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The primary purpose of this study was to investigate and present a theoretical model that identifies the most influential factors affecting the adoption of artificial intelligence (AI) by SMEs to achieve sustainable business performance in Saudi Arabia by integrating the Technology–Organization–Environment (TOE) framework. The authors utilized a quantitative method, using a survey instrument for this research. Data for this research were collected from managers working in six different sectors. Subsequently, based on company size, firms were divided into two groups, allowing multi-group analysis of small and medium-sized businesses to explore group differences. Hence, firm size played a moderating role in the conceptualized model. Data analysis was performed on SmartPLS 3, and the results suggest that dimensions of the TOE framework, such as relative advantage, compatibility, sustainable human capital, market and customer demand, and government support, play a significant role in the adoption of AI. Moreover, this study found a significant influence of AI on SMEs’ operational and economic performance. The multi-group analysis (MGA) results reveal significant group differences, with a medium-sized firm strengthening the relationship between relative advantage and AI adoption compared to small-size firms. The findings lead to practical implications for companies on how to increase the adoption of AI to help SMEs embrace their technological challenges in KSA and obtain sustainable business performance to contribute to the economy.

The installation of photovoltaic (PV) system for electrical power generation has gained a substantial interest in the power system for clean and green energy. However, having the intermittent characteristics of photovoltaic, its integration with the power system may cause certain uncertainties (voltage fluctuations, harmonics in output waveforms, etc.) leading towards reliability and stability issues. In PV systems, the power electronics play a significant role in energy harvesting and integration of grid-friendly power systems. Therefore, the reliability, efficiency, and cost-effectiveness of power converters are of main concern in the system design and are mainly dependent on the applied control strategy. This review article presents a comprehensive review on the grid-connected PV systems. A wide spectrum of different classifications and configurations of grid-connected inverters is presented. Different multi-level inverter topologies along with the modulation techniques are classified into many types and are elaborated in detail. Moreover, different control reference frames used in inverters are presented. In addition, different control strategies applied to inverters are discussed and a concise summary of the related literature review is presented in tabulated form. Finally, the scope of the research is briefly discussed.

The supply chain management (SCM) of COVID-19 vaccine is the most daunting task for logistics and supply managers due to temperature sensitivity and complex logistics process. Therefore, several technologies have been applied but the complexity of COVID-19 vaccine makes the Internet of Things (IoT) a strong use case due to its multiple features support like excursion notification, data sharing, connectivity management, secure shipping, real-time tracking and monitoring etc. All these features can only feasible through choosing and deploying the right IoT platform. However, selection of right IoT platform is also a major concern due to lack of experience and technical knowledge of supply chain managers and diversified landscape of IoT platforms. Therefore, we introduce a decision making model for evaluation and decision making of IoT platforms that fits for logistics and transportation (L&T) process of COVID-19 vaccine. This study initially identifies the major challenges addressed during the SCM of COVID-19 vaccine and then provides reasonable solution by presenting the assessment model for selection of rational IoT platform. The proposed model applies hybrid Multi Criteria Decision Making (MCDM) approach for evaluation. It also adopts Estimation-Talk-Estimation (ETE) approach for response collection during the survey. As, this is first kind of model so the proposed model is validated and tested by conducting a survey with experts. The results of the proposed decision making model are also verified by Simple Additive Weighting (SAW) technique which indicates higher results accuracy and reliability of the proposed model. Similarly, the proposed model yields the best possible results and it can be judged by the precision, accuracy and recall values i.e. 93%, 93% and 94% respectively. The survey-based testing also suggests that this model can be adopted in practical scenarios to deal with complexities which may arise during the decision making of IoT platform for COVID-19 SCM process.

