{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T00:21:48Z","timestamp":1783038108788,"version":"3.54.6"},"reference-count":75,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100006066","name":"Hong Kong Polytechnic University Department of Industrial and Systems Engineering","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100006066","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Electronic Commerce Research and Applications"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.elerap.2026.101613","type":"journal-article","created":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T15:46:30Z","timestamp":1780674390000},"page":"101613","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["An AI-enabled multimodal framework for preference prediction in immersive VR shopping using cross-modal attention and domain adaptation"],"prefix":"10.1016","volume":"78","author":[{"given":"M.W.","family":"Geda","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8215-4190","authenticated-orcid":false,"given":"Yuk Ming","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carman K.M.","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.elerap.2026.101613_b0005","series-title":"A Fusion of Artificial Intelligence and Internet of Things for Emerging Cyber Systems","first-page":"371","article-title":"EEG signals based choice classification for neuromarketing applications","author":"Ali","year":"2022"},{"key":"10.1016\/j.elerap.2026.101613_b0010","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1186\/s43093-024-00371-z","article-title":"Revolutionizing consumer insights: the impact of fMRI in neuromarketing research","volume":"10","author":"Alsharif","year":"2024","journal-title":"Futur. Bus J."},{"key":"10.1016\/j.elerap.2026.101613_b0015","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.foodres.2018.12.031","article-title":"Consumer preferences for nutritional claims: an exploration of attention and choice based on an eye-tracking choice experiment","volume":"116","author":"Ballco","year":"2019","journal-title":"Food Res. Int."},{"key":"10.1016\/j.elerap.2026.101613_b0020","doi-asserted-by":"crossref","DOI":"10.3389\/fnins.2020.594566","article-title":"Is EEG suitable for marketing research? A systematic review","volume":"14","author":"Bazzani","year":"2020","journal-title":"Front. Neurosci."},{"key":"10.1016\/j.elerap.2026.101613_b0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.trc.2021.103435","article-title":"Modelling risk perception using a dynamic hybrid choice model and brain-imaging data: an application to virtual reality cycling","volume":"133","author":"Bogacz","year":"2021","journal-title":"Transp. Res. Part C Emerging Technol."},{"key":"10.1016\/j.elerap.2026.101613_b0030","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0217125","article-title":"Measuring brand association strength with EEG: a single-trial N400 ERP study","volume":"14","author":"Camarrone","year":"2019","journal-title":"PLoS One"},{"key":"10.1016\/j.elerap.2026.101613_b0035","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1109\/TPAMI.2022.3145392","article-title":"Deep ROC analysis and AUC as balanced average accuracy, for improved classifier selection, audit and explanation","volume":"45","author":"Carrington","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.elerap.2026.101613_b0040","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.jretai.2024.06.003","article-title":"Understanding shoppers\u2019 attention to price information at the point of consideration using in-store ambulatory eye-tracking","volume":"100","author":"Chen","year":"2024","journal-title":"J. Retail."},{"key":"10.1016\/j.elerap.2026.101613_b0045","doi-asserted-by":"crossref","first-page":"1120","DOI":"10.1177\/0022243721998375","article-title":"Understanding lateral and vertical biases in consumer attention: an in-store ambulatory eye-tracking study","volume":"58","author":"Chen","year":"2021","journal-title":"J. Mark. Res."