Implementing green retrofitting can save 50–90% of energy use in buildings built worldwide. Government policies in several developed countries have begun to increase the implementation of green retrofitting buildings in those countries, which must rise by up to 2.5% of the lifespan of buildings by 2030. By 2050, it is hoped that more than 85% of all buildings will have been retrofitted. The high costs of implementing green retrofitting amounting to 20% of the total initial construction costs, as well as the uncertainty of costs due to cost overruns are one of the main problems in achieving the implementation target in 2050. Therefore, increasing the accuracy of the costs of implementing green retrofitting is the best solution to overcome this. This research is limited to analyzing the factors that influence increasing the accuracy of green retrofitting costs based on WBS, BIM, and Information Systems. The results show that there are 10 factors affecting the cost accuracy of retrofitting or customizing high-rise office buildings, namely Energy Use Efficiency, Water Use Efficiency, Use of Environmentally Friendly Materials, Maintenance of Green Building Performance during the Use Period, Initial Survey, Project Information Documents, Cost Estimation Process, Resources, Legal, and Quantity Extraction applied. These factors are shown to increase the accuracy of green retrofitting costs.

This research aims to determine the factors driving the success of four large cities in Indonesia in implementing Transit-Oriented Development (TOD) infrastructure policies beyond the eight TOD 3.0 Principles. Only a few studies like this have been conducted. The research uses qualitative methods and is supported by in-depth interviews with stakeholders, community leaders, community groups, and service users. The research findings reveal six themes: policy dialogue, organizational structure and coordination, changes in community habits, resources, dissemination and communication, and transportation and connectivity services. The characteristics of the community in the study area that prioritize deliberation are important determinants in policy dialogue and are involved in determining policy formulation. The city government has established a comprehensive organizational and coordination structure for the village and sub-district levels. The Government controls infrastructure development activities, establishes a chain of command and coordination, and encourages people to change their private car usage habits. The city government combines all this with the principle of deliberation and conveys important information to the public. The research highlights the differences in TOD implementation in Indonesia compared to other countries. Specifically, the existence of policy dialogue and the direct involvement of community members influence the level of program policy formulation and are crucial in controlling urban infrastructure development.

This study explores how Jordanian telecom companies can balance Internet of Things (IoT) driven automation with maintaining genuine consumer-brand connections. It seeks strategies that blend IoT automation with personalized engagement to foster lasting consumer loyalty. Employing qualitative research via semi-structured interviews with IT and customer service managers from Jordanian telecom companies. IoT-driven automation in Jordan’s telecom sector revolutionizes consumer-brand relationships by enabling data-driven personalization. It emphasizes the importance of IoT proficiency, transformed marketing strategies, and the need to balance personalization with consumer privacy. Interviews stress the significance of maintaining authentic human connections amidst automation. Strategies for Jordanian telecom firms include integrating IoT data into CRM systems, employing omnichannel marketing, balancing automation with human interaction, adopting a consumer-centric approach, mitigating security risks, and leveraging IoT insights for adaptive services. These approaches prioritize consumer trust, personalized engagement, and agile service adaptation to meet dynamic consumer preferences. This research provides actionable strategies for telecom firms on effective IoT integration, emphasizing the need to maintain genuine consumer relationships alongside technological advancements. It highlights IoT’s transformative potential while ensuring lasting consumer loyalty and business success. Future research avenues could explore longitudinal studies and the interplay between AI and IoT in telecom services.

This study explores how public relations (PR) can give universities an edge in today’s competitive landscape. By examining past research, conducting interviews in 10 diverse cities in Vietnam, and analyzing case studies, it reveals the powerful link between PR strategies and student involvement. The research shows that well-crafted PR activities, tailored to different student groups and utilizing digital platforms, significantly impact student perceptions and enrollment decisions. It delves deeper than simply confirming PR’s effectiveness, offering insights into how specific PR tactics can resonate with student needs and expectations. Furthermore, it explores how PR influences student retention, highlighting the long-term benefits for universities. This research is a valuable tool for institutions seeking to thrive. By understanding the power of PR in shaping student decisions, universities can tailor their outreach efforts more effectively. Additionally, the study emphasizes the lasting advantages of a strategic PR approach, contributing to a broader discussion on its importance in higher education. Ultimately, these findings benefit both institutions and students, who can expect improved transparency, engagement, and communication within their academic communities.

This paper aims to explore how developing countries like Indonesia have an approach to managing talent to enhance career development using an application system. The application of talent management in the career development of civil servants in Indonesia includes planning, implementing, monitoring, and evaluating career development. Talent management is essential for the government sector and can help improve employee quality, organizational performance, and the achievement of human potential. This research aims to examine the application of talent management in organizations and develop a state civil apparatus information system (SI-ASN) to support the career development process of civil servants. The research methods used include library research and field research, including interviews with competent officials in West Java Province as primary data. The qualitative data was collected in 2022–2023. The results of this study show that the application of talent management for civil servants in Indonesia is considered appropriate, as it directs employees to positions that are in line with their qualifications, competencies and performance. However, it requires an improvement in the methods used, particularly for competency tests, which may be conducted with new methods that are more efficient in terms of budget and time. The study concluded that the application of talent management in the career development of civil servants in Indonesia has a positive impact on the quality of leaders and organizations because it ensures that the appointed leaders are the most competent ones in the field and shows the importance of talent management in succession planning and the career development of civil servants.

The paper considers an important problem of the successful development of social qualities in an individual using machine learning methods. Social qualities play an important role in forming personal and professional lives, and their development is becoming relevant in modern society. The paper presents an overview of modern research in social psychology and machine learning; besides, it describes the data analysis method to identify factors influencing success in the development of social qualities. By analyzing large amounts of data collected from various sources, the authors of the paper use machine learning algorithms, such as Kohonen maps, decision tree and neural networks, to identify relationships between different variables, including education, environment, personal characteristics, and the development of social skills. Experiments were conducted to analyze the considered datasets, which included the introduction of methods to find dependencies between the input and output parameters. Machine learning introduction to find factors influencing the development of individual social qualities has varying dependence accuracy. The study results could be useful for both practical purposes and further scientific research in social psychology and machine learning. The paper represents an important contribution to understanding the factors that contribute to the successful development of individual social skills and could be useful in the development of programs and interventions in this area. The main objective of the research was to study the functionalities of the machine learning algorithms and various models to predict the students’s success in learning.