• Energy Research

Small and medium enterprises (SMEs) are the pillars on which most businesses worldwide rest. Thus, without the support of qualified information systems, it can be very challenging for them to improve their performance and difficult for them to reach sustainability goals. Despite the essentiality of economic sustenance for a competitive advantage in the postmodern industrial era, Jordanian SMEs are hampered with multiple challenges, such as accounting information quality, which supports various organizational decisions. The prevalence of information technology (IT) optimizes accounting operations through accounting-based information. A computerized accounting system (accounting information system, or AIS) facilitates accurate reporting, processes large-scale transactions, and generates meaningful reporting for subsequent evaluation. Given the lack of AIS implementation in SMEs, despite its notable advantages, this study aims to investigate the AIS-implementation antecedents and their implications towards sustainable business performance among Jordanian SMEs. An integrated model was recommended based on the technology–organization–environment (TOE) framework and resource-dependency theory (RDT) for the incorporation of AIS-implementation elements and sustainable business performance into one model. A self-administered questionnaire was disseminated among 194 respondents within the context of Jordanian SMEs for data collection and evaluation using structural equation modelling (SEM). Based on the study outcomes, external pressure, compatibility, financial support, top management support (TMS), and external assistance significantly impacted AIS implementation, which subsequently catalyzed sustainable business performance. Such results could offer useful insights into how organizations could optimize AIS implementation for sustainable business performance and expand the current body of literature on IS- or IT-implementation antecedents and impacts. The implications of this study are that SMEs should develop effective AIS implementation in order to reach sustainability goals. Therefore, we recommend and encourage SMEs decision makers to utilize AIS for their businesses.

In the literature, studies have evidenced the efforts adopted by firms to develop digital technology with the hope of achieving sustainable decisions and competitive performance. However, studies have yet to provide an extensive explanation of the mechanisms used by firms in their digital technology adoption to impact and enhance value, particularly among small and medium enterprises (SMEs). In this regard, accounting information has served as a fundamental basis for business decision-making and the extensive use of digital technology has paved the way for the efficiency and effectiveness of accounting functions in modifying information relating to such functions. More specifically, a digital accounting system (DAS) enables the reporting and processing of large transaction amounts and generates the data required for analysis. However, despite these advantages, SMEs have been slow in their adoption and usage of DASs. Accordingly, this study drew upon resource-based view theory and the technology-organization-environment framework to propose an integrated model for examining the determinants and impact of using DAS among SMEs. The proposed model encapsulates the use and performance aspect of DAS. The study utilized a self-administered survey questionnaire as the primary data collection instrument. Data from 183 SMEs in Jordan were analyzed using partial least squares-structural equation modeling. The findings reveal that compatibility, organizational readiness, top management support and government support all had significant effects on DAS usage, which, in turn, had a positive and significant effect on DAS performance. With regard to the moderating effects, COVID-19 was found to have a moderating role on the DAS usage–DAS performance relationship. The study findings explain the way firms can enhance their DAS use to obtain optimum performance, thereby contributing to the literature on the antecedents and effects of using current information technology/information systems. The study recommends that the government of Jordan prepare and carry out a campaign concerning the importance of DASs for SMEs.

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.

During the COVID-19 pandemic, educational mobile games may play a significant role to facilitate students’ learning. Several studies have indicated that these games using mobile phones may improve students’ learning motivation and effectiveness when they are equipped with appropriate learning strategies. However, investigating the impact of learning strategies in students’ utilization of educational mobile games has received little scholarly attention during the COVID-19 pandemic. Hence, this research proposed two learning games scenarios to fill this gap. In the first scenario, students were offered an educational mobile game with a learning strategy called ‘scaffolding strategy’; while in the second scenario, the same game was offered without the strategy. To achieve this objective, an experimental design with a research model was developed to examine the role of scaffolding learning strategy in students’ use of educational mobile games. In this experimental study, 43 students from two classes participated in the two learning scenarios. The results indicate that educational mobile gaming with the scaffolding learning strategy significantly influenced students’ utilization of the mobile game. In addition, the adoption of the learning strategy significantly affected students’ perceived enjoyment, perceived usefulness, perceived ease of use, and behavioural intention to use, compared with the same game without the learning strategy. The results also indicate that the introduction of the scaffolding learning strategy into the educational mobile game will increase students’ learning effectiveness and motivation.

