Purpose The use of learning management systems (LMSs) such as Google Classroom has increased significantly in higher education institutes during the COVID-19 pandemic. However, only a few studies have investigated instructors’ continued intention to reuse LMS. The purpose of this study is to investigate the factors that influence instructors’ intention to reuse an LMS in higher education institutes. Design/methodology/approach This study adopted a mixed-method research design. In the quantitative section, an integrated model of technology acceptance model and information system success model is proposed to explore the effects of system quality, service quality, information quality, perceived ease of use and perceived usefulness on instructors’ satisfaction and how their satisfaction will influence their intention to reuse Google Classroom in the future. In the qualitative section, to gain more understanding, instructors were asked to identify the challenges that inhibit the adoption of e-Learning technologies in public universities in Iraq and what are their recommendations to rectify them. Findings The findings revealed that service quality had no positive influences on the satisfaction of instructors, while other factors had varying levels of influence, the findings further showed that inadequate internet service and students lack of interest are the biggest challenges instructors faced during their experience with Google Classroom. Research limitations/implications To improve the generalizability of the results, future studies are recommended to include larger samples, in addition, further studies are also advised to take individual traits such as age and gender into consideration. Originality/value The outcomes of this study are expected to benefit researchers, policymakers and LMS developers who are interested in factors that affect instructors’ intention to reuse LMS in higher education institutes in developing countries.

Norms and normative multiagent systems have become the subjects of interest for many researchers. Such interest is caused by the need for agents to exploit the norms in enhancing their performance in a community. The term norm is used to characterize the behaviours of community members. The concept of normative multiagent systems is used to facilitate collaboration and coordination among social groups of agents. Many researches have been conducted on norms that investigate the fundamental concepts, definitions, classification, and types of norms and normative multiagent systems including normative architectures and normative processes. However, very few researches have been found to comprehensively study and analyze the literature in advancing the current state of norms and normative multiagent systems. Consequently, this paper attempts to present the current state of research on norms and normative multiagent systems and propose a norm's life cycle model based on the review of the literature. Subsequently, this paper highlights the significant areas for future work.

This article describes how the Internet of Things (IoT) is a new paradigm shift in information technology (IT). The IoT manifests the phenomenon of ubiquitous computing when objects or ‘things' are connected to the Internet providing automated services related to the things. However, few studies investigated the acceptance of these services by customers. Consequently, the purpose of this article is to investigate the factors that affect the acceptance and use of the IoT services by customers of telecommunication companies in Jordan. A total of 176 respondents participate in this study and the collected data is analyzed using SPSS. The findings indicate that behavioral intention significantly affects the use behavior of IoT services. In addition, IT knowledge is the most important factor that affects the behavioral intention followed by other factors.

Mobile ad hoc network (MANET) can be described as a group of wireless mobile nodes that form a temporary dynamic and independent infrastructure network or a central administration facility. High energy consumption is one of the main problems associated with the MANET technology. The wireless mobile nodes used in this process rely on batteries because the network does not have a steady power supply. Thus, the rapid battery drain reduces the lifespan of the network. In this paper, a new Bat Optimized Link State Routing (BOLSR) protocol is proposed to improve the energy usage of the Optimized Link State Routing (OLSR) protocol in the MANET. The symmetry between OLSR of MANET and Bat Algorithm (BA) is that both of them use the same mechanism for finding the path via sending and receiving specific signals. This symmetry resulted in the BOLSR protocol that determines the optimized path from a source node to a destination node according to the energy dynamics of the nodes. The BOLSR protocol is implemented in a MANET simulation by using MATLAB toolbox. Different scenarios are tested to compare the BOLSR protocol with the Cellular Automata African Buffalo Optimization (CAABO), Energy-Based OLSR (EBOLSR), and the standard OLSR. The performance metric consists of routing overhead ratios, energy consumption, and end-to-end delay which is applied to evaluate the performance of the routing protocols. The results of the tests reveal that the BOLSR protocol reduces the energy consumption and increases the lifespan of the network, compared with the CAABO, EBOLSR, and OLSR.

Recently, development in intelligent transportation systems (ITS) requires the input of various kinds of data in real-time and from multiple sources, which imposes additional research and application challenges. Ongoing studies on Data Fusion (DF) have produced significant improvement in ITS and manifested an enormous impact on its growth. This paper reviews the implementation of DF methods in ITS to facilitate traffic flow analysis (TFA) and solutions that entail the prediction of various traffic variables such as driving behavior, travel time, speed, density, incident, and traffic flow. It attempts to identify and discuss real-time and multi-sensor data sources that are used for various traffic domains, including road/highway management, traffic states estimation, and traffic controller optimization. Moreover, it attempts to associate abstractions of data level fusion, feature level fusion, and decision level fusion on DF methods to better understand the role of DF in TFA and ITS. Consequently, the main objective of this paper is to review DF methods used for real-time and multi-sensor (heterogeneous) TFA studies. The review outcomes are (i) a guideline of constructing DF methods which involve preprocessing, filtering, decision, and evaluation as core steps, (ii) a description of the recent DF algorithms or methods that adopt real-time and multi-sensor sources data and the impact of these data sources on the improvement of TFA, (iii) an examination of the testing and evaluation methodologies and the popular datasets and (iv) an identification of several research gaps, some current challenges, and new research trends.INDEX TERMS Intelligent transportation systems, traffic flow analysis, data fusion; real-time processing, multi-sensor, heterogeneous data, machine learning.