Tourism researchers and the tourism industry rely heavily on data-driven market segmentation analysis for both knowledge development and market insight. Most algorithms used in data-driven market segmentation are exploratory; they do not generate one single stable result. Only when data are well-structured (when very clear, distinct market segments exist in the data) are repeated calculations likely to generate the same segmentation solution. When data lack structure, which is frequently the case in empirical consumer data sets, repeated calculations lead to different solutions. Running a market segmentation analysis once only can therefore lead to an entirely random solution that does not represent a strong foundation for developing a long-term market segmentation strategy. The present study (1) explains the problem, (2) assesses how high the risk is of random solutions occurring in tourism market segmentation studies, and (3) recommends an approach that can be used to avoid random solutions.

Ideation, or the formulation of ideas pertaining to a particular topic, is the precursor to individuals making significant life decisions. Many individuals think about foster caring long before they actually become carers, and it stands to reason that in many cases, carer discontinuation also follows a period of ideation. This being the case, it is possible that by monitoring ideation, interventions could be introduced to prevent placement disruptions occurring, particularly if the sources of dissatisfaction are known. Using a sample of 205 foster carers, a posteriori segmentation analysis identifies groups of carers dissatisfied with the same aspects of their role. One group is particularly dissatisfied with factors that are within the control of foster care agencies and also reports high levels of discontinuation ideation. Recommendations include that the individual support needs of carers be identified such that customized support can be offered, including boosting initial and ongoing training to manage expectations and ensure carers feel prepared for the role. Results also highlight the important role of caseworkers in making carers feel appreciated and taken seriously.

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Clustering functional data is mostly based on the projection of the curves onto an adequate basis and building random effects models of the basis coefficients. The parameters can be fitted with an EM algorithm. Alternatively, distance models based on the coefficients are used in the literature. Similar to the case of clustering multidimensional data, a variety of derivations of different models has been published. Although their calculation procedure is similar, their implementations are very different including distinct hyperparameters and data formats as input. This makes it difficult for the user to apply and particularly to compare them. Furthermore, they are mostly limited to specific basis functions. This paper aims to show the common elements between existing models in highly cited articles, first on a theoretical basis. Later their implementation is analyzed and it is illustrated how they could be improved and extended to a more general level. A special consideration is given to those models designed for sparse measurements. The work resulted in the R package funcy which was built to integrate the modified and extended algorithms into a unique framework.