Search

Social media influencers, or digital creators followed by online audiences of thousands and millions, are a hot marketing tool that grows over 50% annually. Recently, the impact of COVID-19 on travel has severely disrupted this developing market, and heavily impacted travel influencers whose livelihood depends on collaboration with tourism, hospitality, and food businesses. This research presents the first "big data" evaluation of the induced transformations among the top Instagram travel influencers. The findings are based on computer image recognition of photographs published by 140 travel Instagrammers ranging from micro- to mega-influencers before and during the COVID-19 pandemic. In addition, the research proposes a methodology of identification of sponsored posts among travel influencers based on the fuzzy matching of post mentions and a global database of 6 million businesses. [ABSTRACT FROM AUTHOR]

Online public discussions of service failure incidents might result in significant reputational damage for service providers. This study examines the geographical and personal proximity factors that contribute to public involvement in such discussions, using a high-profile incident that happened in a hotel of a popular Chinese budget chain as an example. A geographically weighted regression model is developed to illustrate spatial variations of public response at a prefecture division level. Economic development, area status, and presence of the service provider on the market are identified as significant predictors. This analytical approach can assist hospitality organizations in developing regionally-tailored image recovery strategies. [ABSTRACT FROM AUTHOR]

Managers of protected areas that are popular among tourists need to combine conservation goals with assurances of visitor safety while providing high-quality service. The compromise amongst these goals is reflected in the protected area regulations; however, monitoring of compliance with these regulations is costly and laborious. Social media present a new opportunity to observe visitor behaviour patterns in an unobtrusive and inexpensive manner. We propose a method of using online tourist photographs to monitor unwanted behaviours of the natural area visitors. The specific research object was the Greater Kruger National Park (GKNP) in South Africa, which is one of the largest game parks worldwide. We found that approximately 7% of the photographs depict instances of potentially unwanted behaviour. The results also indicate that the types of unwanted behaviours differ between the federally and privately owned parks, although the overall percentage of unwanted behaviours in the two types of parks is similar. [ABSTRACT FROM AUTHOR]

Automated content analysis of online travel reviews allows analysis of topics of travelers' satisfaction and dissatisfaction, yet its domain is not well researched. We suggest that the "Anna Karenina principle" positing a greater variability of the factors leading to business failure as opposed to those leading to its success results in limitations of topic modeling applied to dissatisfied visitor reviews. We test our hypothesis using TripAdvisor reviews of the Museum of Terracotta Warriors (China). Our findings confirm the hypothesis; we also report the main themes of satisfaction and dissatisfaction of visitors from mainland China and industrialized English speaking countries. [ABSTRACT FROM AUTHOR]

The eWOM has been widely used by scholars and the hospitality industry as a proxy for customers' opinions. However, only a handful of studies examined customers' opinions on different review platforms, which questions the transferability of results obtained from one platform to another. In a systematic investigation, we found that reviews of the same hotels from major eWOM platforms differ in leading eWOM indicators: star ratings, topics, and sentiment, especially between the closed and open review platforms. Over time, a "U-shape" review score distribution emerges signaling bias toward the opinion extremes. The study provides strong evidence for academics and hoteliers not to rely on a single platform to evaluate customer experiences, satisfaction, marketing effectiveness, and the overall image of their brand. We discuss the potential reasons for platform divergence and research avenues to improve our knowledge of inter-platform review variability and develop more rigorous methods employing social media data. • We analyzed the scores, sentiments, and topics of 75,000 hotel reviews from four platforms. • Topics, sentiments, ratings, and other eWOM metrics of the same hotels differ among review platforms. • We observed the highest eWOM contrast between the open and closed review sites. • Over time, a U shape distribution of review metrics emerges showing bias toward opinion extremes. • Academics and hoteliers should use multiple platforms to evaluate customer experiences. [ABSTRACT FROM AUTHOR]

Abstract The study examines the online public response to a high visibility incident of service failure from the crisis communication and image restoration perspectives. A specific incident in a hotel of a popular chain in Beijing, China, which was widely publicized on Chinese social network Weibo, is used as an example. Applying data mining and GIS to the collected data, the study analyzes temporal, spatial, topical, and gender dynamics of public discussions to investigate the usefulness of such data for hospitality providers in crisis. The study finds evidence that the effect of the incident on the general public is moderated by spatial and personal proximity of Weibo users. The discussion topics reflect the nature of the crisis and are affected by communications from the service provider and third parties. Implications for providers of tourism and hospitality services in a crisis of high visibility are discussed. Highlights • Online public discussions of a service failure incident are examined via UGC from Weibo. • Factors of spatial and personal proximity to the incident are examined. • Findings reveal temporal, topical, spatial, and gender implications for service providers. • Crisis Communication Theory is applied to frame the dynamics of public response. • Managerial implications for the hotel chain involved in the incident are discussed. [ABSTRACT FROM AUTHOR]

Following the 2007-2009 financial crisis, governments around the world passed laws that marked the beginning of new period of enhanced regulation of the financial industry. These laws called for a myriad of new regulations, which in the U.S. are created through the so-called notice-and-comment process. Through examining the text documents generated through this process, we study the formation of regulations to gain insight into how new regulatory regimes are implemented following major laws like the landmark Dodd-Frank Wall Street Reform and Consumer Protection Act. Due to the variety of constituent preferences and political pressures, we find evidence that the government implements rules strategically to extend the regulatory boundary by first pursuing procedural rules that establish how economic activities will be regulated, followed by specifying who is subject to the procedural requirements. Our findings together with the unique nature of the Dodd-Frank Act translate to a number of stylized facts that should guide development of formal models of the rule-making process. [ABSTRACT FROM AUTHOR]

