Platforms, Volume 3, Issue 2 (June 2025) – 5 articles

Despite remarkable quantitative growth in Application Programming Interface (API) call volume, Korea’s Open Banking Platform faces a critical qualitative imbalance. This paper investigates this hidden challenge, revealing a significant divergence between quantitative metrics and qualitative indicators. Through time-series analysis of registered accounts and user data, alongside examining financial institution and FinTech dynamics, I identify decelerating platform growth and constrained user base expansion. While API calls exploded, registered account and user growth lagged substantially. Platform growth exhibits player bias, and user base expansion has stagnated despite increased accounts-per-user ratios. These findings indicate that Korea’s Open Banking Platform’s sustainability is threatened by qualitative imbalances masked by quantitative success. I advocate for a shift to data-driven governance, moving beyond API call volume-centric metrics to qualitative indicators focused on user and account base assessment. I call for data-driven policy innovation to foster a more balanced and sustainable platform ecosystem, addressing growth slowdown and user base limitations. Full article

The European automotive industry, particularly in Germany, faces a significant crisis, heavily impacting suppliers reliant on OEMs. To enhance resilience, participation in the platform economy has been proposed as a solution. This study employs a qualitative research approach by conducting 18 expert interviews with automotive companies in the Northern Black Forest region to assess their awareness, perceived potential, and support needs regarding platform-based business models. The findings reveal diverse perspectives: smaller firms perceive higher risks, while larger companies recognize potential but struggle with limited expertise. The results highlight the novelty of the platform economy within the supplier industry and the need for increased awareness, strategic guidance, and tailored support measures. This study provides original insights into regional supplier engagement with platform ecosystems, contributing to the limited research on this topic and offering a foundation for future industry adaptation strategies. Full article

Video-sharing platforms have significantly influenced social justice movements by creating unprecedented opportunities for mobilization and support. However, YouTube’s unique role and platform culture in facilitating social justice movements remain relatively understudied. This research addresses this gap by analyzing video content related to two prominent online social justice movements: #BLM and #StopAAPIHate. We conducted a comprehensive thematic analysis of a dataset comprising 489 videos obtained using the YouTube Data API. Thematic categories were developed to explore the identities of video creators, the type of information conveyed, storytelling techniques, and promotional features utilized. Our findings indicate that public figures, vloggers, and news reporters are the most frequent creators of videos supporting these movements. The primary purpose of these videos is to share movement-related knowledge and personal stories of discrimination. Most creators primarily promote their social media accounts and do not extensively utilize platform features such as live streaming, merchandise sales, donation requests, or sponsorships to actively support these social justice initiatives. Full article

This paper presents an advanced framework for real-time monitoring and analysis of network traffic and endpoint security in large-scale enterprises by addressing the increasing complexity and frequency of cyber-attacks. Our Network Security Traffic Analysis Platform employs a comprehensive technology stack including the Elastic Stack, ZEEK, Osquery, Kafka, and GeoLocation data. By integrating supervised machine learning models trained on the UNSW-NB15 dataset, we evaluate Random Forest (RF), Decision Trees (DT), and Support Vector Machines (SVM), with the Random Forest classifier achieving a notable accuracy of 99.32%. Leveraging Artificial Intelligence and Natural Language Processing, we apply the BERT model with a Byte-level Byte-pair tokenizer to enhance network-based attack detection in IoT systems. Experiments on UNSW-NB15, TON-IoT, and Edge-IIoT datasets demonstrate our platform’s superiority over traditional methods in multi-class classification tasks, achieving near-perfect accuracy on the Edge-IIoT dataset. Furthermore, Network Security Traffic Analysis Platform’s ability to produce actionable insights through charts, tables, histograms, and other visualizations underscores its capability in static analysis of traffic data. This dual approach of real-time and static analysis provides a robust foundation for developing scalable, efficient, and automated security solutions, essential for managing the evolving threats in modern networks. Full article

Efficient supply chain management (SCM) is essential for enterprises seeking to enhance operational efficiency, reduce costs, and mitigate risks while ensuring product quality and customer satisfaction. Addressing quality concerns within the supply chain proactively helps minimize rework, recalls, and returns, leading to significant cost savings and improved profitability. This study presents a machine learning (ML)-driven predictive analytics framework designed to forecast defect rates and optimize quality control processes. The research leverages a dataset sourced from a real-world fashion and beauty startup, hosted in a public repository. The framework employs advanced ML algorithms, including extreme gradient boosting (XGBoost), support vector machines (SVMs), and random forests (RFs), to accurately predict defect rates and derive actionable insights for supply chain optimization. Results demonstrate the effectiveness of predictive analytics in improving supply chain quality management, enabling enterprises to proactively reduce defect rates, minimize costs, and optimize return on investment (ROI). The proposed framework is designed to be scalable and transferable, ensuring adaptability across various industries, including fashion, e-commerce, and manufacturing. These findings underscore the economic and operational benefits of integrating machine learning into supply chain quality control, offering a data-driven, proactive approach to achieving high-efficiency, high-quality supply chain operations. Full article