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Supplemental Material, sj-pdf-1-teu-10.1177_1354816620987198 for Semiparametric APC analysis of destination choice patterns: Using generalized additive models to quantify the impact of age, period, and cohort on travel distances by Maximilian Weigert, Alexander Bauer, Johanna Gernert, Marion Karl, Asmik Nalmpatian, Helmut Küchenhoff and Jürgen Schmude in Tourism Economics

Supplemental Material, sj-pdf-3-teu-10.1177_1354816620987198 for Semiparametric APC analysis of destination choice patterns: Using generalized additive models to quantify the impact of age, period, and cohort on travel distances by Maximilian Weigert, Alexander Bauer, Johanna Gernert, Marion Karl, Asmik Nalmpatian, Helmut Küchenhoff and Jürgen Schmude in Tourism Economics

Supplemental Material, Appendix for Semiparametric APC analysis of destination choice patterns: Using generalized additive models to quantify the impact of age, period, and cohort on travel distances by Maximilian Weigert, Alexander Bauer, Johanna Gernert, Marion Karl, Asmik Nalmpatian, Helmut Küchenhoff and Jürgen Schmude in Tourism Economics

Background of the study: Research on destination choice using aggregated data found people increasingly travel longer distance as new technological developments in transportation occurred (Castro et al. 2020), with economic prosperity in the source market (Sun and Lin 2019) and due to innovations in communication technologies (Yang et al. 2018) that facilitated access to information about destinations at long distance. In addition to these macro-level developments, destination choice, and hence travel distance changes throughout someone’s life cycle due to changing personal circumstances and increasing age (Bernini and Cracolici 2015) and between generations (Lohmann and Danielsson 2001). Hence, longitudinal changes in travel behavior are triggered simultaneously and interactively by age- (i.e., internal), period- (i.e., external), and cohort-effects (i.e., generational) (Oppermann 1995); calling for advanced statistical approaches to separate them. Only a few studies in tourism research have examined alterations in travel behavior based on all three temporal dimensions so far (e.g., Oppermann 1995). Purpose of the study: The purpose of this study is to explain how and why people’s destination choice changes over time. This study aims to estimate the impact of internal (e.g., life-cycle stage, aging, generational membership) and external (e.g., economic development, societal change, technological advancements, political events) temporal factors on individuals’ destination choice using the example of travel distances. Methodology: We analyze a repeated cross-sectional survey of German pleasure travels for the period 1971-2018. The data used in this study were collected in the Reiseanalyse, an annual representative survey of approximately 7,500 German residents (~330,000 respondents and ~227,000 trips in total). To separate the temporal factors we apply statistical age-period-cohort (APC) analysis methods to tourism research and estimate internal temporal developments regarding the individual tourist or external changes in the circumstance of holiday trips. We use generalized additive regression models as a state-of-the-art tool to circumvent the identification problem of APC analyses. We introduce ridgeline matrices and partial APC plots as innovative visualization techniques facilitating the intuitive interpretation of complex temporal structures. Results: The pure APC model (i.e., age, period and cohort as only temporal factors) shows that travel distances vary across all observed temporal dimensions. While short-haul trips are mainly associated with age differences (i.e., increase with age), long-distance travel changes mostly over the period (i.e., increase over time). The impact of generational membership was less pronounced regarding travel distances. The observed tendencies may imply that choosing short-haul destinations depends on personal characteristics and age-related travel constraints such as physical or family restrictions (You and O’leary 2000). Contrarily, long-distance travel might be more constrained by macro-level factors such as developments in transport technology attributed to reduced costs for long-haul travel or economic growth leading to an increase in disposable income, which can be used for more expensive long-distance travel (Sun and Lin 2019). The covariate APC model (i.e., inclusion of additional internal factors shaping travel behavior) reveals how trip duration, household size and income can also affect travel distances in addition to age-, period- and cohort-effects. For example, assuming trips of equal length, the chance for holiday trips over 6,000 km increases more steeply both over time and across generations underlining the higher affordability and easier accessibility of long-haul trips in recent years and for younger cohorts. External factors of destination choice (e.g., economic climate, technological developments) are indirectly included in the period effect, assuming that individual travelers are affected similarly by societal changes and socialization processes of new technology. Conclusions: Often it is the interplay between internal and external factors, related to the tourist and the destination, that shapes travel decision-making and consequently tourism demand. For instance, the individual motivation to travel and the price level at and transport costs to a destination commonly influence tourists’ destination choices (Nicolau and Mάs 2006). Our methodological framework enables to simultaneously incorporate variables on the individual (e.g., income of the traveler) and macro-level (e.g., general economic indices), which leads to more precise estimates of spatio-temporal travel changes. Research implications and limitations: The developed age-period-cohort analysis framework can be easily adapted to investigate other temporal changes in tourism behavior (e.g., transport choice for life-cycle environmental footprint analysis) or the impact of external factors on temporal changes in tourism demand (e.g., comparative analysis of natural and human-induced hazards). Understanding which and how internal and external factors cause changes in travel behavior may lead to better predictions of future tourism demand, supporting touristic stakeholders in tourism planning and management. References: Bernini, C., & Cracolici, M.F. (2015). Demographic change, tourism expenditure and life cycle behaviour. Tourism Management 47 191-205. Castro, R., Lohmann, G., Spasojevic, B., Fraga, C., & Allis, T. (2020). The future past of aircraft technology and its impact on stopover destinations. In: Yeoman, I., & McMahon-Beattie, U. (eds.) The future past of tourism, The future of tourism. Bristol: Channel View Publications 93-104. Lohmann, M., & Danielsson, J. (2001). Predicting travel patterns of senior citizens: How the past may provide a key to the future. Journal of Vacation Marketing 7 (4) 357-366. Nicolau, J.L., & Más, F.J. (2006). The influence of distance and prices on the choice of tourist destinations: The moderating role of motivations. Tourism Management 27 (5) 982-996. Oppermann, M. (1995). Travel life cycle. Annals of Tourism Research 22 (3) 535-552. Sun, Y.Y., & Lin, P.C. (2019). How far will we travel? a global distance pattern of international travel from both demand and supply perspectives. Tourism Economics 25 (8) 1200-1223. Yang, Y., Liu, H., Li, X., & Harrill, R. (2018). A shrinking world for tourists? Examining the changing role of distance factors in understanding destination choices. Journal of Business Research 92 350-359.