Abstract: This research aims to analyze the relationship between brand awareness and purchase intention. Digital marketing purchase intention and word of mouth on purchase intention. This research is quantitative research with an explanatory method that aims to explain the relationship between symptoms. This study intends to conduct hypothesis testing to explain the relationship and influence between variables. The data used in this study are primary data through an online questionnaire method. The questionnaire answers are in the form of a Likert scale which is an interval scale with a scale of 1 to 7. Data processing in this study uses the Partial Least Square (PLS) method with the help of SmartPLS software. This method is Structural Equation Modeling (SEM) which can accommodate the relationship between very complex variables but the data sample size is small. The respondents who were sampled were 436 MSME owners, while the questionnaires that were returned completely and filled out properly amounted to 344 questionnaires. The questionnaires that were valid and reliable from the next stage were distributed to the research respondents whose results would be processed using SmartPLS software. The stages of data processing carried out include evaluation of the measurement model (outer model) and evaluation of the structural model (inner model). Evaluation of the measurement model consists of validity tests and reliability tests. Validity test can be seen from the standardized loading factor value. An indicator is said to be valid when the loading factor value is greater than or equal to 0.7. While the reliability test is seen from the Cronbach's Alpha and Average Variance Extracted (AVE) values. A construct is declared reliable when the Cronbach's Alpha value is greater than or equal to 0.7 and the minimum AVE value is 0.5. Furthermore, the hypothesis test is to see the significance of the relationship between constructs which can be seen from the path coefficient. This calculation looks at the t-statistic and p-value values generated from calculations using SmartPLS. Path coefficients that have a t-statistic value ≥ 1.96 or have a p-value ≤ 0.05 are declared significant. The results of this research are that Brand awareness has a positive and significant effect on purchase intention. Digital Marketing has a positive and significant effect on purchasing intention. Word of Mouth has a positive and significant effect on purchasing intention.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: The purpose of this study is to analyze the influence of social media and big data utilization in improving networking and its implications on decision quality in civil servants in Surabaya City, East Java Province, Indonesia. The sample in this study was 125 respondents consisting of several Civil Servants in the Surabaya City Government, East Java Province, Indonesia. The sampling technique used the random sampling. Data collected through questionnaires were then analyzed using SEM-PLS. The results of the study and data analysis showed that: Utilization of Social Media directly has a positive and significant effect on Networking; Big Data directly has a positive and significant effect on Networking; Utilization of Social Media directly has a positive and significant effect on Decision Quality; Government Big Data directly has a positive and significant effect on Decision Quality; Networking directly has a positive and significant effect on Decision Quality in Civil Servants of Surabaya City, East Java Province, Indonesia. Networking is able to indirectly mediate the utilization of social media and big data on decision quality in civil servants of Surabaya City, East Java Province, Indonesia.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: Skin conditions affect millions of people globally, with symptoms appearing in different body areas. Technological advancements have brought diverse data types, including situations where an image depicting a skin condition can be assigned multiple labels. The Classification Transformer (C-Tran) method, which utilizes transfer learning and transformers, was developed for multi-label classification. Recently, Google introduced a new dataset called SCIN (Skin Condition Image Network), which aims to provide diverse data on skin conditions. This research aimed to use the C-Tran method for the multi-label classification of skin conditions with the SCIN dataset while incorporating additional metadata inputs to improve the metric results. The results show that the multi-label classification process using metadata is far superior to the model without metadata. For example, In the mAP metric, models that utilized metadata scored 82.37, whereas models without metadata only scored 47.02. Similarly, models with metadata achieved 70.83% in the accuracy metric, while models without metadata achieved only 34.72%. Out of the 10,379 data points available with metadata in the SCIN dataset, only 718 were actually utilized for the classification task. It is thought that the inaccurate prediction outcomes are due to unreliable data, even with a confidence level of 4. In this analysis, two metadata categories stood out the most in terms of different measurements: the body part and symptoms metadata categories from the SCIN dataset. With just the body part and symptoms metadata groups, the mAP results achieved a 74.23%, accuracy at 63.89%, CF1 at 68.79%, and OF1 at 73.13%.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: This paper investigates the impact and implementation of Intelligent Tutoring Systems (ITS) on enhancing educational outcomes for engineering students at the Universidad Nacional del Centro del Perú. The model emphasizes the role of ITS in improving academic achievement, student satisfaction, and engagement, considering critical dimensions like emotional attitude, cognitive receptivity, and reflective strategy. Using SmartPLS for data analysis and an application developed in Flutter, the study demonstrates that ITS can positively influence student emotion and performance. Reliability metrics confirm robustness, with Cronbach's alpha values between 0.76 and 0.876 and AVE scores above 0.7. Predictive power is supported by R-squared values of 0.746 for student emotion and 0.723 for ITS impact on academic performance. Path coefficients underscore significant relationships, such as ITS influence on emotional engagement (0.549) and academic satisfaction (0.384). Findings suggest that integrating ITS with emotional and cognitive dimensions can foster higher academic satisfaction and achievement.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: Perceived risk is important in home-based restaurant businesses in tourist areas, especially in influencing tourists' views of risk when deciding to eat at a home-based restaurant. This perception of physical and psychological risks can influence tourists' intentions to dine out. This research aims to measure the magnitude of the influence of perceived risk on tourists' intention to dine out, as well as the role of precautionary measures and digital marketing strategies as moderating variables. Data was collected from 143 respondents who were consumers of home restaurants in several tourist areas in Bali, Indonesia. Data analysis used SmartPLS version 4.0 to test the relationship between research variables. The results show that perceived physical risk does not significantly impact tourists' intention to dine out. In contrast, perceived psychological risk has a strong negative influence on the perceived psychological risk the more likely tourists will dine on site. Health prevention measures implemented by restaurants (precautionary measures) and digital marketing strategies directly affect tourists' intention to dine out. Interesting digital content on social media related to food and beverage products has also been proven to increase this intention. However, precautionary measures and digital marketing as moderating variables do not significantly strengthen the relationship between perceived risk and intention to dine out. This research provides insight for home restaurant business owners to pay attention to consumer risk perceptions and utilize digital marketing strategies effectively. Creating interactive and informative content on social media is very important to increase tourist intent. This research also enriches the literature on consumer behavior and service science in tourism.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: This study explores the impact of TikTok marketing activities in stimulating consumer purchasing intention with trust and reliability during live streaming sessions for Muslim apparel. While there is a growing interest in shopping through live streaming, little study has been done regarding Islamic marketing. To achieve the purpose of the study, a convenience sampling method was adopted to collect data from 225 participants for assessing the effects of TikTok marketing activities on consumer behaviour. The findings suggest that most of the TikTok marketing activities enhance trust and credibility, hence influencing positive intentions to buy among consumers while attending a live stream. The insights also apply to three broader fields of Islamic marketing, consumer behaviour, and sustainable marketing, while again supporting the United Nations SDGs specifically Goal 12: Responsible Consumption and Production. This paper therefore advocates for more sustainable consumption patterns and responsible marketing practices, as it gives confidence and trust in online shopping for ethical consumption of goods in digital commerce. Beyond this, the study highlights how social commerce can be instrumental for the realization of economic growth (Goal 8), innovation within marketing practices, and inclusive and sustainable economic participation via digital platforms.