The development of Indonesia’s aircraft component supporting industry requires a systematic understanding of the factors that drive and inhibit industrial capability enhancement. This study identifies and analyzes 22 critical success factors (CSFs) using the Decision-Making Trial and Evaluation Laboratory method to uncover their causal structure and relative influence. Expert evaluations were obtained from government policymakers and practitioners within the aircraft component supporting industry, and these inputs were used to develop the direct-relation, normalized, and total influence matrices, enabling a comprehensive cause-effect analysis. The results reveal a clear hierarchical structure in which several factors, particularly research and technology, market conditions, performance capability, technological infrastructure and availability, safety, technological reliability, and management support, emerge as primary causal drivers within the ecosystem. These elements exert significant influence on downstream factors, such as industry-government coordination, customer support, measurable demand, team composition, team competency, and contractual aspects. The findings emphasize that strengthening technological infrastructure, regulatory facilitation, and internal managerial capability is essential for improving supplier readiness and integrating domestic firms into global aerospace value chains. This study contributes to the literature by demonstrating the applicability of DEMATEL in structuring industrial development priorities within emerging aerospace economies. Practically, the results offer policymakers and industry leaders a strategic foundation for directing investments, designing capability-building initiatives, and formulating long-term industrial development strategies.

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.

Experience plays a vital role in the decision-making (DM) process. In this paper simulation, modeling, and analysis of past experience over DM has been done using the Iowa gambling task (IGT). The Human DM process is very complex and difficult to model through computational methods because it is a subjective type of process and varies person-to-person. Therefore, this study is an attempt to simulate a DM model similar to the human DM process. For this collection of real data was done and was provided as input to the developed eight Reinforcement Learning (RL) models. The result shows that the performance of the model based on Prospect Utility (PU) learned with Decay Reinforcement Rule (DRI) and Trial Dependency Choice (TDC) is better as compared to other models. It is observed from the analysis of data and also validated that simulation and models output that the experienced group performs better than inexperienced.

As a country surrounded by the ocean, Indonesia is categorized as a country that has marine potential. The fishermen communities’ economy depends on ocean. However, the fishermen communities live below the poverty line and their average income is less than regional minimum wage. In conjunction with the issue, this study seeks to investigate the factors affecting the income of fishermen communities and right decision to fishermen in covering with welfare insurance in Cirebon, Indonesia. The quantitative study is designed using cross-sectional approach. The data collected by applying random sampling with open-ended questions and interview. A total of 100 fishermen’s have participated in this study. The study used some factors in measuring the fishermen community income, namely coastal environment condition, fish catching technology and location, operational capital, climate (season) condition, fishermen’s age, fishermen’s education, and fishing experience. The data are analyzed using the multinomial logistic regression model by assisting the statistical software, i.e., SPSS-23. The results show that coastal environment condition, fish catching technology and location, operational capital, climate (season) condition, fishermen’s age, fishermen’s education, and fishing experience have significant effects on fishermen income. Interestingly, the factor of coastal environment condition and climate (season) condition have significant negative effects on fishermen income. In conclusion, this study identified that two important factors reduced the welfare level of fishermen (via income). Also, in line with that things, the right decision which can provide to support and assist the fishermen community was by providing the welfare insurance. It is purposely to give them the protection from various risks faced by fishermen.

This study examined the impact of activity-based Costing ABC and information technology IT on decision-making and competitive advantage in Sudanese industrial projects. The experimental results of this study were based on a questionnaire administered to accountants and administrators in 61 industrial projects in Sudan, and the data were analyzed using partial least squares. The study's results indicate that using activity-based costing and information technology methods in determining costs, evaluating company profits, and approximating product costs positively affects managerial decisions while improving the competitive advantage of industrial projects. The use of activity-based Costing and information technology affects decision-making and competitive advantage in industrial projects in Sudan. The study recommended that continuous training helps obtain the benefits of using activity-based Costing and information technology in industrial projects. The results show that the result gained from using activity-based Costing and information technology in determining costs, decision-making, and expanding competitive advantage has been affected by attending training programs, gradually, based on learning the capabilities of the activity-based costing system and the information technology system.

