Abstract: Human migration is based on pull factors that individuals evaluate when it comes to moving to a different territory. Likewise, employee attrition is a phenomenon that represents the tendency to a reduction in employees within an organization. This research paper aims to develop and evaluate machine learning algorithms, namely Decision Tree, Random Forest, and Binary Logistic Regression, to predict employee attrition using the IBM dataset available on Kaggle. The objective is to provide organizations with a proactive approach to employee retention and human resource management by creating accurate predictive models. Employee attrition has significant implications for an organization's reputation, profitability, and overall structure. By accurately predicting employee attrition, organizations can identify the factors contributing to it and implement data-driven human resources management practices. This study contributes to improving decision-making processes, including hiring and firing decisions, and ultimately enhances an organization's capital. The IBM dataset used in this study consists of anonymized employee records and their employment outcomes. It provides a comprehensive HR data representation for analysis and prediction. Three machine learning algorithms, Decision Tree, Random Forest, and Binary Logistic Regression, were utilized in this research. These algorithms were selected for their potential to improve accuracy in predicting employee attrition. The Logistic Regression model yielded the highest accuracy of 87.44% among the tested algorithms. By leveraging this study's findings, organizations can develop predictive models to identify factors contributing to employee attrition. These insights can inform strategic decisions and optimize human resource management practices.

	© 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 influence of two key factors, namely communication and disposition, on policy implementation in the educational environment. The main objective of the research is to investigate the impact of communication and disposition on policy implementation, with a specific emphasis on the moderating role of disposition in the relationship between communication and policy implementation. The method used in this research is partial least squares structural equation modelling (PLS-SEM), analysing data from 232 research samples obtained from nine schools in Indonesia. The research results indicate that communication has a significant and positive influence on policy implementation, while disposition also has a significant and positive impact on policy implementation. A more interesting finding is that disposition, in the context of this research, proves to play a crucial role as a moderating variable, enhancing the positive influence between communication and policy implementation. This finding contributes significantly to our understanding of the complexity of factors influencing policy implementation in the educational environment, particularly from the interaction perspective between communication and disposition. The implications of this research can form the basis for the formulation of more effective and contextual policy strategies in the future.

	© 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: Android malware is widespread and it is considered as one of the most threatening attacks recently. The threat is targeting to damage access data or information or leaking them; in general, malicious software consists of viruses, worms, and other malware. Current malware attempts to prevent being detected by any software or anti-virus. This paper describes recent Android malware detection static and interactive approaches as well as several open-source malware datasets. The paper also examines the most current state-of-the-art Android malware identification techniques including identifying by comparative evaluation the gaps between these techniques. As a result, an API-based dynamic malware detection framework is proposed for Android to provide a dynamic paradigm for malware detection. The proposed framework was closely inspected and checked for reliability where meaningful API packages and methods were discovered.

