Browsing by Subject "Performance expectancy"
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ItemArt design major undergraduate's satisfaction towards online education: a case of public universities in Sichuan, ChinaThe paper aims to evaluate the essential determinants that have a significant effect on satisfaction for online education of art design major undergraduate students from four public universities in the Sichuan Province of China. Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Attitude (ATT), Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI) and Satisfaction (SAT) were all investigated to determine if these constructs influence satisfaction of students with online education. The researcher applied a quantitative exploration approach with 487 samples and distributed the quantitative questionnaire to art design major undergraduates at four target universities. The multistage sampling approach was utilized in this survey. Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM) were utilized to determine the relationships of the variables under study. The entire hypotheses were supported by the results of the data analysis, in which social influence indicated the most powerful direct effect on satisfaction. For students to acknowledge and recognize the effectiveness of online education, university administrators and teaching staffs should pay sufficient attention to the factors which has generated significant influence on the satisfaction of the instruction, and consider the correlated teaching adjust or reform in the future according to the findings of this research.
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ItemBehavioral intention and level of usage on convergence media training platform on journalism university students of private universities in Sichuan, ChinaThis study aims to investigate the factors that affect students' behavioral intention and utilization of behavior in the convergence media training platform and to recommend cultivating converged media talents. Taking into account the development of the convergence media training platform, the Theory of Planned Behavior, the Technology Acceptance Model, and the Unified Theory of Plan and Technology Acceptance and Use were set out. The relationship between seven variables including Perceived Usefulness, Perceived Ease of Use, Attitude, Performance Expectation, Behavioral Intention, Social Impact, and Use Behavior was hypothesized. Four hundred and eighty (480) students from three colleges in Sichuan, China were the research samples. The Structural Equation Model (SEM) was utilized to examine the relationship between the variables. Moreover, the consequence revealed that most variables except the relationship between Perceived Ease of Use and Perceived Usefulness as well as Perceived Ease of Use and Attitude did not find a relationship among them. It is possible that students did not find the media convergence platform to be beneficial or simple to operate. In turn, It had no positive influence on attitudes. Hence, it is recommended that teachers and relevant departments strengthen communication and contact with the industry, provide students with more professional teaching content and practical skills training, cultivate a positive social environment, and enhance students' learning attitude and learning efficiency.
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ItemFactors affecting intention to E-learning systems in selected Universities in Phnom Penh, CambodiaThis research investigates factors influencing the intention to use e-learning systems in selected universities in Phnom Penh, Cambodia. The conceptual framework has been developed by adopting previous theoretical studies and research models of the modified unified theory of acceptance and use of technology (UTAUT2). Five hundred questionnaires were collected from undergraduate students through Google form survey with universities’ administration assistance. Multi-stage sampling was used: the first stage is stratified random sampling followed by purposive sampling. Collected data were analyzed using the Structural Equation Model (SEM) and Confirmatory Factor Analysis (CFA) to confirm the model fit and hypothesis testing. The findings showed that performance expectancy, effort expectancy and self-efficacy did not have any influence on behavioral intention. However, social influence had the strongest influence on behavioral intention to use e-learning systems, followed by facilitating conditions. Moreover, facilitating conditions and behavioral intention had a significant influence on use behavior of e-learning systems. This study provided theoretical implications for researchers related to technology adoption and information for training institutions, universities, schools and academic staff on issues they need to focus on when they wish to launch any new system or online services.
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ItemFactors influencing the use of ubiquitous learning in higher education in Sichuan, China in the aftermath of COVID-19 pandemicThis research aims to investigate factors for adoption of ubiquitous learning (u-learning) in higher education in China in the wake of the COVID-19 pandemic. Literature and theoretical models for adoption of ubiquitous learning were examined to find the key factors that would influence ubiquitous learning adoption which include performance expectancy, effort expectancy, social influence, facilitating conditions, intention to use and actual use. The research uses a quantitative, survey-based research design, employing online data collection. The study applied multistage sampling. First, a non-probability sampling method, judgmental sampling was used to draw a population of Chinese higher education students in Sichuan, China at three institutions: – Sichuan Normal University Fine Arts College, Sichuan University of Arts and Sciences Academy of Art and Design, and Dazhou Vocational and Technical College Art Department. Second, stratified random sampling was applied to calculate the number of students to represent each program. Lastly, a sample size of 420 was determined based on the ratio of the number of students in each institution to the total number of populations, were selected through convenience sampling. For analysis of data, Confirmation Factor Analysis (CFA) and structural equation modeling (SEM) were utilized. The analysis showed that intention to use has the strongest effect on actual system use. Furthermore, effort expectancy, facilitating conditions, and social influence except performance expectancy were found to positively affect the intention to use u-learning. Hence, policymakers, universities executives, and educators are recommended to consider these factors to ensure technology adoption success.
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ItemThe effect of behavioral intention to use hybrid education: a case of Chinese undergraduate students(Bangkok : Assumption University Press, 2022) Xie, Haifeng ; Krisana Kitcharoen ; Charnsid Leelakasemsant ; Varghese, Manoj MechankaraPurpose: The purpose of this study is to examining factors affecting undergraduate painting students' behavioral intention toward hybrid education in three public universities in Chongqing, China. Perceived ease of use (PEOU), perceived usefulness (PU), perceived satisfaction (PS), social influence (SI), performance expectancy (PE), Facilitating conditions (FC), and behavioral intention (BI) were used to develop the conceptual framework of this study. Research design, data, and methods: The researchers used quantitative study to distributing questionnaire to 500 participants, who are undergraduate students in the major of painting. The survey was conducted in three sample techniques which are judgmental sampling, quota sampling and convenience sampling methods. An item-objective congruence (IOC) of content validity and Cronbach's Alpha reliability test with 30 pilot samples were earlier assessed. Statistical analyses involve Confirmatory Factor analysis (CFA) and Structural Equation Model (SEM), including model goodness of fit, validity, and reliability. Results: Most hypotheses were supported with the strongest influence between perceived ease of use and perceived usefulness, except facilitation conditions which had no significant influence on behavioral intention. Conclusion: The recommends are that administrators in the educational sector of public institutions should emphasize the main contributors to hybrid learning implementation to increase student engagement and learning efficiency.