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Spatial dependencies and spatial drift in public transport seasonal ticket revenue data

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  • Müller, Sven
  • Wilhelm, Pascal
  • Haase, Knut

Abstract

When firms' customers are located in spatially dispersed areas, it can be difficult to manage service quality on a geographically small scale because the relative importance of service quality might vary spatially. Moreover, standard approaches discussed so far in the marketing science literature usually neglect spatial effects, such as spatial dependencies (spatial autocorrelation for example) and spatial drift (spatial non-stationarity). We propose a comprehensive approach based on spatial econometric methods that covers both issues. Based on the real company data on seasonal ticket revenue of a local public transport service company, we show that addressing such spatial effects of service data can improve management's ability to implement programs aimed at enhancing seasonal ticket revenue. In particular, the article shows how a spatial revenue response function might be specified.

Suggested Citation

  • Müller, Sven & Wilhelm, Pascal & Haase, Knut, 2013. "Spatial dependencies and spatial drift in public transport seasonal ticket revenue data," Journal of Retailing and Consumer Services, Elsevier, vol. 20(3), pages 334-348.
    • Handle: RePEc:eee:joreco:v:20:y:2013:i:3:p:334-348
    DOI: 10.1016/j.jretconser.2013.01.005
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    References listed on IDEAS

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    Cited by:

    1. Katarzyna Kopczewska, 2022. "Spatial machine learning: new opportunities for regional science," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 68(3), pages 713-755, June.
    2. Haase, Knut & Müller, Sven, 2014. "Upper and lower bounds for the sales force deployment problem with explicit contiguity constraints," European Journal of Operational Research, Elsevier, vol. 237(2), pages 677-689.

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