},{"key":"10.1016\/j.elerap.2026.101613_b0050","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.jspi.2019.06.001","article-title":"On Benjamini\u2013Hochberg procedure applied to mid p-values","volume":"205","author":"Chen","year":"2020","journal-title":"J. Statist. Plann. Inference"},{"key":"10.1016\/j.elerap.2026.101613_b0055","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1002\/pchj.645","article-title":"Do you like it or not? Identifying preference using an electroencephalogram during the viewing of short videos","volume":"12","author":"Deng","year":"2023","journal-title":"PsyCh J."},{"key":"10.1016\/j.elerap.2026.101613_b0060","doi-asserted-by":"crossref","first-page":"596","DOI":"10.3390\/bs14070596","article-title":"From E-commerce to the metaverse: a neuroscientific analysis of digital consumer behavior","volume":"14","author":"Fici","year":"2024","journal-title":"Behav. Sci."},{"key":"10.1016\/j.elerap.2026.101613_b0065","doi-asserted-by":"crossref","first-page":"5865","DOI":"10.1109\/JBHI.2024.3419043","article-title":"Cross-modal guiding neural network for multimodal emotion recognition from EEG and eye movement signals","volume":"28","author":"Fu","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.elerap.2026.101613_b0070","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.114909","article-title":"RDAM: domain adaptation under small and class-imbalanced samples","volume":"333","author":"Fu","year":"2026","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.elerap.2026.101613_b0075","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1057\/s41262-020-00221-7","article-title":"A comparative analysis of neuromarketing methods for brand purchasing predictions among young adults","volume":"28","author":"Garczarek-B\u0105k","year":"2021","journal-title":"J. Brand Manag."},{"issue":"1","key":"10.1016\/j.elerap.2026.101613_bib371","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1007\/s12559-026-10585-8","article-title":"EmoDLNet: End-to-End Multi-Scale Spatio-Temporal Deep Learning for EEG-Based Emotion Recognition in Affective Human-Computer Interaction","volume":"18","author":"Geda","year":"2026","journal-title":"Cognitive Computation"},{"key":"10.1016\/j.elerap.2026.101613_b0080","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.physbeh.2019.04.025","article-title":"The application of EEG power for the prediction and interpretation of consumer decision-making: a neuromarketing study","volume":"207","author":"Golnar-Nik","year":"2019","journal-title":"Physiol. Behav."},{"key":"10.1016\/j.elerap.2026.101613_b0085","doi-asserted-by":"crossref","DOI":"10.3389\/fnint.2019.00019","article-title":"Can brain waves really tell if a product will be purchased? inferring consumer preferences from single-item brain potentials","volume":"13","author":"Goto","year":"2019","journal-title":"Front. Integr. Neurosci."},{"key":"10.1016\/j.elerap.2026.101613_b0090","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.ergon.2019.02.006","article-title":"Distinguishing and quantifying the visual aesthetics of a product: an integrated approach of eye-tracking and EEG","volume":"71","author":"Guo","year":"2019","journal-title":"Int. J. Ind. Ergon."},{"key":"10.1016\/j.elerap.2026.101613_b0095","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1002\/cb.1710","article-title":"Consumer neuroscience for marketing researchers","volume":"17","author":"Harris","year":"2018","journal-title":"J. Consum. Behav."},{"key":"10.1016\/j.elerap.2026.101613_b0100","doi-asserted-by":"crossref","DOI":"10.1080\/23311975.2022.2145673","article-title":"Antecedents and consequences of consumers\u2019 attitudes toward live streaming shopping: an application of the stimulus\u2013organism\u2013response paradigm","volume":"9","author":"Ho","year":"2022","journal-title":"Cogent Business & Manage."},{"key":"10.1016\/j.elerap.2026.101613_b0105","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112858","article-title":"MsAD-LEC: estimating large-scale brain effective connectivity network based on multi-subgraph attention diffusion","volume":"309","author":"Ji","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.elerap.2026.101613_b0110","doi-asserted-by":"crossref","unstructured":"Jiang, W.-B., Liu, X.-H., Zheng, W.-L., Lu, B.