{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T09:35:45Z","timestamp":1786700145596,"version":"build-2736575974"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T00:00:00Z","timestamp":1786665600000},"content-version":"vor","delay-in-days":13,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100011151","name":"Key Laboratory of Computer Network and Information Integration, Ministry of Education","doi-asserted-by":"publisher","award":["No. 2024ZD021"],"award-info":[{"award-number":["No. 2024ZD021"]}],"id":[{"id":"10.13039\/501100011151","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J. King Saud Univ. Comput. Inf. Sci."],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    The price of items is an important factor for users\u2019 purchase decisions. Current price-aware recommendation systems mainly focus on modeling user behavior and commodity attributes with the fixed price of items, often ignoring the impact of dynamic price fluctuation on user behavior. Additionally, these algorithms lack consideration of the temporal and global information of user-item actions, making it more difficult to capture user preferences for recent purchases. To address these limitations, we propose a novel Dynamic Global Graph Guided Price-aware Recommendation (DG\n                    <jats:sup>3<\/jats:sup>\n                    PR) method. Specifically, the proposed model comprises four main components: the Dynamic Subgraph Spatial Representation Learning (DSSRL) module, the Dynamic Subgraph Temporal Representation Learning (DSTRL) module, the Global Graph Spatial Representation Learning (GGSRL) module, and the prediction module. In the DSSRL module, a dynamic price grade discretization method is introduced to map item price to the corresponding grade. Subsequently, the DSTRL module captures the dynamic changes of commodities over time. Meanwhile, the GGSRL module learns the global information of the entire heterogeneous graph to capture the user\u2019s purchase preferences globally. During prediction, the temporal and global contextual information is incorporated by the attention mechanism to learn the user\u2019s purchase preferences. By performing such integration, the proposed model effectively facilitates the performance of price-sensitive recommendations in Business-to-Business (B2B) scenarios. To validate the proposed model\u2019s effectiveness, we construct three real-world datasets from a B2B e-commerce platform and conduct extensive experiments. The results demonstrate that the proposed method outperforms the current competing methods by a substantial margin. Specifically, compared to the best-performing baseline LightGCN on the Middle region dataset, our model achieves improvements of 4.01%, 1.32%, and 3.92% in Precision@10, Recall@10, and NDCG@10, respectively, demonstrating its superior capability in capturing dynamic price-aware user preferences.\n                  <\/jats:p>","DOI":"10.1007\/s44443-026-00469-0","type":"journal-article","created":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T09:02:48Z","timestamp":1786698168000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DG$$^{3}$$PR: dynamic global graph guided price-aware recommendation"],"prefix":"10.1007","volume":"38","author":[{"given":"Yingjie","family":"Kuang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenhua","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenyu","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4794-9961","authenticated-orcid":false,"given":"Yuefang","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chang-Dong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,14]]},"reference":[{"issue":"2","key":"469_CR1","doi-asserted-by":"publisher","first-page":"3525","DOI":"10.1007\/s11042-023-15790-3","volume":"83","author":"M Budhathoki","year":"2024","unstructured":"Budhathoki M, Alsadoon A, Dawoud A et al (2024) Knowledge graph for recommendation system: enhanced relation reliability and prediction probability (errapp). Multimedia Tool Appl 83(2):3525\u20133546","journal-title":"Multimedia Tool Appl"},{"key":"469_CR2","doi-asserted-by":"crossref","unstructured":"Chang J, Gao C, Zheng Y et\u00a0al (2021) Sequential recommendation with graph neural