Airbnb has grown very rapidly over the past several years, with millions of tourists having used the service. The purpose of this study was to investigate tourists’ motivations for using Airbnb and...

Why Tourists Choose Airbnb: A Motivation-Based Segmentation Study:

https://ro.uow.edu.au/cgi/viewcontent.cgi?article=1718&context=buspapers

Crisis-resistant tourists

The purpose of this chapter is to explain the rationale for employing TwoStep cluster analysis as a market segmentation method within social marketing. Here, the key stages to be performed and the validation techniques required for effective application of this clustering technique are outlined. To further support the application of this cluster analysis technique as a profiling tool, a review of 25 recent market segmentation studies that have utilised this method is provided. Finally, a case study is provided to demonstrate how TwoStep cluster analysis is employed to segment respondents for an active school travel social marketing campaign that was being developed in Queensland at time of writing. Based on a sample of 537 respondents, three segments were identified and validated, each of which differed significantly based on psychographic, behaviour, geographic and demographic variables. Limitations of the TwoStep Cluster Analysis method are also provided, and opportunities for future research employing TwoStep cluster analysis within a social marketing context conclude this chapter.

Segmentation using two-step cluster analysis

The paper examines which travel risks are more salient for tourists' destination choice. An integrated travel-decision risk typology with survey data from 835 potential tourists is developed and tested. Specifically, this paper explores the interplay of risk types, tourist attributes and destination characteristics. It examines if travel risks linked to nature, health, terrorism, criminality, political instability are more salient for tourists' destination choice, and how risk perceptions influence tourists in the key stages of the decision-making process. Results offer an important baseline for future studies in the post-COVID-19 phase. First, the integrated travel-decision risk typology distinguishes between sociodemographic, psychological and travel-related factors. It shows that past travel experience shapes risk perceptions and impacts tourists’ future destination choice. Second, the study reveals that natural hazards are not the key barrier in the early decision-making stage of the destination choice process. Third, tourist segments that are resilient to certain risks are identified. This paper concludes with implications for the tourism practice with recommendations on how to manage travel risk and decision-making behaviours in the post-COVID-19 phase.

/pdf/which-travel-risks-are-more-salient-for-destination-choice-1q59dotl71.pdf

Which travel risks are more salient for destination choice? An examination of the tourist's decision-making process

Increasing sample size compensates for data problems in segmentation studies

Data analysts in industry and academia make heavy use of market segmentation analysis to develop tourism knowledge and select commercially attractive target segments. Within academic research alone, approximately 5% of published articles use market segmentation. However, the validity of data-driven market segmentation analyses depends on having available a sample of adequate size. Moreover, no guidance exists for determining what an adequate sample size is. In the present simulation study using artificial data of known structure, the impact of the difficulty of the segmentation task on the required sample size is analyzed in dependence of the number of variables in the segmentation base. Under all simulated data circumstances, a sample size of 70 times the number of variables proves to be adequate. This finding is of substantial practical importance because it will provide guidance to data analysts in academia and industry who wish to conduct reliable and valid segmentation studies.

https://journals.sagepub.com/doi/pdf/10.1177/0047287513496475

Required Sample Sizes for Data-Driven Market Segmentation Analyses in Tourism:

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Required Sample Sizes for Data-Driven Market Segmentation Analyses in Tourism: