5.3. Limitations and Future Directions
crowding, COVID-19-related lockdowns, ’ behavior
MAY, COLD
During the first wave of data collection (May 2021), museums and restaurants were closed due to COVID-19-related lockdowns. Many people were hoping for some perspective on the situation before booking a vacation. At the last minute, when they might still have booked, the weather turned out to be cold and very rainy. Thus, it was difficult to collect a large number of participants, and it was difficult to deliver information about attractions that they would actually be able to use. We initiated a second data wave of collection at the end of July and beginning of August, during much more favorable conditions. The situation in domestic tourism in The Netherlands is once again different, however, with all pandemic measures having been lifted at the time of this writing (early 2023), international visitation climbing toward pre-pandemic levels, and domestic tourism at vacation parks remaining popular. Thus, replicating the present study under current conditions could uncover different results, as tourists today face different levels of crowding than in 2021.The accommodations at which we sampled do not comprise a probability sample of accommodation bookings or of vacation parks in Overijssel. More generally, one might ask how representative our sample was of tourists that marketers are hoping to reach with information. Not every tourist will seek out or pay attention to destination information from organizational channels. In that sense, it is likely that the same tourists which downloaded information for an experiment such as this one would also download information purely for use during their vacation. Thus, our participants maybe have been different in ways that made them more susceptible to change their behavior in response to the information provided. It also raises the question if a passive map-based app was an optimal control condition in our experiment. Certainly, more experimental conditions with different channels for the provision of information, as well as a condition with no information at all provided, could lead to deeper insights. In future research we advocate deriving sources of information to be tested from the bottom up, based on observations of or interviews about tourists’ behavior. This could increase the validity and usefulness of the conclusions.Between-individual differences in response to the experimental intervention were controlled for by the random assignment used in our experimental design. However, we also analyzed the within-individual effect of location on experiences, which could be moderated by between-individual variables such as length of stay or travel motivations. A limitation is that we did not include these variables in our analysis. We also did not collect extensive demographic information, such as household composition or place of residence, which can lead to appreciable differences in how vacations are experienced; for example in how far away, new, or different the vacation setting feels. Controlling these variables or modeling these moderations in future research could lead more precise insights into the relationship between location and experience.Analyzing a contextual variable as complicated as location also brings limitations. We chose to use a buffer approach to measure participants’ spatial behavior in relation to attraction locations, meaning when data points were within a certain minimum distance of an attraction, we counted them as being “at” that attraction, as a reasonably simple and accurate estimate. As with all measures of social behavior, it was imperfect, however. Every single data point within a buffer does not mean that the participants visited the attraction, just that they were nearby. Combining buffers with kernel densities or per-participant time data would address this issue. For area-type attractions the boundary polygons or trails would be needed to get more accurate estimate of the visitors.