networks. In: Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval, pp 378\u2013387","DOI":"10.1145\/3404835.3462968"},{"key":"469_CR3","doi-asserted-by":"publisher","first-page":"102573","DOI":"10.1016\/j.jretconser.2021.102573","volume":"61","author":"SS Chen","year":"2021","unstructured":"Chen SS, Choubey B, Singh V (2021) A neural network based price sensitive recommender model to predict customer choices based on price effect. J Retail Consum Serv 61:102573","journal-title":"J Retail Consum Serv"},{"key":"469_CR4","doi-asserted-by":"crossref","unstructured":"Chen S, Chen J, Zhou S et\u00a0al (2024) Sigformer: Sign-aware graph transformer for recommendation. In: Proceedings of the 47th international ACM SIGIR conference on research and development in information retrieval, pp 1274\u20131284","DOI":"10.1145\/3626772.3657747"},{"key":"469_CR5","doi-asserted-by":"crossref","unstructured":"Dadoun A, Defoin-Plate M, Fiig T et\u00a0al (2023) How recommender systems can transform airline offer construction and retailing. Artificial Intelligence and Machine Learning in the Travel Industry: Simplifying Complex Decision Making pp 93\u2013107","DOI":"10.1007\/978-3-031-25456-7_7"},{"issue":"1","key":"469_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3568022","volume":"1","author":"C Gao","year":"2023","unstructured":"Gao C, Zheng Y, Li N et al (2023a) A survey of graph neural networks for recommender systems: challenges, methods, and directions. ACM Trans Recomm Syst 1(1):1\u201351","journal-title":"ACM Trans Recomm Syst"},{"issue":"10","key":"469_CR7","doi-asserted-by":"publisher","first-page":"10281","DOI":"10.1109\/TKDE.2023.3251897","volume":"35","author":"M Gao","year":"2023","unstructured":"Gao M, Li JY, Chen CH et al (2023b) Enhanced multi-task learning and knowledge graph-based recommender system. IEEE Trans Knowl Data Eng 35(10):10281\u201310294","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"7","key":"469_CR8","doi-asserted-by":"publisher","first-page":"3801","DOI":"10.1016\/j.eswa.2014.11.042","volume":"42","author":"S Ghazarian","year":"2015","unstructured":"Ghazarian S, Nematbakhsh MA (2015) Enhancing memory-based collaborative filtering for group recommender systems. Expert Syst Appl 42(7):3801\u20133812","journal-title":"Expert Syst Appl"},{"key":"469_CR9","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.knosys.2018.02.026","volume":"150","author":"A Greenstein-Messica","year":"2018","unstructured":"Greenstein-Messica A, Rokach L (2018) Personal price aware multi-seller recommender system: evidence from ebay. Knowl-Based Syst 150:14\u201326","journal-title":"Knowl-Based Syst"},{"key":"469_CR10","doi-asserted-by":"crossref","unstructured":"Guo H, Tang R, Ye Y et\u00a0al (2017) Deepfm: a factorization-machine based neural network for ctr prediction. arXiv:1703.04247","DOI":"10.24963\/ijcai.2017\/239"},{"key":"469_CR11","doi-asserted-by":"crossref","unstructured":"He R, McAuley J (2016) Fusing similarity models with Markov chains for sparse sequential recommendation. In: 2016 IEEE 16th international conference on data mining (ICDM). IEEE, pp 191\u2013200","DOI":"10.1109\/ICDM.2016.0030"},{"key":"469_CR12","doi-asserted-by":"crossref","unstructured":"He X, Deng K, Wang X et\u00a0al (2020) Lightgcn: Simplifying and powering graph convolution network for recommendation. In: Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, pp 639\u2013648","DOI":"10.1145\/3397271.3401063"},{"key":"469_CR13","unstructured":"Hidasi B, Karatzoglou A, Baltrunas L et\u00a0al (2015) Session-based recommendations with recurrent neural networks. arXiv:1511.06939"},{"issue":"8","key":"469_CR14","doi-asserted-by":"publisher","first-page":"101724","DOI":"10.1016\/j.jksuci.2023.101724","volume":"35","author":"F Horasan","year":"2023","unstructured":"Horasan F, Yurttakal AH, G\u00fcnd\u00fcz S (2023) A novel model based collaborative filtering recommender system via truncated ulv decomposition. J King Saud Univ Comput Inf Sci 35(8):101724","journal-title":"J King Saud Univ Comput Inf Sci"},{"key":"469_CR15","doi-asserted-by":"crossref","unstructured":"Hu WH, Tang SH, Chen YC et\u00a0al (2018) Promotion recommendation method and system based on random forest. In: Proceedings of the 