The policy to accelerate the design of the Detailed Spatial Plan regulation document (RDTR) is a strategic step to enhance ease of doing business and promote sustainable development in Indonesia. Targeting 2036 RDTR sites nationwide, the initiative relies on various policy interventions and technical approaches. However, as of 8 January 2024, only 399 RDTRs (19.59%) were enacted after four years of implementation. This underperformance suggests the need to examine factors influencing the process, including issues at each stage of the RDTR design business process. While often overlooked due to its perceived irrelevance to the core substance of planning, analyzing the process is crucial to addressing operational and procedural challenges. This research identifies critical issues arising from the preparation to the enactment stage of RDTR regulations and proposes necessary policy changes. Using an explanatory approach, the study employs methods such as Analytic Hierarchy Process (AHP), post-review analysis, stakeholder analysis, business process evaluation, and scenario planning. Results show several impediments, including challenges related to commitment, technical and substantive issues, managerial coordination, policy frameworks, ICT support, and data availability. These findings serve as inputs for the development of business process improvement scenarios and reengineering schemes based on Business Process Management principles.

In this paper, we assess the results of experiment with different machine learning algorithms for the data classification on the basis of accuracy, precision, recall and F1-Score metrics. We collected metrics like Accuracy, F1-Score, Precision, and Recall: From the Neural Network model, it produced the highest Accuracy of 0.129526 also highest F1-Score of 0.118785, showing that it has the correct balance of precision and recall ratio that can pick up important patterns from the dataset. Random Forest was not much behind with an accuracy of 0.128119 and highest precision score of 0.118553 knit a great ability for handling relations in large dataset but with slightly lower recall in comparison with Neural Network. This ranked the Decision Tree model at number three with a 0.111792, Accuracy Score while its Recall score showed it can predict true positives better than Support Vector Machine (SVM), although it predicts more of the positives than it actually is a majority of the times. SVM ranked fourth, with accuracy of 0.095465 and F1-Score of 0.067861, the figure showing difficulty in classification of associated classes. Finally, the K-Neighbors model took the 6th place, with the predetermined accuracy of 0.065531 and the unsatisfactory results with the precision and recall indicating the problems of this algorithm in classification. We found out that Neural Networks and Random Forests are the best algorithms for this classification task, while K-Neighbors is far much inferior than the other classifiers.

This study investigates the impact of tourism and institutional quality on environmental preservation, utilizing principal component analysis to generate three composite indices of environmental sustainability for 134 countries from 2002 to 2020. The results reveal that environmental sustainability indices have generally improved in lower- and middle-income nations but have declined in certain high-income countries. The findings also underscore the critical role of institutional quality—particularly regulatory standards, government effectiveness, anti-corruption efforts, and adherence to legal frameworks—in promoting environmental sustainability. However, the study shows that both domestic and international tourism expenditures can have adverse effects on environmental sustainability. Notably, these negative effects are exacerbated in countries with well-developed institutions, which is an unexpected outcome. This highlights the need for careful, thoughtful policymaking to ensure that the tourism sector supports sustainable development, rather than undermining environmental objectives.

This study aims to scrutinize specific long-term sustainability industrial indicators in Thailand as a representative of an emerging economy. The study uses a Bloomberg database comprising all Thai listed companies on the Stock Exchange of Thailand from 2013 to 2023. The research employs a two-step Generalized Method of Moments (GMM) statistics to assess the enduring impact on industrial sustainability. These results provide consistent, significant and positive relationships between asset turnover and sales with all industrial sustainability. The results additionally reveal that some other factors may moderate industrial sustainability but reveal the GDP growth rate and institutional shareholders are less likely to be corporate sustainability to all indicators. The results provide insight into valuable guidance to management teams, financial statements’ users, investors and other stakeholders on designing effective operations and investment strategies to improve sustainability.

This study aims to examine whether banks are compliant with adopting sustainability regulations and guidelines, and how they disclose their sustainable finance activities in sustainability reporting by providing case of Indonesian banking. Previous research provided discussions on the role of governance in supporting many variables as quantitative studies, but failed to demonstrate on going practices of how banking industries implement sustainable finance governance. Hence, this study provides originality by analyzing the extend of disclosures in order to evaluate their commitments in responding to sustainability regulations and guidelines, through disclosures of economic, environment, social, and governance (EESG) information in annual and sustainability reports. The samples were undertaken by examining the contents of sustainability and annual reports published for the financial year 2016 to 30 June 2021, for the Indonesian banks listed in business category 4, business category 3, and international banks, with the total of 202 reports. The results indicate that the implementation of sustainable finance in EESG information increases annually with social performances are the highest information disclosed, while the governance and economic information received the lowest level of disclosure. Results of this study will benefit policymakers, banks, and related companies to understand sustainable finance governance, and reveal the importance the role of banking industries to support Sustainable Development Goals (SDGs). Providing the insights of the ongoing discussions are expected to suggest following actions for further policies to support the implementation of sustainable finance, in particular to establish sustainability governance as a foundation of commitments, beyond complying to regulations.

This study investigates the application of Operational Agility Management in Thai SMEs, examining its impact on Employee Dynamic Capability and the resulting Employee Value Proposition. Using a quantitative approach with a questionnaire survey targeted at Thai SME executives, the research analyzes the relationships between “Value of Work”, “Goal Orientation”, and “Network Communication” as independent variables, “Employee Dynamic Capability” as a mediating variable, and “Employee Value Proposition” as the dependent variable. The findings reveal that Thai Small and Medium-sized Enterprises (SMEs) struggle particularly with “Network Communication” in enhancing their “Employee Value Proposition”, primarily due to their predominant hierarchical command structure. This challenge highlights the need for Thai SMEs to reassess their organizational structures and communication practices to improve employee dynamic capabilities and overall employee value proposition. The study provides novel insights into the application of Operational Agility Management in Thai SMEs, bridging the gap between high-performance management theories and the practical realities faced by SMEs in unpredictable business environments, thus offering a unique perspective on cultivating employee dynamic capabilities in this setting.