	© 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: Considering the increasing demand for cosmetic surgery and the number of private cosmetic surgery clinics, it is essential to measure and manage the quality of services provided by these clinics. Obtaining sufficient knowledge about the content perceived by the clients of the quality of services provided by specialized clinics can affect identifying improvement opportunities and criteria that will cause their competitive advantage, and on the other hand, it also prevents wasting resources. For this purpose, this study aims to identify, evaluate, and prioritize the criteria for quality improvement in cosmetic surgery clinics. First, the effective criteria focus on the quality of medical services have been identified by reviewing the research background. Then, the identified criteria in the case study are customized by the Delphi method, and then the DEMATEL-based analytic network process method (DANP) is applied to reveal their causal relationships between criteria and sub-criteria to determine the direct and indirect influences, and finally, all of them are prioritized. In the end, based on the obtained results and knowledge of experienced medical experts in the case study, some managerial solutions are proposed to improve the quality of the provided medical 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: The selection of an optimal supplier is a critical and open challenge in supply chain management. While experts' assessments significantly influence the supplier selection process, their subjective interactions can introduce unpredictable uncertainty. Existing methods have limitations in effectively representing and handling this uncertainty. The paper aims to address these challenges by proposing a novel approach that leverages q-rung orthopair fuzzy sets (q-ROFSs). The novelty of the proposed approach lies in its ability to accurately capture experts' preferences through the use of q-ROFSs, which offer membership and non-membership degrees, providing a broader expression space compared to conventional fuzzy sets. Additionally, it incorporates social network analysis (SNA) to effectively consider the trust relationships among experts. The proposed approach is divided into three stages. The first stage, presents a novel method to determine experts' weights by combining SNA, the Bayesian formula, and the maximum entropy principle. This approach allows for a more precise representation of varying levels of expertise and influence among experts, addressing the uncertainty arising from subjective interactions. The second stage introduces a hybrid weight determination method to determine criteria weights within the context of q-ROFSs. Entropy plays a crucial role in capturing fuzziness and uncertainty in q-ROFSs, while the projection measure simultaneously provides information about the distance and angle between alternatives. By employing both objective weights estimated using entropy and normalized projection measure and subjective weights derived using an aggregation operator and a score function, the presented approach achieves a comprehensive assessment of criteria importance. To incorporate both subjective and objective weights effectively, game theory is applied which allows us to align decision-making with both quantitative and qualitative aspects, making the method more versatile and applicable. The third stage redefines the traditional Combined Compromise Solution (CoCoSo) method using Bonferroni mean operators which captures interrelationships among arguments to be aggregated. Furthermore, in recognition of the importance of an expert risk preferences and psychological behaviors, we apply regret theory, ensuring that the chosen solutions align more effectively with their underlying preferences and aspirations. The applicability and effectiveness of the proposed approach are demonstrated through a numerical example of green supplier selection. The comparative analysis illustrates the practicality and real-world relevance while the sensitivity analysis, confirms the stability and robustness of the proposed approach.

	© 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 research is to investigate the impact of Artificial Intelligence (AI) on improving the quality of financial reporting in the Islamic banking industry. The study is conducted within the theoretical framework of the Unified Theory of Acceptance and Use of Technology (UTAUT). The study utilized Partial Least Squares Structural Equation Modelling (PLS-SEM) to examine the data collected from a sample of 364 professionals working in the field of Islamic banking. The results of our study suggest that Performance Expectancy, Effort Expectancy, and Social Influence are important factors in predicting individuals' Behavioural Intention to use Artificial Intelligence (AI). Additionally, the presence of Facilitating Conditions further enhances the impact of these factors on individuals' actual Use Behaviour. Significantly, it was shown that Use Behaviour played a significant role in determining the perceived quality of financial reporting. Nevertheless, the study did not find empirical evidence to demonstrate the direct influence of Behavioural Intention on Financial Reporting Quality. This implies that the actual implementation of Artificial Intelligence is required to fully realize its advantages. The use of artificial intelligence (AI) into governance frameworks presents a potentially advantageous pathway for Islamic banks to uphold Shariah principles, while concurrently bolstering accountability and fostering ethical banking practices.

	© 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: It is argued that managers took advantage of Covid-19 pandemic lockdowns and remote auditing and used earnings management (EM) practices extensively. Furthermore, the Covid-19 pandemic created new unsearched crisis-related incentives. This study, therefore, tests whether Covid-19 created a new incentive for managers to manipulate earnings. It also examines the association between corporate social responsibility (CSR) and board independence and EM during Covid-19. A data set of 384 firm-year observations from 2018 to 2021 of non-financial firms listed on the Amman Stock Exchange (ASE) was investigated. Results indicate that Jordanian firms engaged in EM during Covid-19 considerably more than when compared to pre-Covid-19, suggesting that Covid-19 created a new incentive for managers to manipulate earnings. Furthermore, Jordanian firms used income-increasing EM much more when compared to income-decreasing EM. However, when taking Covid-19 into account, no significant association was found between board independence and EM. In addition, the ability of CSR to constrain EM decreased. This adds to the current debate in the literature that even well-established monitoring mechanisms like board independence and CSR are unable to constrain EM practices in a unique business environment caused by Covid-19.

	© 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 examine the influence of the psychosocial work environment on the proclivity for asset misuse in the context of local government management, with a focus on the perceptions of administrators in Central Sulawesi Province. Employing purposive sampling, a total of 39 government units constituted the study's population, with a final sample size of 114 participants, comprising administrators, officers, and managers. WarpPLS software was employed to analyze the data. The findings reveal a positive relationship between rationalization and the inclination toward asset misuse. Additionally, rationalization exhibits a negative impact on the psychosocial work environment. Finally, the psychosocial work environment demonstrates a negative influence on the propensity for asset abuse. These outcomes suggest that the psychosocial work environment plays a pivotal role in mitigating the inclination for asset misuse within the local government of Central Sulawesi Province. This research sheds light on the significance of fostering a positive psychosocial work environment to enhance decision-making processes and reduce the likelihood of fraudulent activities in the management of government assets. Understanding these dynamics is crucial for the development of strategies to promote ethical and responsible asset management in local government entities.