5th Multidisciplinary International Social Networks Conference, pp 1\u20135","DOI":"10.1145\/3227696.3227708"},{"issue":"3","key":"469_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3560486","volume":"41","author":"M Imran","year":"2023","unstructured":"Imran M, Yin H, Chen T et al (2023) Refrs: Resource-efficient federated recommender system for dynamic and diversified user preferences. ACM Trans Inf Syst 41(3):1\u201330","journal-title":"ACM Trans Inf Syst"},{"issue":"11","key":"469_CR17","doi-asserted-by":"publisher","first-page":"6920","DOI":"10.1109\/TKDE.2024.3388453","volume":"36","author":"H Li","year":"2024","unstructured":"Li H, Li C, Feng K et al (2024) Robust knowledge adaptation for dynamic graph neural networks. IEEE Trans Knowl Data Eng 36(11):6920\u20136933","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"469_CR18","doi-asserted-by":"publisher","first-page":"109185","DOI":"10.1016\/j.knosys.2022.109185","volume":"251","author":"T Liang","year":"2022","unstructured":"Liang T, Ma L, Zhang W et al (2022) Content-aware recommendation via dynamic heterogeneous graph convolutional network. Knowl-Based Syst 251:109185","journal-title":"Knowl-Based Syst"},{"key":"469_CR19","doi-asserted-by":"publisher","unstructured":"Lin X, Pan W, Ming Z (2025) Towards interest drift-driven user representation learning in sequential recommendation. In: Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, New York, NY, USA, SIGIR \u201925, pp 1541\u20131551. https:\/\/doi.org\/10.1145\/3726302.3730099","DOI":"10.1145\/3726302.3730099"},{"issue":"5","key":"469_CR20","doi-asserted-by":"publisher","first-page":"102050","DOI":"10.1016\/j.jksuci.2024.102050","volume":"36","author":"X Liu","year":"2024","unstructured":"Liu X, Xiao W, Liu C et al (2024) Meta graph network recommendation based on multi-behavior encoding. J King Saud Univ Comput Inf Sci 36(5):102050","journal-title":"J King Saud Univ Comput Inf Sci"},{"key":"469_CR21","doi-asserted-by":"crossref","unstructured":"Ma H, Yang H, Lyu MR et\u00a0al (2008) Sorec: social recommendation using probabilistic matrix factorization. In: Proceedings of the 17th ACM conference on Information and knowledge management, pp 931\u2013940","DOI":"10.1145\/1458082.1458205"},{"key":"469_CR22","doi-asserted-by":"crossref","unstructured":"Pang Y, Wu L, Shen Q et\u00a0al (2022) Heterogeneous global graph neural networks for personalized session-based recommendation. In: Proceedings of the fifteenth ACM international conference on web search and data mining, pp 775\u2013783","DOI":"10.1145\/3488560.3498505"},{"key":"469_CR23","doi-asserted-by":"crossref","unstructured":"Rendle S, Freudenthaler C, Schmidt-Thieme L (2010) Factorizing personalized markov chains for next-basket recommendation. In: Proceedings of the 19th international conference on World wide web, pp 811\u2013820","DOI":"10.1145\/1772690.1772773"},{"issue":"10","key":"469_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3661821","volume":"56","author":"K Sharma","year":"2024","unstructured":"Sharma K, Lee YC, Nambi S et al (2024) A survey of graph neural networks for social recommender systems. ACM Comput Surv 56(10):1\u201334","journal-title":"ACM Comput Surv"},{"issue":"4","key":"469_CR25","first-page":"90","volume":"2","author":"J Shi","year":"2024","unstructured":"Shi J, Shang F, Zhou S et al (2024) Applications of quantum machine learning in large-scale e-commerce recommendation systems: enhancing efficiency and accuracy. J Ind Eng Appl Sci 2(4):90\u2013103","journal-title":"J Ind Eng Appl Sci"},{"key":"469_CR26","doi-asserted-by":"crossref","unstructured":"Song W, Xiao Z, Wang Y et\u00a0al (2019) Session-based social recommendation via dynamic graph attention networks. In: Proceedings of the Twelfth ACM international conference on web search and data mining, pp 555\u2013563","DOI":"10.1145\/3289600.3290989"},{"key":"469_CR27","doi-asserted-by":"publisher","first-page":"116240","DOI":"10.1016\/j.eswa.2021.116240","volume":"191","author":"Y Song","year":"2022","unstructured":"Song Y, Ye H, Li M et al (2022) Deep multi-graph neural networks with attention fusion for recommendation. Expert Syst Appl 191:116240","journal-title":"Expert Syst