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: This paper introduces a new algorithm in Steganography for concealing secret messages or images within digital images. The algorithm is designed to produce stego images that can be transmitted to recipients without detection by potential attackers, thereby ensuring secure communication channels. The proposed algorithm employs a multi-level randomization technique to embed data within randomly selected cover images, with each byte of the secret image distributed across multiple cover images. This approach contrasts with conventional methods that hide data within a single cover image. Moreover, the algorithm incorporates a load-balancing priority system, a critical feature that ensures uniform stego image quality across the dataset. This strategic approach minimizes variations in Peak-Signal-to-Noise-Ratio (PSNR) values, contributing to consistent performance during data hiding and extraction processes and enhancing communication security. The security and recoverability of the secret image are further improved by a simplified Cipher key system based on SHA-256, which facilitates pseudo-random number generation. This system ensures that the hidden image can be recovered at the receiver's end, even in the face of potential attacks. Experimental results demonstrate comparable PSNR quality to existing methods, particularly when utilizing equal total resolution to deep hiding algorithms. Notably, the proposed algorithm offers an alternative to encryption by leveraging randomization, thereby complicating data extraction for potential attackers by distributing data across multiple images with a randomly generated cipher key.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: In recent years, the adoption of advanced machine learning techniques has revolutionized approaches to solving complex problems, such as identifying occurrences of forest fires. Among these techniques, the use of Convolutional Neural Networks (CNNs) combined with ensemble methods is particularly promising. To investigate the feasibility of detecting fires using video streams from Unmanned Aerial Vehicles (UAVs), the lightweight CNN architecture MobileNetV2 was utilized for real-time detection. Several experiments were conducted on the DeepFire dataset, which comprises an equal number of images with and without fire, to evaluate MobileNetV2's performance. Notably, the architecture's linear bottlenecks and the efficient use of inverted residuals ensure high accuracy without compromising on feature extraction capabilities. For a comprehensive assessment, MobileNetV2 was benchmarked against other models, including DenseNet121, EfficientNetV2S, and VGG16. Accuracy was enhanced by averaging predictions through methods such as voting or summing results. As documented in the literature, MobileNetV2 consistently outperforms other architectures in computational efficiency and provides an excellent balance between efficiency and the quality of learned features over multiple epochs. This study underscores the suitability of MobileNetV2 for real-time applications on drones, particularly for the detection of forest fires in resource-constrained environments. The results show that MobileNetV2 achieves the highest accuracy (0.994), sensitivity (0.994), and specificity (0.998) among the tested models, with low standard deviations across all metrics. In contrast, EfficientNetV2S exhibited the lowest accuracy and sensitivity, both at 0.779, with a specificity of 0.829. The ensemble (Sum) method achieved an average accuracy of 0.989, sensitivity of 0.989, and specificity of approximately 0.988. Therefore, MobileNetV2 not only delivers the highest accuracy and stability but also demonstrates that the choice of ensemble method significantly affects the results.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: This study aims to identify the factors influencing the effectiveness of digital learning among university students. The main focus of the research is to examine the impact of easy-to-use, competency development on websites, and motivation to learn on learning effectiveness. The study employs a quantitative approach with a survey method, where data was collected through online questionnaires distributed to final-year students at Universitas PGRI Malang. Out of 300 distributed questionnaires, 204 were returned and deemed complete, thus used as the final sample for analysis. The data were analyzed using Structural Equation Modeling (SEM) techniques with the help of SmartPLS software to examine the relationships between variables. The results show that ease of use has a significant impact on motivation to learn, but does not have a direct impact on learning effectiveness. Competency development on websites does not have a significant impact on motivation to learn, but it does have a significant impact on learning effectiveness. Additionally, motivation to learn was found to have a significant impact on learning effectiveness and mediates the relationship between ease of use and learning effectiveness, but does not mediate the relationship between competency development on websites and learning effectiveness.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: The objective of this study is to examine the mediating effect of sustainable performance in the relationship between digital marketing tactics and market volatility at an effective level in the Jordanian market. This research uses the Triple Bottom Line (TBL) Theory and the Resource-Based View (RBV) theories to examine the impact of market volatility on sustainability performance, with a focus placed on mediating the effects of digital marketing provided online. Empirical data in the form of field study and statistical analysis to evaluate the relationships between digital marketing tactics, the performance that is sustainable as well volatility within the market through the business were gathered. The findings showed that there is a significant mediating effect of sustainable performance on the relationship between digital marketing strategies and market volatility. The research provides insights into how firms can achieve greater agility and sustainability by rethinking their marketing mix in a volatile market. These findings suggest that the volatile markets should be based on the principle of sustainable performance when planning international digital marketing activities in order to cope with market volatility and competitive advantage.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: Social commerce stands as a pivotal strategy amidst the modern retail environment. However, the distinct cultural and economic landscapes of different nations may lead to variations in understanding shopper behavior. This study endeavors to delve into the efficacy of shopping motivation theory, trust theory, and the information quality model in elucidating purchase intent, all within a comprehensive research framework. Through a survey conducted within the Jordanian context, it was revealed that utilitarian motivations and trust in e-vendors exert a positive influence on customers' inclination to purchase in the context of social commerce. Interestingly, hedonic motivations and the quality of electronic word-of-mouth (eWOM) were found to have no effect on purchase intention. Furthermore, trust in e-vendors was identified to mediate the relationship between eWOM quality and purchasing intention, manifesting as an indirect-only association. The paper concludes with a discussion on the contributions and limitations of the research.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: This research aims to investigate the intricate interplay between Artificial Intelligence (AI), Organizational Databases, Risk Management, and profitability within the Indonesian banking sector. Employing a quantitative research design, the study seeks to understand the mediating role of Risk Management in the relationships among AI, Organizational Databases, and profitability. The key findings underscore the pivotal importance of Risk Management as a mediator, emphasizing the necessity for a comprehensive risk-aware strategy in optimizing the impact of AI and high-quality databases on financial performance. The research contributes theoretically by providing nuanced insights into the dynamic relationships between technology adoption, risk mitigation, and financial success. Limitations include contextual specificity and temporal constraints, acknowledging the need for caution in generalizing findings. The practical contributions involve recommendations for organizations to continuously monitor and adapt their AI implementations and risk management strategies. The study sets the stage for further research, advocating for cross-industry studies, longitudinal analyses, and in-depth case studies to enhance the understanding of these complex dynamics. Overall, this research provides actionable insights for practitioners and contributes to the ongoing discourse on the evolving landscape of technology, risk management, and financial success.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: Given that social media (SM) has become an integral part of the daily lives of university students and their main window to communicate with the world around them, higher education institutions can no longer help but adopt it as a major part of their marketing strategy and as a major communication channel to enhance their ability to retain their current students and attract new students. Therefore, this study aims to reveal the role of Social Media Marketing (SMM) in promoting student satisfaction factors at Al-Ahliyya Amman University (AAU) and improving their experience, which must have a significant successive impact, starting from enhancing the university's image, to expanding the spread of positive eWOM, and ultimately, increase students' loyalty to the university, To create a practical framework for building a marketing strategy that invests in SMM to enhance student loyalty, relevant literature was evaluated. Business school students were surveyed using a paper questionnaire distributed to students to increase interest and accuracy in answering, to extract conclusions from the data and model variables, a structural equation modelling using Smart-PLS was used. The survey results indicate that (AAU) has not invested sufficiently in promoting some of the satisfaction factors of its students and improving their experience. But in general, it affected somewhat positively the loyalty of its students. The research has important theoretical and practical implications.