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Airbnb Experiences is a new type of service launched by Airbnb in November 2016, where users can offer travellers a wide range of activities. This study devotes attention to analysing customer feedback expressed in online reviews published in Airbnb to evaluate those experiences.

A total of 1,110 reviews were collected from 12 categories, including 111 experiences, resulting in 10 reviews per experience. First, the sentiment score was computed based on the text of the reviews. Second, 17 quantitative features encompassing user, Airbnb experience and review information were used to model the score through a support vector machine. Third, a sensitivity analysis was performed to extract knowledge on the most relevant features influencing the sentiment score.

Tourists writing online reviews are not only influenced by their tourist experience but also by their own online experience with the booking and online review platform. The number of reviews made by the user accounted for more than 20 per cent of relevance, while users with more reviews tended to grant more positive reviews.

Current literature is enhanced with a conceptual model grounded on existing studies that assess tourist satisfaction with tour services. Both services online visibility and user characteristics have shown significant importance to tourist satisfaction, adding to the existing body of knowledge.

This study aims to propose a data-driven approach, based on open-source tools, that makes it possible to understand customer satisfaction of the accommodation offer of a whole country.

The method starts by extracting information from all hotels of Portugal available at TripAdvisor through Web scraping. Then, a support vector machine is adopted for modeling the TripAdvisor score, which is considered a proxy of customer satisfaction. Finally, knowledge extraction from the model is achieved using sensitivity analysis to unveil the influence of features on the score.

The model of the TripAdvisor score achieved a mean absolute percentage error of around 5 per cent, proving the value of modeling the extracted data. The number of rooms of the unit and the minimum price are the two most relevant features, showing that customers appreciate smaller and more expensive units, whereas the location of the hotel does not hold significant relevance.

National tourist offices can use the proposed approach to understand what drives tourists’ satisfaction, helping to shape a country’s strategy. For example, licensing new hotels may take into account the unit size and other characteristics that make it more attractive to tourists. Furthermore, the procedure can be replicated at any time and in any country, making it a valuable tool for data-driven decision support on a national scale.

This study aims to present a model drawn on both the extension of the unified theory of acceptance and use of technology (UTAUT2) and the perceived value for explaining consumer behavior toward mobile hospitality services (MHS) from two perspectives: intention to use and recommendation.

The partial least square (PLS) was applied to data gathered from 348 validated responses to a survey to test a number of research hypotheses.

Results found that the proposed conceptual model explains 62 per cent of the intention to use of MHS and 51 per cent of the variation in the recommendation. Perceived value plays a role in explaining both the intention to use and recommend MHS, with both constructs also helping in explaining behavior intention, to which effort expectancy, facilitating conditions and performance expectancy also contribute.

This research goes beyond perceived value by combining it with a cornerstone model, UTAUT2, used in technology adoption studies. The paper addresses updated MHS that include but are not limited to mobile hotel reservations.

This study aims to identify the impact of restaurant pandemic mitigation measures on perceived service quality.

A total of 15,251 reviews were collected from 300 TripAdvisor Lisbon restaurant Web pages between March 2020 and December 2021, when Covid-19 contingency measures were in place. Grounded on the online reviews, a word frequency matrix was created and used as input on partial least squares–structural equation modeling to test the proposed hypotheses.

The results suggest that precaution measures, such as the Vaccination Certificate, negative tests and restaurant layout configuration, positively influenced perceived service quality and consumer satisfaction, moderated by consumers’ beliefs.

This study provides relevant information for restaurant managers, which will help them implement strategies to guarantee service quality, consumer satisfaction and revisit intentions in future pandemic scenarios.

This study aims to discern emerging trends and provide a longitudinal perspective on merchandising research by identifying relationships between merchandising-related subdomains/themes.

This study sourced 657 merchandising-related articles published since 1960, from the Scopus database and 425 from Web of Science. After processing and normalizing the data, this study performed co-word and thematic network analyses. Taking a text mining approach, this study used topic modeling to identify a set of coherent topics characterized by the keywords of the articles.