Appl"},{"key":"469_CR28","doi-asserted-by":"crossref","unstructured":"Sundaram SG, Saravanan P, Ponmalar A et al (2022) Collaborative product recommendation system for e-commerce websites. In: 2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES). IEEE, pp 1\u20137","DOI":"10.1109\/ICSES55317.2022.9914275"},{"key":"469_CR29","doi-asserted-by":"crossref","unstructured":"Tang H, Wu S, Xu G et\u00a0al (2023) Dynamic graph evolution learning for recommendation. In: Proceedings of the 46th international acm sigir conference on research and development in information retrieval, pp 1589\u20131598","DOI":"10.1145\/3539618.3591674"},{"key":"469_CR30","unstructured":"Van\u00a0Meteren R, Van\u00a0Someren M (2000) Using content-based filtering for recommendation. In: Proceedings of the machine learning in the new information age: MLnet\/ECML2000 workshop. Barcelona, pp 47\u201356"},{"key":"469_CR31","doi-asserted-by":"crossref","unstructured":"Vullam N, Vellela SS, Reddy V et\u00a0al (2023) Multi-agent personalized recommendation system in e-commerce based on user. In: 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC). IEEE, pp 1194\u20131199","DOI":"10.1109\/ICAAIC56838.2023.10140756"},{"key":"469_CR32","unstructured":"Wadhwa S, Ranjan A, Xu S et\u00a0al (2020) Personalizing item recommendation via price understanding. In: ComplexRec-ImpactRS@ RecSys, pp 1\u20138"},{"key":"469_CR33","doi-asserted-by":"crossref","unstructured":"Wan M, Wang D, Goldman M et\u00a0al (2017) Modeling consumer preferences and price sensitivities from large-scale grocery shopping transaction logs. In: Proceedings of the 26th international conference on world wide web, pp 1103\u20131112","DOI":"10.1145\/3038912.3052568"},{"key":"469_CR34","doi-asserted-by":"crossref","unstructured":"Wang X, He X, Wang M et\u00a0al (2019) Neural graph collaborative filtering. In: Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval, pp 165\u2013174","DOI":"10.1145\/3331184.3331267"},{"key":"469_CR35","doi-asserted-by":"crossref","unstructured":"Wang Y, Chu Z, Ouyang X et\u00a0al (2024) Llmrg: Improving recommendations through large language model reasoning graphs. In: Proceedings of the AAAI conference on artificial intelligence, pp 19189\u201319196","DOI":"10.1609\/aaai.v38i17.29887"},{"key":"469_CR36","doi-asserted-by":"crossref","unstructured":"Wang Q, Wang J, Ibrahim NF (2025) Sentientityrec: Entity-level sentiment perception graph neural network for news recommendation. J King Saud Univ Comput Inf Sci 37(5):80","DOI":"10.1007\/s44443-025-00087-2"},{"key":"469_CR37","doi-asserted-by":"crossref","unstructured":"Wetprasit S, Cao Q, Seow CK (2022) Recommender system for coupon discount of e-commerce applications. In: 2022 5th International Conference on Data Science and Information Technology (DSIT), IEEE, pp 1\u20136","DOI":"10.1109\/DSIT55514.2022.9943917"},{"key":"469_CR38","doi-asserted-by":"publisher","first-page":"122060","DOI":"10.1016\/j.eswa.2023.122060","volume":"238","author":"B Wu","year":"2024","unstructured":"Wu B, Su X, Liang J et al (2024) Graph gating-mixer for sequential recommendation. Expert Syst Appl 238:122060","journal-title":"Expert Syst Appl"},{"key":"469_CR39","doi-asserted-by":"crossref","unstructured":"Wu J, Yang B, Zhu Q et\u00a0al (2025) Price-aware debiased learning model for recommendation. Neural Networks p 107872","DOI":"10.1016\/j.neunet.2025.107872"},{"key":"469_CR40","unstructured":"Xia Y, Di\u00a0Fabbrizio G, Vaibhav S et\u00a0al (2017) A content-based recommender system for e-commerce offers and coupons. In: Proc. SIGIR Workshop eCommerce, p\u00a07"},{"key":"469_CR41","doi-asserted-by":"crossref","unstructured":"Xiao J, Ye H, He X et\u00a0al (2017) Attentional factorization machines: Learning the weight of feature interactions via attention networks. arXiv:1708.04617","DOI":"10.24963\/ijcai.2017\/435"},{"key":"469_CR42","doi-asserted-by":"crossref","unstructured":"Xu W, Xiao J, Chen J (2024) Leveraging large language models to enhance personalized recommendations in e-commerce. In: 2024 International Conference on Electrical, Communication and Computer Engineering (ICECCE). IEEE, pp 1\u20136","DOI":"10.1109\/ICECCE63537.2024.10823618"},{"key":"469_CR43","unstructured":"Yang B, Yih Wt, He X, et\u00a0al (2014) Embedding