Music festivals, as cultural events that induce tourism flows, intermediate both the cultural and travel experience. The present study analyzes online search behavior of potential attenders to a music festival. We hypothesize that the search process reveals latent patterns of behavior of cultural tourists planning to attend music festivals. To this end, information from Google Trends on queries related to three popular music festivals is used to build a network of search topics. Based on it, alternative exponential random graph model specifications are estimated. Findings support the general result of mediated information flows: music festivals induce planning and traveling queries. However, differences relating to the specificities of the cultural event are also found, in particular those regarding what nodes or queries supply the network with more useful information.
AbbruzzoABridaJGScuderiR (2014) Determinants of individual tourist expenditure as a network: empirical findings from Uruguay. Tourism Management43: 36–45.
2.
AjzenI (1991) The theory of planned behavior. Organizational Behavior and Human Decision Processes50(2): 179–211.
3.
APM (2018) IX anuario de la música en vivo (IX Annual report of live music). Available at: http://www.apmusicales.com. (accessed 9 October 2019).
4.
ArtolaCPintoFde Pedraza GarcíaP (2015) Can internet searches forecast tourism inflows?International Journal of Manpower36(1): 103–116.
5.
Báez-MontenegroADevesa-FernándezM (2017) Motivation, satisfaction and loyalty in the case of a film festival: differences between local and non-local participants. Journal of Cultural Economics41(2): 173–195.
6.
Bangwayo-SkeetePFSkeeteRW (2015) Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach. Tourism Management46: 454–464.
7.
BeiLTChenEYWiddowsR (2004) Consumers’ online information search behavior and the phenomenon of search vs. experience products. Journal of Family and Economic Issues25(4): 449–467.
8.
BjörkPKauppinen-RäisänenH (2015) Contemporary insights to the dynamic pre-trip information sourcing behaviour. Tourism and Hospitality Research15(1): 39–53.
9.
BlazquezDDomenechJ (2018) Big data sources and methods for social and economic analyses. Technological Forecasting and Social Change130: 99–113.
10.
BokelmannBLessmannS (2019) Spurious patterns in Google Trends data—an analysis of the effects on tourism demand forecasting in Germany. Tourism Management75: 1–12.
11.
BonetL (2013) Heritage tourism. In: RizzoIMignosaA (eds) Handbook on the Economics of Cultural Heritage. Edward Elgar Publishing Ltd. ISBN 9780857931009, pp. 386–401. DOI:10.4337/9780857931009.00032.
12.
BorowieckiKJCastiglioneC (2014) Cultural participation and tourism flows: an empirical investigation of Italian provinces. Tourism Economics20(2): 241–262.
13.
BridaJGDalle NogareCScuderiR (2016) Frequency of museum attendance: motivation matters. Journal of Cultural Economics40(3): 261–283.
14.
BuhalisDLawR (2008) Progress in information technology and tourism management: 20 years on and 10 years after the Internet—The state of eTourism research. Tourism Management29(4): 609–623.
15.
ButtsCT (2008) Network: a package for managing relational data in R. Journal of Statistical Software24(2).
16.
CasalóLVFlaviánCGuinalíuM (2010) Determinants of the intention to participate in firm-hosted online travel communities and effects on consumer behavioral intentions. Tourism Management31(6): 898–911.
17.
CelliniRCucciaT (2013) Museum and monument attendance and tourism flow: a time series analysis approach. Applied Economics45(24): 3473–3482.
18.
ChungJYBuhalisD (2009) Information needs in online social networks. Information Technology & Tourism10(4): 267–281.
19.
CoxCBurgessSSellittoC, et al. (2009) The role of user-generated content in tourists’ travel planning behavior. Journal of Hospitality Marketing & Management18(8): 743–764.
20.
CucciaTGuccioCRizzoI (2016) The effects of UNESCO World Heritage List inscription on tourism destinations performance in Italian regions. Economic Modelling53: 494–508.
21.
EisenhardtKMGraebnerME (2007) Theory building from cases: opportunities and challenges. Academy of Management Journal50(1): 25–32.
22.
FaulknerBFredlineELarsonM, et al. (1999) A marketing analysis of Sweden’s Storsjöyran musical festival. Tourism Analysis4(3-4): 157–171.
23.
GergaudOGinsburghV (2017) Measuring the economic effects of events using Google Trends. In: Ateca-AmestoyVMGinsburghVMazzaIO’HaganJPrieto-RodriguezJ (eds) Enhancing Participation in the Arts in the EU. Berlin: Springer, pp. 337–353.
24.
