This study examined the impact of economic uncertainty (EU) on Islamic banks (IBs) and conventional banks' (CBs) efficiency in countries that meet a standard where 1% share of Islamic banking assets is part of their total domestic banking sector assets. In addition, this study explored the moderating effect of country governance (CG) by employing the quantitative methodology based on secondary data from 2006 to 2021. The data analysis was done through ordinary least square, fixed effect model, and the random effect model. EU was found to enhance bank efficiency based on the basic regression results. CG moderated the positive effect of EU on bank efficiency. Additional robustness tests showed that EU was positively related to both types of banks' efficiency. The value of the paper is unique in that few papers have investigated the moderating effect of CG on the impact of EU on banks' efficiency, which enhances comprehension of EU and CG. These results highlight important policy implications whereby banks should continue to invest in and improve their risk management strategies. In addition, governments and regulatory bodies should prioritise good governance practices as these can improve banks' efficiency.
Although there are numerous studies on talent, especially talent identification, development, and selection, both on influencing factors and model construction or talent prediction, they have relatively independently explored some of its stages. Undeniably, talent development is continuous and phased, with specific tasks to be completed at each step, and these steps have certain differences and relationships. The aim of this review is to provide a clear distinction between the entire talent cultivation process, with the purpose of having better methods and measures for each stage to minimize the turnover rate and ensure the integrity of the talent development process. Through searching the Web of Science ™ database, this review was performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Selected were original articles in English containing data or models related to talent detection/identification/development/selection in sports. A total of 16 articles were included in the study by reviewing the literature. This overview presents the differences and relationships between the four stages of talent cultivation, where these different aspects are aim, purpose, approach, and emphasis. The relationship is characterized by continuity, progressive, complementary, and mutually. This finding shows that each stage is not developed independently, but is an integral part of the talent training process. Additionally, better differentiation and strengthening of the links between the various talent cultivation stages are considered to contribute to elite athlete development. This review highlights the differences and relationships that exist at each stage of talent cultivation. Meanwhile, some measures are also proposed to strengthen the connection of these phases and how to reduce the turnover rate of talent, which can provide theoretical references for coaches or stakeholders. Based on the results of the review, it is also recommended that future research on talent cultivation could take into account the intrinsic linkages between the various stages and develop talent training programs in a multidimensional way.
The increasing use of road traffic for land transportation has resulted in numerous road accidents and casualties, including those involving oil and gas tanker vehicles. Despite this, little empirical research has been conducted on the factors influencing tanker drivers' performance. This study aims to address this knowledge gap, particularly in the energy transportation industry, by examining the driving performance factors that affect tanker drivers and incorporating risk assessment measures. The model variables were identified from the literature and used to develop a survey questionnaire for the study. A total of 307 surveys were collected from Malaysian oil and gas tanker drivers, and the driving performance factors were contextually adjusted using the Exploratory Factor Analysis (EFA) approach. The driving performance model was developed using partial least squares structural equation modeling (PLS-SEM). The EFA results categorized driving performance into two constructs: 1) drivers' reaction time with β = 0.320 and 2) attention and vigilance with β value = 0.749. The proposed model provided full insight into how drivers' reaction time, attention, and vigilance impact drivers' performance in this sector, which can help identify potential risks and prevent accidents. The findings are significant in understanding the factors that affect oil and gas drivers' performance and can aid in enhancing oil and gas transportation management by including effective risk assessment measures to prevent fatal crashes.
The generation of power and fuel sustainability that contributes to a cleaner output of exhaust gases is one of the most important objectives the world seeks. In this paper, oxyhydrogen gas is used to retrofit into a two-stroke engine. The water was electrolysed and generated a mixture of oxygen (O2) and hydrogen (H2) or known as oxyhydrogen (HHO) gas via an electrolytic dry cell generator. The HHO was retrofitted experimentally to investigate the engine emissions and exhaust gas temperature from a 1.5 kW gasoline engine. The engine was tested with different power ratings (84-720 W) to investigate the performance and emissions of the engine using gasoline followed by the addition of HHO. The emissions of CO and NOx were measured with different amounts of HHO added. The exhaust temperature was calculated as one of the variables to be considered in relation to pollution. The air-fuel ratios are varied from 12 to 20% in the experiment. The most appropriate air-fuel ratio needed to start the generator with the most environmentally friendly gas emission was analysed. The results showed that the addition of HHO to the engine is successful in reducing fuel consumption up to 8.9%. A higher percentage of HHO added also has improved the emissions and reduced exhaust gas temperature. In this study, the highest quantity of HHO added at 0.15% of the volume fraction reduced CO gas emission by up to 9.41%, NOx gas up to 4.31%, and exhaust gas temperature by up to 2.02%. Generally, adding oxyhydrogen gas has significantly reduced the emissions, and exhaust temperature and provided an eco-friendly environment.
