•This study introduces PLTSs to summarize numerous online review information in a statistical manner.•A cloud model is employed to deal with probabilistic linguistic information.•PLTSs are converted ...and a novel concept of probability linguistic integrated cloud is proposed.•Two information fusion tools are presented to fuse substantial information.•An applicable hotel decision support model is developed for tourists.
Hotel selection on tourism websites has been a prevailing trend in traveling in recent years, and selecting a suitable hotel is a high-risk decision for millions of tourists because of the intrinsic imperceptibility of the provided products. Decision support models for hotel selection have drawn the interest of numerous scholars. The problem is that some existing models cannot address substantial amounts of online information, and they neglect the influence of interrelationships among different criteria on tourists’ decisions. To cover these defects, an applicable hotel decision support model is developed for tourists utilizing online reviews on TripAdvisor.com. Considering a great deal of review information associated with hotels posted by numerous tourists on TripAdvisor.com, probabilistic linguistic term sets (PLTSs) are introduced to summarize this information statistically. Processing qualitative concepts requires effective support of reliable tools; then, a cloud model can be employed to deal with probabilistic linguistic information. First, PLTSs are converted, and a novel concept of probabilistic linguistic integrated cloud (PLIC) is proposed. Moreover, the essential algorithms and distance measure of PLICs are defined. Two information fusion tools are subsequently presented based on Heronian mean operator. Then, the hotel decision support model is established. Finally, a hotel selection problem on TripAdvisor.com is provided to demonstrate the model. Its stability and validity are further verified by a sensitivity analysis and comparison with other extant methods.
•A hotel selection model driven by online textual reviews is explored to assist tourists selecting hotels.•To express tourists’ feelings and differentiate sentiment words’ degree, an improved ...sentiment dictionary is explored.•Linguistic distribution assessments are used to denote semantic and multi-angle information.•To address conflicting evidence and ensure rational results, an evidence theory-based fusion method is proposed.•Promotion strategies of different hotels are formulated through experiments.
Browsing online reviews before selecting a satisfactory hotel has become a trend. Multiple criteria decision making models are powerful tools to provide competitive guidance. The first research gap motivating this study is that online textual reviews perform well in describing abundant perceptions and sentiments hidden in texts, while customer ratings used in existing models ignore them. Moreover, the existing sentiment analysis and hotel selection approaches have limited capacity in differentiating sentiment degrees, expressing natural languages, conveying comprehensive hotel descriptions and managing conflicting attitudes of different tourists. To narrow these gaps, a novel hotel selection model driven by online textual reviews on TripAdvisor.com is constructed. A semantic mapping function and the method of building this dictionary are proposed. Moreover, an evidence theory-based fusion method is proposed, which can guarantee the reliability of the results. Finally, the proposed model is tested in a case study and in robustness and comparative analyses.
This study aims to investigate the hotel selection differences among different types of travellers through online hotel reviews. Specifically, the study performs a detailed examination of the ...differences in hotel key factors, criterion importance and selection results among five types of travellers, namely, business, couples, families, friends and solo. Using a sample of 194,885 online reviews on TripAdvisor.com, this study identifies the hotel key factors and criterion importance by employing the term frequency-inverse document frequency algorithm and Word2Vec algorithm. Additionally, a bounded rationality behavioural decision support model with picture fuzzy information is proposed to address hotel selection problems for different traveller types. Our results suggest that different types of travellers present differences in hotel key factors, criterion importance and selection results. However, families and friends have similar hotel selection results. This study can serve as a reference for hotel managers in understanding traveller preferences and for tourism website optimisation.
•The difference in hotel selection of five traveller types (business, couples, families, friends and solo) is investigated.•Key factors and criteria importance affecting travelers' hotel selection for different types of travelers were identified.•A picture fuzzy TODIM method is proposed for helping five types of travelers to select appropriate hotels.•Five travelers have different preference in key factors and criteria importance.•It is interesting to note that families and friends travelers have similar hotel selection results.
