3.4. Analyses
We analyzed the data in five stages. First, we described the experience and evaluation variables for the sample as a whole. Then, we examined differences on experience and evaluation between the four conditions (popularity-driven/passive, policy-driven/passive, popularity-driven/conversational, policy-driven/conversational) using conditional group means and one-way analysis of variance. The third and fourth stage of data analysis aimed to assess if participants in different conditions visited different locations.In the third stage, we processed GPS data by first eliminating any data points which were not between date of arrival and date of departure or located outside of Overijssel. To analyze spatial patterns of participant distribution we transformed the participant point locations captured by GPS to continuous density representation using bivariate kernel density estimates [The last stage of data analysis involved modeling spatial movements of participants within spatial buffers generated around the attractions (20 m for point attractions, such as museums, restaurants, and monuments and 100 m for area-type attractions, such as parks or playgrounds) as a function of experimental condition. In other words, we modeled the odds that each datapoint collected came from a participant in one of the experimental conditions. Data were nested within participants, as both emotions and spatial behavior tend to be autocorrelated within participants. A multilevel logit model was used. We ran three models with different types of attractions as the outcomes: non-priority attractions, attractions which had popularity-driven priority, and attractions which had policy-driven priority. Finally, we also used these attraction movement variables as predictors of emotions to explore if participants enjoyed their vacation more on days when they engaged in more movements at a specific type of attraction.The popularity-driven passive condition was the reference group for all analyses, meaning all other conditions were always compared to this one, because a popularity-driven passive map-based application is the current default option used by many DMOs, and is thus analogous to a control condition in the present study. For smaller DMOs which do not have their own app, printed materials and conversations in tourist information centers also tend to focus on maps with certain attractions highlighted. Thus, the coefficients of models explaining participant movements based on experimental condition express the differences in movements between participants in the popularity-driven passive condition and each other condition in turn.