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All Journal Social Sciences Insights Journal
Arohman, Rifki
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Social Sciences

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Integration of machine learning in e-commerce: A systematic literature review on consumer behavior prediction and product recommendation Syamsuri, Abd. Rasyid; Arohman, Rifki; Saputra, Muhammad Renaldy; Ikhlash, Muhammad; Damanik, Sri Karyani
Social Sciences Insights Journal Vol. 3 No. 3 (2025): Social Sciences Insights Journal
Publisher : MID Publisher International

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60036/sg7wnx04

Abstract

This systematic literature review examines the integration of machine learning (ML) in e-commerce, focusing on consumer behavior prediction and product recommendation systems. Following PRISMA guidelines, we searched Scopus, Web of Science, IEEE Xplore, and ACM Digital Library, identifying 1,247 records. After screening, 48 peer-reviewed articles (2019-2024) were included. This review makes three novel contributions: (1) a taxonomy of ML algorithms categorizing approaches by function (prediction vs. recommendation) and technique (supervised, unsupervised, deep learning); (2) a comparative analysis of algorithm performance across different e-commerce contexts; and (3) identification of specific research gaps requiring investigation. Findings reveal that hybrid recommendation systems combining collaborative filtering with deep learning achieve superior accuracy (mean improvement of 15-23% over single-method approaches), while gradient boosting methods (XGBoost, LightGBM) demonstrate the highest predictive performance for purchase behavior. Critical challenges include cold-start problems, data sparsity, algorithmic bias, and privacy concerns. We propose an integrative framework mapping ML technique to specific e-commerce applications and identify five priority areas for future research. Limitations include English-language restrictions and potential publication bias toward positive results.
The impact of online shopping features on consumer buying behavior: A systematic literature review Syamsuri, Abd. Rasyid; Arohman, Rifki; Saputra, Muhammad Renaldy; Halim, Abd.; Surbakti, Afridayanti
Social Sciences Insights Journal Vol. 3 No. 3 (2025): Social Sciences Insights Journal
Publisher : MID Publisher International

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60036/x4zvq336

Abstract

This study examines the impact of online shopping features on consumer buying behavior by synthesizing findings from recent scholarly works through a systematic literature review. The research aims to identify how various digital features, including personalization, security mechanisms, interactive technologies, and social commerce elements, influence consumer trust, decision-making, and purchase intentions. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a structured selection process was applied to peer-reviewed journal articles, academic reports, and books published within the last five years, resulting in 25 studies included in the final review. A descriptive synthesis approach was used to categorize themes and compare patterns across different contexts. The findings indicate that functional aspects such as website design and usability strongly affect satisfaction and trust, while interactive and experiential features, including augmented reality, chatbots, and live streaming commerce, enhance engagement and drive purchasing outcomes. Additionally, the review highlights challenges related to privacy concerns, consumer fatigue, and ethical issues, suggesting that sustainable e-commerce strategies require balancing technological innovation with consumer-centric design. The study implies that platforms integrating trust, convenience, and personalization are more likely to achieve long-term consumer loyalty and competitive advantage in digital markets.
The role of AI in enhancing employee experience and HR effectiveness in hybrid work models: A systematic literature review Syamsuri, Abd. Rasyid; Arohman, Rifki; Saputra, Muhamad Renaldy; Esmeralda, Angel II P.
Social Sciences Insights Journal Vol. 3 No. 3 (2025): Social Sciences Insights Journal
Publisher : MID Publisher International

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60036/v4p4q169

Abstract

This systematic literature review examines the role of artificial intelligence (AI) in enhancing employee experience (EX) and human resource (HR) effectiveness within hybrid work models. Following PRISMA guidelines, we systematically searched Scopus, Web of Science, and Google Scholar databases, identifying 847 initial records. After applying inclusion criteria (peer-reviewed articles, published 2019-2024, English language, focusing on AI-HR integration in flexible/hybrid work contexts), 42 studies were included in the final synthesis. The review identifies three primary AI application domains in HR: (1) operational automation (recruitment screening, scheduling, administrative tasks), (2) analytics and decision support (predictive retention modeling, performance analytics), and (3) personalized employee support (adaptive learning, well-being monitoring, conversational agents). Our synthesis reveals that AI positively influences EX outcomes—including engagement, satisfaction, and perceived HR responsiveness—when implemented with transparency, human oversight, and adequate digital infrastructure. However, significant challenges persist, including algorithmic bias in high-stakes decisions, data privacy concerns, skill gaps among HR professionals, and organizational resistance. The review proposes a conceptual framework integrating technological, organizational, and individual factors that moderate AI's effectiveness in hybrid contexts. Key moderating conditions include leadership support, data quality, employee digital literacy, and governance mechanisms. Limitations include potential publication bias, English-language restriction, and the nascent state of longitudinal research in this domain. We conclude with a specific research agenda identifying methodological approaches, contextual variables, and outcome measures warranting future investigation.
Co-Authors A Halim Abd. Rasyid Syamsuri Afridayanti Surbakti Esmeralda, Angel II P. Karyani Damanik, Sri Muhammad Ikhlash Saputra, Muhamad Renaldy Saputra, Muhammad Renaldy