Display omitted
•Numerous online reviews of hotels are described by linguistic distribution assessments that can retain original information effectively.•New comparison method is defined based on ...linguistic scale functions to break through the restriction of previous comparison method for linguistic distribution assessments.•New distance measurement between linguistic distribution assessments is defined based on the linguistic scale functions to calculate the distance between any two linguistic distribution assessments that have different numbers of linguistic terms.•In order to deal with the conflicting criteria and the preferences for criteria in different groups, a mathematical model is designed based on an extended VIKOR approach and the idea of PA operator to select hotels on tourism websites.•A case study of TripAdvisor.com is conducted to select suitable hotels using online reviews of these hotels.
The effect of online reviews on resulting decisions attracted the interests of merchants and researchers in different fields. According to existing studies, the decisions of tourists are highly likely modified after browsing online reviews from other travelers on a tourism website. The manner in which online reviews on tourism websites are utilized to select hotels and support tourists is a noteworthy research problem. Online reviews of a hotel are provided by various tourists with respect to different criteria; thus, each tourist is regarded as a decision-maker. In this instance, the problem of hotel selection is based on online reviews in a tourism website and is expressed as a multi-criteria decision-making problem. In this study, a mathematical model was designed to select appropriate hotels on websites. First, a new comparison method for linguistic distribution assessments was proposed based on linguistic scale functions. Second, a novel distance measurement between linguistic distribution assessments with different numbers of linguistic terms was defined with an adjustable parameter. Third, a model for calculating weights and a mathematical model were constructed according to the idea of prioritized aggregation operator and distance measurement. Finally, a case study of TripAdvisor.com was conducted to select suitable hotels using online reviews of hotels. Data analysis was completed to prove the viability of the designed model.
This study investigates the effect of the COVID-19 pandemic on the hotel selection attributes and customer post-purchase behaviors. Qualitative and quantitative processes comprising an ...importance-performance analysis are used. This mixed-methods approach successfully (1) explores the hotel selection attributes after the COVID-19 pandemic, (2) uncovers the change of importance of these attributes before and after the outbreak of COVID-19, (3) identifies the importance and the performance level of the hotel selection attributes, and (4) explores the roles of the hotel selection attributes that form the overall image of a hotel and the subsequent intentions to revisit a hotel. This study includes a high degree of value, and this is the first empirical research that explores the guests’ hotel choice behaviors before and after the pandemic, which can be helpful for the subsequent guest-behavior studies in the post-pandemic era.
•This research examines the influence of the COVID-19 pandemic on hotel selection attributes.•The hotel selection attributes are explored.•Qualitative and quantitative approaches are used for the attainment of research goals.•Important-performance analysis is applied.
•Subliminal advertising is found to be significantly influential to consumers’ selection of hotels.•Emoji smiling face as a subliminal message does affect consumers’ hotel choice.•Consumers’ theta ...band would significantly increase while viewing hotel videos with subliminal message.•Consumers’ beta brainwaves are significantly lower when watching video with a smiling face emoji as subliminal stimuli embedded in the hotel video.
This study aims to understand how hotel videos embedded with a smiling face emoji as a subliminal message affect consumers’ selection of hotels, with their brain activities measured and collected while they watched the videos. Data was collected from sixteen participants who completed two rounds of experiments. A chi-square test of homogeneity, paired sample t-test, and Bayes factor were performed to address the two proposed research questions. The results of this study reveal that participants’ selection of hotels would be significantly affected by the subliminal stimuli of a smiling face emoji. Meanwhile, neuroscientific data identifies significant differences between participants’ two (theta and beta) out of five bands of brainwaves while they were viewing hotel videos with and without the subliminal message. Suggestions for future studies and practical operations are also discussed.
通过测量和收集消费者在观看视频时的大脑活动, 这项研究旨在了解嵌入笑脸表情作为潜意识信息的酒店视频如何影响消费者对酒店的选择.数据来自16名参与者, 他们完成了两轮实验.本研究采用卡方齐性检验,配对样本t检验及贝叶斯因子来解决上述两个研究问题.研究结果显示, 微笑表情的潜意识刺激会显著影响参与者对酒店的选择.与此同时, 神经科学数据表明, 当参与者在观看带有和不带有潜意识信息的酒店视频时, 五种波段中的两种 (theta和beta) 的脑电波有显著差异.本文对未来研究和实际操作的建议也进行了讨论.
