Big data (BD) analytics has been increasingly gaining attraction in both practice and theory in light of its opportunities, barriers and expected benefits. In particular, emerging economics view big data analytics as having great importance despite the fact that it has been in a constant struggle with the barriers that prevent its adoption. Thus, this study primarily attempted to determine the drivers of big data analytics in the context of a developing economy, Jordan. The study examined the influence of technological, organizational and environmental factors on big data adoption in the Jordanian SMEs context, using PLS-SEM for the analysis. The empirical results revealed that the relative advantage, complexity, security, top management support, organizational readiness and government support influence the adoption of BD, whilst pressure of competition and compatibility appeared to be of insignificant influence. The findings are expected to contribute to enterprise management and strategic use of data analytics in the present dynamic market environment, for both researcher and practitioner circles concerned with the adoption of big data in developing countries.
The most current highly infectious disease, which has become a global health challenge permeating entire sectors of society, is COVID-19. In the education sector, the transmission of COVID-19 has been curbed through the closure of institutions and the facilitation of online learning. The main objective of this study was to propose an integrated model of the unified theory of acceptance and use of technology combined with the DeLone and McLean model, to examine the influence of quality features, namely, performance expectancy (PE), effort expectancy (EE), facilitating conditions (FC), and social influence (SI), on the intentions and satisfaction of users toward mobile learning (m-learning) use in the context of Saudi learning institutions. The study obtained m-learning user data using an online questionnaire, after which the data were exposed to partial least squares structural equation modeling to test the proposed research model. The findings supported the influence of PE, EE, and FC on intention toward m-learning use but did not support the significant influence of SI. Moreover, system, intention, and user satisfaction were found to positively and significantly influence m-learning-system usage, with system, information, and service quality being top drivers of such user intention and satisfaction. The results reflect the required information concerning the strategies of higher institutions to enhance m-learning-system acceptance among students, with general implications for learning acceptance and usage.
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