Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm?
<p>Using 27 million flight bookings for 2 years from a major international airline company, we built a Next Likely Destination model to ascertain customers’ next flight booking. The resulting model achieves an 89% predictive accuracy using historical data. A unique aspect of the model is the i...
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| منشور في: |
2021
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| _version_ | 1864513567901876224 |
|---|---|
| author | Saravanan Thirumuruganathan (11038038) |
| author2 | Soon-gyo Jung (7434773) Dianne Ramirez Robillos (14151015) Joni Salminen (7434770) Bernard J. Jansen (7434779) |
| author2_role | author author author author |
| author_facet | Saravanan Thirumuruganathan (11038038) Soon-gyo Jung (7434773) Dianne Ramirez Robillos (14151015) Joni Salminen (7434770) Bernard J. Jansen (7434779) |
| author_role | author |
| dc.creator.none.fl_str_mv | Saravanan Thirumuruganathan (11038038) Soon-gyo Jung (7434773) Dianne Ramirez Robillos (14151015) Joni Salminen (7434770) Bernard J. Jansen (7434779) |
| dc.date.none.fl_str_mv | 2021-01-13T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s10660-021-09457-0 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Forecasting_the_nearly_unforecastable_why_aren_t_airline_bookings_adhering_to_the_prediction_algorithm_/21597231 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Commerce, management, tourism and services Air transportation and freight services Information and computing sciences Artificial intelligence Data management and data science Human-centred computing Machine learning Prediction Recommendation Airlines Travel User evaluation |
| dc.title.none.fl_str_mv | Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm? |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Using 27 million flight bookings for 2 years from a major international airline company, we built a Next Likely Destination model to ascertain customers’ next flight booking. The resulting model achieves an 89% predictive accuracy using historical data. A unique aspect of the model is the incorporation of self-competence, where the model defers when it cannot reasonably make a recommendation. We then compare the performance of the Next Likely Destination model in a real-life consumer study with 35,000 actual airline customers. In the user study, the model obtains a 51% predictive accuracy. What happened? The Individual Behavior Framework theory provides insights into possibly explaining this inconsistency in evaluation outcomes. Research results indicate that algorithmic approaches in competitive industries must account for shifting customer preferences, changes to the travel environment, and confounding business effects rather than relying solely on historical data.</p><h2>Other Information</h2> <p> Published in: Electronic Commerce Research<br> License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="http://dx.doi.org/10.1007/s10660-021-09457-0" target="_blank">http://dx.doi.org/10.1007/s10660-021-09457-0</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_208e7b95a7bde7a40246721b5245c021 |
| identifier_str_mv | 10.1007/s10660-021-09457-0 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/21597231 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm?Saravanan Thirumuruganathan (11038038)Soon-gyo Jung (7434773)Dianne Ramirez Robillos (14151015)Joni Salminen (7434770)Bernard J. Jansen (7434779)Commerce, management, tourism and servicesAir transportation and freight servicesInformation and computing sciencesArtificial intelligenceData management and data scienceHuman-centred computingMachine learningPredictionRecommendationAirlinesTravelUser evaluation<p>Using 27 million flight bookings for 2 years from a major international airline company, we built a Next Likely Destination model to ascertain customers’ next flight booking. The resulting model achieves an 89% predictive accuracy using historical data. A unique aspect of the model is the incorporation of self-competence, where the model defers when it cannot reasonably make a recommendation. We then compare the performance of the Next Likely Destination model in a real-life consumer study with 35,000 actual airline customers. In the user study, the model obtains a 51% predictive accuracy. What happened? The Individual Behavior Framework theory provides insights into possibly explaining this inconsistency in evaluation outcomes. Research results indicate that algorithmic approaches in competitive industries must account for shifting customer preferences, changes to the travel environment, and confounding business effects rather than relying solely on historical data.</p><h2>Other Information</h2> <p> Published in: Electronic Commerce Research<br> License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="http://dx.doi.org/10.1007/s10660-021-09457-0" target="_blank">http://dx.doi.org/10.1007/s10660-021-09457-0</a></p>2021-01-13T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s10660-021-09457-0https://figshare.com/articles/journal_contribution/Forecasting_the_nearly_unforecastable_why_aren_t_airline_bookings_adhering_to_the_prediction_algorithm_/21597231CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/215972312021-01-13T06:00:00Z |
| spellingShingle | Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm? Saravanan Thirumuruganathan (11038038) Commerce, management, tourism and services Air transportation and freight services Information and computing sciences Artificial intelligence Data management and data science Human-centred computing Machine learning Prediction Recommendation Airlines Travel User evaluation |
| status_str | publishedVersion |
| title | Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm? |
| title_full | Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm? |
| title_fullStr | Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm? |
| title_full_unstemmed | Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm? |
| title_short | Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm? |
| title_sort | Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm? |
| topic | Commerce, management, tourism and services Air transportation and freight services Information and computing sciences Artificial intelligence Data management and data science Human-centred computing Machine learning Prediction Recommendation Airlines Travel User evaluation |