Evaluating the actual use of learning management systems during the covid-19 pandemic: an integrated theoretical model

Exploring the factors that influence academic performance in Jordanian higher education institutions

Since the coronavirus 2019 (COVID-19) pandemic hit the world, many universities have used digital asynchronous learning tools such as Digital Learning Management Systems (DLMS) to continue the educational process. Despite its global usage, only a few studies have investigated its quality in the Jordanian context during the COVID-19 period from a quantitative and qualitative approach perspective. Thus, the current study aims to explore the factors that influence academic performance in Jordanian higher education institutions during the COVID-19 pandemic. A mixed methods research approach was employed to evaluate the quality of the teaching-learning process for Jordanian students in higher education institutions. The triangulated data focused on three core pillars namely if students saw a difference in their grades prior to, during, and after the pandemic, the challenges faced and improvement suggestions. Accordingly, the quantitative approach with an online questionnaire and the qualitative approach with structured interviews were applied to collect the required data from Jordanian students in higher education institutions. The results of the current study revealed that the evaluation of the teaching-learning process quality during the pandemic period affected students' academic performance in different proportions based on their specialization area. In addition, the study results also identified the most important challenges that faced the students during this period and suggested procedures to overcome them and improve the distance learning process. The current study offers empirical evidence on critical success factors underlying digital learning management systems in the COVID-19 era, which can help policymakers in Jordanian universities and the ministry of higher education and scientific research to improve the quality of the teaching-learning process in the Jordanian context.

An empirical evaluation of technology acceptance model for Artificial Intelligence in E-commerce

Artificial Intelligence (AI) has become essential to Electronic-Commerce technology over the past decades. Its fast growth has changed the way consumers do online shopping. Using the Technology Acceptance Model (TAM) as a theoretical framework, this research examines how AI can be made more effective and profitable in e-commerce and how entrepreneurs can make AI technology to assist in achieving their business goals. In this regard, an online survey was conducted from the online purchasers of e-commerce firms. The Partial Least Square (PLS) Smart was used to examine the data. The broadly used TAM was identified as an appropriate hypothetical model for studying the acceptance of AI technology in e-commerce. The findings of this study show that Subjective Norms positively impact Perceived Usefulness (PU) and Pursued Ease of Use (PEU), trust has a positive effect on PEU, and PEU positively impacts PU and attitudes toward use. Similarly, PU also has a positive effect on attitudes toward use and intention to use. Furthermore, the findings do not support the impact of Trust on PU and attitudes towards behavioural intention to use. Lastly, behavioural intention to use positively impacted the actual use of AI technology. This study adds theoretical and practical knowledge for adopting the TAM model in the E-commerce sector. It helps entrepreneurs to implement the TAM model in their business to use AI in a better and more appropriate way.

Employing the TAM in predicting the use of online learning during and beyond the COVID-19 pandemic

Online learning systems have become an applied solution for delivering educational content, especially in developing countries, since the start of the COVID-19 pandemic. The present study is designed to identify the factors influencing the behavioral intention of agricultural students at universities in Iran to use online learning systems in the future. This research uses an extended model in which the constructs of Internet self-efficacy, Internet anxiety, and output quality are integrated into the technology acceptance model (TAM). Data analysis was performed using the SmartPLS technique. The analyses showed the proposed model to be strong in terms of predicting the attitude to online learning and the intention to use it. The extended TAM model fit the data well and predicted 74% of the intention variance. Our findings show attitude and perceived usefulness to have directly affected intention. Output quality and Internet self-efficacy indirectly affected attitude and intention. Research findings can help with the design of educational policies and programs to facilitate education and improve student academic performance.

Adoption of AI-Enabled Tools in Social Development Organizations in India: An Extension of UTAUT Model

Social development organizations increasingly employ artificial intelligence (AI)-enabled tools to help team members collaborate effectively and efficiently. These tools are used in various team management tasks and activities. Based on the unified theory of acceptance and use of technology (UTAUT), this study explores various factors influencing employees' use of AI-enabled tools. The study extends the model in two ways: a) by evaluating the impact of these tools on the employees' collaboration and b) by exploring the moderating role of AI aversion. Data were collected through an online survey of employees working with AI-enabled tools. The analysis of the research model was conducted using partial least squares (PLS), with a two-step model - measurement and structural models of assessment. The results revealed that the antecedent variables, such as effort expectancy, performance expectancy, social influence, and facilitating conditions, are positively associated with using AI-enabled tools, which have a positive relationship with collaboration. It also concluded a significant effect of AI aversion in the relationship between performance expectancy and use of technology. These findings imply that organizations should focus on building an environment to adopt AI-enabled tools while also addressing employees' concerns about AI.