entities and relations for learning and inference in knowledge bases. arXiv:1412.6575"},{"key":"469_CR44","doi-asserted-by":"publisher","first-page":"43460","DOI":"10.1109\/ACCESS.2024.3378111","volume":"12","author":"L Yang","year":"2024","unstructured":"Yang L, Chatelain C, Adam S (2024a) Dynamic graph representation learning with neural networks: a survey. IEEE Access 12:43460\u201343484","journal-title":"IEEE Access"},{"key":"469_CR45","doi-asserted-by":"crossref","unstructured":"Yang S, Ma W, Sun P et\u00a0al (2024b) Sequential recommendation with latent relations based on large language model. In: Proceedings of the 47th International ACM SIGIR conference on research and development in information retrieval, pp 335\u2013344","DOI":"10.1145\/3626772.3657762"},{"key":"469_CR46","doi-asserted-by":"crossref","unstructured":"Yang Y, Xia L, Luo D et al (2024c) Graphpro: Graph pre-training and prompt learning for recommendation. In: Proceedings of the ACM Web Conference 2024, pp 3690\u20133699","DOI":"10.1145\/3589334.3645546"},{"key":"469_CR47","doi-asserted-by":"crossref","unstructured":"Yue Z, Wang Y, He Z et\u00a0al (2024) Linear recurrent units for sequential recommendation. In: Proceedings of the 17th ACM international conference on web search and data mining, pp 930\u2013938","DOI":"10.1145\/3616855.3635760"},{"issue":"3","key":"469_CR48","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1007\/s41019-024-00250-1","volume":"9","author":"L Yufang","year":"2024","unstructured":"Yufang L, Shaoqing W, Keke L et al (2024) Channel-enhanced contrastive cross-domain sequential recommendation. Data Sci Eng 9(3):325\u2013340","journal-title":"Data Sci Eng"},{"issue":"5","key":"469_CR49","first-page":"4741","volume":"35","author":"M Zhang","year":"2022","unstructured":"Zhang M, Wu S, Yu X et al (2022a) Dynamic graph neural networks for sequential recommendation. IEEE Trans Knowl Data Eng 35(5):4741\u20134753","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"469_CR50","doi-asserted-by":"crossref","unstructured":"Zhang X, Xu B, Yang L et\u00a0al (2022b) Price does matter! modeling price and interest preferences in session-based recommendation. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 1684\u20131693","DOI":"10.1145\/3477495.3532043"},{"issue":"1","key":"469_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3664928","volume":"16","author":"Y Zhao","year":"2025","unstructured":"Zhao Y, Wang Y, Liu Y et al (2025) Fairness and diversity in recommender systems: a survey. ACM Trans Intell Syst Technol 16(1):1\u201328","journal-title":"ACM Trans Intell Syst Technol"},{"issue":"02","key":"469_CR52","doi-asserted-by":"publisher","first-page":"1609","DOI":"10.1109\/TKDE.2021.3091160","volume":"35","author":"Y Zheng","year":"2023","unstructured":"Zheng Y, Gao C, He X et al (2023) Incorporating price into recommendation with graph convolutional networks. IEEE Trans Knowl Data Eng 35(02):1609\u20131623","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"469_CR53","doi-asserted-by":"crossref","unstructured":"Zhu Y, Tang Z, Liu Y et\u00a0al (2022) Personalized transfer of user preferences for cross-domain recommendation. In: Proceedings of the fifteenth ACM international conference on web search and data mining, pp 1507\u20131515","DOI":"10.1145\/3488560.3498392"}],"container-title":["Journal of King Saud University Computer and Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-026-00469-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44443-026-00469-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-026-00469-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T09:03:15Z","timestamp":1786698195000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44443-026-00469-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":53,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2026,8]]}},"alternative-id":["469"],"URL":"https:\/\/doi.org\/10.1007\/s44443-026-00469-0","relation":{},"ISSN":["1319-1578","2213-1248"],"issn-type":[{"value":"1319-1578","type":"print"},{"value":"2213-1248","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"8 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 August 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"623"}}