Online query data have emerged as a prevalent source for tourism demand forecasting, but their influence mechanism remains unclear. This study seeks to verify the moderating role of tourists’ online information search behavior on price and income. This empirical study of Hong Kong’s tourism demand among eight English-speaking markets reveals that (1) this behavior moderates how price and income affect tourism demand; (2) regarding price’s effect on tourism demand, all six categories of online information behavior significantly mitigate price’s impact on tourism demand; (3) regarding income’s effect on tourism demand, only information search behaviors related to dining, transportation, and tours exhibit significant moderating effects. This study provides new actionable insights for governments and tourism administrators on online information sharing to enhance tourism marketing.
AlbalateDFagedaX (2016) High speed rail and tourism: empirical evidence from Spain. Transportation Research Part A: Policy and Practice85: 174–185.
2.
AlegreJMateoSPouL (2010) An analysis of households’ appraisal of their budget constraints for potential participation in tourism. Tourism Management31(1): 45–56.
3.
AlegreJMateoSPouL (2013) Tourism participation and expenditure by Spanish households: the effects of the economic crisis and unemployment. Tourism Management39: 37–49.
4.
AlegreJPouLSardM (2019) High unemployment and tourism participation. Current Issues in Tourism22(10): 1138–1149.
5.
AşanKEmeksizM (2018) Outdoor recreation participants’ motivations, experiences and vacation activity preferences. Journal of Vacation Marketing24(1): 3–15.
6.
Bangwayo-SkeetePFSkeeteRW (2015) Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach. Tourism Management46: 454–464.
7.
BellAFairbrotherMJonesK (2019) Fixed and random effects models: making an informed choice. Quality and Quantity53(2): 1051–1074.
8.
BerniniCCracoliciMFNijkampP (2017) Place-based attributes and spatial expenditures behavior in tourism. Journal of Regional Science57(2): 218–244.
9.
BiJLehtoXY (2018) Impact of cultural distance on international destination choices: the case of Chinese outbound travelers. International Journal of Tourism Research20(1): 50–59.
10.
CaseDOGivenLM (2016) Looking for Information: A Survey of Research on Information Seeking, Needs, and Behavior. 4th edition. Emerald Group Publishing.
11.
CebolladaJChuYJiangZ (2019) Online category pricing at a multichannel grocery retailer. Journal of Interactive Marketing46(1): 52–69.
12.
ChenJLiMXieC (2022) Transportation connectivity strategies and regional tourism economy - empirical analysis of 153 cities in China. Tourism Review77(1): 113–128.
13.
ChoiYHickersonBKerstetterD (2018) Understanding the sources of online travel information. Journal of Travel Research57(1): 116–128.
14.
CholettePA (1984) Adjusting sub-annual series to yearly benchmarks. Survey Methodology10(1): 35–49.
15.
ChungHCChungNKimJ (2022) How do online hotel consumers perceive room rates?Journal of Vacation Marketing28(3): 350–365.
16.
DentonFT (1971) Adjustment of monthly or quarterly series to annual totals: an approach based on quadratic minimization. Journal of the American Statistical Association66(333): 99–102.
17.
DergiadesTMavraganiEPanB (2018) Google trends and tourists' arrivals: emerging biases and proposed corrections. Tourism Management66: 108–120.
18.
DivisekeraS (2009) Ex post demand for Australian tourism goods and services. Tourism Economics15(1): 153–180.
19.
DogruTSirakaya-TurkECrouchGI (2017) Remodeling international tourism demand: old theory and new evidence. Tourism Management60: 47–55.
20.
DuncanAHailemariamA (2025) Come and say G’day: using search engine data to understand the dynamics of tourism demand in Australia. Tourism Economics31: 1428–1451.
21.
FesenmaierDRXiangZPanB, et al. (2011) A framework of search engine use for travel planning. Journal of Travel Research50(6): 587–601.
22.
GoldfarbATuckerC (2019) Digital economics. Journal of Economic Literature57(1): 3–43.
23.
GounopoulosDPetmezasDSantamariaD (2012) Forecasting tourist arrivals in Greece and the impact of macroeconomic shocks from the countries of tourists’ origin. Annals of Tourism Research39(2): 641–666.
24.
GunterUSmeralE (2016) The decline of tourism income elasticities in a global context. Tourism Economics22(3): 466–483.
25.
GursoyD (2019) A critical review of determinants of information search behavior and utilization of online reviews in decision making process (invited paper for ‘luminaries’ special issue of International Journal of Hospitality Management). International Journal of Hospitality Management76: 53–60.
26.
GursoyDMcClearyKW (2004) An integrative model of tourists’ information search behavior. Annals of Tourism Research31(2): 353–373.
27.
GursoyDDel ChiappaGZhangY (2017) Preferences regarding external information sources: a conjoint analysis of visitors to Sardinia, Italy. Journal of Travel & Tourism Marketing34(6): 806–820.
