Door-to-door air travel: Exploring trends in corporate reports using text classification models

Ulrike Schmalz, Jürgen Ringbeck and Stefan Spinler

Technological Forecasting and Social Change, 2021, vol. 170, issue C

Abstract: Previous studies have identified key trends affecting the door-to-door air travel chain, to inform organizational decision making. However, the extent to which transport service providers consider strategically relevant trends remains unclear. This study adopts the novel scope of door-to-door air travel in applying multi-labeled text classification models to 52 corporate reports from a sample of transport service providers. Trends identified from a literature review are used to develop seven classes. Two prototype models are developed: a dictionary-based classifier, and a supervised learning model using the multinomial naive Bayes and linear support vector machine classifiers. The latter yields the best model output. The results reveal that providers consider environmentally-friendly air transport and related products to be highly relevant, while disruption management, leveraging passengers data and improving airport feeder traffic through novel mobility concepts are considered to be of medium relevance. These models enable cheaper and quicker analysis of companies textual data. This innovative approach is also applicable to other research questions, such as market studies and finance-related projects.

Keywords: Annual reports; Door-to-door air travel; Small data; Support vector machine; Text classification, (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0040162521002973
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:170:y:2021:i:c:s0040162521002973

DOI: 10.1016/j.techfore.2021.120865

Access Statistics for this article

Technological Forecasting and Social Change is currently edited by Fred Phillips

More articles in Technological Forecasting and Social Change from Elsevier
Bibliographic data for series maintained by Catherine Liu ().