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: This study conducts a bibliometric analysis of data mining publications in the Scopus database, examining the evolution of the field from 2015 to 2024. The study examines the bibliometric structure of data mining in management. Analyzing 2,942 publications, the research identifies significant growth in data mining studies. It reveals gaps in integrating data mining with decision-making, artificial intelligence, forecasting, and sentiment analysis. Despite a large number of publications, interdisciplinary applications of data mining are limited. The scientific publication on data mining and its relationship with decision-making, artificial intelligence, forecasting, and sentiment analysis is found to be weak, showing significant research gaps in these areas. China and the USA are prominent contributors, indicating geographical concentration. The study highlights the need for broader interdisciplinary exploration in data mining beyond traditional areas, urging global researchers to diversify contributions. The analysis focuses solely on publications indexed in Scopus, potentially excluding relevant studies from other databases or sources. This study provides insights into the evolution of data mining research and identifies areas for further interdisciplinary exploration, contributing to the advancement of the field's boundaries.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: This study investigates the utilization of Google Maps reviews to assess hospital service quality. Patient-generated reviews were analyzed using a sentiment analysis framework incorporating the Bidirectional Encoder Representations from Transformers (BERT) classification model. The p control chart was employed to monitor the distribution of negative sentiment. The results of the sentiment analysis revealed a predominance of positive reviews over negative ones. The BERT classifier achieved excellent performance, with AUC values of 99.95% and 93.72% for training and testing data, respectively. However, the p control chart indicated that the hospital's performance still requires improvement, as several observations fell outside the statistically controlled range. Common patient complaints centered on lengthy wait times and queues, highlighting areas for targeted quality enhancement initiatives. This research demonstrates the potential of leveraging patient feedback to inform hospital quality improvement efforts.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: Social media and artificial intelligence (AI) are increasingly playing an important role in the process of designing city parks by influencing people's willingness to pay. This research aims to explore how social media and AI can increase willingness to pay and its impact on the quality of urban park design in Palangkaraya City, Central Kalimantan. Method: This research uses a mixed approach with a combination of quantitative surveys and qualitative interviews. The survey was conducted on 200 respondents from Palangkaraya residents to measure the influ-ence of social media and AI on their willingness to pay. Social media data analysis was con-ducted using AI tools to identify popular garden design preferences. Data was collected from social media platforms as well as in-depth interviews with stakeholders. The research results show that there is a significant positive correlation between social media involvement and peo-ple's willingness to pay. AI data from social media analysis reveals the most popular garden de-sign elements, which then influences design decisions. The quality of park designs that inte-grate feedback from social media and AI recommendations receive higher ratings from the pub-lic. These findings indicate that social media and AI can significantly increase willingness to pay and the quality of urban park design. Active engagement of the public through social me-dia helps formulate designs that better suit their needs, while AI ensures designs that are more innovative and responsive to public preferences. This research emphasizes the importance of using digital tools to improve community engagement and urban planning outcomes.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: The researchers used positivism to study the effects of artificial intelligence (AI) applications on human resource (HR) productivity through employee technical expertise. This study targeted employees in the health care sector in Jordan. This research used a self-reported questionnaire as the primary data collection tool. We developed this questionnaire by reviewing relevant literature and designed it electronically using Google Forms. The procedures followed in analyzing the initial research data included a series of procedures employing SPSS and AMOS software. The study results indicate that Artificial intelligence applications (AIA) produce a positive effect on HR productivity challenges (HRP) by interplaying the mediating role of employees' technical expertise (ETE) in the sector of the service industry. HR productivity challenges were the essential purpose of investigating the impact of Artificial intelligence applications through employees' technical expertise to decrease challenges and find a balance between the employees' skills and AI Apps implementation instead of replacing HR skills.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: These days, due to the increase in the number of connected devices in IoT networks, several types of new cyber threats and attacks are also coming up in IoT. Any cyber-attack can cause significant damage to the IoT networks and loss of service. Therefore, identifying these threats is one of the main steps in risk assessment and should be considered to create a robust security strategy to avoid IoT network breaches. Cyber threats assessment in IoT networks is a prime process due to the evolving nature of cyber-attacks. Therefore, this research focuses on addressing the current gap by performing a comprehensive analysis on identifying the critical threats, vulnerabilities and countermeasures on IoT layers including physical, data link, network, and transport and application layers. The findings of this study indicated that DDoS attacks, Phishing threats were the most common technical threats in the IoT application layer with a percentage of 72% and 66% respectively. In addition, the results found that SQL Injection threat, Cross-Site Scripting (XSS) and Cross-Site Request Forgery (CSRF) attack also were classified as the second level of technical threats in IoT with percentage of 55%, 53% and 52% respectively. In the third level of technical threats in IoT was Password cracking attacks with a percentage of 48%. The results showed that TCP/UDP port scanning, TCP/UDP flooding attack and MQTT attack were the most common technical threats in the IoT transport layer with percentage of 34%, 33% and 31% respectively. In addition, the results found that DNS poisoning threat, SYN-flooding and De-synchronization attack also were classified as the second level of technical threats in IoT with percentage of 27%, 26% and 24% respectively. The third level of technical threats in IoT were lateral movement attacks and DoS attacks with a percentage of 18% and 15% respectively. The framework in this study is considered as a vital tool for practitioners, policymakers, and researchers to identify, classify, and mitigate cyber threats within the IoT systems. The findings from this work can help organizations to understand the types of cyber threats and develop robust strategies against cyber-attacks.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: Big data has become more important in practically all businesses throughout the world in the present era of information technology. Big data as a part of the internet of things, creative accounting practices regarding the meaning, methods and motives and the role of big data as a part of the internet of things on the increase of creative accounting practices. The researchers concluded that big data leads to an increase in the percentage of creative accounting practices in the business environment, due to the fact that big data impacts the auditing process and the detection of creative accounting practices such as income smoothing. Despite the fact that Big Data is most commonly used in creative accounting techniques and its relevance cannot be overstated, research and analyses are insufficient. Given the relevance of big data across all industries, this study attempts to undertake a comprehensive literature analysis on the topic of big data and innovative accounting methods across all industries. As a result, the study will add to the body of knowledge by opening up new avenues for empirical research in big data and creative accounting.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: Artificial intelligence is user-friendly and incorporates a useful number of characteristics that are common across the various services that are provided. By enhancing inventive engagement, artificial intelligence (AI) applications enable a more participatory setting in government agencies. The objective of this research is to find out how the UAE consumers feel about using AI in educational settings. Included in the framework are the characteristics of acceptance, which are: perceived compatibility, trialability, relative advantage, ease of doing business, and technology export. 