Deconstructing Online Hospitality Review Systems

Online hospitality reviews have an important impact on consumers’ travel and hospitality booking decisions in the Internet age. A well-designed online hospitality review system is crucial to reduce the uncertainty of consumers’ decision making, to grasp the actual needs of consumers, and to improve the quality experience of platforms. In this context, this research conducts an empirical study on the design features of online hospitality review systems based on the Kano model. First, the paper analyzes the design features of online hospitality review systems. Then, the paper proposes an improved method to classify design features on the basis of the Kano questionnaire design and survey data. Finally, the paper quantitatively measures their importance in online hospitality review systems. Results can provide scientific basis for online travel platforms or hospitality operators to optimize the design of online hospitality review systems and to obtain reference value to increase the satisfaction of consumers’ decision making.

TRACE: Travel Reinforcement Recommendation Based on Location-Aware Context Extraction

As the popularity of online travel platforms increases, users tend to make ad-hoc decisions on places to visit rather than preparing the detailed tour plans in advance. Under the situation of timeliness and uncertainty of users’ demand, how to integrate real-time context into dynamic and personalized recommendations have become a key issue in travel recommender system. In this article, by integrating the users’ historical preferences and real-time context, a location-aware recommender system called TRACE ( T ravel R einforcement Recommendations Based on Location- A ware C ontext E xtraction) is proposed. It captures users’ features based on location-aware context learning model, and makes dynamic recommendations based on reinforcement learning. Specifically, this research: (1) designs a travel reinforcing recommender system based on an Actor-Critic framework, which can dynamically track the user preference shifts and optimize the recommender system performance; (2) proposes a location-aware context learning model, which aims at extracting user context from real-time location and then calculating the impacts of nearby attractions on users’ preferences; and (3) conducts both offline and online experiments. Our proposed model achieves the best performance in both of the two experiments, which demonstrates that tracking the users’ preference shifts based on real-time location is valuable for improving the recommendation results.

Coping with digital market re-organization: How the hotel industry strategically responds to digital platform power

How do organizations in a sector where powerful platforms have emerged cope with the new constraints and opportunities that platforms induce? A growing number of studies highlight the power of digital platforms to re-organize markets and thereby create new forms of dependence. But there are also indications that organizations are capable of countering platform power especially by demanding their regulation. This paper expands this view to investigate also strategies at the organizational level. It draws on the algorithmic game studies of strategic responses to environmental changes to study how organizations strategically respond to the rise of digital platforms. To show organizations’ capacities to cope with the new digital market environment, we use a qualitative case study of the Swiss hotel sector and its reactions to so-called online travel agencies, based on interviews with hotel managers and professional representatives. We distinguish between three types of hotels—small family-run, luxury, and chain hotels, and identify three types of strategic responses: bypassing, optimizing, and mitigating. Contrary to a platform power perspective, we find some evidence for organizations’ capacity to keep platforms at bay, by limiting dependence through mitigation, and platforms’ reach through bypassing. Hotels also learn to “play the algorithmic game” and take advantage of platforms’ technological affordances, but such strategies seem to accommodate platform power rather than countering it. Finally, we find that hotels with fewer resources (small family-run hotels) are less equipped to counter platform power, suggesting that platforms risk fostering existing hierarchies and segmentation in markets.

Visualizing and Comparing Online Travel Reviews of the Great Walls: A Data Mining Approach

This research employs two samples of heritage sites of the Great Wall of China (Ba daling Great Wall and Mu tianyu Great Wall) and their 21000 reviews on TripAdvisor to visualize and induce feature-related comparisons. Word2vec and D3.js are applied for statistical computing and graphing Minimal Spanning Tree (MST) and ThemeRiver. The applications of MST and ThemeRiver are used to delineate outstanding features and clearer feature relationships. In terms of methodology, we applied an innovative research route to combine MST with ThemeRiver to visualize travellers’ online comments. At the same time, the visual results obtained are combined with qualitative analysis to generate valuable, intuitive summaries that can be used for reference in future research. Practically, the results disclose that although both sites are highly enjoyed by tourists, they are significantly different in terms of service, infrastructure and scenery. This article has implications for policymakers and practitioners with regard to making use of online reviews to gather authentic visitor comments on the Great Wall.

Online Travel Planning for Families with a Child with a Disability

Whereas the search for information when starting to plan a trip is generally perceived as enjoyable, families with a disabled child may associate this step with anxiety and stress. This is due to a lack of information and poor consideration of disabled children’s needs in most tourist websites, making it difficult for such families to find answers to their questions. In this context, the present research attempts to understand the online travel planning process of families with a child with a disability in order to propose solutions adapted to these families’ needs.

Use and Impact of Online Travel Reviews for Planning Free and Easy Holidays

Online travel reviews (OTRs) are used by travelers to plan and book their holiday particular by the free and easy traveler. Hence, it is essential for hoteliers to understand the factors that affect and empower travelers when using OTRs to plan their holidays. This study has adopted three theories: dual-process theory, psychological empowerment (PE) and TPB. These theories were used to test additional dimensions of the informational and normative social influence, its links with perceived empowerment, and their impact on the intention to use OTRs. Using SmartPLS to analyze the data collected from 392 Malaysian, the results show that PE is significantly affected by informational and normative social influence, from travelers’ perception of PE positively affects their attitude toward using OTRs, and attitude significantly influences intention to use OTRs when plans for travel. Furthermore, the research findings contribute to the literature on travelers’ behavioral intention; this study also has direct implications for online travel providers.

Social Network Analysis on Tourists’ Perceived Image of Tropical Forest Park: Implications for Niche Tourism

Tourists’ perceived image is the core of destination marketing. As an important niche tourist destination, the analysis of tourists’ perceived image of tropical forest parks has great value. This study takes the Yalong Bay Tropical Paradise Forest Park as a case site and collects a total of 1,44,022 words from online travel reviews on Ctrip.com via the Python web crawler technology. Firstly, through high-frequency word analysis, we identified 77 core elements and a total of five image themes, which are attraction image, emotional image, service facility image, crowd image, and activity image. Secondly, building on Net Draw analysis, a network structure diagram of the tourist’ perceived image of the Yalong Bay Tropical Paradise Forest Park is constructed. Finally, the overall network and individual network of tourists’ perceived image are analyzed. The results indicate that low overall network density is in possession of core and periphery. Guojianglong Cable Bridge, battery car, and glass path enjoy both high degree centrality and betweenness centrality. They also show significant advantages of structural holes. Therefore, they are on the top of the network. The academic and practical value of tourism image projection and development in tropical forest park is discussed.