-L., 2023. Multimodal Adaptive Emotion Transformer with Flexible Modality Inputs on A Novel Dataset with Continuous Labels, in: Proceedings of the 31st ACM International Conference on Multimedia, MM \u201923. Association for Computing Machinery, New York, NY, USA, pp. 5975\u20135984. https:\/\/doi.org\/10.1145\/3581783.3613797.","DOI":"10.1145\/3581783.3613797"},{"key":"10.1016\/j.elerap.2026.101613_b0115","doi-asserted-by":"crossref","unstructured":"Kaheh, S., Ramirez, M., Wong, J., George, K., 2021. Neuromarketing using EEG Signals and Eye-tracking, in: 2021 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). Presented at the 2021 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), IEEE, Bangalore, India, pp. 1\u20134. https:\/\/doi.org\/10.1109\/CONECCT52877.2021.9622539.","DOI":"10.1109\/CONECCT52877.2021.9622539"},{"key":"10.1016\/j.elerap.2026.101613_b0120","doi-asserted-by":"crossref","first-page":"3803","DOI":"10.1016\/j.eswa.2012.12.095","article-title":"Consumer neuroscience: assessing the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking","volume":"40","author":"Khushaba","year":"2013","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.elerap.2026.101613_b0125","first-page":"91","article-title":"Exploring EEG-based design studies: a systematic review","volume":"35","author":"Kim","year":"2022","journal-title":"Arch. Des. Res."},{"key":"10.1016\/j.elerap.2026.101613_b0130","doi-asserted-by":"crossref","DOI":"10.3389\/fnhum.2024.1347974","article-title":"A method for synchronized use of EEG and eye tracking in fully immersive VR","volume":"18","author":"Larsen","year":"2024","journal-title":"Front. Hum. Neurosci."},{"key":"10.1016\/j.elerap.2026.101613_b0135","doi-asserted-by":"crossref","first-page":"10751","DOI":"10.1109\/JSEN.2022.3168572","article-title":"An EEG data processing approach for emotion recognition","volume":"22","author":"Li","year":"2022","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.elerap.2026.101613_b0140","doi-asserted-by":"crossref","DOI":"10.1016\/j.jretconser.2024.104124","article-title":"The role of price in display complexity\u2019s impact on horticultural plant purchase intention: an eye-tracking study","volume":"82","author":"Li","year":"2025","journal-title":"J. Retail.Consum. Serv."},{"key":"10.1016\/j.elerap.2026.101613_b0145","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.111826","article-title":"MBCFNet: a multimodal brain\u2013computer fusion network for human intention recognition","volume":"296","author":"Li","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.elerap.2026.101613_b0150","doi-asserted-by":"crossref","DOI":"10.3389\/fnins.2021.803507","article-title":"Sleep quality and electroencephalogram delta power","volume":"15","author":"Long","year":"2021","journal-title":"Front. Neurosci."},{"key":"10.1016\/j.elerap.2026.101613_b0155","article-title":"Cascaded convolutional recurrent neural networks for EEG emotion recognition based on temporal\u2013frequency\u2013spatial features","volume":"13","author":"Luo","year":"2023","journal-title":"Appl. Sci."},{"key":"10.1016\/j.elerap.2026.101613_b0160","doi-asserted-by":"crossref","first-page":"3017","DOI":"10.1016\/j.neuron.2024.09.004","article-title":"Beyond neural data: cognitive biometrics and mental privacy","volume":"112","author":"Magee","year":"2024","journal-title":"Neuron"},{"key":"10.1016\/j.elerap.2026.101613_b0165","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1002\/mar.22118","article-title":"Predicting consumer ad preferences: leveraging a machine learning approach for EDA and FEA neurophysiological metrics","volume":"42","author":"Marques","year":"2025","journal-title":"Psychol. Mark."},{"key":"10.1016\/j.elerap.2026.101613_b0170","doi-asserted-by":"crossref","first-page":"1146","DOI":"10.1002\/cb.2253","article-title":"Intelligent neuromarketing framework for consumers\u2019 preference prediction from electroencephalography signals and eye tracking","volume":"23","author":"Mashrur","year":"2024","journal-title":"J. Consum. Behav."},{"key":"10.1016\/j.elerap.2026.101613_b0175","doi-asserted-by":"crossref","DOI":"10.1016\/j.chb.2023.107996","article-title":"Comparing the effects of immersive and non-immersive real estate experience on behavioral intentions","volume":"150","author":"Mauri","year":"2024","journal-title":"Comput. Hum. Behav."},{"key":"10.1016\/j.elerap.2026.101613_b0180","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1177\/14707853221112622","article-title":"A practical review of electroencephalography\u2019s value to consumer research","volume":"65","author":"McInnes","year":"2023","journal-title":"Int. J. Mark. Res."