This study identified the following merchandising-related themes: branding, retail, consumer, behavior, modeling, textile and clothing industry and visual merchandising. Although visual merchandising was the first type of merchandising to be used in-store, only recently has it become an emerging topic in the academic literature. There has been a further trend over the past decade to understand the adoption of simulation technology, such as computer-aided design, particularly in supply chain management in the clothing industry. These and other findings contribute to the discussion of the merchandising concept, approached from an evolutionary perspective.

The conclusions of this study hold implications at the intersection of merchandising, sectors, new technologies, research methodologies and merchandising-practitioner education. Research trends suggest that, in the future, virtual reality and augmented reality using neuroscientific methods will be applied to the e-merchandising context.

The different dimensions of merchandising can be used to leverage store managers’ decision-making process toward an integrated store-management strategy. In particular, by adopting loyalty merchandising tactics, the store can generate emotional attachment among consumers, who will perceive its value and services as unique, thanks to merchandising items designed specifically with that aim in mind. The stimulation of unplanned purchases, the strategic location of products and duration of each merchandising activity in the store, the digitalization of merchandising and the application of findings from neuroscience studies are some of the most relevant practical applications.

This study provides the first-ever longitudinal review of the state of the art in merchandising research, taking a holistic perspective of this field of knowledge spanning a 60-year period. The work makes a valuable contribution to the development of the marketing discipline.

This study aims to present a very recent literature review on tourism demand forecasting based on 50 relevant articles published between 2013 and June 2016.

For searching the literature, the 50 most relevant articles according to Google Scholar ranking were selected and collected. Then, each of the articles were scrutinized according to three main dimensions: the method or technique used for analyzing data; the location of the study; and the covered timeframe.

The most widely used modeling technique continues to be time series, confirming a trend identified prior to 2011. Nevertheless, artificial intelligence techniques, and most notably neural networks, are clearly becoming more used in recent years for tourism forecasting. This is a relevant subject for journals related to other social sciences, such as Economics, and also tourism data constitute an excellent source for developing novel modeling techniques.

The present literature review offers recent insights on tourism forecasting scientific literature, providing evidences on current trends and revealing interesting research gaps.

National tourism offices worldwide implement marketing strategies to influence tourists’ choices. However, there is more than meets the eye when it comes to choosing a city as a tourism destination. The purpose of this paper is to answer which are the characteristics that play a key role in room occupancy.

Diverse characteristics such as the city offer, demographics, natural amenities (e.g. number of beaches) and also politics (e.g. type of government) are combined into a decision tree model to unveil the relevance of each in determining room occupancy. The empirical experiments used data known in 2015 from 43 cities from Europe and the rest of the World to model room occupancy rate in 2016.

While the seasonality effect plays the most significant role, other less studied features such as the type of political party prior to current government were found to have an impact in room occupancy.

This study unveiled that center–right and right governments are generally more sensitive to promote its city as a tourism destination.

Social media has become the main venue for users to express their opinions and feelings, generating a vast number of available and valuable data to be scrutinized by researchers and marketers. This paper aims to extend previous studies analyzing social media reviews through text mining and sentiment analysis to provide useful recommendations for management in the restaurant industry.

The Lexalytics, a text mining artificial intelligence tool, is applied to analyze the text of the online reviews of the restaurants in a touristic Dutch village extracted from the most frequently used social media platforms focusing on the four restaurant quality factors, namely, food and beverage, service, atmosphere and value.

The findings of this research are presented by the identified key themes with comparisons of the customers’ review sentiment between a selected restaurant, Zwaantje, vis-à-vis its bench-mark restaurants set by a specific approach under the abovementioned quality dimensions, in which the food and beverage and service are the most commented by customers. Results demonstrate that text mining can generate insights from different aspects and that the proposed approach is valuable to restaurant management.