GoodreauSMKittsJAMorrisM (2009) Birds of a feather, or friend of a friend? Using exponential random graph models to investigate adolescent social networks. Demography46(1): 103–125.
25.
GuccioCLisiDMartoranaM, et al. (2017) On the role of cultural participation in tourism destination performance: an assessment using robust conditional efficiency approach. Journal of Cultural Economics41(2): 129–154.
26.
HerreroLCSanzJÁBedateA, et al. (2012) Who pays more for a cultural festival, tourists or locals? A certainty analysis of a contingent valuation application. International Journal of Tourism Research14(5): 495–512.
27.
HillerRS (2016) The importance of quality: how music festivals achieved commercial success. Journal of Cultural Economics40(3): 309–334.
28.
HittersE (2007) Porto and Rotterdam as European capitals of culture: toward the festivalization of urban cultural policy. In: RichardsGreg (ed) Cultural Tourism: Global and Local Perspectives. East Sussex: Psychology Press, pp. 281–301.
29.
HoCILinMHChenHM (2012) Web users’ behavioural patterns of tourism information search: from online to offline. Tourism Management33(6): 1468–1482.
30.
HuangCDGooJNamK, et al. (2017) Smart tourism technologies in travel planning: the role of exploration and exploitation. Information and Management54(6): 757–770.
31.
HunterDRHandcockMS (2006) Inference in curved exponential family models for networks. Journal of Computational and Graphical Statistics15(3): 565–583.
32.
JanssenSVerboordM (2015) Cultural mediators and gatekeepers. In: WrightJames D (ed) International Encyclopedia of the Social & Behavioral Sciences, 2nd ed. Vol 5. Oxford: Elsevier. pp. 440–446.
33.
JunSPYooHSChoiS (2018) Ten years of research change using Google Trends: from the perspective of big data utilizations and applications. Technological Forecasting and Social Change130: 69–87.
34.
Karpińska-KrakowiakM (2009) Festivalization of the city. Contemporary examples. Lidé města11(2): 338–350.
35.
LitvinSWGoldsmithREPanB (2008) Electronic word-of-mouth in hospitality and tourism management. Tourism Management29(3): 458–468.
36.
Llodrà-RieraIMartínez-RuizMPJiménez-ZarcoAI, et al. (2015) A multidimensional analysis of the information sources construct and its relevance for destination image formation. Tourism Management48: 319–328.
37.
María MunarA (2011) Tourist-created content: rethinking destination branding. International Journal of Culture, Tourism and Hospitality Research5(3): 291–305.
38.
NoonanDSRizzoI (2017) Economics of cultural tourism: issues and perspectives. Journal of Cultural Economics41(2): 95–107.
39.
PadhiSSPatiRK (2017) Quantifying potential tourist behavior in choice of destination using Google Trends. Tourism Management Perspectives24: 34–47.
40.
PanBFesenmaierDR (2006) Online information search. Vacation planning process. Annals of Tourism Research33(3): 809–832.
41.
PanBLiXR (2011) The long tail of destination image and online marketing. Annals of Tourism Research38(1): 132–152.
42.
PapathanassisAKnolleF (2011) Exploring the adoption and processing of online holiday reviews: a grounded theory approach. Tourism Management32(2): 215–224.
43.
RichardsG (forthcoming) The festivalization of society or the socialization of festivals? The case of Catalunya. In: RichardsG (ed) Cultural Tourism: Global and local perspectives. Binghampton: Haworth Press.
44.
ScuderiRDalle NogareC (2018) Mapping tourist consumption behaviour from destination card data: What do sequences of activities reveal?International Journal of Tourism Research20(5): 554–565.
45.
SGAE (2018) Anuario SGAE de las artes escénicas, musicales y audiovisuales. Available at: http://www.anuariossgae.com/home.html (accessed 10 May 2019).
46.
Stephens-DavidowitzS (2014) The cost of racial animus on a black candidate: evidence using Google search data. Journal of Public Economics118: 26–40.
47.
SullivanOKatz-GerroT (2006) The omnivore thesis revisited: voracious cultural consumers. European Sociological Review23(2): 123–137.
48.
VeccoMSrakarA (2017) Blue notes: Slovenian jazz festivals and their contribution to the economic resilience of the host cities. European Planning Studies25(1): 107–126.
49.
XiangZWangDO’LearyJT, et al. (2015) Adapting to the Internet: trends in travelers’ use of the web for trip planning. Journal of Travel Research54(4): 511–527.
50.
YangXPanBEvansJA, et al. (2015) Forecasting Chinese tourist volume with search engine data. Tourism Management46: 386–397.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