466 questionnaires from various universities have been gathered. The research model was examined using machine learning algorithms (ML) and partial least squares-structural equation modeling (PLS-SEM), which centered on the student's questionnaire responses. The IPMA is also used in this research to evaluate performance and importance of the variables. The theoretical framework of the research links the qualities of the individual variables and those of the technology which makes it new. The findings indicate that the diffusion theory factors outperform the other two factors of ease of doing business and technology export. It ought to be mentioned that when it pertains to the estimated value of the dependent factor, the J48 classifier largely outperformed other classifiers. This study’s findings can guide educational institutions in the UAE to recognize the importance of each acceptance factor in the successful integration of AI technologies. Institutions can use these insights to tailor their strategies, enhancing AI adoption among students and faculty alike. Specifically, the results suggest prioritizing factors from diffusion theory in educational AI implementations, ensuring these technologies are perceived as advantageous and compatible with existing practices. Furthermore, the superiority of the J48 classifier suggests that similar analytical techniques could be employed by educational institutions to continually assess and improve their AI initiatives. The dominance of diffusion theory factors invites further exploration into how these elements specifically influence AI acceptance in other sectors or regions. Additionally, the comparative underperformance of ease of doing business and technology export as factors suggests a need for deeper investigation into how these dimensions can be better leveraged in the context of AI in education. Future research could also explore longitudinal studies to assess the sustainability of AI acceptance over time and experiment with integrating new machine learning algorithms to compare their predictive power against the J48 classifier in different educational settings.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: This study examines the factors influencing Muslim consumers' purchase intentions for Muslim apparel products through TikTok live streaming. Data were collected in Jakarta, with 225 participants selected using convenient sampling. The collected data underwent analysis using exploratory factor analysis, confirmatory factor analysis, and structural equation modelling. The results revealed several key findings. Firstly, the hypothesis that TikTok marketing activities significantly influence perceived innovativeness was supported. Additionally, it was found that TikTok marketing activities significantly affect perceived enjoyment. However, the hypothesis that perceived innovativeness significantly impacts purchase intention was rejected. Furthermore, it was established that perceived innovativeness significantly influences perceived enjoyment, and perceived enjoyment significantly affects purchase intention. These findings contribute to expanding knowledge in social commerce, particularly in understanding the dynamics of consumer behavior within the context of Muslim apparel products marketed through TikTok live streaming. The acceptance of hypotheses regarding the influence of TikTok marketing activities on perceived innovativeness and enjoyment underscores the importance of social media marketing strategies in shaping consumer perceptions and experiences. However, rejecting the hypothesis concerning the direct impact of perceived innovativeness on purchase intention suggests that other factors may mediate this relationship, warranting further investigation.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: This research aims to present and validate a comprehensive model of brand love, encompassing its causes and effects. The model is based on a causal approach and includes a well-established construct of the consumer-brand relationship, celebrities' credibility on brand love through the mediation role of self-brand connection. The research used a quantitative methodology, utilizing samples consisting of individuals who use body care products. Data was obtained from 271 respondents using a Google Form questionnaire. The data were analyzed using Smart PLS structural equation modeling. The research results show that the construction of celebrities' credibility, attractiveness, trustworthiness, and expertise, positively affects self-brand connection and brand love. Self-brand identification mediates the relationship between credibility and brand image in body care.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: The aim of this research is to analyze the influence of the role of AI and big data in increasing the success of e-commerce assistance and its implications for the quality of the capacity development model for MSMEs with disabilities in Serang City, Semarang City and Banjarmasin City, Indonesia. The sample in this study was 184 respondents consisting of 3 (three) regions including Serang City, Semarang City and Banjarmasin City, Indonesia. Sampling technique using technique random sampling. Data collected through questionnaires was then analyzed using SEM-PLS. The results of research and data analysis show that: The role of AI directly has a positive and significant effect on increasing the success of e-commerce assistance; Big Data directly has a positive and significant effect on increasing the success of e-commerce assistance; The role of AI directly has a positive and significant influence on the Quality of the MSME Capacity Development Model; Government Big Data directly has a positive and significant effect on the Quality of the MSME Capacity Development Model; Increasing the Success of E-Commerce Assistance directly has a positive and significant effect on the Quality of the Capacity Development Model for MSMEs with Disabilities in Serang City, Semarang City and Banjarmasin City, Indonesia. Increasing the Success of E-Commerce Assistance is able to mediate indirectly on the Role of AI and Big Data on the Quality of the Capacity Development Model for MSMEs with Disabilities in Serang City, Semarang City and Banjarmasin City, Indonesia.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: Urban forest management has a strategic role in maintaining ecosystem balance and supporting the quality of life of urban communities. Urban forests provide various ecosystem services, such as providing clean air, temperature regulation, carbon storage, and being a habitat for biodiversity. In the midst of increasing urbanization and climate change, the existence of urban forests is increasingly important to support environmental sustainability. However, public awareness of the importance of urban forests and the ecosystem benefits they provide is often inadequate. This is a challenge in efforts to conserve and develop urban forests as an integral part of the urban environment. Palangkaraya City, Central Kalimantan, the Himba Kabui ecotourism program has been launched as an initiative to increase public awareness of urban forests and the ecosystem benefits, they provide. This research aims to analyze the role of stakeholders and artificial intelligence (AI)-based urban forest management strategies in improving public awareness of urban forest ecosystem services and their impact on the success of the Himba Kabui ecotourism program in Palangkaraya City, Central Kalimantan. Stakeholders involved include government, local communities, the private sector, and academia, each of which has an important role in the implementation and development of this program. The use of AI in urban forest management offers various innovations in monitoring, decision making, and conveying information to the public. This study found that the integration of AI technology was able to accelerate increasing public awareness of the importance of urban forests as providers of ecosystem services, such as providing clean air, climate regulation and biodiversity habitat. Additionally, this increased awareness contributes significantly to the success of the Himba Kabui ecotourism program, which aims to promote sustainable tourism and environmental conservation in the region. This research provides the implication that synergy between stakeholders and AI technology can become an effective and sustainable urban forest management model, which not only has a positive impact on the environment but also on the local economy through increasing tourism. It is hoped that these findings can become a reference for urban forest managers and policy makers in developing innovative and participatory environmental management strategies.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: Collecting poverty data through the National Socio-Economic Survey (SUSENAS) demands significant time, costs, and human resources. To enable more efficient policy-making, predicting the poverty rate before the release of Statistics Indonesia (BPS) data is essential. This research compares day and night satellite images to predict per capita expenditure in East Java, Indonesia, which has the highest number of poor people. The satellite images are processed using a transfer learning approach that employs a pretrained Convolutional Neural Network (CNN) model with VGG-16 architecture as a feature extractor. These extracted features are then used as independent variables to predict East Java's per capita expenditure using Support Vector Regression (SVR) with RBF and polynomial kernels. The findings indicate that night images are more reliable than day images, with the best model being a combination of transfer learning and the SVR polynomial kernel using night images. The prediction mapping aligns well with the unmodeled night image, demonstrating the effectiveness of this approach in predicting per capita expenditure.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: In this research, the goal was to improve diabetes prediction by combining Multilayer Perceptron Neural Network (MLPNN) with Memetic Algorithm (MA) and Arithmetic Optimization Algorithm (AOA). The method suggested used a preprocessing step to choose a representative subset of attributes from the initial set. Next, the method suggested utilized a combination of the MA and AOA algorithms to optimize feature selection, resulting in a refined dataset that served as input for the Neural Network. Ultimately, the suggested approach utilized the multilayer perceptron neural network (MLPNN) to train the network with hidden layer neurons. The experimental findings indicated a 95% high accuracy rate was achieved. Machine learning classifiers achieved better accuracy compared to classifiers in previous studies, with Decision Tree and Logistic Regression classifiers each reaching 93.57% and 93.33% accuracy, respectively.