Interest in applying Big Data to tourism is increasing, and automated sentiment analysis has been used to extract public opinion from various sources. This article evaluates the suitability of different types of automated classifiers for applications typical in tourism, hospitality, and marketing studies by comparing their performance to that of human raters. While the commonly used performance indices suggest that on easier-to-classify data sets machine learning methods demonstrate performance comparable to that by human raters, other performance measures such as Cohen’s kappa show that the results of machine learning are still inferior to manual processing. On more difficult and noisy data sets, automated analysis has poorer performance than human raters. The article discusses issues pertinent to selection of appropriate sentiment analysis software and offers a word of caution against using automated classifiers uncritically. [ABSTRACT FROM AUTHOR]

This study examined whether people living in the US connect their sensory experiences with local temperature to climate change and whether mass media influences the process. We used the volume of Twitter messages containing words “climate change” and “global warming” as the indicator of attention that public pays to the issue. Specifically, the goals were: (1) to investigate whether people immediately notice substantial local weather anomalies such as deviations from long-term mean temperatures and connect them to climate change by contributing to climate change discourse on Twitter and (2) to examine the role of mass media in this process. Over 2 million tweets were collected for a two-year period (2012–2013) and were assigned to 157 urban areas in the continental US. The rate of tweeting on climate change was regressed on the time variables, number of climate change publications in the mass media, and a number of temperature variables. The analysis was conducted at the two levels of aggregation – national and local. The high significance of the mass media and temperature variables in the majority of regression models suggests that both the weather and mass media coverage control public interest to the topic. However, no convincing evidence was found that the media acts as a mediator in the relationship between local weather and climate change discourse. Overall, the findings confirmed that the public recognize extreme temperature anomalies and connect these anomalies to climate change. [ABSTRACT FROM AUTHOR]

Disaster risk management, including response and recovery, are essential elements of sustainable development. With the recent increase in natural hazards, the importance of techniques to understand, model and predict the evacuation and returning behavior of affected individuals is rising. Studies have found that influence from real world social ties affects mobility decisions during disasters. Despite the rapid spread of social media platforms, little has been quantitatively understood about the influence of social ties on online social media on such decisions. Information provided by who at what timing influences users' decision-making process by how much during disasters? In this study, we answer these research questions by proposing a data-driven framework that can predict post-disaster mobility decisions and simultaneously unravel the influence of various information on online social media. More specifically, our method quantifies the influence of information provided by different types of social media accounts on the peoples' decisions to return or stay displaced after evacuation. We tested our approach using real world data collected from more than 13 million unique Twitter users during Hurricane Sandy. Experiments verified that we can improve the predictive accuracy of return and displacement behavior, and also quantify the influence of online information. In contrast to popular beliefs, it was found that information posted by the crowd influenced the decisions more than information disseminated by official accounts. Improving our understanding of influence dynamics on online social media could provide policy makers with insights on how to disseminate information on social media more effectively for better disaster response and recovery, which may contribute towards building sustainable urban systems. [ABSTRACT FROM AUTHOR]

Automated content analysis of online travel reviews allows identification of topics of travelers' satisfaction, yet its domain is not well researched. We suggest that the Anna Karenina principle positing a greater variability of the factors leading to business failure as opposed to those leading to success can be applied to the domain of visitors' reviews of historic and cultural attractions. The larger variability of issues in reviews of dissatisfied visitors is likely to result in limitations for automated topic modeling. We confirm our proposition using TripAdvisor reviews of the Terracotta Army museum in China, and validate the outcome with two additional sites. The study strongly suggests that application of unsupervised topic mining algorithms to negative reviews may be problematic and the results should be treated with caution. The main themes of dissatisfaction of visitors to all three sites are reported and practical implications for management of the attractions are discussed. [ABSTRACT FROM AUTHOR]

The crucial role of sensory dimensions in customer experiences has been supported in literature. However, traditional self-reported sensory measurements have limited capacity in capturing the multi-dimensional experiences sensed by individuals and articulating the distinct effect of different sensory dimensions on actual behavior. This study is the first attempt to test the effects of positive and negative experiences involving all five senses (sight, smell, sound, taste, and touch) on customer ratings. The sensory experiences reported in social media reviews were captured and explored using text mining and sentiment analysis. The findings show that although the majority of customers' experiences were positive, the negative sensory experiences had higher effect on customer rating. Furthermore, the five senses had different weights in forming overall experience, which provides theoretical contributions to the literature on sensescapes, prospect theory, and discourses on satisfiers and dissatisfiers. • New methodological technique to analyze big online review data. • New multidimensional sensory experience sentiment scale. • Application of the scale to restaurant reviews demonstrate the effect of five sensory experiences on customer ratings. • Taste, touch and sight experiences are the most important components of memorable restaurant experiences. • The negative sensory experiences have higher effect on customer rating, compared to their positive counterparts. [ABSTRACT FROM AUTHOR]