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10.7717/peerj-cs.850

http://scihub22266oqcxt.onion/10.7717/peerj-cs.850
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35174269!8802791!35174269
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suck abstract from ncbi

pmid35174269
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  • Determining the factors affecting customer satisfaction using an extraction-based feature selection approach #MMPMID35174269
  • Wu W; Riantama D
  • PeerJ Comput Sci 2022[]; 8 (ä): e850 PMID35174269show ga
  • The coronavirus disease 2019 (COVID-19) causes tremendous damages to the world, including threats to human's health and daily activities. Most industries have been affected by this pandemic, particularly the tourism industry. The online travel agencies (OTAs) have suffered from the global tourism market crisis by air travel lockdown in many countries. How online travel agencies can survive at stake and prepare for the post-COVID-19 future has emerged as an urgent issue. This study aims to examine the critical factors of customers' satisfaction to OTAs during the COVID-19 pandemic. A text mining method for feature selection, namely LASSO, was used to deal with online customer reviews and to extract factors that shape customers' satisfaction to OTAs. Results showed that refunds, promptness, easiness and assurance were ranked as the most competitive factors of customers' satisfaction, followed by bad reviews & cheap and excellent service & comparison. New factors to customers' satisfaction were revealed during the global tourism recession. Findings provide OTAs guidelines to reset services priorities during the pandemic crisis.
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  • suck abstract from ncbi

    e850 ä.8 2022