The adoption of big data analytics (BDA) is increasing pace both in practice and in theory, owing to the prospects and its potential advantages. Numerous researchers believe that BDA could provide significant advantages, despite constant battles with the constraints that limit its implementation. Here, we suggest an incorporated model to investigate the drivers and impacts of BDA adoption in the Jordanian hotel industry based on the technology–organisation–environment framework and the resource-based view theory. The suggested model incorporates both the adoption and performance components of BDA into a single model. For data collection, in this study, we used an online questionnaire survey. The research model was verified based on responses from 119 Jordanian hotels. This study yielded two significant findings. First, we discovered that relative advantage, organizational readiness, top management support, and government regulations have a major impact on BDA adoption. The study results also reveal a strong and favourable association between BDA adoption and firm performance. Finally, information sharing was found to have a moderating effect on the association between BDA adoption and firm performance. The data revealed how businesses might increase their BDA adoption for improved firm performance. The present study adds to the limited but growing body of literature investigating the drivers and consequences of technology acceptance. The findings of this study can serve as a resource for scholars and practitioners interested in big data adoption in emerging nations.

Understanding how the public perceives various risks and hazards associated with our well-being and health is crucial for government and policymakers. The present research aimed to assess the public's perception of various risks and hazards associated with their well-being and health. The study combined two well-known approaches to risk assessment, namely, the analytical hierarchical process (AHP) and the psychometric paradigm. Seven risk attributes were chosen from the risk perception literature to evaluate 27 risks and hazard activities using a survey questionnaire developed based on the psychometric paradigm literature. The collected data was then analyzed using AHP to determine the priority weight for each risk attribute. The results showed that the most important risk attribute was voluntariness of risk, followed by chronic-catastrophic and newness of risk. Furthermore, the study found that natural hazards were ranked the highest, followed by refugee influx and fire hazards. In contrast, the mobile phone was perceived as the lowest type of risk. The findings can be used by policymakers to develop effective and sustainable risk communication strategies. Policymakers can use the research findings to create effective and sustainable risk communication strategies that help the government to inform and educate the public about potential risks, improve coordination among agencies and stakeholders, and enhance public trust in government decision-making.

Nowadays, social media applications (SMAs) which are quite popular among students have a significant influence on education sustainability. However, there is a lack of research that explores elements of the constructivist learning approach with the technology acceptance model (TAM) in higher education. Therefore, this research aimed to minimize the literature gap by examining the SMA factors used for active collaborative learning (ACL) and engagement (EN) to affect the students’ academic performance in measuring education sustainability, as well as examining their satisfaction from its use. This study employed constructivism theory and TAM as the investigation model, and applied a quantitative method and analysis through surveying 192 university students at King Faisal University. Using structural equation modeling (SEM), the responses were sorted into nine factors and analyzed to explain students’ academic performance in measuring education sustainability, as well as their satisfaction. The results were analyzed with structural equation modelling; it was shown that all the hypotheses were supported and positively related to sustainability for education, confirming significant relationships between the use of SMAs and the rest of the variables considered in our model (interactivity with peers (IN-P), interactivity with lecturers (IN-L), ACL, EN, perceived ease of use (PEOU), perceived usefulness (PU), SMA use, student satisfaction (SS), and students’ academic performance (SAP).

Unmanned aerial vehicles (UAVs) coupled with machine learning approaches have attracted considerable interest from academicians and industrialists. UAVs provide the advantage of operating and monitoring actions performed in a remote area, making them useful in various applications, particularly the area of smart farming. Even though the expense of controlling UAVs is a key factor in smart farming, this motivates farmers to employ UAVs while farming. This paper proposes a novel crop-monitoring system using a machine learning-based classification with UAVs. This research aims to monitor a crop in a remote area with below-average cultivation and the climatic conditions of the region. First, data are pre-processed via resizing, noise removal, and data cleaning and are then segmented for image enhancement, edge normalization, and smoothing. The segmented image was pre-trained using convolutional neural networks (CNN) to extract features. Through this process, crop abnormalities were detected. When an abnormality in the input data is detected, then these data are classified to predict the crop abnormality stage. Herein, the fast recurrent neural network-based classification technique was used to classify abnormalities in crops. The experiment was conducted by providing the present weather conditions as the input values; namely, the sensor values of temperature, humidity, rain, and moisture. To obtain results, around 32 truth frames were taken into account. Various parameters—namely, accuracy, precision, and specificity—were employed to determine the accuracy of the proposed approach. Aerial images for monitoring climatic conditions were considered for the input data. The data were collected and classified to detect crop abnormalities based on climatic conditions and pre-historic data based on the cultivation of the field. This monitoring system will differentiate between weeds and crops.