Matches in SemOpenAlex for { <https://semopenalex.org/work/W3017855717> ?p ?o ?g. }
- W3017855717 endingPage "102912" @default.
- W3017855717 startingPage "102912" @default.
- W3017855717 abstract "Based on internet big data from multiple sources (i.e., the Baidu search engine and two online review platforms, Ctrip and Qunar), this study forecasts tourist arrivals to Mount Siguniang, China. Key findings of this empirical study indicate that (a) tourism demand forecasting based on internet big data from a search engine and online review platforms can significantly improve forecasting performance; (b) compared with tourism demand forecasting based on single-source data from a search engine, demand forecasting based on multisource big data from a search engine and online review platforms demonstrates better performance; and (c) compared with tourism demand forecasting based on online review data from a single platform, forecasting performance based on multiple platforms is significantly better." @default.
- W3017855717 created "2020-05-01" @default.
- W3017855717 creator A5035511249 @default.
- W3017855717 creator A5058395981 @default.
- W3017855717 creator A5064920618 @default.
- W3017855717 date "2020-07-01" @default.
- W3017855717 modified "2023-10-17" @default.
- W3017855717 title "Forecasting tourism demand with multisource big data" @default.
- W3017855717 cites W1975994995 @default.
- W3017855717 cites W1986078433 @default.
- W3017855717 cites W1992777957 @default.
- W3017855717 cites W2000842688 @default.
- W3017855717 cites W2006746888 @default.
- W3017855717 cites W2012327977 @default.
- W3017855717 cites W2015122449 @default.
- W3017855717 cites W2020832745 @default.
- W3017855717 cites W2025210352 @default.
- W3017855717 cites W2032170121 @default.
- W3017855717 cites W2034201819 @default.
- W3017855717 cites W2038642139 @default.
- W3017855717 cites W2067284569 @default.
- W3017855717 cites W2067505258 @default.
- W3017855717 cites W2086074129 @default.
- W3017855717 cites W2126831543 @default.
- W3017855717 cites W2126977778 @default.
- W3017855717 cites W2130778342 @default.
- W3017855717 cites W2154291363 @default.
- W3017855717 cites W2323881768 @default.
- W3017855717 cites W2324562510 @default.
- W3017855717 cites W2330583869 @default.
- W3017855717 cites W2335082123 @default.
- W3017855717 cites W2400682180 @default.
- W3017855717 cites W2500086770 @default.
- W3017855717 cites W2526724998 @default.
- W3017855717 cites W2547820680 @default.
- W3017855717 cites W2564352171 @default.
- W3017855717 cites W2585078266 @default.
- W3017855717 cites W2760894977 @default.
- W3017855717 cites W2774914982 @default.
- W3017855717 cites W2789902053 @default.
- W3017855717 cites W2794726811 @default.
- W3017855717 cites W2884741544 @default.
- W3017855717 cites W2887630604 @default.
- W3017855717 cites W2894665096 @default.
- W3017855717 cites W2901637508 @default.
- W3017855717 cites W2902021342 @default.
- W3017855717 cites W2907651231 @default.
- W3017855717 cites W2911964244 @default.
- W3017855717 cites W2921553645 @default.
- W3017855717 cites W2939094371 @default.
- W3017855717 cites W2968235412 @default.
- W3017855717 cites W3122283149 @default.
- W3017855717 cites W3123115705 @default.
- W3017855717 cites W3125170714 @default.
- W3017855717 cites W3125342939 @default.
- W3017855717 cites W3126014112 @default.
- W3017855717 cites W4212883601 @default.
- W3017855717 doi "https://doi.org/10.1016/j.annals.2020.102912" @default.
- W3017855717 hasPublicationYear "2020" @default.
- W3017855717 type Work @default.
- W3017855717 sameAs 3017855717 @default.
- W3017855717 citedByCount "89" @default.
- W3017855717 countsByYear W30178557172020 @default.
- W3017855717 countsByYear W30178557172021 @default.
- W3017855717 countsByYear W30178557172022 @default.
- W3017855717 countsByYear W30178557172023 @default.
- W3017855717 crossrefType "journal-article" @default.
- W3017855717 hasAuthorship W3017855717A5035511249 @default.
- W3017855717 hasAuthorship W3017855717A5058395981 @default.
- W3017855717 hasAuthorship W3017855717A5064920618 @default.
- W3017855717 hasBestOaLocation W30178557172 @default.
- W3017855717 hasConcept C110875604 @default.
- W3017855717 hasConcept C124101348 @default.
- W3017855717 hasConcept C136764020 @default.
- W3017855717 hasConcept C144133560 @default.
- W3017855717 hasConcept C162853370 @default.
- W3017855717 hasConcept C166957645 @default.
- W3017855717 hasConcept C171089853 @default.
- W3017855717 hasConcept C18918823 @default.
- W3017855717 hasConcept C191935318 @default.
- W3017855717 hasConcept C193809577 @default.
- W3017855717 hasConcept C205649164 @default.
- W3017855717 hasConcept C2522767166 @default.
- W3017855717 hasConcept C26517878 @default.
- W3017855717 hasConcept C38652104 @default.
- W3017855717 hasConcept C41008148 @default.
- W3017855717 hasConcept C75684735 @default.
- W3017855717 hasConcept C97854310 @default.
- W3017855717 hasConceptScore W3017855717C110875604 @default.
- W3017855717 hasConceptScore W3017855717C124101348 @default.
- W3017855717 hasConceptScore W3017855717C136764020 @default.
- W3017855717 hasConceptScore W3017855717C144133560 @default.
- W3017855717 hasConceptScore W3017855717C162853370 @default.
- W3017855717 hasConceptScore W3017855717C166957645 @default.
- W3017855717 hasConceptScore W3017855717C171089853 @default.
- W3017855717 hasConceptScore W3017855717C18918823 @default.
- W3017855717 hasConceptScore W3017855717C191935318 @default.
- W3017855717 hasConceptScore W3017855717C193809577 @default.