},{"key":"10.1016\/j.elerap.2026.101613_b0185","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2024.112312","article-title":"Preference learning based on adaptive graph neural network for multi-criteria decision support","volume":"167","author":"Meng","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.elerap.2026.101613_b0190","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1108\/JIMA-09-2017-0098","article-title":"A study of unconscious emotional and cognitive responses to tourism images using a neuroscience method","volume":"10","author":"Michael","year":"2019","journal-title":"J. Islamic Market."},{"key":"10.1016\/j.elerap.2026.101613_b0195","doi-asserted-by":"crossref","DOI":"10.1016\/j.jretconser.2020.102202","article-title":"The use of event related potentials brain methods in the study of Conscious and unconscious consumer decision making processes","volume":"58","author":"Ozkara","year":"2021","journal-title":"J. Retail.Consum. Serv."},{"key":"10.1016\/j.elerap.2026.101613_b0200","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1037\/npe0000156","article-title":"Exploring the relationships between perception of product quality, product ratings, and consumers\u2019 personality traits: an eye-tracking study","volume":"15","author":"Pascucci","year":"2022","journal-title":"J. Neurosci. Psychol. Econ."},{"key":"10.1016\/j.elerap.2026.101613_b0205","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1509\/jmr.14.0048","article-title":"Consumer neuroscience: applications, challenges, and possible solutions","volume":"52","author":"Plassmann","year":"2015","journal-title":"J. Mark. Res."},{"key":"10.1016\/j.elerap.2026.101613_b0210","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.ergon.2019.06.006","article-title":"Can eye movements be effectively measured to assess product design?: Gender differences should be considered","volume":"72","author":"Qu","year":"2019","journal-title":"Int. J. Ind. Ergon."},{"key":"10.1016\/j.elerap.2026.101613_b0215","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2024.102231","article-title":"Data fusion in neuromarketing: Multimodal analysis of biosignals, lifecycle stages, current advances, datasets, trends, and challenges","volume":"105","author":"Quiles P\u00e9rez","year":"2024","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.elerap.2026.101613_b0220","doi-asserted-by":"crossref","DOI":"10.1016\/j.array.2021.100072","article-title":"Emotion recognition from EEG-based relative power spectral topography using convolutional neural network","volume":"11","author":"Rahman","year":"2021","journal-title":"Array"},{"key":"10.1016\/j.elerap.2026.101613_b0225","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.jocm.2015.04.001","article-title":"Using facial EMG and eye tracking to study integral affect in discrete choice experiments","volume":"14","author":"Rasch","year":"2015","journal-title":"J. Choice Model."},{"key":"10.1016\/j.elerap.2026.101613_b0230","doi-asserted-by":"crossref","DOI":"10.1111\/psyp.13658","article-title":"Interindividual differences in brain dynamics of early visual processes: impact on score accuracy in the mental rotation task","volume":"57","author":"Ruggeri","year":"2020","journal-title":"Psychophysiology"},{"key":"10.1016\/j.elerap.2026.101613_b0235","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1002\/arcp.1006","article-title":"The neuropsychology of consumer behavior and marketing","volume":"1","author":"Shaw","year":"2018","journal-title":"Consumer Psychol. Rev."