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Augmenting Household Expenditure Forecasts with Online Employee-generated Company Reviews
We assess the ability of online employee-generated content in predicting consumption expenditures. In so doing, we aggregate millions of employee expectations for the next six-month business outlook of their employer and build an employee sentiment index. We test whether forward-looking employee sen...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8655719/ https://www.ncbi.nlm.nih.gov/pubmed/34899083 http://dx.doi.org/10.1093/poq/nfab017 |
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author | Symitsi, Efthymia Stamolampros, Panagiotis Karatzas, Antonios |
author_facet | Symitsi, Efthymia Stamolampros, Panagiotis Karatzas, Antonios |
author_sort | Symitsi, Efthymia |
collection | PubMed |
description | We assess the ability of online employee-generated content in predicting consumption expenditures. In so doing, we aggregate millions of employee expectations for the next six-month business outlook of their employer and build an employee sentiment index. We test whether forward-looking employee sentiment can contribute to baseline models when forecasting aggregate consumption in the United States and compare its performance to well-established, survey-based consumer sentiment indexes. We reveal that online employee opinions have incremental information that can be used to augment the accuracy of consumption forecasting models and inform economic policy decisions. |
format | Online Article Text |
id | pubmed-8655719 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-86557192021-12-10 Augmenting Household Expenditure Forecasts with Online Employee-generated Company Reviews Symitsi, Efthymia Stamolampros, Panagiotis Karatzas, Antonios Public Opin Q Articles We assess the ability of online employee-generated content in predicting consumption expenditures. In so doing, we aggregate millions of employee expectations for the next six-month business outlook of their employer and build an employee sentiment index. We test whether forward-looking employee sentiment can contribute to baseline models when forecasting aggregate consumption in the United States and compare its performance to well-established, survey-based consumer sentiment indexes. We reveal that online employee opinions have incremental information that can be used to augment the accuracy of consumption forecasting models and inform economic policy decisions. Oxford University Press 2021-09-01 /pmc/articles/PMC8655719/ /pubmed/34899083 http://dx.doi.org/10.1093/poq/nfab017 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of American Association for Public Opinion Research. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Articles Symitsi, Efthymia Stamolampros, Panagiotis Karatzas, Antonios Augmenting Household Expenditure Forecasts with Online Employee-generated Company Reviews |
title | Augmenting Household Expenditure Forecasts with Online Employee-generated Company Reviews |
title_full | Augmenting Household Expenditure Forecasts with Online Employee-generated Company Reviews |
title_fullStr | Augmenting Household Expenditure Forecasts with Online Employee-generated Company Reviews |
title_full_unstemmed | Augmenting Household Expenditure Forecasts with Online Employee-generated Company Reviews |
title_short | Augmenting Household Expenditure Forecasts with Online Employee-generated Company Reviews |
title_sort | augmenting household expenditure forecasts with online employee-generated company reviews |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8655719/ https://www.ncbi.nlm.nih.gov/pubmed/34899083 http://dx.doi.org/10.1093/poq/nfab017 |
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