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This study aims to explore the use of time series analyses to examine changes in travelers’ preferences in accommodation features by disentangling seasonal, trend and the COVID-19 pandemic’s once-off disruptive effects.

Longitudinal data are retrieved by online traveler reviews (n = 519,200) from the Canary Islands, Spain, over a period of seven years (2015 to 2022). A time series analysis decomposes the seasonal, trend and disruptive effects of six prominent accommodation features (view, terrace, pool, shop, location and room).

Single accommodation features reveal different seasonal patterns. Trend analyses indicate long-term trend effects and short-term disruption effects caused by Covid-19. In contrast, no long-term effect of the pandemic was found.

The findings stress the need to address seasonality at the single accommodation feature level. Beyond targeting specific features at different guest groups, new approaches could allow dynamic price optimization. Real-time insight can be used for the targeted marketing of platform providers and accommodation owners.

A novel application of a time series perspective reveals trends and seasonal changes in travelers’ accommodation feature preferences. The findings help better address travelers’ needs in P2P offerings.

This paper aims to identify key service attributes in peer-to-peer (P2P) accommodation from online reviews and formulate service improvement strategies based on the unsatisfactory service encounters mined from the reviews.

The methodology involves topic modelling using latent Dirichlet allocation, sentiment analysis and process analysis based on process chain network (PCN).

The text analytics results showed that negative P2P accommodation experiences are caused by the lack of hot water for shower, poor sleep quality and unpleasant check-in.

The PCN analysis shows that the surrogate interactions of the P2P accommodation platform with both the guest and the host impact consumer experiences. This highlights that the key to managing consumer experiences lies in the non-human resources such as information, rather than direct interactions between process entities.

The information on the P2P accommodation platform should be in a more interactive format such as video and 360 degrees camera. Hosts should ensure a good condition of the physical products such as water heaters and beds before guests' arrival. Professional videography and handyperson services should be provided by the platform to help hosts deliver a preferred consumer experience. Flexible and strict check-in polices should also be introduced to smoothen the check-in process.

This study is built on multi-attribute utility theory. It is also one of the first to study P2P accommodation services from an operations management perspective. It demonstrates how text analytics serves as an additional supplement for service improvement.

This paper aims to introduce a hierarchical fuzzy system for an online review analysis named FLORA. FLORA enables tourists to decide their destination without reading numerous reviews from experienced tourists. It summarizes reviews and visualizes them through a hierarchical structure. The visualization does not only present overall quality of an accommodation, but it also presents the condition of the bed, hospitality of the front desk receptionist and much more in a snap.

FLORA is a complete system which acquires online reviews, analyzes sentiments, computes feature scores and summarizes results in a hierarchical view. FLORA is designed to use an overall score, rated by real tourists as a baseline for accuracy comparison. The accuracy of FLORA has achieved by a novel sentiment analysis process (as part of a knowledge acquisition engine) based on semantic analysis and a novel rating technique, called hierarchical fuzzy calculation, in the knowledge inference engine.

The performance comparison of FLORA against related work has been assessed in two aspects. The first aspect focuses on review analysis with binary format representation. The results reveal that the hierarchical fuzzy method, with probability weighting of FLORA, is achieved with the highest values in precision, recall and F-measure. The second aspect looks at review analysis with a five-point rating scale rating by comparing with one of the most advanced research methods, called fuzzy domain ontology. The results reveal that the hierarchical fuzzy method, with probability weighting of FLORA, returns the closest results to the tourist-defined rating.

This research advances knowledge of online review analysis by contributing a novel sentiment analysis process and a novel rating technique. The FLORA system has two limitations. First, the reviews are based on individual expression, which is an arbitrary distinction and not always grammatically correct. Consequently, some opinions may not be extracted because the context free grammar rules are insufficient. Second, natural languages evolve and diversify all the time. Many emerging words or phrases, including idioms, proverbs and slang, are often used in online reviews. Thus, those words or phrases need to be manually updated in the knowledge base.