},{"key":"10.1016\/j.elerap.2026.101613_b0240","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TNSRE.2023.3241846","article-title":"Improved domain adaptation network based on wasserstein distance for motor imagery EEG classification","volume":"31","author":"She","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10.1016\/j.elerap.2026.101613_b0245","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.inffus.2016.09.003","article-title":"Combining eye tracking, pupil dilation and EEG analysis for predicting web users click intention","volume":"35","author":"Slanzi","year":"2017","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.elerap.2026.101613_b0250","doi-asserted-by":"crossref","first-page":"32","DOI":"10.3390\/bioengineering4020032","article-title":"Time-frequency distribution of seismocardiographic signals: a comparative study","volume":"4","author":"Taebi","year":"2017","journal-title":"Bioengineering"},{"key":"10.1016\/j.elerap.2026.101613_b0255","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.129315","article-title":"SEDA-EEG: a semi-supervised emotion recognition network with domain adaptation for cross-subject EEG analysis","volume":"622","author":"Tan","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.elerap.2026.101613_b0260","doi-asserted-by":"crossref","DOI":"10.1509\/jmr.13.0564","article-title":"Using EEG to predict consumers\u2019 future choices","author":"Telpaz","year":"2015","journal-title":"J. Mark. Res."},{"key":"10.1016\/j.elerap.2026.101613_b0265","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.118722","article-title":"A hybrid neuro-experimental decision support system to classify overconfidence and performance in a simulated bubble using a passive BCI","volume":"212","author":"Toma","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.elerap.2026.101613_b0270","doi-asserted-by":"crossref","DOI":"10.3389\/fncom.2024.1516440","article-title":"Multimodal consumer choice prediction using EEG signals and eye tracking","volume":"18","author":"Usman","year":"2025","journal-title":"Front. Comput. Neurosci."},{"key":"10.1016\/j.elerap.2026.101613_b0275","doi-asserted-by":"crossref","DOI":"10.3389\/fcomp.2022.780580","article-title":"Multimodal EEG and eye tracking feature fusion approaches for attention classification in hybrid BCIs","volume":"4","author":"Vortmann","year":"2022","journal-title":"Front. Comput. Sci."},{"key":"10.1016\/j.elerap.2026.101613_b0280","doi-asserted-by":"crossref","DOI":"10.3389\/fnins.2023.1148855","article-title":"EEGformer: a transformer\u2013based brain activity classification method using EEG signal","volume":"17","author":"Wan","year":"2023","journal-title":"Front. Neurosci."},{"key":"10.1016\/j.elerap.2026.101613_b0285","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.cogr.2021.04.001","article-title":"Review of the emotional feature extraction and classification using EEG signals","volume":"1","author":"Wang","year":"2021","journal-title":"Cognit. Rob."},{"key":"10.1016\/j.elerap.2026.101613_b0290","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1515\/tnsci-2019-0003","article-title":"Test and evaluation of advertising effect based on EEG and eye tracker","volume":"10","author":"Wang","year":"2019","journal-title":"Transl. Neurosci."},{"key":"10.1016\/j.elerap.2026.101613_b0295","doi-asserted-by":"crossref","first-page":"467","DOI":"10.2147\/PRBM.S301286","article-title":"How emotional interaction affects purchase intention in social commerce: the role of perceived usefulness and product type","volume":"14","author":"Wang","year":"2021","journal-title":"PRBM"},{"key":"10.1016\/j.elerap.2026.101613_b0300","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1080\/09544828.2023.2172662","article-title":"Research on the correlation mechanism between eye-tracking data and aesthetic ratings in product aesthetic evaluation","volume":"34","author":"Wang","year":"2023","journal-title":"J. Eng. Des."},{"key":"10.1016\/j.elerap.2026.101613_b0305","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2020.101095","article-title":"Prediction of product design decision making: an investigation of eye movements and EEG features","volume":"45","author":"Wang","year":"2020","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.elerap.2026.101613_b0310","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1080\/2326263X.2023.2287719","article-title":"Feasibility of decoding visual information from EEG","volume":"11","author":"Wilson","year":"2024","journal-title":"Brain-Comput. Interfaces"},{"key":"10.1016\/j.elerap.2026.101613_b0315","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.125452","article-title":"Unsupervised multi-source domain adaptation via contrastive learning for EEG classification","volume":"261","author":"Xu","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.elerap.2026.101613_b0320","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.1108\/MRR-06-2023-0456","article-title":"Marketing beyond reality: a systematic literature review on metaverse","volume":"47","author":"Yadav","year":"2024","journal-title":"Manag. Res. Rev."