In higher education learning, e-learning systems have become renowned tools worldwide. The evident importance of e-learning in higher education has resulted in a prenominal increase in the number of e-learning systems delivering various forms of services, especially when traditional education (face-to-face) was suddenly forced to move online due to the COVID-19 outbreak. Accordingly, assessing e-learning systems is pivotal in the interest of effective use and successful implementation. By relying on the related literature review, an extensive model is developed by integrating the information system success model (ISSM) and the technology acceptance model (TAM) to illustrate key factors that influence the success of e-learning systems. Based on the proposed model, theory-based hypotheses are tested through structural equation modeling employing empirical data gathered through a survey questionnaire of 537 students from three private universities in Jordan. The findings demonstrate that quality factors, including instructor, technical system, support service, educational systems, and course content quality, have a direct positive influence on students’ satisfaction, perceived usefulness, and system use. Moreover, self-regulated learning negatively affects students’ satisfaction, perceived usefulness, and system use. Students’ satisfaction, perceived usefulness, and system use are key predictors of their academic performance. These findings provide e-learning stakeholders with important implications that guarantee the effective, successful use of e-learning that positively affects students’ learning.

In this article, unmanned aerial vehicles (UAVs) are expected to play a key role in improving the safety and reliability of transportation systems, particularly where data traffic is nonhomogeneous and nonstationary. However, heterogeneous data sharing raises plenty of security and privacy concerns, which may keep UAVs out of future intelligent transportation systems (ITS). Some of the well-known security and privacy issues in the UAV-enabled ITS ecosystem include tracking UAVs and vehicle locations, unauthorized access to data, and message modification. Therefore, in this article, we contribute to the sum of knowledge by combining the hyperelliptic curve cryptography (HECC) techniques, digital signature, and hash function to present a privacy-preserving authentication scheme. The security features of the proposed scheme are assessed using formal security analysis methods, i.e., real-or- random (ROR) oracle model. To examine the performance of the proposed scheme, a comparison with other existing schemes has been carried out. The results reveal that the proposed scheme outperforms its counterpart schemes in terms of computation and communication costs.

Information systems security is considered one of the key issues concerning organizations’ management. Despite the massive investment that organizations make to safeguard their systems, there are still many internal security breaches. The increase in insider threats to information systems can be related to the employees’ compliance toward information security policy. Several review papers were conducted to explore information security policy compliance behavior research. However, the literature lacks insight into the positive and negative (direct or indirect) influence of human and organizational theories and their factors influencing information security policy compliance behavior. Therefore, this paper provides a systematic literature review synthesizing the psychological theories, organizational theories, and other internal and external factors on information security policy compliance researches. The results analysis of 87 studies showed that the general deterrence theory, theory of planned behavior, and protection motivation theory are the most frequently used. The influencing factors of theories are mostly similar in the results. Furthermore, information security education, training and awareness, trust, and leadership, among many other internal and external factors, are highly used. This study is one of the first researches that explores the relationship types among the influencing factors; emphasizing the direct and indirect effect, and information security policy compliance behavior. This paper also identifies some gaps in information security policy compliance behavior research and proposes future works. In addition, it provides a theoretical contribution and practical insight in the context of information security policy compliance.

Predicting stock market prices is an important and interesting task in academic and financial research. The volatile nature of the stock market means that predicting stock market prices is a challenging task. However, recent advancements in machine learning, especially in deep learning techniques, have made it possible for researchers to use such techniques to predict future stock trends based on historical financial data, social media news, financial news, and stock technical indicators (STIs). This work focused on the prediction of closing stock prices based on using ten years of Yahoo Finance data of ten renowned stocks and STIs by using 1D DenseNet and an autoencoder. The calculated STIs were first used as the input for the autoencoder for dimensionality reduction, resulting in less correlation between the STIs. These STIs, along with the Yahoo finance data, were then fed into the 1D DenseNet. The resultant features obtained from the 1D DenseNet were then used as input for the softmax layer residing inside the 1D DenseNet framework for the prediction of closing stock prices for short-, medium-, and long-term perspectives. Based on the predicted trends of the stock prices, our model presented the user with one of three suggested signals, i.e., buy, sell, or hold. The experimental results showed that the proposed approach outperformed the state-of-the-art techniques by obtaining a minimum MAPE value of 0.41.

Finances represent one of the key requirements to perform any useful activity for humanity. Financial markets, e.g., stock markets, forex, and mercantile exchanges, etc., provide the opportunity to anyone to invest and generate finances. However, to reap maximum benefits from these financial markets, effective decision making is required to identify the trade directions, e.g., going long/short by analyzing all the influential factors, e.g., price action, economic policies, and supply/demand estimation, in a timely manner. In this regard, analysis of the financial news and Twitter posts plays a significant role to predict the future behavior of financial markets, public sentiment estimation, and systematic/idiosyncratic risk estimation. In this paper, our proposed work aims to analyze the Twitter posts and Google Finance data to predict the future behavior of the stock markets (one of the key financial markets) in a particular time frame, i.e., hourly, daily, weekly, etc., through a novel StockSentiWordNet (SSWN) model. The proposed SSWN model extends the standard opinion lexicon named SentiWordNet (SWN) through the terms specifically related to the stock markets to train extreme learning machine (ELM) and recurrent neural network (RNN) for stock price prediction. The experiments are performed on two datasets, i.e., Sentiment140 and Twitter datasets, and achieved the accuracy value of 86.06%. Findings show that our work outperforms the state-of-the-art approaches with respect to overall accuracy. In future, we plan to enhance the capability of our method by adding other popular social media, e.g., Facebook and Google News etc.

With the rapid increase in social media users and netizens globally, the proclivity for online shopping using social commerce (SC) platforms cannot be ignored. Trust has been recognised as a constant challenge in the context of social commerce due to the lack of face-to-face interaction. Therefore, there is a dire need to enhance the trust of consumers in social commerce platforms. However, the research in the formation of trust in social commerce and antecedents remains limited. In addition, the existing SC research failed to include its multidimensional view to investigate user behaviour. This study fills this gap and extends existing knowledge by developing a model exploring the antecedents of trust in social commerce. Drawing upon the social-technical theory and trust lens, this study attempts to identify the role of (i) structural assurance (SA) and SC platforms as an institution-based trust, (ii) trust in sellers and trust in SC community as trusting beliefs, and (iii) trust in online payment as a cognitive trust on trust and intention of the social commerce. This research employs a dataset (n = 406) collected using an online survey; the research subjects were recruited from Australia, the United States, and the United Kingdom. This study uses the partial least squares structural equation modeling (PLS-SEM) approach to analyse the data and to confirm the hypothesis proposed in the research model. The empirical findings show that trust in social commerce influences behavioural intention. In addition, trust in the SC platform, the SC community, and online payment influence the trust in SC. Likewise, SA and trust in the SC platform have a significant relationship with trust in sellers, the SC community, and online payment. Finally, this study discusses the theoretical contributions and practical insights to several limitations and suggests directions for future research.