This research contributes to the tourism business and assists travelers by introducing comprehensive and easy to understand information about each accommodation to travelers. Although the FLORA system was originally designed and tested with accommodation reviews, it can also be used with reviews of any products or services by updating data in the knowledge base. Thus, businesses, which have online reviews for their products or services, can benefit from the FLORA system.

This research proposes a FLORA system which analyzes sentiments from online reviews, computes feature scores and summarizes results in a hierarchical view. Moreover, this work is able to use the overall score, rated by real tourists, as a baseline for accuracy comparison. The main theoretical implication is a novel sentiment analysis process based on semantic analysis and a novel rating technique called hierarchical fuzzy calculation.

Based on text content analysis using big data, this study aims to explore differences in guest perceptions of peer-to-peer accommodations before and after COVID-19 to provide suggestions for the development of these properties in China postpandemic.

A guest perception dictionary was established by collecting Ctrip customer reviews of peer-to-peer accommodations. After data cleaning, thematic word analysis and semantic association network analysis were used to explore perceptions and thematic differences before and after COVID-19.

This research constructed a multidimensional framework of guest-perceived values for peer-to-peer accommodation in the context of COVID-19. The findings showed that the emphasis on functionality in peer-to-peer accommodation changed; perceived emotional values associated with peer-to-peer stays were more complex; perceived social values decreased, host–guest interactions were reduced and online communication became a stronger trend; tourist preferences for types of experiences changed, and people changed their destination selections; perceived conditional value was reflected in perceived risks, and the perceptions of environmental health, service and physical risks increased.

This research has constructed a multidimensional framework of tourist perceived value on the basis of peer-to-peer accommodation context and epidemic background and has thus shown the changes in tourist perceived value of peer-to-peer accommodation before and after COVID-19.

To the best of authors’ knowledge, this research constitutes the first attempt to explore the perceptual differences for peer-to-peer accommodations before and after COVID-19 based on an extensive data set of online reviews from multiple provinces of China.

This chapter aims to find out perceived value creation while engaging with the Airbnb business. Whilst values have been found leading to participation, values resulting from actual participation are yet to be explored. By taking the approach of service-dominant logic and cocreation, topped with the discourse analysis of the written accounts of Airbnb guests, this study has discovered several values that result from cocreation during participation in Airbnb business models. Several avenues for the continuation of this study are suggested to build upon the acquired knowledge from this exploratory research.

Integrative reviews are an important method for understanding research in the field of special education. Reviews can help practitioners decide what methods to use in the classroom, researchers clarify directions for new research, and policymakers guide education improvement programs. We discuss the steps for conducting an integrative review, illustrating the process with a case study of an integrative review of large-scale testing accommodations for students with disabilities.

Most of the research on collaborative consumption platforms (CCPs) has focused on motivational drives, and little research has been conducted on the problem of unbalanced information sharing, also known as the “lemons problem,” and signals. The paper aims to discuss these issues.

This study conducted a netnography and an experiment.

The netnographic study showed that participants tend to use low ratings and negative reviews as cues implying more searches, use ratings as an anchor to adjust other information, and employ differing cognitive information-processing styles. The experimental results show that, in a normal environment (when ratings are high), visualizers (verbalizers) have more of an intention to use CCPs when they are exposed to abundant pictures (textual cues); however, when the cues lead to a further information search (when the ratings are low), this search behavior pattern is reversed: visualizers (verbalizers) have more of an intention to use CCPs when they are exposed to abundant textual cues (pictures).

This study extends previous research by showing that people frequently use differing heuristics depending on the context; that ratings have an anchoring effect and guide people in selecting a signal to use and condition how they use it; and that visualizers prefer text cues to pictorial cues when trying to make informed decisions under a condition that points to a further information search. These results are opposite of previous assertion.

Marketers are advised to provide a mechanism by which users can extract the cues they need and reduce the less urgent ones; devise a mechanism that screens participants and divides them into two categories: those who post honest evaluations and those who do not; and reduce the opportunistic behaviors of partners on both sides.