This paper presents an empirical investigation on the perceptions of individual investors about the impact of accounting disclosures on their decision makings on the stock exchange market. The study uses some quantitative methods with survey questionnaires sent to investors participating in the Vietnam stock market. By using SPSS 22, the research develops a research model with 11 accounting information groups (factors) which are measured by 82 items. The results indicate that the income information significantly affected investors’ decisions on buying, selling and holding securities while the information on assets, the accounting principles and policies had little impact on individuals’ investment decisions. The voluntary disclosures, however, show more effects than mandatory disclosures on the investors' decision making.

This article examines the nexus between Big data governance, dynamic capability and decision-making. Survey data was garnered from firms in Jordan and fuzzy sets (fsQCA) approach was applied. Fuzzy set configural modeling revealed two causal models for high decision-making effectiveness conditions. First, Big data governance, number of employees and firm age are necessary for higher levels of effective decision-making. Second, dynamic capability, number of employees and firm age are necessary for higher levels of effective decision-making. Insights for practitioners in an emerging economy and possible areas of future research are highlighted.

Company activities can yield both favorable and adverse outcomes. Among the detrimental effects are various environmental crises stemming from companies' failure to responsibly manage and uphold ethical business practices within their operational environment. This study aims to scrutinize the impact of artificial intelligence and social media on augmenting public administration decision-making and its repercussions on corporate social responsibility within enterprises situated in Sidoarjo, East Java Province, Indonesia. The sample comprises 130 respondents representing companies actively engaged in corporate social responsibility endeavors. Employing purposive sampling technique, data collected from surveys were subjected to Structural Equation Modeling-Partial Least Squares (SEM-PLS) analysis. The research outcomes and data analysis reveal that artificial intelligence significantly and directly influences corporate decision-making as well as public administration of social responsibility. Likewise, the utilization of social media by both corporations and government exhibits a direct and positive correlation with corporate decision-making and corporate social responsibility. Public administration decision-making also demonstrates a direct and significant impact on corporate social responsibility within companies in Sidoarjo, East Java Province, Indonesia. Moreover, public administration decision-making partially mediates the effects of artificial intelligence utilization and social media utilization on public administration of social responsibility within companies in Sidoarjo, East Java Province, Indonesia.

This study examines the robust relationships between Roblox's business model, collaborative learning, participation in the decision-making process, and cooperative literacy within the Indonesian Roblox community. All hypotheses were substantiated, emphasizing the significance of understanding the business model, promoting collaborative learning, and encouraging active involvement in decision-making activities for fostering cooperative literacy among players. The research employed an exploratory research design with a quantitative approach. Sampling bias and self-reported data are acknowledged limitations, along with the cross-sectional design's inability to establish causality. To address these constraints, future research should employ longitudinal methods, diverse data collection approaches, and intervention studies. Cross-cultural research comparing the Indonesian Roblox community with other cultural contexts is also encouraged. Practical recommendations include integrating features that support collaborative learning and decision-making participation within the Roblox platform. Collaboration between educational institutions and Roblox to use the platform as an educational tool is suggested, offering students a unique opportunity to develop cooperative literacy skills. These findings offer valuable insights into cooperative literacy and community engagement within the Roblox ecosystem, providing a roadmap for its development. This research contributes to the platform's growth and success in Indonesia, making it a more cooperative and informed community.

Fuzzy Analytical Network Process (F-ANP) method is able to consider the complex relationships among different levels of decisions, transactions, and feedbacks of criteria and alternatives to calculate the weights of the elements. The large number of pair-wise comparisons in F-ANP and also difficulties in understanding the way of comparisons for the expert, have reduced the efficiency and practicality of this method. In this paper, in order to eliminate the above-mentioned problems, it has been tried to provide an approach using the Fuzzy Best-Worst method, called F-BWANP. The proposed method, requires less comparison data and leads to more consistent comparisons, which means that more reliable results can be obtained, while making it much easier for responding by experts. Finally, in order to describe the proposed method and evaluate its capability, a numerical example is provided.