28.
HanSGuptaSLehmannDR (2001) Consumer price sensitivity and price thresholds. Journal of Retailing77(4): 435–456.
29.
HelsonH (1964) Adaptation-Level Theory: An Experimental and Systematic Approach to Behavior. Harper & Row.
30.
Hong Kong Tourism Board (2019) A Statistical Review of Hong Kong Tourism 2019. Hong Kong: HKTB.
31.
HöpkenWEberleTFuchsM, et al. (2021) Improving tourist arrival prediction: a big data and artificial neural network approach. Journal of Travel Research60(5): 998–1017.
32.
HuMSongH (2020) Data source combination for tourism demand forecasting. Tourism Economics26(7): 1248–1265.
33.
HuangCDGooJNamK, et al. (2017) Smart tourism technologies in travel planning: the role of exploration and exploitation. Information & Management54(6): 757–770.
34.
HuiTYuenC (1998) An econometric study on Japanese tourist arrivals in British Columbia and its implications. Service Industries Journal18(4): 38–50.
35.
JangSS (2004) The past, present, and future research of online information search. Journal of Travel & Tourism Marketing17(2-3): 41–47.
36.
JinHMoscardoGMurphyL (2017) Making sense of tourist shopping research: a critical review. Tourism Management62: 120–134.
37.
KangSJodiceLWNormanWC (2020) How do tourists search for tourism information via smartphone before and during their trip?Tourism Recreation Research45(1): 57–68.
38.
KangSKimWGParkD (2021) Understanding tourist information search behaviour: the power and insight of social network analysis. Current Issues in Tourism24(3): 403–423.
39.
KimJLeeC (2017) Role of tourism price in attracting international tourists: the case of Japanese inbound tourism from South Korea. Journal of Destination Marketing & Management6(1): 76–83.
40.
KosenkoRRahtzD (1988) Buyer market price knowledge influence on acceptable price range and price limits. Advances in Consumer Research15: 328–333.
41.
KuhlthauCC (1991) Inside the search process: information seeking from the user's perspective. Journal of the American Society for Information Science42(5): 361–371.
42.
LawRBuhalisDCobanogluC (2014) Progress on information and communication technologies in hospitality and tourism. International Journal of Contemporary Hospitality Management26(5): 727–750.
43.
LawRLiGFongDKC, et al. (2019) Tourism demand forecasting: a deep learning approach. Annals of Tourism Research75: 410–423.
44.
LeeMCunninghamLF (2001) A cost/benefit approach to understanding service loyalty. Journal of Services Marketing15(2): 113–130.
45.
LeeHChungNNamY (2019) Do online information sources really make tourists visit more diverse places? Based on the social networking analysis. Information Processing & Management56(4): 1376–1390.
46.
LiGSongHWittSF (2005) Recent developments in econometric modeling and forecasting. Journal of Travel Research44(1): 82–99.
47.
LiCCuiGPengL (2017a) The signaling effect of management response in engaging customers: a study of the hotel industry. Tourism Management62: 42–53.
48.
LiXPanBLawR, et al. (2017b) Forecasting tourism demand with composite search index. Tourism Management59: 57–66.
49.
LimC (1999) A meta-analytic review of international tourism demand. Journal of Travel Research37(3): 273–284.
50.
LiuXWangDGretzelU (2022) On-site decision-making in smartphone-mediated contexts. Tourism Management88: 104424.
51.
LynchJGArielyD (2000) Wine online: search costs affect competition on price, quality, and distribution. Marketing Science19(1): 83–103.
52.
MartinCAWittSF (1987) Tourism demand forecasting models: choice of appropriate variable to represent tourists' cost of living. Tourism Management8(3): 233–246.
53.
MartinsLFGanYFerreira-LopesA (2017) An empirical analysis of the influence of macroeconomic determinants on world tourism demand. Tourism Management61: 248–260.
54.
MishraDPHeideJBCortSG (1998) Information asymmetry and levels of agency relationships. Journal of Marketing Research35(3): 277–295.
55.
NaudéWASaaymanA (2005) Determinants of tourist arrivals in Africa: a panel data regression analysis. Tourism Economics: The Business and Finance of Tourism and Recreation11(3): 365–391.
56.
NesselKKościółekSWszendybył-SkulskaE, et al. (2021) Benefit segmentation in the tourist accommodation market based on eWOM attribute ratings. Information Technology & Tourism23(2): 265–290.
57.
PadhiSSPatiRK (2017) Quantifying potential tourist behavior in choice of destination using Google trends. Tourism Management Perspectives24: 34–47.
58.
PageSSongHWuDC (2012) Assessing the impacts of the global economic crisis and swine flu on inbound tourism demand in the United Kingdom. Journal of Travel Research51(2): 142–153.