The intensification of the Internet of Health Things devices created security concerns due to the limitations of these devices and the nature of the healthcare data. While dealing with the security challenges, several authentication schemes, protocols, processes, and standards have been adopted. Consequently, making the right decision regarding the installation of a secure authentication solution or procedure becomes tricky and challenging due to the large number of security protocols, complexity, and lack of understanding. The major objective of this study is to propose an IoHT-based assessment framework for evaluating and prioritizing authentication schemes in the healthcare domain. Initially, in the proposed work, the security issues related to authentication are collected from the literature and consulting experts’ groups. In the second step, features of various authentication schemes are collected under the supervision of an Internet of Things security expert using the Delphi approach. The collected features are used to design suitable criteria for assessment and then Graph Theory and Matrix approach applies for the evaluation of authentication alternatives. Finally, the proposed framework is tested and validated to ensure the results are consistent and accurate by using other multi-criteria decision-making methods. The framework produces promising results such as 93%, 94%, and 95% for precision, accuracy, and recall, respectively in comparison to the existing approaches in this area. The proposed framework can be picked as a guideline by healthcare security experts and stakeholders for the evaluation and decision-making related to authentication issues in IoHT systems

This research applies an extended theory of planned behavior (TPB) to empirically investigate consumers’ intentions in Pakistan to purchase energy-efficient appliances (EEAs). Most developing countries face energy crises. As a result, many countries consider EEAs to be part of the solution to energy-related problems and teach sustainable consumption behavior to consumers. Previous studies have neglected developing countries in this context, yet developing markets have great potential for EEA adoption. To understand EEA adoption, we incorporated such variables as warm glow benefits, utilitarian environmental benefits, normative beliefs, and moral obligations as antecedents to TPB variables. The moderating effect of eco-literacy between attitude, subjective norms, perceived behavioral control (PBC), and purchase intention toward EEAs are also examined. Data was gathered through a survey questionnaire from 673 Pakistani consumers to empirically test the proposed hypotheses. The results reveal that utilitarian environmental benefits and warm glow benefits significantly influence attitudes toward EEAs. The findings also show a positive effect of normative beliefs on subjective norms. The interaction effect of eco-literacy positively influences the relationship between attitude and purchase intention, with similar results for subjective norms and purchase intention. However, no significant moderating effect of eco-literacy is found between PBC and purchase intention. Furthermore, we performed multi-group analysis to explore significant group differences by utilizing socio-demographic variables such as gender, age, education, and income. The results show significant group differences, with females’ purchasing behavior, younger consumers, and educated consumers being more readily influenced. Finally, insights for policymakers, suggestions and future directions are discussed.

Purpose The purpose of this study is to develop an extended theory of planned behavior (TPB) model by adding religious commitment (RC) and self-efficacy as internal variables and investigating the effect of these variables on attitudes toward halal cosmetics. In addition, this study also examined the moderating role of halal literacy in the relationships between attitudes (ATT), subjective norms (SN), perceived behavioral control (PBC) and intentions to purchase halal cosmetics. Design/methodology/approach The method of data collection used was self-administered surveys with customers in two stores in Karachi, Pakistan, yielding 267 valid questionnaires. To guarantee validity and reliability, convergent and discriminant validity analyses were conducted, and structural equation modeling was advanced to assess the relationships between variables using smart partial least squares 3.0 software. The interaction moderation technique has been used to examine the moderating effect of halal literacy on the purchase intention (PI) of halal cosmetics. Findings The results show that RC and self-efficacy both significantly impact the attitudes of Gen Y. Normative beliefs also had a significant relationship with SN. Further, ATT and SN had a significant relationship with PI of halal cosmetics, while PBC was nonsignificant. Furthermore, halal literacy is found to have a positive moderating influence on ATT and PI, and SN and PI. Finally, the moderating effect of halal literacy does not exist in the relationship between PBC and PI. Research limitations/implications Participants’ characteristics should vary for future studies, and larger sample sizes may yield different results. It is critical for managers working in the cosmetic industry to monitor Muslim consumption patterns to develop strategies to reach Muslim consumers. This study reveals the effect of RC, self-efficacy and the moderating role of halal literacy on the behavioral attitudes of a booming market sector, which can guide marketing managers in developing more effective advertising campaigns. Originality/value This paper contributes to the halal consumption literature by exploring RC and self-efficacy as constructs for the very first time in the TPB model. To the best of the authors’ knowledge, this study is the first to explore the influence of halal literacy on Gen Y Pakistani Muslim consumer behavioral intention toward halal cosmetic products using the TPB model. The paper offers an extended TPB model framework that may be of interest to scholars, marketers and policymakers.

