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

Link:
Autor/in:
Erscheinungsjahr:
2013
Medientyp:
Text
Schlagworte:
  • Market segmentation
  • Segmentation
  • Market segments
  • Industry
  • Research
  • Marketing
  • Seasonal ticket revenue
  • Spatial econometrics
  • Spatial non-stationarity
  • Geographically weighted regression
  • Revenue response function
  • Public transport
  • Spatial autocorrelation
  • Market segmentation
  • Segmentation
  • Market segments
  • Industry
  • Research
  • Marketing
Beschreibung:
  • 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. © 2013 Elsevier Ltd.
Lizenz:
  • info:eu-repo/semantics/closedAccess
Quellsystem:
Forschungsinformationssystem der UHH
Interne Metadaten
Quelldatensatz
oai:www.edit.fis.uni-hamburg.de:publications/5458afaa-bd74-4371-a241-62edf1d57f8d