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

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  • Schmalz, Ulrike
  • Ringbeck, Jürgen
  • Spinler, Stefan

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.

Suggested Citation

  • Schmalz, Ulrike & Ringbeck, Jürgen & Spinler, Stefan, 2021. "Door-to-door air travel: Exploring trends in corporate reports using text classification models," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    • Handle: RePEc:eee:tefoso:v:170:y:2021:i:c:s0040162521002973
    DOI: 10.1016/j.techfore.2021.120865
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