Hotel selection method based on online evaluations has become a hot research topic. The existing models based on online ratings or reviews from one website have a disadvantage of information being ...definite and information amount being small. Therefore, this paper proposes a hotel selection model based on Probabilistic linguistic Term Set (PLTS) which integrates online ratings and reviews from multiple websites: (1) Unifying the rating information’s evaluation attributes among different websites based on the PLTS similarity calculation method, putting forward the transformation method of linguistic scale to unify the rating information’s evaluation scale among different websites; (2) Analyzing the sentiment of review texts and putting forward the aggregation model of user reviews based on different groups' risk attitudes; (3) Improving the linguistic scale function to introduce the unbalanced effect of positive and negative evaluations; (4) According to preference differences among different groups, putting forward the attribute weight calculation method and providing recommendation results for different groups. Take four hotels on TripAdvisor, Ctrip and Hostelworld websites for case studies. The results show that information can be used to a greater extent by integrating online ratings and reviews from multiple websites, thus providing consumers with more objective and reliable decision-making results.
Urban tourism is a worldwide form of tourism and is one of the most important social and economic impetus for urban development. The urban tourism market has been increasingly dominated by the demand ...for personalized experiences. Accordingly, this study aims to design personalized itineraries with hotel selection for multi-day urban tourists. A two-level heuristic approach is proposed, which embeds genetic algorithm, variable neighborhood search, and differential evolution algorithm into the structure of memetic algorithm. A case study in Xiamen, a coastal city in Southeast China, is carried out to evaluate the performance of our approach. Results of paired sample t-tests show that our proposed approach is remarkably superior to existing methods. In addition, compared with previous methods, our approach can design more reasonable and personalized itineraries for tourists.
•We design personalized itineraries with hotel selection for multi-day urban tourists.•We regard this issue as a bilevel optimization problem involving hotel sequence optimization and day trip design.•We propose a two-level heuristic approach to optimize the solutions by embedding GA, VNS, and DEA into the MA structure.•A case study in Xiamen is conducted to evaluate the performance our approach.•Our approach can design more reasonable and personalized routes for tourists.
•A tourist recommendation system is developed to design personalized day tour routes for urban tourists considering hotel selection.•A hybrid heuristic algorithm combined with DPSO, JADE and LS ...procedure is proposed.•Our approach performs significantly better than current methods.•The hybrid evolution mechanism can achieve better efficiency.
With the development of tourism, digital technology is increasingly being applied in the design of tourist routes. This study takes into account that tourists are experience-driven in tourism activities and hotel selections. In this study, the tourist trip design problem with hotel selection is formulated based on bi-objective optimization with total utility of the points of interest maximization and the average utility of the hotels maximization, and a three-step hybrid algorithm combined with discrete particle swarm optimization, an adaptive differential evolution with an optional external archive, and a local search is designed to identify the optimal route. To examine the performance of the designed algorithm, a numerical experiment was conducted. The results of Wilcoxon rank sum tests verified that the proposed algorithm performed distinctly better than extant approaches. Moreover, the results also indicate that the two main innovative mechanisms about initialization and hybrid evolution play a critical role in improving the algorithm's efficiency for the tourist trip design problem with hotel selection.
•A new method based on online reviews for hotel selection is proposed.•The numerical scale function is improved based on the online reviews.•A new method called two-steps aspiration satisfaction ...function is defined.•Three different time aspirations and development speeds are put forward.•The management suggestions are provided for decision-makers and alternatives.
With the development of the Internet, online reviews provide a new perspective to evaluate alternatives. From the perspective of selecting a hotel as the long-term partner, the comprehensive performances of hotels in a long-time dimension become the vital factor. In addition, the behaviors of decision-makers should also be taken into account in the decision making. Due to the above reasons, this paper provides a multi-stage multi-attribute decision-making method based on online reviews for hotel selection by considering the aspirations of different development speeds. Firstly, this paper considers the performances of hotels in multiple stages. Secondly, this paper establishes the aspiration satisfaction function that considers different risk preferences and aspirations to make the decision process more flexibility. Thirdly, this paper provides three different development speeds when setting the multi-stage time aspirations to offer more choices. Finally, this paper illustrates the effectiveness by a practical example and provides management suggestions for both decision-makers and alternatives based on the simulation results.
PDF
