{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T01:42:59Z","timestamp":1783561379076,"version":"3.55.0"},"reference-count":44,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T00:00:00Z","timestamp":1755043200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Social Science Foundation of China","award":["22FYB068"],"award-info":[{"award-number":["22FYB068"]}]},{"name":"National Social Science Foundation of China","award":["KYCX24_3860"],"award-info":[{"award-number":["KYCX24_3860"]}]},{"name":"Postgraduate Research &amp; Practice Innovation Program of Jiangsu Province","award":["22FYB068"],"award-info":[{"award-number":["22FYB068"]}]},{"name":"Postgraduate Research &amp; Practice Innovation Program of Jiangsu Province","award":["KYCX24_3860"],"award-info":[{"award-number":["KYCX24_3860"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>The quantification of emotional value and accurate prediction of purchase intention has emerged as a critical interdisciplinary challenge in the evolving emotional economy. Focusing on Generation Z (born 1995\u20132009), this study proposes a hybrid algorithmic framework integrating text-based sentiment computation, feature selection, and random forest modeling to forecast purchase intention for therapeutic toys and interpret its underlying drivers. First, 856 customer reviews were scraped from Jellycat\u2019s official website and subjected to polarity classification using a fine-tuned RoBERTa-wwm-ext model (F1 = 0.92), with generated sentiment scores and high-frequency keywords mapped as interpretable features. Next, Boruta\u2013SHAP feature selection was applied to 35 structured variables from 336 survey records, retaining 17 significant predictors. The core module employed a RF (random forest) model to estimate continuous \u201cpurchase intention\u201d scores, achieving R2 = 0.83 and MSE = 0.14 under 10-fold cross-validation. To enhance interpretability, RF model was also utilized to evaluate feature importance, quantifying each feature\u2019s contribution to the model outputs, revealing Social Ostracism (\u03b2 = 0.307) and Task Overload (\u03b2 = 0.207) as dominant predictors. Finally, k-means clustering with gap statistics segmented consumers based on emotional relevance, value rationality, and interest level, with model performance compared across clusters. Experimental results demonstrate that our integrated predictive model achieves a balance between forecasting accuracy and decision interpretability in emotional value computation, offering actionable insights for targeted product development and precision marketing in the therapeutic goods sector.<\/jats:p>","DOI":"10.3390\/a18080506","type":"journal-article","created":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T09:43:01Z","timestamp":1755078181000},"page":"506","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Algorithmic Modeling of Generation Z\u2019s Therapeutic Toys Consumption Behavior in an Emotional Economy Context"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-2604-1324","authenticated-orcid":false,"given":"Xinyi","family":"Ma","sequence":"first","affiliation":[{"name":"School of Business, Yangzhou University, Yangzhou 225127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Qin","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Lv","sequence":"additional","affiliation":[{"name":"School of Business, Yangzhou University, Yangzhou 225127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102345","DOI":"10.1016\/j.jretconser.2020.102345","article-title":"\u2018Instagram made Me buy it\u2019: Generation Z impulse purchases in fashion industry","volume":"59","author":"Djafarova","year":"2021","journal-title":"J. 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