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: This study aims to evaluate an intervention that moved beyond the dimensions of digital information application (DTMs) in science education in higher education. Using an integrated methodology, there is a large and growing body of evidence for the prominence placed on both perceived experience and quality tutors in science courses in educating institutions with broader ambivalence toward limited Digital Literacy. The model and Hypotheses were tested with data collected from 158 participants. Our results suggest that user perceptions about the value of DIE may be contingent on several factors beyond any intrinsic greater experience in learning and teaching Science. The quality of tutors may lead to a greater perceived usefulness regarding the technology. Also, the level of communication flow can affect how much science students are willing to use technology. Academic institutions will have to reassess the utility of digital information technology as an instrument for improving science education. This research focused on educational contexts where DIE profoundly influences teaching and learning science. Further research could focus on other educational fields, math, or language to broaden the understanding of technology's role in diverse educational contexts.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: The intention to reuse Garuda Indonesia Airline became the leading indicator in the minimum standard of air transport services. The main problems in this research were the less attractive promotion efforts that hindered passengers from reusing Garuda Indonesia Airline and unclear web check-in procedures, which were difficult for some passengers to understand. This research aimed to know the direct and indirect influences of service quality and web check-in on the intention to reuse Garuda Indonesia Airline mediated by passenger trust at Soekarno-Hatta Airport. The research was quantitative, with the data collecting method using a questionnaire distributed online through a purposive sampling technique to 210 passengers of Garuda Indonesia Airline. The data was analyzed using the partial least squares-structural equation modeling (PLS-SEM) method. This research showed five direct influences: the influence of service quality and web check-in on passenger trust, the influence of service quality, web check-in, and passenger trust on the intention to reuse Garuda Indonesia Airline. Passenger trust could mediate the influence of service quality and web check-in on the intention to reuse Garuda Indonesia Airline at Soekarno-Hatta Airport. From the results of this research, it could be concluded that service quality and web check-in through passenger trust are needed to reach the intention of reusing Garuda Indonesia Airline.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: The current research aims to recognize the mediating role effect of the e-behavior (henceforth e-behavior) on the relationship between the electronic word of mouth (henceforth e-WOM) and the electronic decision of purchase (henceforth e-DOP) among the students of Jadara University/ Jordan. The research adopted a descriptive analytical methodology in data collection and analyses; and developed a questionnaire to measure the variables of e-behavior, e-WOM, and e-POD. Students using the university Facebook website completed the questionnaire, 400 retrieved questionnaires were valid for statistical analysis. Smart PLS software was used to analyze the collected data. The study found statistical differences of e-WOM on the e-DOP, significant differences were also found of e-behavior in the relationship between e-WOM and e-DOP. The study recommends companies to take interest in e-WOM and to add it to its marketing strategies, because e-WOM effects taking an e-DOP by potential customers and enhances positive purchase behavior.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: This study aimed to investigate digital drivers of digital transformation success in public sector organizations. Based on prior related studies, three digital drivers were selected as key drivers, which are digital government, digital leadership, and digital HRM. Gathering data by online questionnaires from public sector employees, the study based on SmartPLS 3.0 statistics found significant and positive impacts of these three drivers on digital transformation success. Interestingly, the results refer to the success of digital transformation is greatly subject to digital HRM and possibly this effect is due to the fact that the basic aim of digital government and digital leadership is to enhance the operations of the digitization process through adopting digital-oriented public administration mentality, creating public value, setting shared digital vision and strategy, communicating digital change goals, initiating digital organizational culture, which is basically guided and can be attained through efficient and effective digital HRM practices. Hence, the study contributes to the literature through underlying three digital drivers of digital transformation success. It calls scholars for considering these drivers when examining success factors of digital transformation and practitioners when redesigning organizations to adapt digital change.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: This study aims to identify the impact of digital capabilities on working from home employee productivity in the UAE banking industry. In order to achieve the objective of this study, data were collected by questionnaire from employees working in e-businesses such as online shopping agents, numbering (268). After verifying the validity and reliability, the effects were examined using simple regression to examine the direct effect and path analysis to examine the indirect effect. The results of the study indicated that the study community was highly electronic, and the cost variable was the highest application of investment, applying digital leadership capabilities followed by digital work environment. The results showed a strong relationship between the elements of the independent variable (digital capabilities) and working from home employee productivity. The study recommended repeating such studies on the same industry and other industries, and the study recommended examining its generalization to developing contexts by applying the study and the same variables.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: This research paper aims to test the effects of ChatGPT on students’ performance while using training to moderate this effect. The current paper uses a quantitative, descriptive, cause-effect approach. A cross-sectional sampling approach was used to collect the data online from 117 students in three Jordanian universities (Princess Sumaya University, University of Jordan, and German Jordanian University) by using a survey questionnaire. Data has been tested for its validity and reliability before testing hypotheses. The results indicated that the students agreed on the importance of ChatGPT (ease of use, accuracy, and plagiarism), however, most of the respondents did not agree on the importance of training on ChatGPT and they say it is easy and does not need training. The results also show that there are significant correlations among ChatGPT dimensions (ease of use, accuracy, and plagiarism). However, there is a significant correlation between training and plagiarism only, and there is an insignificance between training and both ease of use and accuracy, which supports the respondents' viewpoint that the training is not important. Finally, findings indicate that there is a significant strong correlation between all other variables (ease of Use, accuracy, and plagiarism) and students' performance, and a weak relationship with training. Finally, results show that there is a significant impact of ChatGPT (Accuracy, ease of use, and plagiarism) on students’ performance, where plagiarism has rated the highest significant effect, then accuracy, while ease of use has an insignificant effect. Moreover, results demonstrated that training has an insignificant moderation effect between ChatGPT and students’ performance.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: This study investigates auditors' willingness to adopt AI-based audit tools using the AI Device Use Acceptance (AIDUA) model, focusing on the factors influencing acceptance and the moderating role of technology readiness. Data were collected from 153 certified external auditors in Jordan, representing a 30% response rate. The findings reveal that social influence and hedonic motivation positively impact performance expectancy, while anthropomorphism influences effort expectancy. Emotions significantly affect auditors' willingness to adopt AI-based audits, moderated by their technology readiness. This study contributes to the literature by utilizing the AIDUA framework to understand AI acceptance in auditing, offering insights into the unique aspects of AI technologies. The results highlight the importance of understanding auditors' perceptions and readiness, providing valuable implications for practitioners and policymakers to develop strategies for effective AI integration in auditing. Future research should explore these dynamics in diverse cultural contexts and over extended periods to enhance the generalizability of the findings.