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.

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.

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.

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.

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.

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.

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.

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.

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.

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.