	© 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 investigates the relationships between investors’ behavioral biases and compares their relative importance. For this purpose, a survey is conducted, and analytical methods are used. The sample for this study has been 512 individual investors of the Tehran Stock Exchange who completed an online questionnaire. The respondents replied about their behavior in different situations to analyze the prevalence of asymmetric discounting, mental accounting, shifting risk preference, loss aversion, regret aversion, overconfidence, proxy decision making, ambiguity aversion bias, anchoring, and herd behavior as significant fields of behavioral biases in their investment decisions. The data is analyzed using two different analytical techniques. A model based on structural equations is designed and tested to analyze the relations between these fields. Another integrated method, the DEMATEL-based analytic network process, is also used to prioritize and rank these behavioral biases. Finally, the results are compared and confirmed by each other. Analyzing the results proves the existence of 19 positive and statistically significant relations between these fields. Thus, an increase or decrease in the intensity of a particular field of behavioral biases in one’s decisions significantly affects the intensity of other fields. The present study finds that shifting risk preference, anchoring, loss aversion, and regret aversion are the most important fields of behavioral biases based on their prevalence among investors and their correlations with other biases.

	© 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 socio-economic development of each country requires the contribution of businesses to social goals and the state budget. Improving tax compliance behavior is consistent with improving the ability of businesses to contribute to the budget. The objective of the study is to evaluate factors affecting tax compliance behavior for businesses. The study is conducted on 202 enterprises in Thai Nguyen, a locality located in the key economic region of Vietnam and with a high level of economic development. Through quantitative analysis of the research results, it found that: the system legislation and socio-economic conditions have the strongest influence on businesses' tax compliance behavior. However, the influence of business characteristics and tax authority characteristics on tax compliance behavior is lower.

	© 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: There were still contradictory results from earlier research on the relationship between organizational innovation, performance, and management control systems (MCS). In order to account for these contradictory results, future studies ought to concentrate on the empirical analysis of Simon's MCS theory. The purpose of the study was to assess the mediation and moderation model in relation to performance, innovation, and MCS. The study intends to broaden the scope by employing a more thorough definition and measurement of research variables. Because the Partial Least Square (PLS) can concurrently assess the existence of a dual dependency relationship of a latent variable, the study used PLS to test the hypothesis. The mediated hypothesis which holds that MCS indirectly affects performance through innovation seems to be supported by these data. All things considered, this study contributes to the understanding of the ambiguous and contradictory conclusions of earlier studies that examined the connection between MCS, innovation, and 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: Financial reports provide information about a company's assets, liabilities, equity, income, expenses and cash flow. This information can be used by various parties such as investors, creditors, government and management to make business decisions and assess company performance. Companies in obtaining good financial reports need to detect fraudulent financial statements first. Financial statement fraud can be detrimental to investors and creditors because it gives a wrong picture of a company's financial performance. This study aims to examine the effect of big data competence and the tone of the top internal auditors on the detection of financial statement fraud, as well as to mediate the effect of big data competence on the detection of financial statement fraud through self-efficacy. This research uses a sample of 183 respondents who are internal auditors in companies in Indonesia. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results of the study show that big data competence has no significant effect on the detection of financial statement fraud, but has a positive and significant effect on self-efficacy. In addition, the internal auditor's tone of the top also has a positive and significant effect on the detection of financial statement fraud. Finally, self-efficacy partially mediates the relationship between big data competence and fraud detection of financial statements. This research provides important implications for practitioners and decision makers in developing internal auditor competence in the field of big data and paying attention to tone of the top as an important factor in detecting fraudulent financial statements. In addition, this research also contributes to strengthening the understanding of the relationship between big data competence, tone of the top, self-efficacy, and fraud detection of financial statements in the Indonesian context.