The paper provides a relatively big scale in numbers and resources of social media reviews to further explore the most important service dimensions in the restaurant industry in a specific tourist area. It also offers a useful framework to apply the text mining business intelligence tool by comparison of peers for local small business restaurant practitioners to improve their management skills beyond manually reading social media reviews.

The purpose of this paper is to further our knowledge of what influences users to adopt mobile augmented reality in tourism (MART). A conceptual model is proposed, combining the extension of Unified Theory of Acceptance and Usage of Technology (UTAUT2) with task technology fit (TTF), to explain behavioural intention and user behaviour of MART adopters.

A questionnaire was completed by a sample of 335 respondents in Portugal. Both UTAUT2 and TTF were combined into a new model from which several hypotheses were drawn based upon the literature.

The results have shown that the model explains 72 per cent of the variance in behaviour intention to use MART and 45 per cent of the variance in user behaviour.

MART is becoming increasingly known to travellers as it provides the user diverse and useful information with a real relationship with the world. By studying behaviour and what influences consumers to use MART, this study aims to advance the research into new technologies in tourism.

本论文旨在扩展对于消费者在旅游行业中使用移动增强现实科技（MART）的知识。本论文结合科技接受和使用全模型（UTAUT2）和任务科技配置度模型（TTF）, 提出一个新型的理论模型, 用于更深度理解MART使用者的使用意图和行为。

本论文采用问卷采样形式, 采样地点在葡萄牙, 一共采集335份样本数据。由UTAUT2和TTF模型整合的新模型得到了理论认证。

本论文新模型解释了72%的MART消费者行为意图和45%消费者使用行为。

MART如今越来越受到游客的认识, 其科技通过一种与现实更贴近的手段, 向游客提供多样且实用的信息。本论文通过研究消费者使用MART的行为, 对旅游产业新科技的应用有着很深的理论贡献。

Online travel reviews platforms have become innovative information systems due to the incorporation of sophisticated gamification elements such as visually appealing badges. This study aims to analyze three features of the review after leveling up a badge: review length (number of words), sentiment scoring and period between two successive reviews (number of days until the next review is written).

A total of 77,000 online TripAdvisor reviews written by 100 frequent travelers and contributors are analyzed using a data mining approach. A data-based sensitivity analysis is then conducted to provide an understanding of the data mining trained models.

The results show evidence that badges appealing for self-pride (“badge passport”) and for peer-recognition (“badge helpful”) have significant influence across the lifespan of online review, whereas badges simply awarded by counting the contributions have little effect.

To the best of the authors’ knowledge, this study provides the first analysis of how an experienced traveler is influenced as the badges and points are being awarded. Intrinsic motivational factor to award badges for standard contributions scarcely influence user behavior. Badges need to be designed to reward accomplishments that are not so trivial to be achieved and that do not depend entirely on the user.

在线旅游评论平台已成为创新的信息系统, 这也是由于结合了复杂的游戏化元素, 例如具有视觉吸引力的徽章。本研究旨在分析升级徽章后评论的三个特征：评论长度（字数）、情绪评分和两次连续评论之间的时间段（距离撰写下一次评论的天数）。

本研究使用数据挖掘方法分析了由 100 位常旅客和贡献者撰写的总共 77,000 条在线 TripAdvisor 评论。然后进行基于数据的敏感性分析 (DSA), 以提供对数据挖掘训练模型的理解。

结果表明, 具有自我自豪感（“徽章护照”）和同行认可（“徽章有帮助”）的徽章在在线评论的整个生命周期中具有显着影响, 而仅通过计算贡献来授予徽章几乎没有影响。

本研究首次分析了经验丰富的旅行者在获得徽章和积分时受到的影响。奖励标准贡献徽章的内在动机因素几乎不会影响用户行为。需要设计徽章来奖励那些并非微不足道且不完全取决于用户的成就。