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  • IJF_Demand_forecasting_with_user-generated_online_information

    Rights statement: This is the author’s version of a work that was accepted for publication in International Journal of Forecasting. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Forecasting, ??, ?, 2018 DOI: 10.1016/j.ijforecast.2018.03.005

    Accepted author manuscript, 333 KB, PDF-document

    Embargo ends: 6/06/20

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

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Demand forecasting with user-generated online information

Research output: Contribution to journalJournal article

E-pub ahead of print
<mark>Journal publication date</mark>6/06/2018
<mark>Journal</mark>International Journal of Forecasting
Number of pages36
<mark>State</mark>E-pub ahead of print
Early online date6/06/18
<mark>Original language</mark>English

Abstract

Recently, there has been substantial research on augmenting aggregate forecasts with individual consumer data from internet platforms, such as search traffic or social network shares. Although the majority of studies report increased accuracy, many exhibit design weaknesses including lack of adequate benchmarks or rigorous evaluation. Furthermore, their usefulness over the product life-cycle has not been investigated, which may change, as initially, consumers may search for pre-purchase information, but later for after-sales support. In this study, we first review the relevant literature and then attempt to support the key findings using two forecasting case studies. Our findings are in stark contrast to the literature, and we find that established univariate forecasting benchmarks, such as exponential smoothing, consistently perform better than when online information is included. Our research underlines the need for thorough forecast evaluation and argues that online platform data may be of limited use for supporting operational decisions.

Bibliographic note

This is the author’s version of a work that was accepted for publication in International Journal of Forecasting. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Forecasting, ??, ?, 2018 DOI: 10.1016/j.ijforecast.2018.03.005