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Online review bias research has predominantly focused on self-selection biases on the user’s side. By collecting online reviews from multiple platforms and examining their biases in the unique digital environment of “Chinanet,” this paper aims to shed new light on the multiple sources of biases embedded in online reviews and potential interactions among users, technical platforms and the broader social–cultural norms.

In the first study, online restaurant reviews were collected from Dianping.com, one of China's largest review platforms. Their distribution and underlying biases were examined via comparisons with offline reviews collected from on-site surveys. In the second study, user and platform ratings were collected from three additional major online review platforms – Koubei, Meituan and Ele.me – and compared for possible indications of biases in platform's review aggregation.

The results revealed a distinct exponential-curved distribution of Chinese users’ online reviews, suggesting a deviation from previous findings based on Western user data. The lack of online “moaning” on Chinese review platforms points to the social–cultural complexity of Chinese consumer behavior and online environment that goes beyond self-selection at the individual user level. The results also documented a prevalent usage of customized aggregation methods by review service providers in China, implicating an additional layer of biases introduced by technical platforms.

Using an online–offline design and multi-platform data sets, this paper elucidates online review biases among Chinese users, the world's largest and understudied (in terms of review biases) online user group. The results provide insights into the unique social–cultural cyber norm in China's digital environment and bring to light the multilayered nature of online review biases at the intersection of users, platforms and culture.

Recent tourism research has adopted social media analytics (SMA) to examine tourism destination image (TDI) and gain timely insights for marketing purposes. Comparing the methodologies of SMA and intercept surveys would provide a more in-depth understanding of both methodologies and a more holistic understanding of TDI than each method on their own. This study aims to investigate the unique merits and biases of SMA and a traditional visitor intercept survey.

This study collected and compared data for the same tourism destination from two sources: responses from a visitor intercept survey (n = 1,336) and Flickr social media photos and metadata (n = 11,775). Content analysis, machine learning and text analysis techniques were used to analyze and compare the destination image represented from both methods.

The results indicated that the survey data and social media data shared major similarities in the identified key image phrases. Social media data revealed more diverse and more specific aspects of the destination, whereas survey data provided more insights in specific local landmarks. Survey data also included additional subjective judgment and attachment towards the destination. Together, the data suggested that social media data should serve as an additional and complementary source of information to traditional survey data.

This study fills a research gap by comparing two methodologies in obtaining TDI: SMA and a traditional visitor intercept survey. Furthermore, within SMA, photo and metadata are compared to offer additional awareness of social media data’s underlying complexity. The results showed the limitations of text-based image questions in surveys. The findings provide meaningful insights for tourism marketers by having a more holistic understanding of TDI through multiple data sources.

With the growing online review manipulation and fake reviews in the hospitality industry, it is not uncommon that a consumer encounters disconfirmation when comparing the existing online reviews with his/her own product or service evaluation. The purpose of this study is to investigate the influence of review disconfirmation on customer online review writing behavior.

This study used a mixed-method combining online secondary big data modeling and experimental design.

Review disconfirmation influences customers’ emotional responses embedded in the review; a customer who encounters review disconfirmation tends to exert more reviewing effort, manifested by writing longer reviews; negativity bias exists in disconfirmation effects, in that negative review disconfirmation shows more significant and stronger effects than positive review disconfirmation.

Findings from this study provide important managerial implications for business owners and marketers who attempt to influence online reviews. The study suggests that fictitious online review manipulation might be detrimental to the business.

This research contributes to two literature streams, including research on the social influence of online consumer reviews, and the relationship between disconfirmation and consumers’ post-consumption behavior, by extending the influence of disconfirmation from the offline context to the online context.

The purpose of this paper is to review the impact electronic word-of-mouth (eWOM) has had on the hospitality and tourism industry and discuss the changes that will affect its future. The paper’s touchpoint is the authors’ earlier paper (Litvin et al., 2008), which proposed that eWOM was to become a major influence as a conduit of travelers’ views and opinions.

The paper summarizes the arguments of the authors’ earlier paper, describing ways in which eWOM has evolved into the influential system it has become, with special emphasis on the growth of mobile media as a platform for eWOM dissemination.

The authors conclude that eWOM has fulfilled its promise to become a major influence on the hospitality and tourism industry and will continue to play an essential role in hospitality marketing for the foreseeable future.

The authors provide examples of successful media campaigns and propose strategies for hospitality and tourism businesses.

eWOM has emerged to become a highly influential element of modern marketing strategy. This look back at an early eWOM paper, with reflection on changes that have occurred and a view to the future, is of value as validation of an often cited article that set the stage for much subsequent hospitality research.

