{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T19:08:42Z","timestamp":1771009722134,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T00:00:00Z","timestamp":1770940800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72374188"],"award-info":[{"award-number":["72374188"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Graduate Education Research Fund of USTC","award":["USTC-GERF24001"],"award-info":[{"award-number":["USTC-GERF24001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JTAER"],"abstract":"<jats:p>In the era of e-commerce, consumers\u2019 innovative insights are crucial for brands to improve their products and meet consumers\u2019 potential needs. Although user-generated content (UGC) platforms have accumulated a vast amount of consumer voices, brands often face the dilemma of \u201crich data but scarce insights\u201d: valuable consumer innovation insights are not only scarce but also expressed implicitly. Traditional methods have difficulty reliably identifying such insights in this task. Therefore, this study, for the first time, introduces a large language model (LLM)-augmented Siamese framework, aiming to improve the identification and generalization of consumer innovation insights in multi-platform UGC. Based on four Chinese social media platforms and a dataset of 133,538 comments, we conducted three experiments and performed a systematic comparison under five model configurations. The results show that our approach significantly outperforms traditional benchmarks and strong baselines in most experimental settings, while maintaining stable competitiveness in cross-platform tests. Finally, we showed how the identified innovation insights can be transformed into structured outputs for product planning, thereby facilitating the integration of consumer insights into the brand\u2019s innovation process.<\/jats:p>","DOI":"10.3390\/jtaer21020064","type":"journal-article","created":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T17:51:32Z","timestamp":1771005092000},"page":"64","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["From UGC to Brand Product Improvement: Mining Consumer Innovation Insights Across Social Media Platforms"],"prefix":"10.3390","volume":"21","author":[{"given":"Jiacheng","family":"Wang","sequence":"first","affiliation":[{"name":"School of Management, University of Science and Technology of China, Hefei 230026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Management, University of Science and Technology of China, Hefei 230026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1287\/mnsc.1120.1599","article-title":"Crowdsourcing new product ideas over time: An analysis of the dell ideastorm community","volume":"59","author":"Bayus","year":"2013","journal-title":"Manag. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"223","DOI":"10.25300\/MISQ\/2017\/41:1.03","article-title":"Digital innovation management","volume":"41","author":"Nambisan","year":"2017","journal-title":"MIS Q."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"750","DOI":"10.1111\/jpim.12312","article-title":"A bibliometric review of open innovation: Setting a research agenda","volume":"33","author":"Randhawa","year":"2016","journal-title":"J. Prod. Innov. Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"123551","DOI":"10.1016\/j.techfore.2024.123551","article-title":"The implication of user-generated content in new product development process: A systematic literature review and future research agenda","volume":"206","author":"Nasrabadi","year":"2024","journal-title":"Technol. Forecast. Soc. Change"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s11747-015-0456-3","article-title":"Institutions and axioms: An extension and update of service-dominant logic","volume":"44","author":"Vargo","year":"2016","journal-title":"J. Acad. Mark. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1108\/ITP-08-2013-0144","article-title":"Building brand loyalty through user engagement in online brand communities in social networking sites","volume":"28","author":"Zheng","year":"2015","journal-title":"Inf. Technol. People"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3088","DOI":"10.3390\/jtaer19040149","article-title":"The nexus of influencers and purchase intention: Does consumer brand co-creation behavior matter?","volume":"19","author":"Kilumile","year":"2024","journal-title":"J. Theor. Appl. Electron. Commer. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"103898","DOI":"10.1016\/j.jretconser.2024.103898","article-title":"Gaining insights for service improvement through unstructured text from online reviews","volume":"80","author":"Zhang","year":"2024","journal-title":"J. Retail. Consum. Serv."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.2307\/41703503","article-title":"Business intelligence and analytics: From big data to big impact","volume":"36","author":"Chen","year":"2012","journal-title":"Mis Q."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1016\/j.jbusres.2015.07.001","article-title":"Big data consumer analytics and the transformation of marketing","volume":"69","author":"Erevelles","year":"2016","journal-title":"J. Bus. Res."},{"key":"ref_11","first-page":"429","article-title":"\u201cSticky information\u201d and the locus of problem solving: Implications for innovation","volume":"40","year":"1994","journal-title":"Manag. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1287\/mksc.2018.1123","article-title":"Identifying customer needs from user-generated content","volume":"38","author":"Timoshenko","year":"2019","journal-title":"Mark. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.3390\/jtaer19020064","article-title":"Online review helpfulness and information overload: The roles of text, image, and video elements","volume":"19","author":"Wang","year":"2024","journal-title":"J. Theor. Appl. Electron. Commer. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"953","DOI":"10.1287\/mksc.2016.0993","article-title":"Sentence-based text analysis for customer reviews","volume":"35","author":"Allenby","year":"2016","journal-title":"Mark. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1111\/poms.12303","article-title":"An integrated text analytic framework for product defect discovery","volume":"24","author":"Abrahams","year":"2015","journal-title":"Prod. Oper. Manag."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"104034","DOI":"10.1016\/j.jretconser.2024.104034","article-title":"Using machine learning to develop customer insights from user-generated content","volume":"81","author":"Mustak","year":"2024","journal-title":"J. Retail. Consum. Serv."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Shen, Z., Zhao, C., and Li, Y. (2026). Customer requirements analysis and product service improvement framework using multi-source user-generated content and dual importance\u2013performance analysis: A case study of fresh e-ecommerce. J. Theor. Appl. Electron. Commer. Res., 21.","DOI":"10.3390\/jtaer21010019"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"103028","DOI":"10.1016\/j.technovation.2024.103028","article-title":"Anomaly detection in consumer review analytics for idea generation in product innovation: Comparing machine learning and deep learning techniques","volume":"134","author":"Cui","year":"2024","journal-title":"Technovation"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"102389","DOI":"10.1016\/j.ipm.2020.102389","article-title":"Mining product innovation ideas from online reviews","volume":"58","author":"Zhang","year":"2021","journal-title":"Inf. Process. Manag."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Reimers, N., and Gurevych, I. (2019). Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv.","DOI":"10.18653\/v1\/D19-1410"},{"key":"ref_21","unstructured":"Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1109\/TBDATA.2025.3536934","article-title":"Auggpt: Leveraging chatgpt for text data augmentation","volume":"11","author":"Dai","year":"2025","journal-title":"IEEE Trans. Big Data"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ding, B., Qin, C., Zhao, R., Luo, T., Li, X., Chen, G., Xia, W., Hu, J., Luu, A.T., and Joty, S. (2024). Data augmentation using large language models: Data perspectives, learning paradigms and challenges. arXiv.","DOI":"10.18653\/v1\/2024.findings-acl.97"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Gao, T., Yao, X., and Chen, D. (2021). Simcse: Simple contrastive learning of sentence embeddings. arXiv.","DOI":"10.18653\/v1\/2021.emnlp-main.552"},{"key":"ref_25","first-page":"31","article-title":"A survey on text classification: From traditional to deep learning","volume":"13","author":"Li","year":"2022","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1177\/0022242919873106","article-title":"Uniting the tribes: Using text for marketing insight","volume":"84","author":"Berger","year":"2020","journal-title":"J. Mark."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"104110","DOI":"10.1016\/j.jretconser.2024.104110","article-title":"How loud is consumer voice in product deletion decisions? retail analytic insights","volume":"82","author":"Zhu","year":"2025","journal-title":"J. Retail. Consum. Serv."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1485","DOI":"10.1287\/mnsc.1110.1370","article-title":"Deriving the pricing power of product features by mining consumer reviews","volume":"57","author":"Archak","year":"2011","journal-title":"Manag. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1509\/jmr.12.0106","article-title":"Mining marketing meaning from online chatter: Strategic brand analysis of big data using latent dirichlet allocation","volume":"51","author":"Tirunillai","year":"2014","journal-title":"J. Mark. Res."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Maalej, W., and Nabil, H. (2015). Bug report, feature request, or simply praise? On automatically classifying app reviews. 2015 IEEE 23rd International Requirements Engineering Conference (RE), IEEE.","DOI":"10.1109\/RE.2015.7320414"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Govindarajan, V.S., Chen, B., Warholic, R., Erk, K., and Li, J.J. (2020, January 8\u201312). Help! Need advice on identifying advice. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online.","DOI":"10.18653\/v1\/2020.emnlp-main.427"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1007\/s12525-019-00351-0","article-title":"Supporting customer-oriented marketing with artificial intelligence: Automatically quantifying customer needs from social media","volume":"30","author":"Goutier","year":"2020","journal-title":"Electron. Mark."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"e2412105121","DOI":"10.1073\/pnas.2412105121","article-title":"Patterns of linguistic simplification on social media platforms over time","volume":"121","author":"Loru","year":"2024","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Feng, S.Y., Gangal, V., Wei, J., Chandar, S., Vosoughi, S., Mitamura, T., and Hovy, E. (2021). A survey of data augmentation approaches for nlp. arXiv.","DOI":"10.18653\/v1\/2021.findings-acl.84"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wei, J., and Zou, K. (2019). Eda: Easy data augmentation techniques for boosting performance on text classification tasks. arXiv.","DOI":"10.18653\/v1\/D19-1670"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"899","DOI":"10.1111\/isj.12286","article-title":"User idea implementation in open innovation communities: Evidence from a new product development crowdsourcing community","volume":"30","author":"Liu","year":"2020","journal-title":"Inf. Syst. J."