{"status":"OK","data":{"id":505736,"identifier":"OPYONR","persistentUrl":"https://doi.org/10.34894/OPYONR","protocol":"doi","authority":"10.34894","separator":"/","publisher":"DataverseNL","publicationDate":"2025-08-19","storageIdentifier":"surf://10.34894/OPYONR","effectiveDatasetFileCountLimit":10000,"datasetFileUploadsAvailable":9953,"datasetType":"dataset","locks":[],"latestVersion":{"id":31304,"datasetId":505736,"datasetPersistentId":"doi:10.34894/OPYONR","datasetType":"dataset","storageIdentifier":"surf://10.34894/OPYONR","versionNumber":1,"internalVersionNumber":55,"versionMinorNumber":0,"versionState":"RELEASED","latestVersionPublishingState":"RELEASED","deaccessionLink":"","distributionDate":"2022-12-05","lastUpdateTime":"2025-08-19T06:33:18Z","releaseTime":"2025-08-19T06:33:18Z","createTime":"2025-08-19T06:31:09Z","publicationDate":"2025-08-19","citationDate":"2025-08-19","effectiveDatasetFileCountLimit":10000,"datasetFileUploadsAvailable":9953,"termsOfUse":"MIT – license_uri","fileAccessRequest":true,"metadataBlocks":{"citation":{"displayName":"Citation Metadata","name":"citation","fields":[{"typeName":"title","multiple":false,"typeClass":"primitive","value":"Reproducibility package (software and synthetic data) for \"Attention Trajectories Capture Utility Accumulation and Predict Brand Choice\""},{"typeName":"author","multiple":true,"typeClass":"compound","value":[{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"Martinovici, Ana"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"https://ror.org/057w15z03","expandedvalue":{"scheme":"http://www.grid.ac/ontology/","termName":"Erasmus University Rotterdam","@type":"https://schema.org/Organization"}}}]},{"typeName":"datasetContact","multiple":true,"typeClass":"compound","value":[{"datasetContactName":{"typeName":"datasetContactName","multiple":false,"typeClass":"primitive","value":"Martinovici, Ana"},"datasetContactAffiliation":{"typeName":"datasetContactAffiliation","multiple":false,"typeClass":"primitive","value":"Rotterdam School of Management "}}]},{"typeName":"dsDescription","multiple":true,"typeClass":"compound","value":[{"dsDescriptionValue":{"typeName":"dsDescriptionValue","multiple":false,"typeClass":"primitive","value":"

Abstract of the associated paper:

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\n

Trajectories of attention capture the accumulation of brand utility during complex decision-making tasks. Thus, attention trajectories, as reflected in eye movements, predict the final brand choice of 85% of consumers before they implement it. Even when observing eye movements in only the first quarter of the decision process, attention already predicts brand choice much better (45%) than chance levels (20%). This superior prediction performance is due to a “double attention lift” for the chosen brand. Thus, the chosen brand receives progressively more attention towards the moment of choice, and more of this attention is devoted to integrating information about the brand rather than to comparing it with other options. In contrast, the currently owned brand grabs attention early in the task, and its attention gain persists for brand-loyal and shifts for brand-switching consumers. A new attention-and-choice model used in tandem with the Bayesian K-fold Cross-Validation methodology on eye-tracking data from 325 representative consumers uncovered these attention trajectory effects. The findings contribute to closing important knowledge gaps in the attention and choice literature and have implications for marketing research and managerial practice. 

\n


\n

Brief description of the reproducibility package:

\n


\n

This package includes code needed to reproduce numerical results presented in the paper starting from a synthetic dataset. In addition, it provides an example of how to structure code for a complex project, and how to improve the likelihood of numerical reproducibility by using Make. The readme file provides links to additional information about Make and describes what the user needs to do in order to execute the code. 

"}}]},{"typeName":"subject","multiple":true,"typeClass":"controlledVocabulary","value":["Other"]},{"typeName":"keyword","multiple":true,"typeClass":"compound","value":[{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Numerical Reproducibility"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Attention"}}]},{"typeName":"topicClassification","multiple":true,"typeClass":"compound","value":[{"topicClassValue":{"typeName":"topicClassValue","multiple":false,"typeClass":"primitive","value":"Other Economics Not Elsewhere Classified"}}]},{"typeName":"notesText","multiple":false,"typeClass":"primitive","value":"

This entry is a forty-seven-file data package totaling 4.0 MB, containing files in , .txt, .Rmd, .RData, .pdf, .csv, .Rproj, .stan, .rds, .R, .html and .md formats.

If you use this dataset, please cite: Martinovici, Ana (2022). Reproducibility package (software and synthetic data) for \"Attention Trajectories Capture Utility Accumulation and Predict Brand Choice\". Erasmus University Rotterdam (EUR). Software. https://doi.org/10.25397/eur.21603288

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