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All Journal Advances: Jurnal Ekonomi & Bisnis
Muhammad Bakri
Universitas Wira Bhakti, Makassar, Indonesia
Economics, Econometrics & Finance

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Explainable Artificial Intelligence (XAI) in Personalized Marketing: A Systematic Literature Review of Algorithms, Interpretability Techniques, and Consumer Trust Implications Yassir Yassir; Muhammad Bakri
Advances: Jurnal Ekonomi & Bisnis Vol. 4 No. 3 (2026): May - June
Publisher : Yayasan Pendidikan Bukhari Dwi Muslim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60079/ajeb.v4i3.798

Abstract

Purpose: This study aims to systematically review the role of Explainable Artificial Intelligence (XAI) in personalized marketing by examining AI algorithms, interpretability techniques, and their implications for consumer trust. Research Method: A systematic literature review was conducted by analyzing peer-reviewed journal articles and conference papers related to AI, XAI, and personalized marketing. The study synthesizes findings across technical and behavioral dimensions to provide an integrated understanding of the research domain. Results and Discussion: The results indicate that machine learning, deep learning, and recommender systems are the primary algorithms used in personalized marketing. However, increasing model complexity reduces interpretability, creating a need for XAI techniques such as LIME, SHAP, and attention mechanisms. The findings further reveal that XAI enhances consumer trust by improving transparency, understandability, and fairness, although contextual factors, including privacy concerns and user characteristics, influence this relationship. Implications: This study contributes theoretically by integrating technical and behavioral perspectives into a unified framework. In practice, it provides managers with guidance on designing transparent and trustworthy AI systems and highlights the need for ethical, user-centered AI implementation. Originality: This study is original in integrating technical perspectives on XAI with behavioral perspectives on consumer trust in personalized marketing. It offers a unified framework explaining how explainability supports transparency, fairness, and trust in AI-driven marketing.
The Role of Artificial Intelligence and HR Analytics in Enhancing Strategic Human Resource Decision-Making Muhammad Bakri; Yassir Yassir
Advances: Jurnal Ekonomi & Bisnis Vol. 4 No. 3 (2026): May - June
Publisher : Yayasan Pendidikan Bukhari Dwi Muslim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60079/ajeb.v4i3.799

Abstract

Purpose: This study aims to systematically analyze the roles of Artificial Intelligence (AI) and HR Analytics in enhancing strategic human resource decision-making, focusing on their integration, impact, and implementation challenges. Research Method: This research employs a Systematic Literature Review (SLR) to analyze peer-reviewed journal articles on AI, HR Analytics, and strategic HR decision-making. Relevant studies were identified, screened, and synthesized to ensure a structured and comprehensive evaluation. Results and Discussion: The findings indicate that AI and HR Analytics significantly enhance decision quality, speed, and predictive accuracy, enabling a shift from reactive to proactive decision-making. HR Analytics plays a mediating role in transforming workforce data into actionable insights. However, the impact varies depending on organizational readiness, data quality, and analytical capability. Key challenges include data integration, skill gaps, and ethical concerns such as algorithmic bias and transparency. Implications: This study provides theoretical contributions by offering an integrated framework linking AI, HR Analytics, and strategic decision-making. Practically, organizations should strengthen data governance, analytical capabilities, and leadership support to maximize the benefits of AI-driven HR systems. Originality: This study offers originality by integrating AI and HR Analytics into a unified framework that explains how both technologies support strategic human resource decision-making while addressing organizational and ethical implementation challenges.
Co-Authors Yassir Yassir