Analyzing key influences of tourists’ acceptance of online reviews in travel decisions

Chong, Alain Yee‐Loong; Khong, Kok Wei; Ma, Teng; McCabe, Scott; Wang, Yi

doi:10.1108/intr-05-2017-0212

Cited by 98 publications

(111 citation statements)

References 70 publications

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“…In the three columns, there are keywords related to positive feelings (e.g., good, great). The crowdedness-related keywords crowd/s/ed/ing (7386), overcrowd/ed/ing (186), busy (2623), line/s (3392), queue/s (1139), wait/ed/ing (5243), and await/ing (72), together with ticket/s/ing (8563), and the dirtiness-related keywords abandoned (79), derelict (11), dirty (293), garbage (53), junk (101), rubbish (51), ruined (230), and trash (75), together with graffiti (420), that may indicate sustainability problems, appear in the attractions column. The riskiness-related keywords crime/inal/inally (103), danger/ous (122), pick pocket pickpocket/s (422), robbed robbery (66), steal stole/n (416), and thief/ves (97) also appear in the attractions column.…”

Section: Resultsmentioning

confidence: 99%

“…Another, on visitors from New Zealand [52], revealed that perceived usefulness and empowerment led to the use of TGC to plan the trip. A third, about Chinese travellers [53], showed that eWoM had a significant utility and influence in the planning and decision making related to the trip. The influence of OTRs in travel decision making has also been demonstrated through artificial intelligence methodologies [54].…”

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confidence: 99%

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Destination Image Analytics Through Traveller-Generated Content

Mariné-Roig

2019

Sustainability

119

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The explosion of content generated by users, in parallel with the spectacular growth of social media and the proliferation of mobile devices, is causing a paradigm shift in research. Surveys or interviews are no longer necessary to obtain users’ opinions, because researchers can get this information freely on social media. In the field of tourism, online travel reviews (OTRs) hosted on travel-related websites stand out. The objective of this article is to demonstrate the usefulness of OTRs to analyse the image of a tourist destination. For this, a theoretical and methodological framework is defined, as well as metrics that allow for measuring different aspects (designative, appraisive and prescriptive) of the tourist image. The model is applied to the region of Attica (Greece) through a random sample of 300,000 TripAdvisor OTRs about attractions, activities, restaurants and hotels written in English between 2013 and 2018. The results show trends, preferences, assessments, and opinions from the demand side, which can be useful for destination managers in optimising the distribution of available resources and promoting sustainability.

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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Destination Image Analytics Through Traveller-Generated Content

Mariné-Roig

2019

Sustainability

119

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“…6). The relationship between the above variables and consumers' adoption of eTourism technology has been supported in many eTourism research (e.g., Chong et al 2018;Kim 2016;Chang et al 2016).…”

Section: Tam++ Models Technology Acceptance Model (Tam)mentioning

confidence: 90%

Acceptance and Adoption of eTourism Technologies

Pourfakhimi

Duncan

Ould

et al. 2020

Handbook of E-Tourism

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The field of eTourism research encompasses a plethora of research on users' adoption and acceptance of technologies. As a multidimensional phenomenon, an in-depth understanding of eTourism technology acceptance requires crossing the boundaries of tourism and hospitality, information and communication technologies, and marketing. Taking such a multidisciplinary approach enables researchers to integrate knowledge from the broader disciplines of psychology, sociology, and economics to construct a deep understanding of users' behavior. However, while there have been some recent advances in broadening the horizons of research in this field, the majority of eTourism technology acceptance research relies on a few classical technology acceptance and consumer behavior theories. This chapter presents a summarized overview of the most important determinants of technology acceptance behavior and critically reviews most influential theoretical models that have been used as the foundation of the majority of existing research in this field. Subsequently, some major areas of theoretical and empirical gaps in our understanding of eTourism technology acceptance will be discussed to provide researchers with a pathway towards further expanding the boundaries of research in this field. This chapter assists emerging researchers in this field to gain an overall understanding of the progress of research so far. It also directs emerging researchers towards developing alternative research agendas to diversify the theoretical foundations of eTourism technology acceptance research and expand the boundaries of knowledge in this field beyond the status quo.

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“…In the information management and marketing literature focusing on the antecedents of ORH, the term online review usefulness seems to be used in a broad (and sometimes loose) way and relates to the extent to which an OR is perceived as useful by consumers, sometimes with perceived usefulness measured as one of the core constructs/variables of an acceptance or adoption model such as the technology acceptance model [25,26] and its subsequent variations [139]. As such, usefulness pertains to online consumers' expectations that using ORs will enhance their performance in information seeking, which becomes a driver of consumers' attitude and intention to use ORs themselves [9,10,20,31].…”

Section: Orh and Financial Performancementioning

confidence: 99%

Online Review Helpfulness and Firms’ Financial Performance: An Empirical Study in a Service Industry

Mariani

Borghi

2020

International Journal of Electronic Commerce

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This study aims to bridge a significant research gap in the electronic word-of-mouth (eWOM) literature: measuring the effect of the degree of online review helpfulness (ORH) on firms' financial performance. As studies of the impact of ORH on firm performance in the context of service industries in general and more specifically in the hospitality sector are virtually nonexistent, this work intends to offer insights to eWOM researchers by analyzing if and to what extent ORH affects the financial performance of hospitality firms. Based on a re-visitation of the antecedents of ORH stemming from information adoption models, social influence theory and dual process theory, we analyze the moderating effects of the degree of ORH on the relationships between online review valence/volume and firms' financial performance. Based on the examination of 395,964 online reviews related to 261 higherend hotels located in London, the third most visited destination worldwide, we find that the degree of ORH positively moderates the positive effect of the reviews' valence on financial performance, while it does not moderate significantly the positive effect of the reviews' volume on financial performance. Theoretical contributions to the nascent research stream taking an outcome-oriented approach to the study of eWOM helpfulness and managerial implications are discussed.

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Analyzing key influences of tourists’ acceptance of online reviews in travel decisions

Cited by 98 publications

References 70 publications

Destination Image Analytics Through Traveller-Generated Content

Destination Image Analytics Through Traveller-Generated Content

Acceptance and Adoption of eTourism Technologies

Online Review Helpfulness and Firms’ Financial Performance: An Empirical Study in a Service Industry

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