Restricted accessResearch articleFirst published online 2022-8
Semiparametric APC analysis of destination choice patterns: Using generalized additive models to quantify the impact of age,period,and cohort on travel distances
This study investigates how age, period, and birth cohorts are related to altering travel distances. We analyze a repeated cross-sectional survey of German pleasure travels for the period 1971–2018 using a holistic age–period–cohort (APC) analysis framework. Changes in travel distances are attributed to the life cycle (age effect), macro-level developments (period effect), and generational membership (cohort effect). We introduce ridgeline matrices and partial APC plots as innovative visualization techniques facilitating the intuitive interpretation of complex temporal structures. Generalized additive models are used to circumvent the identification problem by fitting a bivariate tensor product spline between age and period. The results indicate that participation in short-haul trips is mainly associated with age, while participation in long-distance travel predominantly changed over the period. Generational membership shows less association with destination choice concerning travel distance. The presented APC approach is promising to address further questions of interest in tourism research.
AlegreJPouL (2004) Micro-economic determinants of the probability of tourism consumption. Tourism Economics10(2): 125–144.
2.
BauerAScheiplFKüchenhoffH, et al. (2018) An introduction to semiparametric function-on-scalar regression. Statistical Modelling18(3-4): 346–364.
3.
BeckenSCarmignaniF (2020) Are the current expectations for growing air travel demand realistic?Annals of Tourism Research80: 102840.
4.
BeldonaS (2005) Cohort analysis of online travel information search behavior: 1995-2000. Journal of Travel Research44(2): 135–142.
5.
BerniniCCracoliciMF (2015) Demographic change, tourism expenditure and life cycle behaviour. Tourism Management47: 191–205.
6.
BowenDClarkeJ (2009) Contemporary Tourist Behaviour: Yourself and Others as Tourists. Wallingford: CABI Tourism Texts. CABI.
7.
CarstensenB (2007) Age–period–cohort models for the lexis diagram. Statistics in Medicine26(15): 3018–3045.
8.
CastroRLohmannGSpasojevicB, et al. (2020) The future past of aircraft technology and its impact on stopover destinations. In: YeomanIMcMahon-BeattieU (eds) The Future Past of Tourism, The Future of Tourism. Bristol: Channel View Publications, pp. 93–104.
9.
ChenSCShoemakerS (2014) Age and cohort effects: the American senior tourism market. Annals of Tourism Research48: 58–75.
10.
ClaytonDSchifflersE (1987) Models for temporal variation in cancer rates. II: age–period–cohort models. Statistics in Medicine6(4): 469–481.
11.
ClementsMSArmstrongBKMoolgavkarSH (2005) Lung cancer rate predictions using generalized additive models. Biostatistics6(4): 576–589.
12.
CohenSAPrayagGMoitalM (2014) Consumer behaviour in tourism: concepts, influences and opportunities. Current Issues in Tourism17(10): 872–909.
CooperCHallCM (2016) Contemporary Tourism: An International Approach. 3rd edn. Oxford: Goodfellow Publishers Ltd.
15.
DigglePDigglePJHeagertyP, et al. (2002) Analysis of Longitudinal Data. Oxford: Oxford University Press.
16.
EilersPHMarxBD (1996) Flexible smoothing with B-splines and penalties. Statistical Science11: 89–102.
17.
Eugenio-MartinJLCampos-SoriaJA (2011) Income and the substitution pattern between domestic and international tourism demand. Applied Economics43(20): 2519–2531.
18.
FannonZMondenCNielsenB (2018) Age-period-cohort modelling and covariates, with an application to obesity in England 2001-2014 (No. 2018-W05). Economics Group, Nuffield College: University of Oxford.
19.
FienbergSEMasonWM (1979) Identification and estimation of age-period-cohort models in the analysis of discrete archival data. Sociological Methodology10: 1–67.
20.
Forschungsgemeinschaft Urlaub und Reisen eV (FUR) (2020a) Reiseanalyse. How Germany travels. Available at: https://reiseanalyse.de/reiseanalyse-2018/(accessed 14 May 2020).
FrickRBelartBSchmiedM, et al. (2014) Langstreckenmobilität: Aktuelle Trends und Perspektiven: Grundlagenstudie. Grundlagenstudie.
23.
FuWJ (2000) Ridge estimator in singulah oesiun with application to age-period-cohort analysis of disease rates. Communications in Statistics-Theory and Methods29(2): 263–278.
24.
GloverPPrideauxB (2009) Implications of population ageing for the development of tourism products and destinations. Journal of Vacation Marketing15(1): 25–37.
25.
GösslingSScottDHallCM, et al. (2012) Consumer behaviour and demand response of tourists to climate change. Annals of Tourism Research39(1): 36–58.
26.
GuilletBDLeeALawR, et al. (2011) Factors affecting outbound tourists’ destination choice: the case of Hong Kong. Journal of Travel & Tourism Marketing28(5): 556–566.
JackmanMLordeTNaitramS, et al. (2020) Distance matters: the impact of physical and relative distance on pleasure tourists’ length of stay in Barbados. Annals of Tourism Research80: 102794.
32.
JapkowiczNShahM (2011) Evaluating Learning Algorithms: A Classification Perspective. Cambridge, MA: Cambridge University Press.
33.
KarlM (2018) Risk and uncertainty in travel decision-making: tourist and destination perspective. Journal of Travel Research57(1): 129–146.
34.
KarlMWinderGBauerA (2017) Terrorism and tourism in Israel: analysis of the temporal scale. Tourism Economics23(6): 1343–1352.
35.
KimHXiangZFesenmaierDR (2015) Use of the internet for trip planning: a generational analysis. Journal of Travel & Tourism Marketing32(3): 276–289.