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"103172","DOI":"10.1016\/j.im.2019.103172","article-title":"Building social translucence in a crowdsourcing process: A case study of miui.com","volume":"57","author":"Zhang","year":"2020","journal-title":"Inf. Manag."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.ipm.2013.08.006","article-title":"The impact of preprocessing on text classification","volume":"50","author":"Uysal","year":"2014","journal-title":"Inf. Process. Manag."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1037\/h0031619","article-title":"Measuring nominal scale agreement among many raters","volume":"76","author":"Fleiss","year":"1971","journal-title":"Psychol. Bull."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Lin, Y.C., Chen, S.A., Liu, J.J., and Lin, C.J. (2023). Linear classifier: An often-forgotten baseline for text classification. arXiv.","DOI":"10.18653\/v1\/2023.acl-short.160"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_42","unstructured":"Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3504","DOI":"10.1109\/TASLP.2021.3124365","article-title":"Pre-training with whole word masking for Chinese bert","volume":"29","author":"Cui","year":"2021","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1111\/jpim.12656","article-title":"Augmenting human innovation teams with artificial intelligence: Exploring transformer-based language models","volume":"40","author":"Bouschery","year":"2023","journal-title":"J. Prod. Innov. Manag."},{"key":"ref_45","unstructured":"Liu, A., Feng, B., Xue, B., Wang, B., Wu, B., Lu, C., Zhao, C., Deng, C., Zhang, C., and Ruan, C. (2024). Deepseek-v3 technical report. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.aiopen.2022.03.001","article-title":"Data augmentation approaches in natural language processing: A survey","volume":"3","author":"Li","year":"2022","journal-title":"Ai Open"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Tenney, I., Das, D., and Pavlick, E. (2019). Bert rediscovers the classical nlp pipeline. arXiv.","DOI":"10.18653\/v1\/P19-1452"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_49","unstructured":"Izmailov, P., Podoprikhin, D., Garipov, T., Vetrov, D., and Wilson, A.G. (2018). Averaging weights leads to wider optima and better generalization. arXiv."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1287\/mnsc.42.2.173","article-title":"New product development: The performance and time-to-market tradeoff","volume":"42","author":"Cohen","year":"1996","journal-title":"Manag. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"4572","DOI":"10.1111\/poms.13860","article-title":"Managing innovation portfolios: From project selection to portfolio design","volume":"31","author":"Si","year":"2022","journal-title":"Prod. Oper. Manag."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"111909","DOI":"10.1016\/j.jss.2023.111909","article-title":"Not all requirements prioritization criteria are equal at all times: A quantitative analysis","volume":"209","author":"Svensson","year":"2024","journal-title":"J. Syst. Softw."},{"key":"ref_53","first-page":"66","article-title":"Managing your innovation portfolio","volume":"90","author":"Nagji","year":"2012","journal-title":"Harv. Bus. Rev."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Valverde-Albacete, F.J., and Pel\u00e1ez-Moreno, C. (2014). 100% classification accuracy considered harmful: The normalized information transfer factor explains the accuracy paradox. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0084217"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Davis, J., and Goadrich, M. (2006, January 25\u201329). The relationship between precision-recall and roc curves. Proceedings of the 23rd International Conference on Machine Learning, Pittsburgh, PA, USA.","DOI":"10.1145\/1143844.1143874"},{"key":"ref_56","first-page":"8052","article-title":"Generalizing to unseen domains: A survey on domain generalization","volume":"35","author":"Wang","year":"2022","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1109\/TIT.1982.1056489","article-title":"Least squares quantization in pcm","volume":"28","author":"Lloyd","year":"1982","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_58","first-page":"2579","article-title":"Visualizing data using t-sne","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"nwae403","DOI":"10.1093\/nsr\/nwae403","article-title":"A survey on multimodal large language models","volume":"11","author":"Yin","year":"2024","journal-title":"Natl. Sci. Rev."},{"key":"ref_60","first-page":"1","article-title":"Domain-adversarial training of neural networks","volume":"17","author":"Ganin","year":"2016","journal-title":"J. Mach. Learn. Res."}],"container-title":["Journal of Theoretical and Applied Electronic Commerce Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/0718-1876\/21\/2\/64\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T18:03:37Z","timestamp":1771005817000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/0718-1876\/21\/2\/64"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,13]]},"references-count":60,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["jtaer21020064"],"URL":"https:\/\/doi.org\/10.3390\/jtaer21020064","relation":{},"ISSN":["0718-1876"],"issn-type":[{"value":"0718-1876","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,13]]}}}