This paper proposes a nonlinear threshold cointegration framework to study how energy prices affect Malaysia's nominal exchange rate, considering the money supply, income, and interest rate. The study employs a threshold cointegration approach utilizing threshold autoregressive and momentum threshold autoregressive models. The momentum threshold vector error correction model determines the short-run adjustment of exchange rate deviation from the long-run equilibrium level. The findings reveal that the nonlinear adjustment process to capture the short-run deviation in the long-run equilibrium path is primarily influenced by energy prices, money supply, and interest rates. These results highlight the importance of considering the impact of energy prices on exchange rate policies when formulating and implementing economic policies in Malaysia. The findings can also be valuable for decision-makers to comprehend the future dynamics of exchange rates and make well-informed decisions.
For the classical multi-objective optimal power flow (MOOPF) problem, only traditional thermal power generators are used in power systems. However, there is an increasing interest in renewable energy sources and the MOOPF problem using wind and solar energy has been raised to replace part of the thermal generators in the system with wind turbines and solar photovoltaics (PV) generators. The optimization objectives of MOOPF with renewable energy sources vary with the study case. They are mainly a combination of 2-4 objectives from fuel cost, emissions, power loss and voltage deviation (VD). In addition, reasonable prediction of renewable power is a major difficulty due to the discontinuous, disordered and unstable nature of renewable energy. In this paper, the Weibull probability distribution function (PDF) and lognormal PDF are applied to evaluate the available wind and available solar power, respectively. In this paper, an enhanced multi-objective mayfly algorithm (NSMA-SF) based on non-dominated sorting and the superiority of feasible solutions is implemented to tackle the MOOPF problem with wind and solar energy. The algorithm NSMA-SF is applied to the modified IEEE-30 and standard IEEE-57 bus test systems. The simulation results are analyzed and compared with the recently reported MOOPF results.
The extracts of E. alte offer promising potential as renewable resources for various chemical derivative products aimed at addressing antibiotic resistance. These extracts exhibited significant activity against methicillin-resistant Staphylococcus aureus (MRSA), a strain known for its resistance to multiple antibiotics. The extracts were found to be effective against several common antibiotics, including Imipenem, Ampicillin, Penicillin G, Oxacillin, and Amoxicillin-clavulanate. GC-MS analysis revealed that the phytoconstituents of E. alte extracts, obtained using both methanol and ethyl acetate, consist of a diverse range of 83 and 160 phytocompounds, respectively. These organic compounds serve as important biochemical precursors for the synthesis of vitamins E and K1, and exhibit antioxidant, antimicrobial, and anti-inflammatory properties in both plants and microorganisms. Notable compounds identified include fatty acids (such as palmitic acid, dodecanoic acid, sebacic acid, pentadecanoic acid, myristic acid, stearic acid, behenic acid, and linoelaidic acid), phytosterols (Campesterol, β-sitosterol, Stigmast-5-ene), sugars (D-fructose, Fructofuranans), terpenoids (Phytol, citronellol), and phenolic acids (Protocatechoic acid, shikimic acid). The antimicrobial activity of all E. alte extracts was found to be superior to that of mupirocin and ciprofloxacin, as observed in susceptibility testing against MRSA ATCC 43300 and other pathogenic bacteria and fungi. It is likely that the combined action of the antimicrobial components within the E. alte extract bypasses the mechanisms employed by MRSA to protect itself from antibiotics. Further experiments are needed to investigate the individual effects of each pure compound and their potential synergistic interactions, which may enhance their overall performance.
The calcium looping cycle (CaL) possesses outstanding CO2 capture capacity for future carbon-capturing technologies that utilise CaO sorbents to capture the CO2 in a looping cycle. However, sorbent degradation and the presence of inert materials stabilise the sorbent, thereby reducing the CO2 capture capacity. Consequently, the CaO sorbent that has degraded must be replenished, increasing the operational cost for industrial use. CaO sorbents have been modified to enhance their CO2 capture capacity and stability. However, various CaO sorbents, including limestone, dolomite, biogenesis calcium waste and industrial waste, exhibit distinct behaviour in response to these modifications. Thus, this work comprehensively reviews the CO2 capture capacity of sorbent improvement based on various CaO sorbents. Furthermore, this study provides an understanding of the effects of CO2 capture capacity based on the properties of the CaO sorbent. The properties of various CaO sorbents, such as surface area, pore volume, particle size and morphology, are influential in exhibiting high CO2 capture capacity. This review provides insights into the future development of CaL technology, particularly for carbon-capturing technologies that focus on the modifications of CaO sorbents and the properties that affect the CO2 capture capacity.