36.
KupperLLJanisJMKarmousA, et al. (1985) Statistical age-period-cohort analysis: a review and critique. Journal of Chronic Diseases38(10): 811–830.
37.
LeeHAGuilletBDLawR, et al. (2012) Robustness of distance decay for international pleasure travelers: a longitudinal approach. International Journal of Tourism Research14(5): 409–420.
38.
LehtoXYJangSAchanaFT, et al. (2008) Exploring tourism experience sought: a cohort comparison of Baby Boomers and the Silent Generation. Journal of Vacation Marketing14(3): 237–252.
39.
LiXLiXHudsonS (2013) The application of generational theory to tourism consumer behavior: an American perspective. Tourism Management37: 147–164.
40.
LohmannMDanielssonJ (2001) Predicting travel patterns of senior citizens: how the past may provide a key to the future. Journal of Vacation Marketing7(4): 357–366.
41.
MarraGWoodSN (2012) Coverage properties of confidence intervals for generalized additive model components. Scandinavian Journal of Statistics39(1): 53–74.
42.
McKercherBMakB (2019) The impact of distance on international tourism demand. Tourism Management Perspectives31: 340–347.
43.
McKercherBChanALamC (2008) The impact of distance on international tourist movements. Journal of Travel Research47(2): 208–224.
44.
McKercherBLaiBYangL, et al. (2020) Travel by Chinese: a generational cohort perspective. Asia Pacific Journal of Tourism Research25(4): 341–354.
45.
MoutinhoL (1987) Consumer behaviour in tourism. European Journal of Marketing17: 872–909.
46.
NelderJAWedderburnRW (1972) Generalized linear models. Journal of the Royal Statistical Society: Series A (General135(3): 370–384.
47.
NicolauJLMásFJ (2006) The influence of distance and prices on the choice of tourist destinations: the moderating role of motivations. Tourism Management27(5): 982–996.
48.
OppermannM (1995) Travel life cycle. Annals of Tourism Research22(3): 535–552.
49.
PendergastD (2010) Getting to know the Y Generation. In: BenckendorffPMoscardoGPendergastD (eds) Tourism and Generation Y. Wallingford: CAB International, pp. 1–15.
50.
Pennington-GrayLSprengRA (2002) Analyzing changing preferences for pleasure travel with cohort analysis. Tourism Analysis6: 109–121.
51.
Pennington-GrayLFridgenJDStynesD (2003) Cohort segmentation: an application to tourism. Leisure Sciences25(4): 341–361.
52.
Pennington-GrayLKerstetterDLWarnickR (2002) Forecasting travel patterns using Palmore’s cohort analysis. Journal of Travel & Tourism Marketing13(1-2): 125–143.
53.
R Core Team (2019) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available at: https://www.R-project.org/ (accessed 4 January 2021).
54.
RomagosaF (2020) The COVID-19 crisis: opportunities for sustainable and proximity tourism. Tourism Geographies22: 690–694.
55.
ScheinerJHolz-RauC (2013) A comprehensive study of life course, cohort, and period effects on changes in travel mode use. Transportation Research Part A: Policy and Practice47: 167–181.
56.
ScheweCDNobleSM (2000) Market segmentation by cohorts: the value and validity of cohorts in America and abroad. Journal of Marketing Management16(1-3): 129–142.
57.
SchmidVJHeldL (2007) Bayesian age-period-cohort modeling and prediction-BAMP. Journal of Statistical Software21(8): 1–15.
58.
SmallmanCMooreK (2010) Process studies of tourists’ decision-making. Annals of Tourism Research37(2): 397–422.
59.
SongHLiG (2008) Tourism demand modelling and forecasting—a review of recent research. Tourism Management29(2): 203–220.
60.
SongHQiuRTParkJ (2019) A review of research on tourism demand forecasting: launching the annals of tourism research curated collection on tourism demand forecasting. Annals of Tourism Research75: 338–362.
61.
SunYYLinPC (2019) How far will we travel? A global distance pattern of international travel from both demand and supply perspectives. Tourism Economics25(8): 1200–1223.
62.
TaylorCEKnudsonDM (1973) Area preferences of midwestern campers. Journal of Leisure Research5(2): 39–48.
63.
ToblerWR (1970) A computer movie simulating urban growth in the Detroit region. Economic Geography46(sup1): 234–240.
WickhamH (2016) ggplot2: Elegant Graphics for Data Analysis. Berlin: Springer.
66.
WilkeC (2018) ggridges: ridgeline plots in “ggplot2.” R package version 0.5. 0. R foundation for statistical computing, Vienna, Austria.
67.
WongIAFongLHNLawR (2016) A longitudinal multilevel model of tourist outbound travel behavior and the dual-cycle model. Journal of Travel Research55(7): 957–970.
68.
WongIALawRZhaoX (2017) When and where to travel? A longitudinal multilevel investigation on destination choice and demand. Journal of Travel Research56(7): 868–880.
69.
WoodSN (2017) Generalized Additive Models: An Introduction with R. Boca Raton, FL: CRC Press.
70.
YangYLandKC (2008) Age–period–cohort analysis of repeated cross-section surveys: fixed or random effects?Sociological Methods & Research36(3): 297–326.
71.
YangYLandKC (2013) Age-Period-Cohort Analysis: New Models, Methods, and Empirical Applications. Boca Raton, FL: CRC Press.
72.
YangYLiuHLiX, et al. (2018) A shrinking world for tourists? Examining the changing role of distance factors in understanding destination choices. Journal of Business Research92: 350–359.
73.
YouXO’learyJT (2000) Age and cohort effects: an examination of older Japanese travelers. Journal of Travel & Tourism Marketing9(1-2): 21–42.
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