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p-Index From 2021 - 2026
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All Journal International Journal of Research and Applied Technology (INJURATECH)
Danisa, Salma
Unknown Affiliation
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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How Consumer Engagement is Fuelling the Next Wave of Global E-Marketplace Growth Danisa, Salma
International Journal of Research and Applied Technology (INJURATECH) Vol. 4 No. 2 (2024): Vol 4 No 2 (2024)
Publisher : Universitas Komputer Indonesia

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Abstract

The transformation of the global electronic commerce landscape is currently shifting from a purely transactional model toward an ecosystem centered on consumer engagement. This research aims to analyze how digital engagement mechanisms—such as social features, gamification, and AI-driven personalization—have become the primary drivers of e-marketplace growth in the global market. Through a systematic literature review, this study finds that two-way interactions between platforms and users not only enhance retention but also significantly reduce customer acquisition costs. The findings indicate that "engagement" is no longer merely a supplementary element but a core growth engine defining the new wave of e-commerce. This article offers a theoretical contribution in the form of an Engagement-Driven Growth framework and provides practical recommendations for platform managers to prioritize experiential value over mere transaction volume.
Algorithmic Bias in Political Content Curation on the Twitter/X Platform: A Machine Learning Perspective Danisa, Salma
International Journal of Research and Applied Technology (INJURATECH) Vol. 4 No. 2 (2024): Vol 4 No 2 (2024)
Publisher : Universitas Komputer Indonesia

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Abstract

This study explores the mechanisms of algorithmic bias in the curation of political content on the Twitter/X platform through the lens of Machine Learning (ML). Amidst increasing global polarization, recommendation algorithms are frequently accused of facilitating the creation of echo chambers. This paper highlights how the objective functions of ML models, specifically the maximization of user engagement, inadvertently amplify extremist and partisan content. Utilizing a systematic literature review, the research identifies that bias originates not only from training data (data bias) but also from architectural reinforcement mechanisms (reinforcement bias). The findings suggest that the interaction between user behavior and algorithmic feedback loops creates a self-perpetuating cycle of polarization. This study contributes a technical mapping of how collaborative filtering and deep learning algorithms contribute to the fragmentation of the digital public sphere. The results are intended to serve as a foundational framework for developers and regulators in designing curation systems that are more transparent and politically neutral.
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