The transformation of the workplace in the digital era has pushed organizations to adopt more adaptive and inclusive leadership styles, particularly in managing and retaining talent. Inclusive leadership is believed to foster a psychologically safe work environment, build trust, and enhance employee engagement—key factors influencing talent retention decisions. This study aims to examine the impact of inclusive leadership on talent retention, by exploring the mediating roles of psychological safety, trust, and employee engagement. A quantitative approach was employed through an online survey involving 150 respondents from the professional and technology sectors. Data analysis was conducted using multiple linear regression and Structural Equation Modeling (SEM) with the support of SPSS and SmartPLS. The findings reveal that inclusive leadership significantly influences talent retention, both directly and indirectly through the three mediating variables. These results highlight the importance of leadership styles that are attuned to employees’ psychological needs. Theoretically, this study contributes to the growing body of literature on leadership and employee retention. Practically, it offers strategic recommendations for organizations to develop sustainable human resource management policies focused on employee well-being—particularly in the context of an increasingly digital and flexible work environment.

Advancements in fintech algorithms have improved decision-making efficiency in credit scoring, investment advice, and financial product offerings. However, these automated systems raise ethical concerns related to algorithmic bias, lack of transparency, and accountability. Social inequalities embedded in historical data risk reinforcing discrimination in digital financial services, particularly in Southeast Asia’s evolving regulatory environment. This study explores ethical dilemmas in algorithmic decision-making across fintech platforms and assesses company responses. Using a qualitative multiple-case study of three Indonesian fintech firms in peer-to-peer lending, e-wallet, and robo-advisory sectors, data were gathered through semi-structured interviews and internal document analysis. Results indicate algorithmic bias as the most critical issue, followed by transparency and accountability gaps. Peer-to-peer lending firms demonstrate better ethical readiness via regular audits, while others show limited mitigation efforts. The study proposes a conceptual model emphasizing fairness, transparency, and accountability, offering practical insights for regulators and industry to strengthen ethical governance in Indonesia’s AI-based fintech ecosystem.

The use of Artificial Intelligence (AI) in digital marketing is rapidly expanding, enabling highly personalized strategies for consumers. However, this advancement also raises serious concerns about data privacy, especially amid varying regulations such as the GDPR (Europe), CCPA (United States), and local policies across Southeast Asia. This study examines how AI technologies like Natural Language Processing (NLP) and predictive analytics can adaptively balance personalization with privacy protection. It also explores the emotional dimension of consumer responses—particularly trust and anxiety—and how these emotions shape perceptions of digital marketing strategies under different regulatory contexts. A mixed methods approach was employed, combining survey data from 400 respondents across three regions and in-depth interviews with 20 extreme-case participants. The analysis utilized tools such as SmartPLS, NVivo, and visual platforms like Tableau AI and MonkeyLearn. Findings reveal that limiting the collection of sensitive data can increase consumer acceptance by up to 23% without compromising marketing effectiveness. Consumer trust emerged as a key mediating factor, while anxiety amplified the demand for transparency. In Southeast Asia, incentive-based strategies were found to be 35% more effective than regulatory approaches. These findings underscore the importance of integrating technological, emotional, and cultural dimensions when designing ethical and context-aware digital marketing strategies.

Globalization has increased the complexity of business, particularly for multinational companies facing the challenges of diversity and inclusion in the workplace. Diversity and inclusion have been recognized as strategic factors that can drive innovation, productivity, and operational efficiency. This study aims to analyze the impact of diversity and inclusion policies on organizational performance in the context of multinational companies. Using a quantitative approach, data were collected through a questionnaire survey involving 200 respondents from various multinational companies. Analysis was conducted using multiple linear regression to measure the contribution of diversity and inclusion policies to organizational performance. The results show that diversity contributes 45% to the improvement of organizational performance, while inclusion contributes 37%. The R² value of 0.6416 indicates that the model can explain 64.16% of the variability in organizational performance based on these two variables. Furthermore, the effective implementation of diversity and inclusion policies has been shown to increase employee engagement by 28% and operational efficiency by 15%. This study emphasizes that diversity and inclusion should be regarded as essential elements in an organizational strategy to enhance competitiveness in the global market. This research contributes to the literature on diversity and inclusion by providing relevant empirical evidence to support strategic decision-making in multinational companies. The study also offers practical guidance for managers in designing more inclusive policies to maximize human resource potential.

Main Problem: There are differences in the influence of AI and Big Data on employee performance in previous research. Objective: Focus research by reviewing and analyzing whether Artificial Intelligence or AI has an influence on employee performance. Next, analyze whether Big Data affects employee performance. Originality: The Influence of AI and Big Data on Employee Performance. Method: To select the research sample, the proportional area random sampling method and regression equation were used to process the data with the help of the SPSS application. Results: In this research and discussion, it can be concluded that AI Big Data has an influence on the performance of employees at PT. Widya Robotics Yogyakarta, General Ahmad Yani International Airport Semarang, PT. Global Data Inspiration Yogyakarta.

This community service aims as village development literacy for the Village Head, Village Officials and Village Youth. The workshop was held in Bandungan, Semarang Regency, and program implementation was carried out in 5 selected districts in Central Java Province. This activity is supported by LPPM ITB Semarang and Unicef, which is included in the Program for Handling Out of School Children in Central Java Province. Through the lecture method with visual media supporting devices and ending with a question and answer session, it is very effective in delivering material. This was shown by the enthusiasm of the participants in the question and answer interaction at the discussion forum.

An unavoidable fact in utilizing the optimization of the company's economic aspects, either directly or indirectly, is the minimization of the impact on environmental conditions. This study seeks to examine empirically to develop models and improve sustainable performance by using various research variables that have been reviewed by various previous researchers. such as corporate governance. The model in the bag at a manufacturing company in Central Java with a composition of 200 samples and data analysis using Amos 25 to obtain inferential statistical results using regression analysis.The results of this study indicate that all hypotheses proposed in this model are accepted and can provide a form of contribution to the development of science related to the acceptance of all hypotheses in the model.Further research in making empirical models based on integration models should add other variables outside of corporate governance and CRS disclosure to improve environmental performance such as corporate social performance, green supply chain management, and institutional pressure. The contribution of the theoretical aspects shown by the development of these findings can contribute to agency theory, and this operational management theory can provide recommendations for managers to improve company performance based on corporate governance.