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: The study aimed to examine the impact of artificial intelligence on the development of electronic financial services. The population of the study involves employees from Jordanian commercial banks distributed across branches and the general administration. The researcher used a survey as the study instrument to collect data from the study sample, with 356 surveys distributed via a link. The Statistical Package for the Social Sciences (SPSS) was used to analyze the data. The results showed an impact of using artificial intelligence on the development of electronic financial services in Jordanian commercial banks. One of the prominent recommendations was for the management of commercial banks to keep pace with the advancements and developments in artificial intelligence and the expert systems environment by providing advanced operating and storage systems to keep up with the developments in electronic financial services.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: This study sought to empirically test the novel Country-of-Reference (COR)-effect by investigating the impact of Westernization on Jordanian youth’s purchasing behavior. The constructs examined were (Social-Identity) Self-Discrepancy, eWOM, and Herd Behavior in relation to Western COR and subsequently, purchase behavior. The data was collected in two phases; semi-structured in-depth interviews and online surveys. 12 interviews were conducted in total, following the saturation principle. Moreover, 232 questionnaire responses were gathered, through convenience sampling and snowballing techniques. The study substantiated the existence of strong links between (social identity) self-discrepancy, eWOM, herding behavior, and Western COR. In addition, proved Western COR’s mediating role between the variables and purchase behavior. Conclusively, all the proposed hypotheses were corroborated qualitatively and quantitatively. Researchers looking to further study the COR-effect should consider possible time, budget, and language restraints. As well as extend the literature to varying age groups and geographical areas. Moreover, it might be interesting to explore the COR concept in diverse industries and through a comparative research approach. This research paper analyzed a new notion of understanding consumer behavior. More specifically, it is the first to assess the role Westernization plays in Jordanian youth consumers’ consumption behaviors. Further, the findings presented in this study can aid businesses and boost the economy, through encouraging “shop local” communication strategies.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: In this digital era, the use of the Internet in business transactions through e-commerce platforms has created many conveniences, the development of Internet technology has given rise to crimes called cyber crime or crimes through the Internet network. The purpose of this study is to investigate the relationship between cybercrime and purchase intention on e-commerce platforms, and the relationship between e-brand trust and purchase intention on e-commerce platforms. This research method is a quantitative method to analyze the relationship between variables, research data was obtained by distributing online questionnaires through social media platforms, and questionnaires containing statement items were designed using a Likert scale of 1 to 5. The respondents of this study were 535 consumers who had shopped online on e-commerce platforms determined by the simple random sampling method. Data analysis used the PLS Partial least squares SEM (PLS-SEM) technique. In the study, the outer model which is called the measurement model has a meaning between indicators connected by other variables. The measurement model of convergent validity, discriminant, and reliability was used. The standard loading factor value in the concurrent validity test must be > 0.7 or greater than the established criteria. The same applies to the discriminant validity test, which uses a larger value for the loading factor. The construct reliability test uses Cronbach's alpha and the composite reliability value. The hypothesis testing uses partial least square (PLS) which is the inner model test result, namely the R-square output, path coefficient, or t-statistic. The convincing t-statistic result > 1.96 is that Ha is accepted and Ho is rejected. If the probability number (p-value) <0.005 is included, then Ha is accepted. If the p-value is <0.05 (or 5%), the t-statistic is > 1.96, and the beta coefficient is positive, then Ha can also be accepted. The results of the analysis show that cybercrime has a positive effect on consumer purchasing intentions on e-commerce platforms and e-brand trust hurts consumer purchasing intentions on e-commerce platforms.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: The study aims to bridge this gap by examining how the credibility of information on SM influences educational decisions in higher education (HE) among students. The research questions addressed are: (1) how does SM information credibility influence education decision-making? (2) How often does informative information on SM have a kind with corresponding understanding from traditional sources to guide educational decisions? What difficulties are there in how students judge SM regarding the credibility of information? While social media (SM) has been argued to be an important stakeholder in our lives, its role in educational decision-making regarding information credibility is not explicated. This study, combining surveys and interviews in a mixed-methods approach, showed that providing authentic SM information appreciably increases students' ability to make an informed choice when choosing relevant courses, gain insight into possible learning aids, and assist them with their career plans. The findings highlight the importance of digital literacy for SM-content credibility evaluability in students. The study also outlined critical strategies for educators and institutions to support students in assessing the reliability of sources and train them in skills that accompany a responsible thumb online. The conclusion takeaway from the study is that credible information on SNS can impact and change educational decision-making.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: In recent years, opinions and communication can be easily expressed through social media networks that have allowed users to communicate and share their opinions and views, resulting in massive user-generated content. This content may contain text that is hateful to large groups or specific individuals. Therefore, in most website policies, automatic hate speech detection is required, and early automatic detection or filtering of such content is critical and necessary in online social networks, especially with large and increasingly user-generated content. This paper presents a suggested model to enhance the detection performance of hate speech using deep learning models with two types of word embedding models, the first model is Arabic models based on Wor2Vec including AraVec and Mazajak. The second is word embedding techniques models based on BERT including three pre-trained models namely ARABERT, MARBERT and CAMeLBERT. Common metrics in text classification are used including precision, recall, accuracy, and F1 score for model assessment. The experimental results show fine-tuned Arabic BERT models outperform Word2Vec based models, and that MARBERT outperforms both ARABERT and CAMeLBERT across all deep learning architectures, highlighting its superior ability to classify Arabic text. Additionally, BLSTM models show the highest performance on ARABERT, MARBERT, and CAMeLBERT, achieving an accuracy of 0.9945 with MARBERT.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: The current study aims to assess the factors that could affect students’ use of ChatGPT. The study proposed a theoretical model that included six factors. Data was collected from 518 students using a questionnaire. The data were analyzed using Structural Equation Modeling to identify the relationships and test the hypothesis. The findings revealed that performance expectancy and social trust significantly influenced students’ intentions to use ChatGPT. Contrary to expectations, both social influence and effort expectancy had insignificant effects. By elucidating the core factors affecting the utilization of ChatGPT, trust had a significant impact on the intention to use ChatGPT. Furthermore, trust mediates the relationship between perceived security and intention to use ChatGPT, this study can provide valuable insights for policymakers. Moreover, this study contributes to the existing literature by setting the foundation for future research seeking a deeper understanding of the factors influencing the use of other AI technologies in teaching and learning in Electronic Public Facilities.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: This study examines the effect of the quality of e-services provided by e-commerce on customer satisfaction and loyalty during the COVID-19 pandemic. This study used a quantitative approach to involve 118 respondents who traded in online markets. The sampling method used is purposive sampling. The data were analyzed using a structural equation model using the partial least squares technique. Research results indicate that out of the five hypothetical relationships raised by the researcher, one relationship related to remuneration was found to lack a statistically significant positive effect. Besides that, the remaining four hypothetical relationships, including efficiency, confidentiality, accountability, and customer satisfaction, demonstrated positive and statistically significant. The implication of this study highlights the need to cultivate and improve the quality of e-services in the online market, especially in the context of the COVID-19 pandemic. In this way, businesses can provide consumers with a rich shopping experience, enhancing customer satisfaction and laying the foundation for long-term customer loyalty.