The publishers of International Journal of Crowd Science wish to retract the article “Quality-time-complexity universal intelligence measurement”, by Wen Ji, Jing Liu, Zhiwen Pan, Jingce Xu, Bing Liang, and Yiqiang Chen, which appeared in volume 2, issue 1, 2018. It has come to our attention that due to an error in the submission process the above article is an earlier version of the article published in International Journal of Crowd Science, volume 2, issue 2, 2018 (DOI: https://doi.org/10.1108/IJCS-04-2018-0007). The duplicate publication was the result of an inadvertent administrative error by the authors. The authors and publishers of the journal sincerely apologise to the readers.

The purpose of this paper is to introduce the seven articles on festival and events research in this special issue of IJCTHR.

The article gives summary information and perspectives on the articles that appear in the issue.

This paper categorizes seven articles into empirical studies and conceptual pieces and summarizes their contributions to literature. The seven papers explore festivals and events from the following perspectives: participants' values, motivations, and spending; organizers' risk management; sociological examination; sustainability practices; and practical advice.

The value of this paper lies in its review of current approaches to researching festivals and events, and advice provided for future research on the topic.

The accurate measure of the economic contribution of festivals and special events is a challenge. Using a case study, the purpose of this paper is to demonstrate a previously un‐captured economic contribution from increased hotel rates during the period of festival or event; the “rising tide” effect.

This paper uses a case study on Charleston's hotel occupancy changes, and how the changes coincide with the occurrence of festivals and events in the community, to demonstrate the increased tourism income due to rising accommodation prices during festivals and events.

The study validates the increased tourism income due to rising accommodation prices during festivals and events, which can provide a significant boost to the economy of a local community.

Festival organizations, as well as hoteliers and other beneficiaries of tourist spending during festivals and events, should note how this additional contribution benefits them and their communities.

Many economic contributions of festivals/events overstate their values. The current study first demonstrates a previously un‐captured economic contribution using a case study approach.

The purpose of this paper is to examine the spending patterns of non‐local participants and spectators at a medium‐sized international sport event, to segment their spending patterns and consider implications for the quality of each segment's event experience.

Spending in nine sectors of the economy is measured via self‐report, and respondents are segmented into five groups: spectators, athletes, coaches, officials, and other participants (e.g. media, medical staff). The daily and aggregate spend for each segment in each economic sector is calculated and compared. Regression analysis tests differences among segments for each economic sector.

Participants account for 39 per cent of aggregate spend; coaches are the biggest spenders; athletes spend relatively little. The segments spend differently on hospitality, private transportation, grocery, and retail, with spectators spending significantly more than the participant groups on hospitality and private transportation, and significantly less on groceries and merchandise. Spending in sectors normally associated with celebration and festivity accounts for only 8 per cent of total spend.

Findings are derived from a single event, but are consistent with other work, suggesting that inadequate attention is given to opportunities for festive celebration, especially among athletes.

Coaches are a particularly useful target market for retailers, whereas hoteliers and service stations should target their marketing at spectators. Event organizers should do more to build festivals.

This paper identifies the ways that different segments organize their spending at an event, and demonstrates that greater attention to festivals could enhance a sport event's overall impact.

The purpose of this paper is to motivate hospitality leaders and local festival and special event management to grow their events from simply local events to those that attract tourists and tourists' dollars to the community.

Previous research is discussed but no new research is offered.

This commentary challenges event managers to elevate their local events into attractors bringing tourists and tourist spending to the community.

Festivals and special events should consider growing their events such that they attract new monies to the community, put heads in beds, and generate revenue for tourism providers and other merchants across the local economy. The article offers suggestions to tourism officials for the distribution of public support funding to help local festivals successfully grow their events.

Readers will find the Charleston model discussed herein of value as they consider the politics of funding festivals and special events in their community. The paper offers suggestions and warnings for the growth.

The purpose of this paper is to investigate the usefulness of search query volume data in forecasting demand for hotel rooms and identify the best econometric forecasting model.

The authors used search volume data on five related queries to predict demand for hotel rooms in a specific tourist city and employed three ARMA family models and their ARMAX counterparts to evaluate the usefulness of these data. The authors also evaluated three widely used causal econometric models – ADL, TVP, and VAR – for comparison.

All three ARMAX models consistently outperformed their ARMA counterparts, validating the value of search volume data in facilitating the accurate prediction of demand for hotel rooms. When the three causal econometric models were included for forecasting competition, the ARX model produced the most accurate forecasts, suggesting its usefulness in forecasting demand for hotel rooms.

To demonstrate the usefulness of this data type, the authors focused on one tourist city with five specific tourist‐related queries. Future studies could focus on other aspects of tourist consumption and on more destinations, using a larger number of queries to increase accuracy.

Search volume data are an early indicator of travelers' interest and could be used to predict various types of tourist consumption and activities, such as hotel occupancy, spending, and event attendance.

The paper's findings validate the value of search query volume data in predicting hotel room demand, and the paper is the first of its kind in the field of tourism and hospitality research.