This research introduces the Multi-Objective Liver Cancer Algorithm (MOLCA), a novel approach inspired by the growth and proliferation patterns of liver tumors. MOLCA emulates the evolutionary tendencies of liver tumors, leveraging their expansion dynamics as a model for solving multi-objective optimization problems in engineering design. The algorithm uniquely combines genetic operators with the Random Opposition-Based Learning (ROBL) strategy, optimizing both local and global search capabilities. Further enhancement is achieved through the integration of elitist non-dominated sorting (NDS), information feedback mechanism (IFM) and Crowding Distance (CD) selection method, which collectively aim to efficiently identify the Pareto optimal front. The performance of MOLCA is rigorously assessed using a comprehensive set of standard multi-objective test benchmarks, including ZDT, DTLZ and various Constraint (CONSTR, TNK, SRN, BNH, OSY and KITA) and real-world engineering design problems like Brushless DC wheel motor, Safety isolating transformer, Helical spring, Two-bar truss and Welded beam. Its efficacy is benchmarked against prominent algorithms such as the non-dominated sorting grey wolf optimizer (NSGWO), multiobjective multi-verse optimization (MOMVO), non-dominated sorting genetic algorithm (NSGA-II), decomposition-based multiobjective evolutionary algorithm (MOEA/D) and multiobjective marine predator algorithm (MOMPA). Quantitative analysis is conducted using GD, IGD, SP, SD, HV and RT metrics to represent convergence and distribution, while qualitative aspects are presented through graphical representations of the Pareto fronts. The MOLCA source code is available at: https://github.com/kanak02/MOLCA.
Malaria remains a major public health problem worldwide, including in Southeast Asia. Chemotherapeutic agents such as chloroquine (CQ) are effective, but problems with drug resistance and toxicity have necessitated a continuous search for new effective antimalarial agents. Here we report on a virtual screening of ∼300 diarylpentanoids and derivatives, in search of potential Plasmodium falciparum lactate dehydrogenase (PfLDH) inhibitors with acceptable drug-like properties. Several molecules with binding affinities comparable to CQ were chosen for in vitro validation of antimalarial efficacy. Among them, MS33A, MS33C and MS34C are the most promising against CQ-sensitive (3D7) with EC50 values of 1.6, 2.5 and 3.1 μM, respectively. Meanwhile, MS87 (EC50 of 1.85 μM) shown the most active against the CQ-resistant Gombak A strain, and MS33A and MS33C the most effective P. knowlesi inhibitors (EC50 of 3.6 and 5.1 μM, respectively). The in vitro cytotoxicity of selected diarylpentanoids (MS33A, MS33C, MS34C and MS87) was tested on Vero mammalian cells to evaluate parasite selectivity (SI), showing moderate to low cytotoxicity (CC50 > 82 μM). In addition, MS87 exhibited a high SI and the lowest resistance index (RI), suggesting that MS87 may exert effective parasite inhibition with low resistance potential in the CQ-resistant P. falciparum strain. Furthermore, the in vivo toxicity of the molecules on early embryonic development, the cardiovascular system, heart rate, motor activity and apoptosis were assessed in a zebrafish animal model. The overall results indicate the preliminary potential of diarylpentanoids, which need further investigation for their development as new antimalarial agents.
