Exploring TripAdvisor

Yoo, Kyung‐Hyan; Σιγάλα, Μαριάννα; Gretzel, Ulrike

doi:10.1007/978-3-642-54089-9_17

Cited by 59 publications

(37 citation statements)

References 19 publications

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“…In this process, entrepreneurs should harmonize value co-creation by acquiring the ability to strategically select the right stakeholders providing the most adequate resources and to establish durable relationships leading to win-win situations, thus an equal distribution of (Economic and social) value among actors [55].…”

Section: Smart Tourism Ecosystems: An Application Model For Tourismmentioning

confidence: 99%

Social Innovation in Smart Tourism Ecosystems: How Technology and Institutions Shape Sustainable Value Co-Creation

et al. 2018

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Abstract:In the service era, markets are reconceptualized as systems of actors interconnected through networked relationships based on resources exchange and producing value co-creation. Two of the main contemporary service research theories, Service-dominant logic and Service science, propose different organizational layouts for producing and harmonizing value co-creation: Service ecosystems and smart service systems. However, these two models show some limitations. So, this work aims at drawing an integrated model, the so called Smart service ecosystem that can be applied to hypercompetitive and experience-based sectors. The model was tested in the tourism sector by using a case study methodology. Ten interviews were administered to key informants to analyze their perception about the main dimensions of the smart service ecosystems. By adopting a holistic view, the results obtained can allow the elaboration of a framework which pinpoints: (1) the main stakeholder groups (actors); (2) the kind of resources exchanged (resource integration); (3) the tools employed (technology); (4) the institution exchange among users (institutions). Applying the model obtained to the tourism sector this work explores the main element-steps for managing and optimizing value co-creation and sustainability in the long run and thus for transitioning from innovation to social innovation.

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Section: Smart Tourism Ecosystems: An Application Model For Tourismmentioning

confidence: 99%

Social Innovation in Smart Tourism Ecosystems: How Technology and Institutions Shape Sustainable Value Co-Creation

et al. 2018

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“…This selection matches Baka (2016), who considers TripAdvisor the world's largest source of UGC in the domain of tourism, and other authors [11,46], explaining the advantages of collecting a set of open data in TripAdvisor because of the huge amount of user-generated reviews that it hosts. Moreover, Yoo et al [47] note that TripAdvisor's reputation management system helps to determine the helpfulness of reviews and/or reviewers (which enables viewing profiles, other reviews, votes, and ratings) and motivates users to contribute reliable reviews (through intrinsic and extrinsic motivations).…”

Section: Data Collectionmentioning

confidence: 99%

Measuring Destination Image through Travel Reviews in Search Engines

Mariné-Roig

2017

Sustainability

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Abstract:In recent years, mobile phones and access points to free Wi-Fi services have been enhanced, which has made it easier for travellers to share their stories, pictures, and video clips online during a trip. At the same time, online travel review (OTR) websites have grown significantly, allowing users to post their travel experiences, opinions, comments, and ratings in a structured way. Moreover, Internet search engines play a crucial role in locating and presenting OTRs before and throughout a trip. This evolution of social media and information and communication technologies has upset the classic sources of information of the projected tourist destination image (TDI), allowing electronic word-of-mouth to occupy a prominent position. Hence, the aim of this paper is to propose a method based on big data technologies for analysing and measuring the perceived (and transmitted) TDI from OTRs as presented in search engines, emphasising the cognitive, spatial, temporal, evaluative, and affective TDI dimensions. To test this approach, a massive analysis of metadata processed by search engines was performed on 387,414 TripAdvisor OTRs on 'Things to Do' in Île de France, an outstanding smart tourist destination. The results obtained are consistent and allow for the extraction of insights and business intelligence.

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“…The ability to anticipate concerns also give hotel managers the opportunity to surprise their guests, which can be seen as an important aspect in the hospitality and tourism industry (Yoo et al, 2016). Being the topic trends estimated straight from reviews by an unsupervised approach, it is possible to state that a topic rise or decline can signal a safe direction for further investment aiming to increase customer satisfaction.…”

Section: Open Accessmentioning

confidence: 99%

“…In this sense, the tourism and hospitality industry has been interested in customer perceptions for improving operations in the services industry (Han, Mankad, Gavirneni, & Verma, 2016). Nowadays, travel platforms like TripAdvisor collect volunteered information, openly distributing user reviews to firms and managers, challenging them with a great volume of data (Yoo, Sigala, & Gretzel, 2016) and building reputational economies for the tourism and hospitality industry (Langley & Leyshon, 2017).…”

Section: Introductionmentioning

confidence: 99%

Business Analytics in Tourism: Uncovering Knowledge from Crowds

Marcolin

Becker

Wild

et al. 2019

BAR, Braz. Adm. Rev.

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Business Analytics leverages value from data, thus being an important tool for the decisionmaking process. However, the presence of data in different formats is a new challenge for analysis. Textual data has been drawing organizational attention as thousands of people express themselves daily in text, like the description of customer perceptions in the tourism and hospitality area. Despite the relevance of customer data in textual format to support decision making of hotel managers, its use is still modest, given the difficulty of analyzing and interpreting the large amounts of data. Our objective is to identify the main evaluation topics presented in online guest reviews and reveal changes throughout the years. We worked with 23,229 hotel reviews collected from TripAdvisor website through WebScrapping packages in R, and used a text mining approach (Latent Semantic Analysis) to analyze the data. This contributes with practical implications to hotel managers by demonstrating the applicability of text data and tools based on open-source solutions and by providing insights about the data and assisting in the decisionmaking process. This article also contributes in presenting a stepwise text analysis, including capturing, cleaning and formatting publicly available data for organizational specialists.

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Exploring TripAdvisor

Cited by 59 publications

References 19 publications

Social Innovation in Smart Tourism Ecosystems: How Technology and Institutions Shape Sustainable Value Co-Creation

Social Innovation in Smart Tourism Ecosystems: How Technology and Institutions Shape Sustainable Value Co-Creation

Measuring Destination Image through Travel Reviews in Search Engines

Business Analytics in Tourism: Uncovering Knowledge from Crowds

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