	© 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: While decent work has emerged as the central theme of the psychology of work theory and a global concept and directive for promoting social, political, and economic justice, it has garnered increasing scientific and political attention in the past two decades. However, until now, no defined measurement scale for the pharmaceutical supply chain exists. The present study aims to design and validate key performance indicators (KPIs) for 'decent work' in the pharmaceutical industry supply chain of Iran, using the Decent Work Daffi Scale (2017) as a reliable framework with five sub-scales and 15 items, tested and validated. For the validation of the Decent Work Scale, a quantitative survey study was conducted among selected pharmaceutical industry experts with a sample size of 228 individuals in the year 2023. The current study adopted an exploratory factor analysis approach using SPSS software and a confirmatory factor analysis through AMOS version 24 software. In this context, the factor structure, convergent validity, discriminant validity, and Cronbach's alpha coefficients were examined. The results showed that the five-factor structure outperforms the one-factor model with evidence supporting the convergent, discriminant, and predictive validity of the five-factor scale. Thus, the measurement of decent work in the pharmaceutical industry of Iran comprises five sub-scales: occupational safety conditions, access to healthcare, adequate remuneration, Free time and rest, and alignment of organizational values with family and societal values. This scale can serve as a useful tool for industrial and organizational psychology research, as well as for studies on the sustainability of social supply chains.

	© 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 examines how generational cohorts influence households’ choices regarding housing tenure and considers the diverse preferences and socio-economic factors that shape decisions—using survey data on 425 families in Ho Chi Minh City, Vietnam. The data is analyzed using a multinomial logit model. The results indicate that generation significantly positively affects housing tenure choice, such that, unlike older cohorts, younger generations are more inclined to rent houses as their preferred housing option. Furthermore, permanent income plays a significant role in shaping housing tenure choices. On the other hand, social-economic variables, namely education, gender of references, family structures, and area of residence, were significant in influencing housing tenure decisions. This finding highlights the importance of housing policies prioritizing affordable and accessible rental options in large cities.

	© 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: Several models have been developed to predict financial difficulties and corporate bankruptcy. In this research various models were employed, including the Altman model (referred to as the Z-Score), the Springate model (known as the S-Score), the Zmijewski model (designated as the X-Score), and the Grover model (referred to as the G-Score). These techniques serve the purpose of evaluating the likelihood of encountering financial difficulties, which in turn determines the probability of PT Garuda Indonesia (Persero) Tbk going bankrupt. The study utilized secondary data sourced from financial statements spanning the years from 2020 to 2022. The application of the Altman model for bankruptcy prediction revealed that PT Garuda Indonesia (Persero), Tbk experienced financial distress throughout the period from 2020 to 2022. According to the Springate model, the company was in a state of distress and declared bankruptcy in 2020 and 2022, while 2021 fell into a grey area. The Zmijewski model indicated that the company was on the brink of bankruptcy, with financial difficulties and a potential risk of bankruptcy within the next three years. Grover's model predicted bankruptcy for the company in 2020 and 2022, but indicated safety in 2021. Notably, the Taffler model emerged as the most accurate in forecasting bankruptcy, boasting a 100% accuracy rate with no errors. Meanwhile, the Zmijewski model achieved an 81.25% accuracy rate with an error rate of 18.75%, and the Springate model exhibited the lowest accuracy in bankruptcy prediction, scoring only 12.50% accuracy with an error rate of 87.50%.

	© 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 applies the Motivation - Opportunity – Ability model on social networking platforms to study tourists’ selection of green hotels for their participation in sports events. The study analyzed the intention to choose green hotels of 324 Vietnamese tourists staying at 4- and 5-star hotels with green labels in Vietnam. The research results show that the Motivations, Opportunity, and Ability factors in using social networks have a positive impact on the belief in green hotels, thereby promoting the intention to choose green hotels when tourists participate in sports events in Vietnam. This study also provides some practical implications for green hotels in using social networks to enhance the trust and intention to choose a hotel of tourists participating in sports events. At the same time, the study also proposes some suggestions for directing human resources training towards the organization of tourism-integrated sports events to create responsible tourism 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: Condition-based maintenance (CBM) of industrial machines depends on the continuous, real-time monitoring of the machine’s operational condition via smart sensors attached to different components on the machine. The problem of regularly spaced missing data, which can occur due to a variety of hardware or software issues, is one that is often overlooked in the literature surrounding CBM in industrial machines. Such missing data can cause issues in interpreting the true operational state of the machine, which can reduce the effectiveness of CBM processes. In this paper, we examine the capabilities of five data imputation techniques for handling this regular missing data and examine the impact these techniques have on machine learning (ML) classification algorithms for machine fault diagnosis. We examine the following techniques: simple mean imputation, mean imputation with outliers removed, best and worst-case imputation, and previous day imputation. Each of these methods is configured with the specific parameters that they will only consider data from the previous 24 hours, to ensure that the data is recent, and adequately represents the current status of the machine. The efficacy of each method at accurately reconstructing the missing data and the impact they have on ML classification is recorded in the results. The models are evaluated on a real-world dataset and are evaluated on a variety of common performance metrics.

