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IMPROVING ORGANIZATIONAL PERFORMANCE THROUGH ENVIRONMENTAL PASSION AND ENVIRONMENTAL PERFORMANCE Adi, Susilo; Wardi, Agustinus
Dinamika: Jurnal Manajemen Sosial Ekonomi Vol 4 No 1 (2024): DINAMIKA : Jurnal Manajemen Sosial Ekonomi
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/dinamika.v4i1.472

Purposes: This study aims to develop an empirical research model in improving organizational performance. The conceptual model is proposed based on Green Human Resource Management, Environmental Passion and Environmental Performance. Research Methodology: Using 200 respondents in the manufacturing industry in Central Java, the model was tested to obtain research data, the data were analyzed using inferential statistical data analysis using regression techniques and AMOS software 25. Result: The results showed that all proposed concepts were acceptable and the model proposed is quite feasible to be developed in improving organizational performance. Limitations: The limitations of this study are still using concepts that are abstract in nature and have not been able to provide specific dimensions for analysis with respect to endogenous variables, namely organizational performance. Contribution: In this study, this research develops new concepts related to concepts from stakeholder theory, other atomic theories and operations management theories. The result of this research contributes as recommendations for practitioners in improving organizational performance referring to environmental performance and the concept of sustainability in the field of management.

BUILDING RESILIENCE THROUGH AI: PREDICTIVE ANALYTICS FOR SUPPLY CHAIN RISK MANAGEMENT IN THE POST-COVID GLOBAL MARKET Henny, Henny; Qosidah, Nanik; Wardi, Agustinus
Dinamika: Jurnal Manajemen Sosial Ekonomi Vol. 5 No. 1 (2025): DINAMIKA : Jurnal Manajemen Sosial Ekonomi
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/c44y6a86

The COVID-19 pandemic has exposed fundamental vulnerabilities in global supply chain systems, such as over-reliance on single suppliers and a lack of operational visibility. This has highlighted the urgent need for a new approach to risk management—one that leverages smart technologies. Artificial Intelligence (AI) has emerged as a promising solution, thanks to its capabilities in predictive analytics and adaptive, data-driven decision-making in real time. This study aims to develop an AI-based predictive system framework to enhance the resilience of global supply chains in the face of post-pandemic disruptions. Using the Design Science Research (DSR) methodology, the research designs and evaluates a system that integrates algorithms such as LSTM, Random Forest, Natural Language Processing (NLP), and Reinforcement Learning. It also applies a federated learning approach to ensure data privacy among supply chain partners. The study analyzes over 12,000 data entries from diverse sources, including IoT devices, weather data, demand trends, and social media. The system's effectiveness is evaluated through a combination of quantitative methods (PLS-SEM analysis on 103 respondents) and qualitative methods (interviews with 12 industry executives). The findings show that AI-driven predictive analytics significantly improve supply chain resilience (β = 0.67; p < 0.001), with demand forecasting accuracy increasing by up to 40% and delivery times reduced by 30%. Conceptually, the study contributes by designing a resilient model that integrates real-time visibility, adaptability, and cross-organizational collaborative learning. Unlike traditional approaches focused solely on automation, this framework offers a more holistic solution, addressing key gaps in the literature. The implication is clear: AI is becoming a strategic asset in building sustainable, resilient supply chains amid ongoing global uncertainty.

THE ROLE OF INCLUSIVE LEADERSHIP IN ENHANCING TALENT RETENTION AMONG REMOTE WORKFORCE: A MULTINATIONAL STUDY Purnama, Kusna Djati; Wardi, Agustinus; Aditya, Galuh
Dinamika: Jurnal Manajemen Sosial Ekonomi Vol. 5 No. 1 (2025): DINAMIKA : Jurnal Manajemen Sosial Ekonomi
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/rkx6dd80

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.

PERSONALIZATION VERSUS PRIVACY: INVESTIGATING THE TRADE-OFFS IN AI-DRIVEN DIGITAL MARKETING STRATEGIES Aditya, Galuh; Wardi, Agustinus; Fitriani, Nining
Dinamika: Jurnal Manajemen Sosial Ekonomi Vol. 5 No. 1 (2025): DINAMIKA : Jurnal Manajemen Sosial Ekonomi
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/cjwtch17

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.

NAVIGATING ETHICAL DILEMMAS IN ALGORITHMIC DECISION-MAKING: A CASE-BASED STUDY OF FINTECH PLATFORMS Wardi, Agustinus; Aditya, Galuh
Jurnal Akuntansi dan Bisnis Vol. 5 No. 1 (2025): Mei 2025 : Jurnal Akuntansi dan Bisnis
Publisher : LPPM Universitas Sains Dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jiab.v5i1.1044

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.

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