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20240329122006.0 |
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211030s2021 xx o ||| 0 eng d |
| 040 |
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|a EBLCP
|b eng
|c EBLCP
|d YDX
|d EBLCP
|d SFB
|d OCLCF
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|a 1280392122
|a 1280462788
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|a 1281978375
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|a 9781802625103
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|b 000073108779
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|a (OCoLC)1281957780
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|z (OCoLC)1280462788
|z (OCoLC)1280600455
|z (OCoLC)1281978375
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|a G155.A1
|b .B54 2021
|
| 082 |
0 |
4 |
|a 910.68
|2 23
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| 049 |
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|a UAMI
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| 100 |
1 |
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|a Okumus, Fevzi.
|
| 245 |
1 |
0 |
|a Big Data Analytics and Forecasting in Hospitality and Tourism
|h [electronic resource].
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| 260 |
|
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|a Bradford, West Yorkshire :
|b Emerald Publishing Limited,
|c 2021.
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| 300 |
|
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|a 1 online resource (389 p.).
|
| 490 |
1 |
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|a International Journal of Contemporary Hospitality Management Ser. ;
|v v.6
|
| 500 |
|
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|a Description based upon print version of record.
|
| 505 |
0 |
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|a Cover -- Guest editorial -- Tourism demand nowcasting using a LASSO-MIDAS model -- Forecasting daily attraction demand using big data from search engines and social media -- High-frequency forecasting from mobile devices' bigdata: an application to tourism destinations' crowdedness -- A segmented machine learning modeling approach of social media for predicting occupancy -- Which search queries are more powerful in tourism demand forecasting: searches via mobile device or PC? -- Timing matters: crisis severity and occupancy rate forecasts in social unrest periods
|
| 505 |
8 |
|
|a Are environmental-related online reviews more helpful? A big data analytics approach -- Listening to your employees: analyzing opinions from online reviews of hotel companies -- Artificial intelligence for hospitality big data analytics: developing a prediction model of restaurant review helpfulness for customer decision-making -- Asymmetric relationship between customer sentiment and online hotel ratings: the moderating effects of review characteristics -- Toward travel pattern aware tourism region planning: a big data approach
|
| 505 |
8 |
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|a Extracting revisit intentions from social media big data: a rule-based classification model -- Spatial-temporal evolution patterns of hotels in China:1978-2018 -- Destination image through social media analytics and survey method -- Do the flipped impacts of hotels matter to the popularity of Airbnb? -- The decision tree for longer-stay hotel guest: the relationship between hotel booking determinants and geographical distance -- Using social media photos as a proxy to estimate the recreational value of (im)movable heritage: the Rubjerg Knude(Denmark) lighthouse
|
| 590 |
|
|
|a ProQuest Ebook Central
|b Ebook Central Academic Complete
|
| 650 |
|
0 |
|a Tourism
|x Management.
|
| 650 |
|
0 |
|a Tourism
|x Marketing.
|
| 650 |
|
7 |
|a Tourism
|x Management
|2 fast
|
| 650 |
|
7 |
|a Tourism
|x Marketing
|2 fast
|
| 758 |
|
|
|i has work:
|a Big Data Analytics and Forecasting in Hospitality and Tourism (Text)
|1 https://id.oclc.org/worldcat/entity/E39PD3QR7JYKvxYFw8YGYKtXFq
|4 https://id.oclc.org/worldcat/ontology/hasWork
|
| 776 |
0 |
8 |
|i Print version:
|a Okumus, Fevzi
|t Big Data Analytics and Forecasting in Hospitality and Tourism
|d Bradford, West Yorkshire : Emerald Publishing Limited,c2021
|
| 830 |
|
0 |
|a International Journal of Contemporary Hospitality Management Ser.
|
| 856 |
4 |
0 |
|u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=6788010
|z Texto completo
|
| 938 |
|
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|a ProQuest Ebook Central
|b EBLB
|n EBL6788010
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| 938 |
|
|
|a YBP Library Services
|b YANK
|n 17683998
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| 994 |
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|a 92
|b IZTAP
|
