were distributed, and 368 (73.6%) useable questionnaires were analyzed using structural equation modeling in this study.

Synthetically speaking, human, organizational and technology factors, as antecedents to medical professionals' continuance intention of the cloud-based e-learning system have been examined, and the results strongly support the research model with all hypothesized links being significant.

Particularly, it is worth mentioning that the application of capturing both ECM and flow theory for completely explaining three types of factors (i.e. human, organizational and technology factors) as external variables to medical professionals' cloud-based e-learning continuance intention is well documented, that is, information systems (IS) and nonIS determinants are simultaneously evaluated, and extrinsic and intrinsic motivators are both taken into consideration in this study's theoretical development of medical professionals' cloud-based e-learning continuance intention to acquire a more comprehensive and robust analysis.

The purpose of this paper is to examine the roles of task-technology fit (TTF), learning-technology fit (LTF) and cognitive absorption (CA) in determining medical professionals’ cloud-based electronic learning (e-learning) system continuance intention and performance outcomes and evaluate whether medical professionals’ perceived impact on learning can affect their perceived impact on tasks within medical institutions.

Sample data for this study were collected from medical professionals at six hospitals in Taiwan. A total of 600 questionnaires were distributed, and 373 (62.2%) usable questionnaires were analyzed using structural equation modeling in this study.

In this study, medical professionals’ perceived TTF and LTF as antecedents to their cloud-based e-learning continuance intention and performance outcomes were validated, and medical professionals’ perceived impact on learning had a positive effect on their perceived impact on tasks. Synthetically speaking, this study’s results strongly support the research model with all hypothesized links being significant.

It is particularly worth mentioning that this study introduces a new construct, “LTF,” to conceptualize, define and measure it, and further contributes to the application of capturing both expectation–confirmation model and CA (i.e. an intrinsic motivator) for completely explaining medical professionals’ perceived TTF and LTF as external variables to their cloud-based e-learning continuance intention and performance outcomes.

The purpose of this study is to propose a hybrid model integrating the expectation-confirmation model with the views of cognitive absorption (CA) theory and updated DeLone and McLean information system success model to examine whether quality factors as antecedents to medical professionals’ beliefs can affect their continuance intention of the cloud-based e-learning system.

This study’s sampling frame was taken from among medical professionals working in hospitals with over 300 beds in Taiwan which had implemented the cloud-based learning management system (LMS) with a blend of asynchronous and synchronous technologies. Sample data for this study were collected from medical professionals at six hospitals in Taiwan. The data for this study were gathered by means of a paper-and-pencil survey, and each sample hospital that participated in this study was asked to identify a contact person who could distribute the survey questionnaires to medical professionals who had experience in using the cloud-based LMS in their learning. A total of 600 questionnaires were distributed, and 378 (63.0%) usable questionnaires were analyzed using structural equation modeling in this study.

This study proved that medical professionals’ perceived learner–content interaction quality, learner–system interaction quality, service quality, cloud storage service quality and learner–human interaction quality all positively caused their perceived usefulness, confirmation and CA elicited by the cloud-based e-learning system, which jointly explained their satisfaction with the system, and resulted in their continuance intention of the system.

Several limitations and suggestions may open avenues for future research. First, the limitation of self-reported measures should be considered; future research may combine with qualitative data (e.g. semi-structured, narrative, in-depth interviews, focus group interviews and open-ended questions) to get more complete interpretations of medical professionals’ cloud-based e-learning continuance intention. Next, this study’s data were collected from hospitals in Taiwan only; given this study’s limited scope, future research may generalize this study’s sample to the respondents of other national cultural backgrounds and make cross-country comparisons to enhance the completeness of this study. Finally, this study’ results were based on cross-sectional data; future research may use a longitudinal analysis by taking into account the evolution of medical professionals’ cloud-based e-learning continuance intention over time.