},{"key":"10.1016\/j.elerap.2026.101613_b0325","doi-asserted-by":"crossref","first-page":"19087","DOI":"10.1007\/s11042-017-4580-6","article-title":"Analysis of EEG signals and its application to neuromarketing","volume":"76","author":"Yadava","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"10.1016\/j.elerap.2026.101613_b0330","doi-asserted-by":"crossref","DOI":"10.1016\/j.bbr.2021.113128","article-title":"Examining the effect of online advertisement cues on human responses using eye-tracking, EEG, and MRI","volume":"402","author":"Yen","year":"2021","journal-title":"Behav. Brain Res."},{"key":"10.1016\/j.elerap.2026.101613_b0335","first-page":"1","article-title":"EEG-based emotion recognition with autoencoder feature fusion and MSC-TimesNet model","author":"Yin","year":"2025","journal-title":"Comput. Methods Biomech. Biomed. Eng."},{"key":"10.1016\/j.elerap.2026.101613_b0340","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2024.112320","article-title":"Enhancing driver attention and road safety through EEG-informed deep reinforcement learning and soft computing","volume":"167","author":"Yousaf","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.elerap.2026.101613_b0345","doi-asserted-by":"crossref","DOI":"10.3389\/fnbeh.2024.1331396","article-title":"EEG-based study of design creativity: a review on research design, experiments, and analysis","volume":"18","author":"Zangeneh Soroush","year":"2024","journal-title":"Front. Behav. Neurosci."},{"key":"10.1016\/j.elerap.2026.101613_b0350","doi-asserted-by":"crossref","first-page":"6502","DOI":"10.3390\/app13116502","article-title":"Review of studies on user research based on EEG and eye tracking","volume":"13","author":"Zhu","year":"2023","journal-title":"Appl. Sci."},{"key":"10.1016\/j.elerap.2026.101613_b0355","doi-asserted-by":"crossref","first-page":"2102","DOI":"10.1109\/TAFFC.2025.3554399","article-title":"Multi-modal cross-subject emotion feature alignment and recognition with EEG and eye movements","volume":"16","author":"Zhu","year":"2025","journal-title":"IEEE Trans. Affect. Comput."},{"key":"10.1016\/j.elerap.2026.101613_b0360","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2022.101601","article-title":"A new approach for product evaluation based on integration of EEG and eye-tracking","volume":"52","author":"Zhu","year":"2022","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.elerap.2026.101613_b0365","doi-asserted-by":"crossref","DOI":"10.3389\/fphy.2020.629620","article-title":"Differential entropy feature signal extraction based on activation mode and its recognition in convolutional gated recurrent unit network","volume":"8","author":"Zhu","year":"2021","journal-title":"Front. Phys."},{"key":"10.1016\/j.elerap.2026.101613_b0370","doi-asserted-by":"crossref","first-page":"15102","DOI":"10.1038\/s41598-019-51567-1","article-title":"A consumer neuroscience study of conscious and subconscious destination preference","volume":"9","author":"Zo\u00ebga Rams\u00f8y","year":"2019","journal-title":"Sci. Rep."}],"container-title":["Electronic Commerce Research and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1567422326000426?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1567422326000426?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T00:03:01Z","timestamp":1783036981000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1567422326000426"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":75,"alternative-id":["S1567422326000426"],"URL":"https:\/\/doi.org\/10.1016\/j.elerap.2026.101613","relation":{},"ISSN":["1567-4223"],"issn-type":[{"value":"1567-4223","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"An AI-enabled multimodal framework for preference prediction in immersive VR shopping using cross-modal attention and domain adaptation","name":"articletitle","label":"Article Title"},{"value":"Electronic Commerce Research and Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.elerap.2026.101613","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"101613"}}