The current study addresses consumers’ use of information posted by other consumers on CCPs and demonstrates that participants use low ratings and negative reviews as cues implying more searches, use ratings as an anchor to adjust other information, and employ differing cognitive information-processing styles. Previous research rarely addressed these information search behaviors of consumers on CCPs.

This study aims to conduct a critical review of the research on the sharing economy to identify its key intellectual foundations and their evolution and offers thematic and methodological recommendations for future research to advance the domain.

A tri-method approach using bibliometric (co-citation) analysis, thematic content analysis and a quantitative systematic literature review was conducted on sharing economy research in hospitality and tourism journals, up to and including May 2020.

The findings from the three methods were coherent and provide a clear picture of the fact that while research on the sharing economy in hospitality and tourism has achieved significant depth, the breadth of the understanding of this area remains somewhat limited. Each of the three periods of research on the sharing economy identified in the study were marked by a focus on specific thematic areas, with largely Western-situated researchers demonstrating limited theoretical engagement and using a limited range of methods and perspectives (disciplinary, stakeholders and sectors) to examine these themes.

Recommendations for future thematic research opportunities are provided using a multi-level perspective. The present review does not include research that incorporates the impacts of COVID-19, which has significantly disrupted the hospitality and tourism industry and is the focus of current research in the field. However, the review represents the largest and most comprehensive assessment of the state of research on the sharing economy prior to COVID-19, and, as such, can serve as a valuable baseline for future reviews of sharing economy research during and after the pandemic.

In contrast with previous literature reviews, the present review is comprehensive in its scope, methodology and temporal coverage of sharing economy research. It also examines the evolution of research on the topic, enabling a more nuanced identification of gaps and future thematic and methodological research opportunities.

To the general public, the Civil Service is often perceived as an organisational entity: in reality, it embraces a complex network of organisations (the various departments) whose common goal is to further government policies, but whose nature, size and activities mirror the variety and scope of the policy arena itself. The Gowrie Report to the Prime Minister — a review of office accommodation management — synthesised the findings from reviews of eight of these departments. Its terms of reference were clear — ‘to examine provision of office and administrative accommodation and whether it meets current and foreseen needs; to examine departmental arrangements for managing and budgeting for accommodation; to consider the value for money of these arrangements; and to make recommendations’.

This study aims to analyze Chinese and Western visitors’ on‐line reviews of Shenzhen under five dimensions: catering, accommodation, transportation, travel and shopping.

This study used a multidimensional discourse analysis to assess visitors’ experience, perception and emotional evaluations of Shenzhen in reviews on TripAdvisor.com.

The findings suggest that both Chinese and Western visitors show positive perceptions of Shenzhen’s transportation convenience and options and show great interest in folk culture. Both groups focus on shopping, service and price but believe that the prices in Shenzhen are high.

The classification of reviews highlights tourism elements in Shenzhen, which can offer implications for destination image formation for similar destinations.

This study offers suggestions on how to build an appealing destination that meets both Chinese and Western tourists’ needs.

This study enriches the theory of tourism image perception of Shenzhen as it relates to language and culture.

This study offers implications on building an appealing destination that meets Chinese and Western tourists’ expectations.

深圳目的地形象感知：基于中西方游客在线评论的话语分析

摘要

本研究从餐饮、住宿、交通、旅游、购物五个维度分析中西方游客对深圳的评价。

本研究采用多维语篇分析来评估访问者对深圳的体验、感知和情感评价。

调查结果表明, 中国和西方游客对深圳的交通便利和选择都表现出积极的看法, 并对民俗文化表现出浓厚的兴趣。两组都关注购物、服务和价格, 但认为深圳的物价偏高。

评论的分类突出了深圳的旅游元素，可以为相似目的地形象的构建提供启示。

本研究丰富了深圳旅游形象感知与语言相关的理论。它为建设一个满足中国和西方游客期望的吸引人的目的地提供了启示。

旅游目的地形象, 话语分析, 深圳, 多维话语分析, 中西方游客

文章类型： 研究型论文