{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:52:29Z","timestamp":1787028749991,"version":"build-2736575974"},"reference-count":62,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T00:00:00Z","timestamp":1767830400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Kocaeli University Scientific Research Projects Coordination Unit","award":["FAA-2025-4585"],"award-info":[{"award-number":["FAA-2025-4585"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JTAER"],"abstract":"<jats:p>Crowdfunding platforms like Kickstarter have reshaped early-stage financing by allowing entrepreneurs to connect directly with potential supporters. As a fast-expanding part of digital commerce, crowdfunding offers significant opportunities but also substantial risks for both entrepreneurs and platform operators, making predictive analytics an essential capability. Although crowdfunding shares some operational features with traditional e-commerce, its mix of financial uncertainty, emotionally charged storytelling, and fast-evolving social interactions makes it a distinct and more challenging forecasting problem. Accurately predicting campaign outcomes is especially difficult because of the high-dimensionality and diversity of the underlying textual and behavioral data. These factors highlight the need for scalable, intelligent data science methods that can jointly exploit structured and unstructured information. To address these issues, this study proposes a novel AI-based predictive framework that integrates a Convolutional Block Attention Module (CBAM)-enhanced symmetric autoencoder for compressing high-dimensional Generative AI (GenAI) BERT embeddings with meta-heuristic feature selection and advanced classification models. The framework systematically couples attention-driven feature compression with optimization techniques\u2014Genetic Algorithm (GA), Jaya, and Artificial Rabbit Optimization (ARO)\u2014and then applies Long Short-Term Memory (LSTM) and Gradient Boosting Machine (GBM) classifiers. Experiments on a large-scale Kickstarter dataset demonstrate that the proposed approach attains 77.8% accuracy while reducing feature dimensionality by more than 95%, surpassing standard baseline methods. In addition to its technical merits, the study yields practical insights for platform managers and campaign creators, enabling more informed choices in campaign design, promotional tactics, and backer targeting. Overall, this work illustrates how advanced AI methodologies can strengthen predictive analytics in digital commerce, thereby enhancing the strategic impact and long-term sustainability of crowdfunding ecosystems.<\/jats:p>","DOI":"10.3390\/jtaer21010028","type":"journal-article","created":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T09:01:29Z","timestamp":1767862889000},"page":"28","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Crowdfunding as an E-Commerce Mechanism: A Deep Learning Approach to Predicting Success Using Reduced Generative AI Embeddings"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2152-5490","authenticated-orcid":false,"given":"Hakan","family":"Gunduz","sequence":"first","affiliation":[{"name":"Faculty of Engineering, Software Engineering Department, Kocaeli University, Kocaeli 41001, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2341-2975","authenticated-orcid":false,"given":"Muge","family":"Klein","sequence":"additional","affiliation":[{"name":"Faculty of Economics and Administrative Sciences, Business Administration Department, Turkish-German University, Istanbul 34820, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3357-592X","authenticated-orcid":false,"given":"Ela Sibel","family":"Bayrak Meydanoglu","sequence":"additional","affiliation":[{"name":"Faculty of Economics and Administrative Sciences, Business Administration Department, Turkish-German University, Istanbul 34820, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1016\/j.jbusvent.2013.07.003","article-title":"Crowdfunding: Tapping the right crowd","volume":"29","author":"Belleflamme","year":"2014","journal-title":"J. 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