Quantifying potential tourist behavior in choice of destination using Google Trends.

Abstract This study uses the Information Processing Approach and the Theory of Planned Behavior of tourists to extract four time-series constructs from 63 relevant and semantically related keywords on “Kerala Tourism” using Google Trends data. Analyzing these data helps in formulating various strategies to boost tourism in a given region and, subsequently, in proposing a structured methodology that applies different econometric models to predict monthly arrivals of both global and domestic tourists to Kerala. The output of these models showed significant improvement in prediction of tourist arrivals when using these constructs in the ARIMAX models. Moreover this study provides a framework for forecasting tourist arrivals to a destination and helps to predict behaviors influencing tourist destination selection using Google Trends data. These analyses are expected to guide policy makers in understanding and making appropriate decisions to deploy resources at potential tourist destination sites to enhance the potential experience of the tourist.

[1]  Sandra J. Milberg,et al.  Understanding travelers’ intentions to visit a short versus long-haul emerging vacation destination: The case of Chile , 2017 .

[2]  Les Oxley,et al.  Modeling and Forecasting the Demand for Electricity in New Zealand: A Comparison of Alternative Approaches , 2003 .

[3]  A. Morrison,et al.  Travel blogs on China as a destination image formation agent: A qualitative analysis using Leximancer , 2015 .

[4]  Bing Pan,et al.  Forecasting hotel room demand using search engine data. , 2012 .

[5]  H. Stanley,et al.  Quantifying Trading Behavior in Financial Markets Using Google Trends , 2013, Scientific Reports.

[6]  Craig A Stow,et al.  Mining web-based data to assess public response to environmental events. , 2015, Environmental pollution.

[7]  Hal Hodson.  [dn25217] Google Flu Trends gets it wrong three years running , 2014 .

[8]  Mahalia Jackman,et al.  Research Note: Nowcasting Tourist Arrivals in Barbados – Just Google it! , 2015 .

[9]  Chris Chatfield,et al.  Introduction to Statistical Time Series. , 1976 .

[10]  K. Dhama,et al.  Global Tourist Guide to Oral Care - A Systematic Review. , 2016, Journal of clinical and diagnostic research : JCDR.

[11]  Hendrik,et al.  Trip Guidance: A Linked Data Based Mobile Tourists Guide , 2014 .

[12]  C. Lawrence,et al.  The relationship between place attachment, the theory of planned behaviour and residents’ response to place change , 2016 .

[13]  Andreas G. Georgantopoulos.  Tourism Expansion and Economic Development: VAR/VECM Analysis and Forecasts for the Case of India , 2012 .

[14]  Jian Ma,et al.  A MIDAS modelling framework for Chinese inflation index forecast incorporating Google search data , 2015, Electron. Commer. Res. Appl..

[15]  Xu Xu,et al.  Perceived pollution and inbound tourism in China , 2017 .

[16]  K. Goulias,et al.  Modeling travel behavior and sense of place using a structural equation model , 2013 .

[17]  Yongjun Sung,et al.  Predicting selfie-posting behavior on social networking sites: An extension of theory of planned behavior , 2016, Comput. Hum. Behav..

[18]  S. Stephens-Davidowitz.  The cost of racial animus on a black candidate: Evidence using Google search data☆ , 2014 .

[19]  Haiyan Song,et al.  A meta-analysis of international tourism demand forecasting and implications for practice , 2014 .

[20]  C. Artola,et al.  Can internet searches forecast tourism inflows , 2015 .

[21]  Azlee Zabidi,et al.  Malaysian tourism interest forecasting using Nonlinear Auto-Regressive Moving Average (NARMA) model , 2014, 2014 IEEE Symposium on Wireless Technology and Applications (ISWTA).

[22]  Ladislav Kristoufek,et al.  Power-law correlations in finance-related Google searches, and their cross-correlations with volatility and traded volume: Evidence from the Dow Jones Industrial components , 2015, 1502.00225.

