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Segmenting Visitors to New Zealand: An Activity-Based Typology: An Abstract

  • Girish PrayagEmail author
  • Peter Fieger
Conference paper
Part of the Developments in Marketing Science: Proceedings of the Academy of Marketing Science book series (DMSPAMS)

Abstract

While previous studies applying the push-pull framework have contributed to improve our understanding of the underlying motivation of visitors and their destination choice, these studies are based on cross-sectional data that offer a limited perspective of how motivation to visit a destination evolves overtime. There is currently no pooled cross-sectional study of either the push or pull factors of a destination. Accordingly, the purpose of this study is to examine the influence of the pull factors of a destination, New Zealand, over a 19-year period (1997–2015). The research questions for this study are: what types of activity-based tourist profile can be identified among visitors to New Zealand over this period? and are there differences between these profiles on tourist spending, length of stay, travel style (package versus independent), and demographic variables? The data for this study is sourced from the International Visitor Survey (IVS) by New Zealand’s Ministry for Business, Innovation and Employment (MBIE), which is the primary source of data for international visitors’ travel behavior in New Zealand. We employ a pooled cross-sectional design that uses data spanning the period from 1997 to 2015 to identify segments of tourists that have chosen similar activities during their holidays in New Zealand. The useable sample size for this study consists of 62,288 individuals. Nine typologies of visitors were found such as individuals favoring nature-based activities (Type 1). These individuals also have a propensity for adventure (Type 2), cultural (Type 3), and walking-based activities (Type 6). Other notable combinations include adventure (Type 2)/nightlife-based (Type 8) activities, cultural (Type 3)/high-value rides (Type 4)/museum (Type 5) activities, high-value rides (Type 4)/museum and zoo (Type 5) activities, and a negative relationship between golfing and fishing (Type 7)/nightlife (Type 8) activities. Of interest is also how the visitor typologies have changed over this time frame. The time-dependent changes in rotated factor correlations of most identified typologies reveal remarkable pattern fluctuations over time. The nine typologies were profiled on three travel behavior characteristics (travel style, tourist spending, and length of stay).

References Available Upon Request

Copyright information

© Academy of Marketing Science 2019

Authors and Affiliations

  1. 1.University of CanterburyChristchurchNew Zealand
  2. 2.University of New EnglandArmidaleAustralia

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