Micro-expressions (ME) are rapidly occurring expressions that reveal the true emotions that a human being is trying to hide, cover, or suppress. These expressions, which reveal a person's actual feelings, have a broad spectrum of applications in public safety and clinical diagnosis. This study provides a comprehensive review of the area of ME recognition. A bibliometric and network analysis techniques is used to compile all the available literature related to ME recognition. A total of 735 publications from the Web of Science (WOS) and Scopus databases were evaluated from December 2012 to December 2022 using all relevant keywords. The first round of data screening produced some basic information, which was further extracted for citation, coupling, co-authorship, co-occurrence, bibliographic, and co-citation analysis. Additionally, a thematic and descriptive analysis was executed to investigate the content of prior research findings, and research techniques used in the literature. The year wise publications indicated that the published literature between 2012 and 2017 was relatively low but however by 2021, a nearly 24-fold increment made it to 154 publications. The three topmost productive journals and conferences included IEEE Transactions on Affective Computing (n = 20 publications) followed by Neurocomputing (n = 17) and Multimedia tools and applications (n = 15). Zhao G was the most proficient author with 48 publications and the top influential country was China (620 publications). Publications by citations showed that each of the authors acquired citations ranging from 100 to 1225. While publications by organizations indicated that the University of Oulu had the most published papers (n = 51). Deep learning, facial expression recognition, and emotion recognition were among the most frequently used terms. It has been discovered that ME research was primarily classified in the discipline of engineering, with more contribution from China and Malaysia comparatively.
A new Schiff base, 1-(E)-(4-((E) 4nitrobenzylidene) amino) phenyl)imino) methyl)naphthalen-2-ol (4NMN), was prepared from the reaction of p-phenylenediamine with 2-hydroxy-1-naphthaldehyde and 4-nitrobenzaldehyde and characterized with spectroscopic analysis. UV-VIS and NMR. Frontier molecular orbitals, molecular electrostatic potential, and chemical reactivity descriptors of the synthesized compound were studied using molecular modeling methods. The antibacterial and antifungal activities of the Schiff base were studied for its minimum inhibitory concentration. The compound showed a higher effect on yeast than against bacteria. Density functional theory (DFT) calculations were performed to study the mechanism of reaction for the synthesis of 4NMN, and the results were consistent with the experimental findings. 4NMN exhibited moderate antibacterial and antifungal activities and demonstrated higher inhibition potential against different resistant strains compared to the reference drug gentamycin. The absorption and fluorescence spectra of 4NMN were measured in different solvents, and the effect of relative polarity and acidity on the medium was observed. An inner filter effect was observed at high concentrations, and the compound showed considerable fluorescence enhancement with increasing medium viscosity and fluorescence quenching by the addition of traces of Cr1+ and Cu2+. Additionally, molecular docking studies were conducted to investigate the efficiency of antibacterial and antifungal targets.
Parkinson's disease is a neurodegenerative condition defined by the progressive death of dopaminergic neurons in the brain. The diagnosis of Parkinson's disease often uses time-consuming clinical evaluations and subjective assessments. Electrochemical Impedance Spectroscopy (EIS) is a useful technique for electroanalytical devices due to its label-free performance, in-situ measurements, and low cost. The development of reliable diagnostic tools for Parkinson's disease can be significantly enhanced by exploring novel techniques like faradaic and non-faradaic EIS detection methods. These techniques have the ability to identify specific biomarkers or changes in electrochemical properties linked to Parkinson's disease, allowing for an early and accurate diagnosis. Faradaic EIS detection methods utilize redox processes on the electrode surface, while non-faradaic EIS methods rely on charge transfer or capacitive properties. EIS can identify biomarkers or changes in electrical properties as indicators of Parkinson's disease by measuring impedance at different frequencies. By combining both faradaic and non-faradaic EIS approaches, it may be possible to obtain a comprehensive understanding of the electrochemical changes occurring in Parkinson's disease patients. This may lead to the development of more effective diagnostic techniques and potentially opening up new avenues for personalized treatment strategies. This review explores the current research on faradaic and non-faradaic EIS approaches for diagnosing Parkinson's disease using electrochemical impedance spectroscopy.
Continuous gesture recognition can be used to enhance human-computer interaction. This can be accomplished by capturing human movement with the use of the Inertial Measurement Units in smartphones and using machine learning algorithms to predict the intended gestures. Echo State Networks (ESNs) consist of a fixed internal reservoir that is able to generate rich and diverse nonlinear dynamics in response to input signals that capture temporal dependencies within the signal. This makes ESNs well-suited for time series prediction tasks, such as continuous gesture recognition. However, their application has not been rigorously explored, with regard to gesture recognition. In this study, we sought to enhance the efficacy of ESN models in continuous gesture recognition by exploring diverse model structures, fine-tuning hyperparameters, and experimenting with various training approaches. We used three different training schemes that used the Leave-one-out Cross-validation (LOOCV) protocol to investigate the performance in real-world scenarios with different levels of data availability: Leaving out data from one user to use for testing (F1-score: 0.89), leaving out a fraction of data from all users to use in testing (F1-score: 0.96), and training and testing using LOOCV on a single user (F1-score: 0.99). The obtained results outperformed the Long Short-Term Memory (LSTM) performance from past research (F1-score: 0.87) while maintaining a low training time of approximately 13 seconds compared to 63 seconds for the LSTM model. Additionally, we further explored the performance of the ESN models through behaviour space analysis using memory capacity, Kernel Rank, and Generalization Rank. Our results demonstrate that ESNs can be optimized to achieve high performance on gesture recognition in mobile devices on multiple levels of data availability. These findings highlight the practical ability of ESNs to enhance human-computer interaction.