	© 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: Sukawati Art Market is one of the distinguished art markets in Bali for decades, which is also close to the Sukawati Public Market. The government is making efforts to revitalize the Sukawati Public Market to become the Sukawati Art Market, while the Sukawati Public Market is being relocated to a new location far from settlements. So that during the two years of the market revitalization and reallocation program it was quiet and triggered the emergence of traders around the old market area which disturbed public order. The purpose of this research is to analyze the polemic of stakeholders objectives regarding the Sukawati Market revitalization and relocation program, using MACTOR analysis. The results of the analysis show that local government, village government, and custom village are regulatory actors, while traditional markets' sellers are the main target of the project and serve as mediation to other actors. Actors still have polemic interests, especially traders outside the market. Therefore the local government needs to control, educate, and make innovation to facilitate the sellers around outside the traditional market. The revitalization program and market relocation are successful, the traditional markets’ sellers return to selling, and the market will be busy again.

	© 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 proposes an original view to determine the effect of P2P loans on MSME and non-MSME bank loans after the COVID-19 pandemic as a whole and then focuses on the island of Java (more developed areas) and outside Java (areas which are still undeveloped). The approach used in this study uses panel data regression from 33 provinces in Indonesia during Jan-Dec 2022 after the COVID-19 pandemic. The results of this study confirm that P2P lending is not a disrupter for bank credit, the details of the results are: (1) P2P lending has a significant positive effect on overall MSME banking credit, but has no significant effect on overall non-MSME banking credit; (2) P2P lending has no significant effect on MSME banking credit in Java, but has a significant positive effect on non-MSME banking credit in Java after the COVID-19 pandemic; (3) P2P lending has a significant positive effect on MSME banking credit outside Java after the COVID-19 pandemic, but has no significant effect on non-MSME banking credit in Java post the COVID-19 pandemic.

	© 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 contemporary business landscape, effective interpretation of customer sentiment, accurate demand forecasting, and precise price prediction are pivotal in making strategic decisions and efficiently allocating resources. Harnessing the vast array of data available from social media and online platforms, this paper presents an integrative approach employing machine learning, deep learning, and probabilistic models. Our methodology leverages the BERT transformer model for customer sentiment analysis, the Gated Recurrent Unit (GRU) model for demand forecasting, and the Bayesian Network for price prediction. These state-of-the-art techniques are adept at managing large-scale, high-dimensional data and uncovering hidden patterns, surpassing traditional statistical methods in performance. By bridging these diverse models, we aim to furnish businesses with a comprehensive understanding of their customer base and market dynamics, thus equipping them with insights to make informed decisions, optimize pricing strategies, and manage supply chain uncertainties effectively. The results demonstrate the strengths and areas for improvement of each model, ultimately presenting a robust and holistic approach to tackling the complex challenges of modern supply chain management.

	© 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 gain an overview of the evaluation results and the many challenges to implementing machining competency test certification (CTC) in Vocational High Schools (VHS). The research approach to evaluating this program is a qualitative method using the analysis of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The program evaluation design in this study uses the Assessment Evaluability and Performance Monitoring (AEPM) model, which has four evaluation components: Context, Inputs, Activities, and Performance Monitoring. The subjects involved in data collection through the distribution of questionnaires of five VHS in the Special Capital Region of Jakarta. The technique of determining all subjects using the Purposive Sampling technique. The results showed the level of effectiveness of the implementation of the machining CTC program. Some dimensions need to be strengthened, especially for the “less and “very lacking” category. Finally, the approach presented in this research using the AEPM model is a step forward in the analysis of the CTC program. This approach can easily be replicated in other countries with similar aims as this 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)