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: The study surveys the effects of religiosity and Knowledge of halal products on both product assessment and digital purchasing interest. The study also investigates the effects of religiosity and halal product assessment on digital product purchase interest. This research method is to use quantitative methods to test the relationship between dependent and independent variables. The population of this study is consumers of halal products and the sample of this study is 564 consumers of halal products determined by the simple random sampling method. The research analysis uses the structural equation modeling partial least squares (SEM-PLS) method and uses data processing tools with SmartP LS 4.0 software. The research questionnaire contains statement items using a 7-point Likert scale, namely (1) strongly disagree, (2) disagree, (3) somewhat disagree, (4) neutral, (5) somewhat agree, (6) agree and (7) strongly agree. The independent variables of this research are Religiosity, Knowledge of halal products, and Product assessment and the dependent variable is digital purchasing interest. The stages of research data analysis are the outer model test including reliability and validity tests and the inner model test including termination tests and hypothesis tests. Based on the results of research data analysis, it can be concluded as follows: Religiosity has a positive and significant effect on the assessment of halal products, meaning that the higher the religiosity obtained by customers, the higher the assessment of halal products. Knowledge of halal products has a positive and significant effect on the assessment of halal products, meaning that the higher the knowledge of halal products, the higher the assessment of halal products. Religiosity has a positive and significant effect on interest in buying halal products, meaning that the higher the religiosity obtained by customers, the interest in buying halal products will increase. Knowledge of halal products has a positive and significant effect on buying interest, meaning that the higher the customer's perceived knowledge of halal products, the more buying interest will increase. The assessment of halal products has a positive and significant effect on interest in buying halal products, meaning that the higher the understanding of the assessment of halal products, the higher the level of interest in buying halal products.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: Technological transformation not only changes the way we communicate and do business but also changes the way the government provides public services to the community. One manifestation of this transformation is the implementation of Information Systems (IS) for public services, to provide services that are more efficient, transparent, and responsive. By implementing an IS, the administration and data management process becomes more efficient. This research method uses a quantitative method approach, The research data are obtained by distributing online questionnaires via the Google Form platform and the respondents for this research were 576 senior employees of the immigration department in Indonesia who were determined using a simple random sampling method. Research data analysis uses structural equation modeling (SEM). The variables in this research are the dependent variables, namely information Technology System integration and Information Technology System Security (ITSS). The dependent variable is the Efficiency and Accuracy of Immigration Documents (EAID) and User Acceptance and Satisfaction (UAS). Based on data analysis, it is concluded that TSS integration had a positive and significant relationship with EAID, Information technology system integration had a positive and significant relationship with UAS, ITSS had a positive and significant relationship with EAID, ITSS had a positive and significant relationship with UAS and user acceptance and satisfaction had a positive and substantial relationship with the EAID. Implementing IS opens the door to more efficient, transparent, and responsive public services. By leveraging technology, governments can streamline administrative processes, increase citizen participation, and create an environment where every citizen can benefit from better public services. In carrying out this transformation, the government must remain focused on data security, privacy, and community empowerment so that people truly feel the positive impact of technological developments in public services.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: In the dynamic realm of educational technology, the integration of Chatbots and artificial intelligence (AI) has become crucial in augmenting student support and engagement. The shift towards digital education platforms demands capable and prompt systems for addressing student inquiries. Chatbots and AI have seen widespread integration across educational settings, but their targeted application in handling student queries is a relatively unexplored area of educational support. There is a notable research void regarding the actual effectiveness of Chatbots in responding to student queries. Most existing studies focus broadly on AI's educational applications, often overlooking the specifics of student interactions and query resolution. This study addresses this gap by thoroughly assessing the impact of Chatbots and AI in managing student queries. Utilizing a quantitative approach, the research involved 572 university students in the UAE who have interacted with Chatbots for query resolution. The study employed the PLS-SEM model for analysis. Results indicated that information and system quality, along with perceived learning value and satisfaction, are significant factors influencing the acceptance of Chatbots. These findings are crucial for understanding the adoption and efficiency of AI tools in student query management. Some aspects of the model, however, did not show significant effects on Chatbots usage. Nonetheless, this research provides valuable insights for educational bodies and tech developers. The empirical evidence on Chatbots’ effectiveness in handling student queries can guide future AI integrations in educational contexts. It also offers best practice guidelines for creating AI support systems that align with student needs. Furthermore, the study contributes to the development of policies on AI use in education, underscoring the need to match technological progress with educational objectives and student well-being.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: The aim of this research is to empirically test a new marketing model from a digital marketing perspective based on social media marketing, which is a crucial factor for consumers in the digital era when making decisions (especially tourists) to visit the Togean Islands marine tourism destination in Tojo Una-Una Regency. Social media marketing comprises three dimensions known as 4C: context, communication, collaboration, and connection. This type of research employs exploratory or confirmatory research methods. The data analysis method utilizes the PLS-SEM version 4 approach, and the research sample consists of 160 respondents, including both foreign tourists and Indonesian tourists. The research results indicate that the communication dimension has the highest loading factor value of 0.813, followed by collaboration with 0.770, connection with 0.745, and context with 0.703. Furthermore, the path coefficient value for the collaboration dimension is the highest compared to the other three dimensions, at 0.813. These findings imply that decision-makers can derive meaningful insights for redesigning new marketing models in the tourism sector amidst the digitalization era.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: This study investigates the impact of cross-Border E-commerce development on China’s foreign trade. The software SPSS is used to calculate the value of each independent variable CBEC transaction volume, business infrastructure, professional talents, and development potential, and the software STATA version 18 is used to perform all the regression analyses. The findings reveal that efficient CBEC business infrastructure, including electronic payments, logistics, and digital support systems advancements, significantly enhances trade facilitation. Additionally, developing and cultivating professional CBEC talents are critical in sustaining trade growth, though there remains a significant talent gap in high-end, composite skills. Furthermore, the study highlights the immense potential of CBEC to broaden trade channels, improve global competitiveness, and foster innovation in small and medium-sized enterprises (SMEs). The analysis indicates steady growth in CBEC transactions and infrastructure, alongside an increasing internet penetration rate, supporting the sector's expansion. The study concludes with recommendations for policymakers and businesses, emphasizing the need to enhance infrastructure, cultivate professional talents, and strengthen market potential to ensure sustainable CBEC development and boost foreign trade. These insights provide a comprehensive understanding of the mechanisms CBEC influences foreign trade, offering a valuable reference for future research and policy formulation.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: This study observed more than 150 respondents in the digital trading environment, explaining how the important role of website characteristics and service quality influences customer purchase intensity. PLS-SEM analysis of the data was performed using SMARTPLS version 3 software. After that, the samples were collected through an online questionnaire by setting several criteria to make the resulting perceptions accurate. The study's findings show that while a website's features have a big impact on customer trust, customer loyalty, and purchase intensity can all be directly impacted by the quality of the product or service offered. However, unfortunately, customer trust and satisfaction cannot mediate website characteristics in their influence on customer purchase intensity. Overall, this study expands the understanding in the field of trade that service quality and website characteristics significantly influence increasing customer buying intensity.