59.
PanBChenguang WuDSongH (2012) Forecasting hotel room demand using search engine data. Journal of Hospitality and Tourism Technology3(3): 196–210.
60.
ParkORyuJ (2019) Cognitive fit effects of online reviews on tourists’ information search. Information Technology & Tourism21(3): 313–335.
61.
ParkSLeeJSongW (2017) Short-term forecasting of Japanese tourist inflow to South Korea using Google trends data. Journal of Travel & Tourism Marketing34(3): 357–368.
62.
PengBSongHCrouchGI, et al. (2015) A meta-analysis of international tourism demand elasticities. Journal of Travel Research54(5): 611–633.
63.
RatchfordBTTalukdarDLeeM (2001) A model of consumer choice of the internet as an information source. International Journal of Electronic Commerce5(3): 7–21.
64.
RosenbaumMSSpearsDL (2005) Who buys that? Who does what? Analysis of cross-cultural consumption behaviours among tourists in Hawaii. Journal of Vacation Marketing11(3): 235–247.
65.
Simon TsaiCLuP (2012) Authentic dining experiences in ethnic theme restaurants. International Journal of Hospitality Management31(1): 304–306.
66.
SmeralE (2009) The impact of the financial and economic crisis on European tourism. Journal of Travel Research48(1): 3–13.
67.
SmeralESongH (2015) Varying elasticities and forecasting performance. International Journal of Tourism Research17(2): 140–150.
68.
SongHLiG (2008) Tourism demand modelling and forecasting—A review of recent research. Tourism Management29(2): 203–220.
69.
SongHZhangH (2024) Tourism demand modelling and forecasting: a horizon 2050 paper. Tourism Review80: 8–27.
70.
SongHWittSFJensenTC (2003a) Tourism forecasting: accuracy of alternative econometric models. International Journal of Forecasting19(1): 123–141.
71.
SongHWongKKFChonKKS (2003b) Modelling and forecasting the demand for Hong Kong tourism. International Journal of Hospitality Management22(4): 435–451.
72.
SongHQiuRTRParkJ (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.
73.
SongHQiuRTRParkJ (2023) Progress in tourism demand research: theory and empirics. Tourism Management94: 104655.
74.
StiglerGJ (1961) The economics of information. Journal of Political Economy69(3): 213–225.
75.
SunSLiYGuoJ, et al. (2022) Tourism demand forecasting: an ensemble deep learning approach. Tourism Economics28(8): 2021–2049.
76.
TsuiWHKFungMKY (2016) Causality between business travel and trade volumes: empirical evidence from Hong Kong. Tourism Management52: 395–404.
77.
VolchekKLiuASongH, et al. (2019) Forecasting tourist arrivals at attractions: search engine empowered methodologies. Tourism Economics25(3): 425–447.
78.
WangY (2009) The impact of crisis events and macroeconomic activity on Taiwan’s international inbound tourism demand. Tourism Management30(1): 75–82.
79.
Waqas-AwanARosselló-NadalJSantana-GallegoM (2021) New insights into the role of personal income on international tourism. Journal of Travel Research60(4): 799–809.
80.
WenLLiuCSongH, et al. (2021) Forecasting tourism demand with an improved mixed data sampling model. Journal of Travel Research60(2): 336–353.
81.
WerthnerHKleinS (1999) Information Technology and Tourism: A Challenging Relationship. Springer-Verlag Wien.
82.
WilsonTD (1999) Models in information behaviour research. Journal of Documentation55(3): 249–270.
83.
WłodarczykJ (2024) Information and income distribution: the perspective of information economics. In: RabanDRWłodarczykJ (eds) The Elgar Companion to Information Economics. Edward Elgar Publishing, pp. 81–104.
84.
WuXBlakeA (2023) Does the combination of models with different explanatory variables improve tourism demand forecasting performance?Tourism Economics29(8): 2032–2056.
85.
WuDCSongHShenS (2017) New developments in tourism and hotel demand modeling and forecasting. International Journal of Contemporary Hospitality Management29(1): 507–529.
86.
XiangZWangDO LearyJT, et al. (2015) Adapting to the internet. Journal of Travel Research54(4): 511–527.
87.
YangCLinHHanC (2010) Analysis of international tourist arrivals in China: the role of world heritage sites. Tourism Management31(6): 827–837.
88.
YangYPanBSongH (2014) Predicting hotel demand using destination marketing organization’s web traffic data. Journal of Travel Research53(4): 433–447.
89.
YangXPanBEvansJA, et al. (2015) Forecasting Chinese tourist volume with search engine data. Tourism Management46: 386–397.
ZhangLPanBSmithWW, et al. (2009) An exploratory study of travelers’ use of online reviews and recommendations. Information Technology & Tourism11(2): 157–167.