To efficiently and accurately track the Global Maximum Power Point (GMPP) of the PV system under Varying Environmental Conditions (VECs), numerous hybrid Maximum Power Point Tracking (MPPT) techniques were developed. In this research work, different hybrid MPPT techniques are categorized into three types: a combination of conventional algorithms, a combination of soft computing algorithms, and a combination of conventional and soft computing algorithms are discussed in detail. Particularly, about 90 hybrid MPPT techniques are presented, and their key specifications, such as accuracy, speed, cost, complexity, etc., are summarized. Along with these specifications, numerous other parameters, such as the PV panel’s location, season, tilt, orientation, etc., are also discussed, which makes its selection easier according to the requirements. This research work is organized in such a manner that it provides a valuable path for energy engineers and researchers to select an appropriate MPPT technique based on the projects’ limitations and objectives.

Climate change in context of environmental issues is pushing most of the countries to set the goals in order to achieve carbon neutrality and sustainable development. The recognition of Sustainable Development Goal (SDG) thirteen (13) is aided by the objective of this study which is to take an urgent action to combat climate change. In this context, this study investigates the effect of technological progress, income, and foreign direct investment on carbon dioxide emission by taking into consideration the moderating effect of economic freedom in 165 global countries from 2000 to 2020. The study employed ordinary least squares (OLS), fixed effects (FE), and two-step system generalized method of moments for analysis. The findings reveal that economic freedom, income per capita, foreign direct investment, and industry increase carbon dioxide emission while technological progress reduces emission in global countries. Surprisingly, economic freedom indirectly increases the level of carbon emissions by technological progress; however, economic freedom indirectly decreases the level of carbon emissions by income per capita. In this regard, this study favors clean eco-friendly technologies and seeks methods for development without harming the environment. Furthermore, the findings of this study have considerable policy suggestions for the sample countries.

This study proposes a bi-level framework for real-time crash risk forecasting (RTCF) for signalised intersections, leveraging the temporal dependency among crash risks of contiguous time slices. At the first level of RTCF, a non-stationary generalised extreme value (GEV) model is developed to estimate the rear-end crash risk in real time (i.e., at a signal cycle level). Artificial intelligence techniques, like YOLO and DeepSort were used to extract traffic conflicts and time-varying covariates from traffic movement videos at three signalised intersections in Queensland, Australia. The estimated crash frequency from the non-stationary GEV model is compared against the historical crashes for the study locations (serving as ground truth), and the results indicate a close match between the estimated and observed crashes. Notably, the estimated mean crashes lie within the confidence intervals of observed crashes, further demonstrating the accuracy of the extreme value model. At the second level of RTCF, the estimated signal cycle crash risk is fed to a recurrent neural network to predict the crash risk of the subsequent signal cycles. Results reveal that the model can reasonably estimate crash risk for the next 20–25 min. The RTCF framework provides new pathways for proactive safety management at signalised intersections.

Accurate landslide susceptibility mapping (LSM) relies on a detailed landslide inventory and relevant influencing factors. In this study, Sentinel 2 remote sensing imagery is employed to establish a comprehensive landslide inventory leveraging attention U-Net backbone networks in Zhangjiajie City of Hunan Province, China. Subsequently, the refined landslide inventory, with more precise boundaries, is integrated into LSM process. A multi-scale sampling three-dimensional convolutional neural network (3D-CNN) is introduced into LSM, facilitating the extraction of multi-scale neighbourhood characteristics and deep information of relevant topographical, hydrological, meteorological, geological, and human activity factors. Experimental results demonstrate that this method achieves the highest accuracy and area under the receiver operating characteristic (ROC) curve. Moreover, its recall and F1-score significantly higher surpass those of other small-, medium-, and large-scale models, with the F1-score being more than 10% higher. This superior performance is attributed to its proficiency in discerning the nonlinear spatial correlation between landslide occurrences and influencing factors. The comprehensive consideration of scale characteristics through the multi-scale sampling strategy outperforms the single-scale CNN models across all evaluation metrics. This study furnishes a suite of high-precision methodologies for landslide hazard assessment in Zhangjiajie City, thereby offering invaluable support to decision-makers engaged in large-scale land use planning and geologic disaster prevention.