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Consistent with their goal of becoming a centre for educational excellence in teaching, learning and research, the authorities of Chinese higher education introduced their academic database system as one of their e-library services. However, the existing literature exhibits inadequate empirical measurements of academic databases in all aspects of higher education throughout the world during the COVID-19 pandemic. To address this gap, this study aims to validate the technology satisfaction model (TSM) for measuring students' satisfaction in using academic databases for their learning and research purposes. This study also analysed local and international academic databases to explore whether these databases could play a moderating role in shaping learners' satisfaction. The data were collected through a survey of 500 respondents studying at a research university in Shanghai. The results, which were analysed by structural equation modelling and the Rasch model, showed that students' satisfaction is determined by three valid predictors: computer self-efficacy, perceived usefulness and ease of use, and causal direct and indirect relationships among these variables in the use of local and international academic databases. Our new findings on the moderating effect of local (i.e. Chinese) and international (e.g. English) academic databases highlighted that the TSM has successfully estimated dual databases and produced insignificant, dissimilar results. This study could aid local and international educators, researchers, information science professionals and others in measuring the perception of academic databases for learning and research. This research could also serve as a guideline for researchers and psychometricians in measuring innovative learning technologies using structural equation modelling and the Rasch model. This is the unique contribution of this study, which concludes that local and international academic databases are almost equally important for postgraduate students at a research university in China. Moreover, these students are satisfied in using these academic databases to learn and do research. [ABSTRACT FROM AUTHOR]

The concept of big data has been extensively considered as a technological modernisation in organisations and educational institutes. Thus, the purpose of this study is to determine whether the modified technology acceptance model (MTAM) is viable for evaluating the performance of librarians in the use of big data analytics in academic libraries. This study used an empirical research method for collecting data from 211 librarians working in Pakistan's universities. On the basis of the findings of the MTAM analysis by structural equation modelling, the performances of the academic libraries were comprehended through the process of big data. The main influential components of the performance analysis in this study were the big data analytics capabilities, perceived ease of access and the usefulness of big data practices in academic libraries. Subsequently, the utilisation of big data was significantly affected by skills, perceived ease of access and the usefulness of academic libraries. The results also suggested that the various components of the academic libraries lead to effective organisational performance when linked to big data analytics. [ABSTRACT FROM AUTHOR]

In today's technology-enriched environment, online research databases have become an indispensable tool in learning and research activities. Libraries worldwide have incorporated them as an integral part of their current service delivery. This study intends to determine whether the technology satisfaction model (TSM) validly measures postgraduate students' satisfaction of using online research databases in an academic library. It further explores the relationships among antecedents within the model. To measure the components of the TSM, the stratified random sampling strategy was used to conduct a survey with 300 postgraduate students from six faculties of COMSATS University Islamabad (CUI) in Pakistan. The data were statistically analysed using the Rasch model and structural equation modelling (SEM). The results of the study illustrate direct and indirect associations among the exogenous, endogenous and mediating variables of the TSM, with the studied postgraduate students' computer self-efficacy playing a crucial role in influencing their satisfaction. The TSM shows that 69% of the variance in the postgraduate students' satisfaction can be explained by perceived usefulness, computer self-efficacy and perceived ease of use, which confirm that the TSM has a strong predictive power and is viable for measuring endogenous variable. The findings also make a significant contribution to the testing of databases for academics, service providers and researchers in higher education. [ABSTRACT FROM AUTHOR]