The interactive effects of online reviews on the determinants of Swiss hotel performance: A neural network analysis

Phillips, Paul A.; Zigan, Krystin; Silva, Maria Manuela Santos; Schegg, Roland

doi:10.1016/j.tourman.2015.01.028

Cited by 174 publications

(123 citation statements)

References 62 publications

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“…In one pertinent study, Londoño and Hernandez-Maskivker [27] applied sentiment analysis to analyze online reviews on TripAdvisor to identify environmental factors of the analyzed hotels. Likewise, Phillips et al [28] used sentiment analysis to analyze online reviews of Swiss hotels obtained from 69 different sources to extract indicators of environmental management. Table 5 provides a summary of the main studies relevant to the present research.…”

Section: Literature Reviewmentioning

confidence: 99%

“…[27] Identified practices related to environmental sustainability using sentiment analysis of TripAdvisor's comments [28] Examined the determinants of Swiss hotel performance using an artificial neural network model that builds based on previous e-WOM studies of 59,688 online comments obtained from 69 different sources. This allowed the authors to identify those indicators, including the environmental ones, which improve the management of hotels in Switzerland.…”

Section: References Summarymentioning

confidence: 99%

“…For instance, Phillips et al [28] developed artificial neural networks to determine the Swiss hotel performance and thus identified those environmental factors that allow for improving the management of hotels in this country. To this end, the authors extracted 59,688 online comments from 69 different sources and performed sentiment analysis to extract the environmental factors that were most present Furthermore, Pak and Paroubek [40] developed different methodologies to analyze Twitter comments.…”

Section: Sentiment Analysismentioning

confidence: 99%

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Do Online Comments Affect Environmental Management? Identifying Factors Related to Environmental Management and Sustainability of Hotels

2018

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Abstract:The main aim of this study was to identify the key indicators related to environmental management and sustainability of hotels as perceived by travelers during their trips. The methodology used was a sentiment analysis with an algorithm developed in Python trained with data mining and machine learning, with the MonkeyLearn library in the hotel industry sector under the eWOM model (e-Word of Mouth). The results with negative, positive and neutral feelings were submitted to a textual analysis with the qualitative analysis software Nvivo Pro 12. The sample consisted of the 25 best hotels in Switzerland according to Traveler's Choice from TripAdvisor ranking 2018 that draws from more than 500,000 reviews. For data extraction, we connected to the TripAdvisor API, obtaining a sample of n = 8331 reviews of the hotels that made up the ranking. The results of the study highlight the key factors related to environmental management detected by travelers during their stay in hotels and can be meaningfully used by managers or hotel managers to improve their services and enhance the value provided by their policies of sustainability and respect for the environment. The limitations of the present study relate to the size of the sample and the number of hotels included in the present analysis.

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Section: Literature Reviewmentioning

confidence: 99%

Section: References Summarymentioning

confidence: 99%

Section: Sentiment Analysismentioning

confidence: 99%

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Do Online Comments Affect Environmental Management? Identifying Factors Related to Environmental Management and Sustainability of Hotels

2018

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“…For recommendation systems, it is important to determine the information on which the recommendation is based. Given the pervasive nature of the Internet in everyday life, the studies aim to establish new recommendation systems primarily based on consumer reviews on the Internet [125][126][127].…”

Section: Tourism and Consumer Reviews Online Or Through Mobile Applicmentioning

confidence: 99%

The Role of Mobile Technology in Tourism: Patents, Articles, News, and Mobile Tour App Reviews

Kim

2017

Sustainability

107

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Abstract:The purpose of this research is to identify the status and role of mobile technology in achieving sustainable and smart tourism, and to suggest future research and strategy directions for academia and managers in practice. This research utilized multiple sources, such as patents, academic articles, and news, and selected methodologies optimized for the purpose of each study. Study 1 used Netminer, a social network analysis program, to analyze the relationships between patent's International Patent Classification (IPC) codes. Study 2 used the T-LAB program for content analysis to analyze the texts of patents, journal articles, and news. Study 3 used the Leximancer program, which utilizes relative frequency to analyze mobile app consumer reviews. In study 1, we identified various forms of data related technologies and mobile technologies for smart city systems and maps. In study 2, we found the environment, sustainability, business, and market themes to be related to mobile technology. In study 3, we explored consumers' attitudes and preferences for mobile travel app using their reviews. Advances in mobile technology are expected to create innovative experiences for consumers, foster a sustainable competitive advantage for tourism destinations and tourism-related suppliers, and create sustainable competencies for smart tourism.

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“…There has been a lot of literature reviews proving that online reviews have become the important factor in consumer's observation of business reputation and public praise [1], and the key indicator of whether consumers are willing to buy [2]. Online reviews are related to brand trust and willingness to buy, which can affect the consumption decision of hotel [3], restaurant [4], etc.…”

Section: Related Researchmentioning

confidence: 99%

Based on Logistic Regression the online Reviews False Identification Model Study

Zhang¹

2017

dtssehs

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Abstract. In this paper, a questionnaire survey was made on the key factors to identify the false comments from the perspective of consumer perception, including words, number, comment tone, detailed degree, three days before the comments number, first week comments, total comments number, 2 weeks before comments number, additional comments number, photo show number, good review, medium comments or bad comments, sellers reputation, from 0 to 9 o'clock and 21 measurement items, in the case of taobao commodity comments, artificial traversal 60 kinds of commodities in 6885 comments to collect data, it is concluded that additional comments number, slide show number, and medium review number of significant differences when consumers perceive false comments. By constructing the Logistic regression model, detailed degree, three days before the comments number and slide show number are the key influencing factors, overall percentage prediction rate was 85%, the discriminant function effect is better. The results show that the three indexes can be used as a consumer in judging comment whether false goods, shopping is the basis of risk.

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The interactive effects of online reviews on the determinants of Swiss hotel performance: A neural network analysis

Cited by 174 publications

References 62 publications

Do Online Comments Affect Environmental Management? Identifying Factors Related to Environmental Management and Sustainability of Hotels

Do Online Comments Affect Environmental Management? Identifying Factors Related to Environmental Management and Sustainability of Hotels

The Role of Mobile Technology in Tourism: Patents, Articles, News, and Mobile Tour App Reviews

Based on Logistic Regression the online Reviews False Identification Model Study

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