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: The current problem of the study is to explore the mediating role of financial reporting quali-ty in the impact of artificial intelligence (AI) on the quality of external auditing in Jordanian commercial banks. A descriptive analytical approach was used. The target population in this research consists of all 13 Jordanian commercial banks listed on the Amman Stock Ex-change. The researcher was able to collect 198 questionnaires that were approved to be filled out by employees of Jordanian commercial banks. The present research in Jordanian com-mercial banks discovered that the impact of AI on external auditing quality is moderated by the quality of financial reporting. The study recommends paying attention to the quality of external auditing, as the auditing process must be carried out efficiently and effectively in accordance with auditing standards. In order for errors and violations discovered during the audit process to be detected, the quality of financial reports must be audited. It also high-lights the need to enhance the use of artificial intelligence in the bank to raise the efficiency of the banking systems and thus raise the bank’s efficiency.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: Management of customary forests through green accounting is an important approach in efforts to achieve Sustainable Development Goals (SDGs). Customary forests, which constitute an important cultural and ecological heritage for local communities, are often threatened by unsustainable exploitation (deforestation) activities. Therefore, Green Accounting is a business concept that focuses on the efficiency and effectiveness of long-term resource use in integrating the customary forest environmental functions and providing social benefits. Therefore, the implementation of green accounting in customary forest management aims to measure and monitor the economic, social and environmental impacts of extractive activities on these forests. This research aims to analyze the relationship between digital green accounting variables on financial performance, environmental performance on sustainable development and digital green accounting towards sustainable development. This research method is quantitative causal which tests the relationship between several variables. The population of this research is indigenous community leaders and the sample of respondents for this research is 432 indigenous community leaders determined using a simple random sampling method. Data analysis for this research uses structural equation modelling (SEM) partial least squares (PLS) with data processing tools using SmartPLS 4.0 software. Research data was obtained by distributing online questionnaires using social media. The independent variables of this research are digital green accounting, environmental performance and the dependent variable is sustainable development. The stages of research data analysis are the outer model test including reliability and validity tests and the inner model test including termination tests and hypothesis tests. Based on the results of data analysis, it is concluded that digital green accounting has a positive and significant relationship to financial performance, environmental performance has a positive and significant relationship to sustainable development and digital green accounting has a positive and significant relationship to sustainable development.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: The development of information technology is currently growing rapidly, affecting all aspects of life. There are various benefits obtained from advances in information technology, one of which is the ease of communicating and accessing information. This transformation is an opportunity to increase accessibility in various sectors, such as government, health, education, social, economic and other sectors. This research aims to analyze the relationship between digital transformation and public accountability, digital transformation with public transparency and transparency with public accountability. The research uses quantitative methods to test the relationship between variables. The respondents of this research were 478 government public service office employees who were determined using a simple random sampling method. Research data analysis uses structural equation modelling (SEM) partial least squares (PLS) with research data analysis tools using SmartPLS 3.0 software. Based on the results of data analysis, it is concluded that digital transformation had a positive and significant relationship to public accountability, digital transformation had a positive and significant relationship to public transparency and transparency had a positive and significant relationship to public accountability. The process of digital transformation in public administration greatly influences how public services are delivered and how government functions. Leveraging digital technology is an opportunity to increase accessibility in various fields such as government, health, education, economics, social and politics. Public administration can increase transparency, efficiency, public participation and data-based decision-making. Organizations pursuing digital transformation must be flexible, innovative and able to adapt quickly. Several important benefits of transparency include preventing corruption, making it easier to identify weaknesses and strengths of a policy, and increasing accountability in the delivery of public services by government agencies. In addition, a transparent attitude will increase trust in government institutions to decide on certain policies, as well as being able to encourage a conducive investment climate and increase business certainty.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: The rapid advancement of drone technology has expanded their applications across various sectors, necessitating robust real-time decision-making systems. Traditional algorithms often falter in dynamic and unpredictable environments. This paper introduces a fuzzy logic-based approach to enhance the decision-making capabilities of autonomous drones. Utilizing Monte Carlo simulations, the proposed model was evaluated through three distinct experiments involving 300, 600, and 950 scenarios respectively. The first experiment demonstrated an obstacle avoidance efficiency of 82.00%, an 8.00% reduction in energy consumption, a decision accuracy of 95.33%, and a mission success rate of 79.33%. The second experiment showed an avoidance efficiency of 82.50%, maintaining the energy consumption reduction at 8.00%, with a decision accuracy of 95.83% and a mission success rate of 78.33%. The third experiment achieved an avoidance efficiency of 82.11%, with an 8.00% reduction in energy consumption, a decision accuracy of 95.26%, and a mission success rate of 78.31%. These results highlight the superior performance of fuzzy logic in real-time decision-making for autonomous drones compared to traditional methods.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: The main objective of spatiotemporal analysis is to offer precise predictions of outcomes. The objective of this study is to assess the accuracy of the Bayesian Latent Gaussian Model in predicting outcomes by utilizing both time-varying and fixed spatial weight matrices. The results of the Monte Carlo simulation suggest that when there is moderate spatial autocorrelation (between 0.3 and 0.7), it is strongly advised to use a time-varying spatial weight matrix. This approach yields the most precise predictions and minimizes any distortion in parameter estimates. Furthermore, we provide an illustrative case study where we simulate the effects of exposure to multiple pollutants on tuberculosis. The analysis revealed that particulate matter 10 (PM10), nitrogen oxides (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3), have a positive influence on the risk of TB, with spatial effects that change over time. The model demonstrates that a rise of 1 mg/m³ in the levels of PM10, NO2, SO2, CO, and O3 is linked to corresponding increases in TB cases by 2.1%, 21.17%, 13.20%, 6.72%, and 6.59%, respectively. NO2 and SO2 have the most significant influence on the risk of tuberculosis (TB). These findings enhance our comprehension of the spatial correlation of TB over time and promote further investigation to determine the most efficacious strategies for mitigating the dissemination of TB.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: The research design uses a quantitative approach, especially correlational, verification or hypothesis testing based on empirical data in the field. The population of this research is all Islamic banking customers, totaling around 33,600,000 people. The sample size for this study was 178 people, which was determined using the Joseph F. Hair formula. The research sample was selected randomly using stratified random sampling techniques to represent the relevant population. Data was collected using a questionnaire which was distributed to selected samples using a Google form. Primary data was analyzed using SMART PLS. The results indicate that the role of AI has an impact on the Quality of Strategic Decisions; Social Dynamics have an impact on the Quality of Strategic Decisions; Political Support has an impact on the Quality of Strategic Decisions; The role of AI has an impact on the Progress of Islamic Banking, Social Dynamics has an impact on the Progress of Islamic Banking; Political Support has an impact on the Progress of Islamic Banking; The quality of strategic decisions has an impact on the progress of Islamic banking; Quality of Strategic Decisions mediates the relationship between the Role of AI and the Progress of Islamic Banking; Quality of Strategic Decisions mediates the relationship between Social Dynamics and Islamic Banking Progress; and finally, Quality of Strategic Decisions mediates the relationship between Political Support and Islamic Banking Progress.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)