This study fully evaluates interaction-related and cloud-related quality determinants through an understanding of medical professionals’ state of CA in explaining their cloud-based e-learning system continuance intention that is difficult to expound with only their utilitarian perception of the system. Hence, the results contribute to deep insights into an all-round quality evaluation in the field of medical professionals’ cloud-based e-learning continuance intention, and extrinsic and intrinsic motivators are both taken into consideration in this study’s theoretical development of medical professionals’ cloud-based e-learning continuance intention to acquire a more comprehensive and robust analysis.

Today, the high cost of e-learning systems’ implementation and the difficulty of managing the infrastructures motivate educational institutions toward application of cloud-based e-learning systems. This new system should be aligned with the academics’ aims and pedagogical principles to be beneficial for learners and instructors. Therefore, the vendor selection of learning systems is one of the most important processes to migrate toward cloud-based e-learning. The purpose of this paper is to develop a new framework to facilitate the vendor selection of cloud-based e-learning systems in the cloud market.

To identify the initial criteria as to the vendor selection of cloud-based e-learning services, a literature review is done. To enrich the initial criteria, a focus group of experts is investigated, and the framework developed; then, a survey analysis is conducted to validate the proposed framework. The extracted criteria and sub-criteria are weighted and prioritized using best-worst method (BWM).

The results indicate that the main dimensions of vendor selection framework as regards cloud-based e-learning systems are managerial, technological and pedagogical factors. The rank orders and weights of the mentioned aspects and their sub-criteria are calculated using the BWM.

The proposed framework helps managers to get a big picture of requirements as to cloud-based e-learning and more effectively to select appropriate vendors in this initiative. In the vendor selection process, managers must pay attention to technological issues as well as managerial and pedagogical considerations.

Cloud-based e-learning systems are getting increasingly essential to offer training courses more efficiently in educational institutions. Although the intersection between cloud computing and e-learning has increasingly grown in both practical and academic contexts, there are little studies on how educational institutions and organizations could be able to select appropriate cloud-based e-learning systems. This paper explores the ignored but critically important subject of cloud-based e-learning. The main contribution of this paper is to propose a novel and integrated framework containing the important aspects of vendor selection in cloud-based e-learning services. The proposed framework comprises managerial, technological and pedagogical aspects simultaneously as well as sub-criteria denoting each aspect.

One of the most significant threats of COVID-19 in the world is the closure of universities, schools, training courses and even companies and organizations. In such a situation and with the free time that has arisen, this threat of education closure can become a golden opportunity for learning and progress in virtual education. E-learning uses information technology (IT) to distribute knowledge and information for training and education. Also, cloud computing is a technology utilized in the IT domain. It can be employed in performing e-learning. Therefore, the main goal of this study is to assess the impact of cultural characteristics, economic situations, skills and knowledge on the development and success of CELS in the COVID-19 era.

Cloud-based e-learning system (CELS) provides all e-learning requirements like software and hardware resources to promote conventional e-learning technologies. The CELS stands on several factors of diverse aspects that have been of high significance in CELS success. So, these systems must be checked to analyze their significance rate and successfully carry out their effectiveness. On the other hand, these days, the 2019 coronavirus disease (COVID-19) changes our daily lifestyles. Therefore, the present investigation provides a new model investigating the development and success of CELS in the COVID-19 era. Also, an online questionnaire was used to gather the data. The content validity of the questionnaire was obtained by applying the opinions of ten experts from e-learning specialists. The collected data are analyzed using LISREL and Smart PLS software.

The results from the path coefficient and the sample t-test have implied that skills and knowledge positively influence CELS in the COVID-19 era. In addition, the relationship between cultural characteristics and CELS in the COVID-19 era has been positive and significant. The relationship between the economic situations and the CELS in the COVID-19 era is positive and significant.

The proposed model helps managers get a big picture of CELS necessities and more effectively in the COVID-19 era. This research has a unique impact on universities to develop an e-learning platform to facilitate the education process in the COVID-19 era. It provides guidelines for educational institutions to effectively implement the learning management system to facilitate students' education.