[23]  Kevin K. F. Wong,et al.  Tourism forecasting: To combine or not to combine? , 2007 .

[24]  Heesup Han,et al.  Application of the Theory of Planned Behavior to green hotel choice: Testing the effect of environmental friendly activities , 2010 .

[25]  Jane Probert,et al.  Festivals as agents for behaviour change: A study of food festival engagement and subsequent food choices , 2015 .

[26]  Chris Brooks,et al.  Introductory Econometrics for Finance , 2002 .

[27]  Irem Önder,et al.  Forecasting Tourism Demand with Google Trends For a Major European City Destination , 2016 .

[28]  Kayhan Tajeddini,et al.  Tourism in Switzerland: how perceptions of place attributes for short and long holiday can influence destination choice. , 2016 .

[29]  M. Conner,et al.  The Theory of Planned Behaviour , 2004 .

[30]  W. Cobb.  Trending now: Using big data to examine public opinion of space policy , 2015 .

[31]  M. Conner,et al.  Efficacy of the Theory of Planned Behaviour: a meta-analytic review. , 2001, The British journal of social psychology.

[32]  Natalie Kink,et al.  Search Engines as Substitutes for Traditional Information Sources? An Investigation of Media Choice , 2008, Inf. Soc..

[33]  Wonho Song,et al.  Short-term forecasting of Japanese tourist inflow to South Korea using Google trends data , 2017 .

[34]  Haiyan Song,et al.  Forecasting international tourist flows to Macau , 2006 .

[35]  Chulmo Koo,et al.  The influence of tourism website on tourists' behavior to determine destination selection: : A case study of creative economy in Korea , 2015 .

[36]  Miguel A. Rodríguez,et al.  Internet vs. travel agencies on pre-visit destination image formation: An information processing view , 2008 .

[37]  Haiyan Song,et al.  Combining statistical and judgmental forecasts via a web-based tourism demand forecasting system , 2013 .

[38]  Donyaprueth Krairit,et al.  Tourists' external information search behavior model: the case of Thailand , 2011 .

[39]  Amir F. Atiya,et al.  Combination of long term and short term forecasts, with application to tourism demand forecasting , 2011 .

[40]  Prosper F. Bangwayo-Skeete,et al.  Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach , 2015 .

[41]  Jon Kepa Gerrikagoitia,et al.  New Trends of Intelligent E-marketing Based on Web Mining for E-shops , 2015 .

[42]  M. Thea Sinclair,et al.  The Economics of Tourism , 1991 .

[43]  David M. Pennock,et al.  Predicting consumer behavior with Web search , 2010, Proceedings of the National Academy of Sciences.

[44]  H. Varian,et al.  Predicting the Present with Google Trends , 2009 .

[45]  I. Ajzen.  Attitudes, Personality and Behavior , 1988 .

[46]  H. Varian,et al.  Predicting the Present with Google Trends , 2012 .

[47]  Kenneth G. Stewart.  Introduction to Applied Econometrics , 2005 .

[48]  I. Ajzen.  The theory of planned behavior , 1991 .

[49]  Rob Law,et al.  Forecasting tourism demand with composite search index , 2017 .

[50]  Xin Li,et al.  How does Google search affect trader positions and crude oil prices , 2015 .

[51]  Aitor Almeida,et al.  A method for automatic generation of fuzzy membership functions for mobile device's characteristics based on Google Trends , 2013, Comput. Hum. Behav..

[52]  Hailin Li,et al.  Accurate and efficient classification based on common principal components analysis for multivariate time series , 2016, Neurocomputing.

[53]  Vipin Kumar,et al.  Trends in big data analytics , 2014, J. Parallel Distributed Comput..

[54]  Xin Yang,et al.  Forecasting Chinese tourist volume with search engine data , 2015 .

[55]  J. Lee,et al.  Risk, uncertainty and the theory of planned behavior: A tourism example , 2010 .