With the increasing popularity of e-commerce live streaming, understanding purchase intentions for cultural heritage products in this context is crucial, prompting the analysis based on the ABC attitude theory. This study investigates how creative products of intangible cultural heritage influence consumers' purchase intentions within the framework of e-commerce live streaming. It further explores the mediating influence of consumer satisfaction in this association, utilizing the ABC attitude theory. The research design encompasses dimensions related to creative products of intangible cultural heritage, e-customer satisfaction, and purchase intention. Confirmatory factor analysis and Structural Equation Modeling (SEM) are utilized to examine the research hypotheses. The study reveals that perceived product quality, perceived interactivity, and professionalism among streamer factors, as well as perceived entertainment among scene factors, positively influence consumer satisfaction. Additionally, intangible cultural heritage has a positive effect on consumers' purchase intention toward creative products of intangible cultural heritage. Furthermore, consumer satisfaction serves as a mediating factor in the relationship between perceived product quality, perceived professionalism and interactivity, perceived entertainment, and consumers' purchase intention. This study is limited to creative products of intangible cultural heritage and involved a relatively small sample size. Moreover, certain perceptual dimensions of e-commerce live streaming were excluded from the analysis. Originality/value - This study contributes to the existing literature on the ABC theory by developing a model that examines consumer satisfaction and purchase intention. The model employed in this study integrates perceptual dimensions pertaining to creative products of intangible cultural heritage within the framework of e-commerce live streaming, utilizing the ABC theory. Additionally, this research contributes to addressing a gap in the existing literature by specifically focusing on the category of creative products of intangible cultural heritage within the broader discourse on dimensions in e-commerce live streaming. Thus, this study presents numerous diverse perspectives that can serve as a basis for further research into the integration of e-commerce live streaming and cultural and creative products associated with intangible cultural heritage.
Social media's significance in higher education has increased due to its capacity to enhance participation, communication, teamwork, and information sharing. Important notifications, updates, and reminders can be promptly received by all members of the university community, assuring that information is shared with everyone. The objective of this study is to develop a model for a Customer Relationship Management (CRM) system in higher education that is based on social media and intends to increase student satisfaction, loyalty, and profitability. It blends the idea of trust with Delone Mclean success model. Partial Least Squares-Structural Equation Modeling (PLS-SEM) was used to evaluate the data that was gathered from 606 Jordanian private university students via an online survey. The findings demonstrated that user satisfaction affects social media usage and that system quality, information quality, service quality, and trust must all be considered to attain user satisfaction. This study examines how to create a CRM system based on social media in Jordanian universities. The study makes significant contribution to the development criteria for evaluating social media-based CRM systems in higher education institutions, and its broad conceptual model cloud be expanded and tested in future studies. This study is the first to investigate the use of social media to develop a CRM system for Jordanian universities. This is a novel study, and the work significantly create a set of criteria for evaluating social media-driven CRM systems in higher education. The study's expansive the model may serve as the base for more research in this field.
In this study, the fabrication of titanium dioxide/reduced graphene oxide (TiO2/rGO) utilising banana peel extracts (Musa paradisiaca L.) as a reducing agent for the photoinactivation of Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) was explored. The GO synthesis was conducted using a modified Tour method, whereas the production of rGO involved banana peel extracts through a reflux method. The integration of TiO2 into rGO was achieved via a hydrothermal process. The successful synthesis of TiO2/rGO was verified through various analytical techniques, including X-ray diffraction (XRD), gas sorption analysis (GSA), Fourier-transform infrared (FT-IR) spectroscopy, ultraviolet-visible diffuse reflectance spectroscopy (UV-Vis DRS), scanning electron microscope-energy dispersive X-ray (SEM-EDX) and transmission electron microscopy (TEM) analyses. The results indicated that the hydrothermal-assisted green synthesis effectively produced TiO2/rGO with a particle size of 60.5 nm. Compared with pure TiO2, TiO2/rGO demonstrated a reduced crystallite size (88.505 nm) and an enhanced surface area (22.664 m2/g). Moreover, TiO2/rGO featured a low direct bandgap energy (3.052 eV), leading to elevated electrical conductivity and superior photoconductivity. To evaluate the biological efficacy of TiO2/rGO, photoinactivation experiments targeting E. coli and S. aureus were conducted using the disc method. Sunlight irradiation emerged as the most effective catalyst, achieving optimal inactivation results within 6 and 4 h.