CELS are getting increasingly essential to offer training courses more efficiently in educational institutions. Although the intersection between cloud computing and e-learning has increasingly grown in both practical and academic contexts, few studies on the impact of cultural characteristics, economic situations, skills and knowledge on the development and success of CELS in the COVID-19 era. This paper explores the ignored but critically important subject of CELS. This paper's main contribution is to present a new and integrated model containing the essential aspects of the development and success of CELS in the COVID-19 era. The proposed framework comprises cultural characteristics, economic situations, skills and knowledge aspects simultaneously, as well as sub-criteria denoting each element.

This study aims to assesses the factors that affected the development of cloud-based accounting education (DCBAE) and students' academic performance at Oman Universities.

In this paper a questionnaire was used to collect data for a sample of students. Partial least squares (PLS) were used to assess the hypotheses and model.

Results indicate that human factors and cultural and social factors have a direct positive effect on the DCBAE and students' academic performance.

This study is carried out after five semesters since the beginning of the COVID-19 pandemic. Thus, it can help universities (accounting education in particular) in the Sultanate of Oman know the factors that affect the development of accounting education and adopt policies and strategies that depend on cloud computing in education operations, even after the COVID-19 pandemic.

The main contribution of this study is to evaluate the factors that affected the DCBAE and the academic performance of students in the Sultanate of Oman from March 15, 2020 to June 2022, a period that witnessed the application of cloud-based education, either in whole or in part using a questionnaire about the opinions of students.

This paper aims to provide a literature review on the cloud-based platforms for the education sectors. The several aspects of cloud computing adoption in education, remote/distance learning and the application of cloud-based design and manufacturing (CBDM) have been studied and theorised.

A four-step methodology was adopted to analyse and categorise the papers obtained through various search engines. Out of 429 research articles, 72 papers were shortlisted for the detailed analysis.

Many factors that influence cloud computing technology adoption in the education sector have been identified in this paper. The research findings on several research items have been tabulated and discussed. Based on the theoretical research done on cloud computing for education, cloud computing for remote/distance learning and CBDM, cloud computing could enhance the educational systems in mainly developing countries and improve the scope for remote/distance learning.

This study is limited to papers published only in the past decade from 2011 to 2020. Besides, this review was unable to include journal articles published in different languages. Nevertheless, for the effective teaching and learning process, this paper could help understand the importance and improve the process of adopting cloud computing concepts in educational universities and platforms.

This study is a novel one as a research review constituting cloud computing applications in education and extended for remote/distance learning and CBDM, which have not been studied in the existing knowledge base.

The purpose of this study was to investigate the factors that might influence the intention and use behaviour of e-learning systems by students in state universities in Sri Lanka.

The theoretical model for this study was primarily drawn from unified theory of acceptance and Use of Technology 2 (UTAUT2). Exogenous variables included performance expectancy, effort expectancy, social influence, work life quality, hedonic motivation, internet experience and facilitating condition, and their influence on behavioural intention and use behaviour were studied. Instrument was developed using validated items from past literature. Data for this quantitative study were collected from undergraduate and postgraduate students from 15 Sri Lankan state universities by self-administering and Web-form during second quarter of 2018. Structural equation modelling was used to see the insights from the valid data using IBM’s SPSS 25 and AMOS 22.

Results of the confirmatory factor analysis and subsequent evaluation of the structural model confirmed the proposed hypotheses, and it was found that constructs of UTAUT2 have a significant impact on and play an important role in behavioural intention to use and use behaviour of e-learning system by state university students in Sri Lanka.

The adoption of an e-learning system in Sri Lankan state universities is fairly low. Hence, investigation of what determinants might be contributing for adoption is important to enhance the learning experience of students and help them improve their knowledge. This paper contributes by delineating the factors that influence the acceptance and use of e-learning systems by students of state universities in Sri Lanka.