Poverty, an intricate global challenge influenced by economic, political, and social elements, is characterized by a deficiency in crucial resources, necessitating collective efforts towards its mitigation as embodied in the United Nations' Sustainable Development Goals. The Gini coefficient is a statistical instrument used by nations to measure income inequality, economic status, and social disparity, as escalated income inequality often parallels high poverty rates. Despite its standard annual computation, impeded by logistical hurdles and the gradual transformation of income inequality, we suggest that short-term forecasting of the Gini coefficient could offer instantaneous comprehension of shifts in income inequality during swift transitions, such as variances due to seasonal employment patterns in the expanding gig economy. System Identification (SI), a methodology utilized in domains like engineering and mathematical modeling to construct or refine dynamic system models from captured data, relies significantly on the Nonlinear Auto-Regressive (NAR) model due to its reliability and capability of integrating nonlinear functions, complemented by contemporary machine learning strategies and computational algorithms to approximate complex system dynamics to address these limitations. In this study, we introduce a NAR Multi-Layer Perceptron (MLP) approach for brief term estimation of the Gini coefficient. Several parameters were tested to discover the optimal model for Malaysia's Gini coefficient within 1987-2015, namely the output lag space, hidden units, and initial random seeds. The One-Step-Ahead (OSA), residual correlation, and residual histograms were used to test the validity of the model. The results demonstrate the model's efficacy over a 28-year period with superior model fit (MSE: 1.14 × 10-7) and uncorrelated residuals, thereby substantiating the model's validity and usefulness for predicting short-term variations in much smaller time steps compared to traditional manual approaches.
Co-curricular activities equip students with essential skills and knowledge for personal and professional growth. Despite their importance, many students exert minimal effort to complete the assigned tasks. Instructors perceive that the lack of emphasis on final exams in co-curricular subjects reduces student effort and commitment. Moreover, poor time management and lack of effort in completing tasks have increased across various subjects in recent years. Therefore, it is important to investigate the factors that contribute to student commitment towards co-curricular subjects. In this study, the submission status of 339 tasks was retrieved from the student learning system to measure student commitment based on whether tasks were submitted on time, delayed, or not submitted. A chi-square test f was used to investigate the relationship between students' demographic characteristics and their commitment. The findings revealed a significant association between student commitment and the type of task given (p 0.05), indicating that the year of the study could not determine the level of commitment to the course. Overall, these findings could be used to guide the preparation of tasks and assignments in co-curricular subjects to enhance student commitment and holistic development.
Learning Analytics Tools (LATs) can be used for informed decision-making regarding teaching strategies and their continuous enhancement. Therefore, LATs must be adopted in higher learning institutions, but several factors hinder its implementation, primarily due to the lack of an implementation model. Therefore, in this study, the focus is directed towards examining LATs adoption in Higher Learning Institutions (HLIs), with emphasis on the determinants of the adoption process. The study mainly aims to design a model of LAT adoption and use it in the above context to improve the institutions' decision-making and accordingly, the study adopted an extended version of Technology Acceptance Model (TAM) as the underpinning theory. Five experts validated the employed survey instrument, and 500 questionnaire copies were distributed through e-mails, from which 275 copies were retrieved from Saudi employees working at public HLIs. Data gathered was exposed to Partial Least Square-Structural Equation Modeling (PLS-SEM) for analysis and to test the proposed conceptual model. Based on the findings, the perceived usefulness of LAT plays a significant role as a determinant of its adoption. Other variables include top management support, financial support, and the government's role in LATs acceptance and adoption among HLIs. The findings also supported the contribution of LAT adoption and acceptance towards making informed decisions and highlighted the need for big data facility and cloud computing ability towards LATs usefulness. The findings have significant implications towards LATs implementation success among HLIs, providing clear insights into the factors that can enhance its adoption and acceptance. They also lay the basis for future studies in the area to validate further the effect of LATs on decision-making among HLIs institutions. Furthermore, the obtained findings are expected to serve as practical implications for policy makers and educational leaders in their objective to implement LAT using a multi-layered method that considers other aspects in addition to the perceptions of the individual user.