The value of rating diversity within multidimensional rating system: Evidence from hotel booking platform

Liu, Xianwei; Li, Chunhong; Nicolau, Juan Luis; Han, Meini

doi:10.1016/j.ijhm.2023.103434

Cited by 10 publications

(7 citation statements)

References 43 publications

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“…Based on a large dataset of individual ratings on Booking.com platform, we provide empirical evidence that long-stayers assign less polarized scores. On the other hand, our work adds to an emerging literature on review helpfulness (Lee et al, 2021;Liu et al, 2023;Mudambi and Schuff, 2010;Zhang et al, 2023;Zhao et al, 2013) by showing how greater ratings' volatility dampers the informational content of online reviews, making the signal about expected quality noisier and less useful.…”

Section: Introductionmentioning

confidence: 60%

“…This literature has shown that informative and readable reviews from consumers with high reputation are deemed as more helpful (Liang et al, 2019;Liu and Park, 2015). Review diversity (different ratings for each item that composes the overall score) improves usefulness for the case of negative reviews (Liu et al, 2023) whereas high arousal makes reviews less helpful (Chatterjee, 2020). Moreover, emoticons enhance usefulness when the review is narrative-based (Huang et al, 2020).…”

Section: 2noisy Reviews and Heteroskedastic Ratingsmentioning

confidence: 99%

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Noisy Signals: Do Ratings’ Volatility Depend on the Length of the Consumption Span?

Boto‐García

Leoni

2023

SSRN Journal

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Section: Introductionmentioning

confidence: 60%

Section: 2noisy Reviews and Heteroskedastic Ratingsmentioning

confidence: 99%

Noisy Signals: Do Ratings’ Volatility Depend on the Length of the Consumption Span?

Boto‐García

Leoni

2023

SSRN Journal

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“… Furthermore, exploring methods to assess the reliability and credibility of data sources, such as establishing a rating system for social media platforms or news outlets, can enhance the accuracy of sentiment analysis [81]. Looking ahead, the potential of machine learning and AI in this field is vast.…”

Section: Resultsmentioning

confidence: 99%

Integrating Social Media Data and Historical Stock Prices for Predictive Analysis: A Reinforcement Learning Approach

Li,

Zhang

2023

IJACSA

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The reliance on data collection for assessing individual behavior and actions has intensified, particularly with the proliferation of digital platforms. People often use the Internet to express their opinions and experiences about various products and services on social media and personal websites. Concurrently, the stock market, a key driver of commercial and industrial growth, has seen a surge in research focused on predicting market trends. The vast array of information on social media regarding public sentiment towards current events, coupled with the known impact of financial news on stock prices, has led to the application of data mining techniques for understanding market volatility. This research proposes a novel method that integrates social media data, encompassing public sentiment, news, and historical stock prices, to predict future stock trends. The approach involves two primary phases. The first phase develops a sentiment analysis (SA) model using three dilated convolution layers for feature extraction and classification. Addressing the challenge of unbalanced classification, a reinforcement learning (RL)-based strategy is employed, wherein an agent receives varied rewards for accurate classification, with a bias towards the minority class. Additionally, a unique clustering-based mutation operator within a differential equation (DE) framework is introduced to initiate the backpropagation (BP) process. The second phase incorporates an attention-based long short-term memory (LSTM) model, merging historical stock prices with sentiment data. An experimental analysis of the study dataset is conducted to determine optimal values for significant parameters, including the reward function.

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“…However, rarely studies are seen in the marketing literature especially in Saudi Arabian context that aid in understanding how aspects of hotel servicescape lead to satisfaction and revisit intentions in consumers. Today's modern internet hotel booking technologies have also made it easily possible to familiarize the consumers well before their actual visit [11,12]. The images, videos of hotel rooms and even 360-degree views of the hotels are easily provided through hotel booking websites and smartphone applications [11,12].…”

Section: Introductionmentioning

confidence: 99%

“…Today's modern internet hotel booking technologies have also made it easily possible to familiarize the consumers well before their actual visit [11,12]. The images, videos of hotel rooms and even 360-degree views of the hotels are easily provided through hotel booking websites and smartphone applications [11,12]. Even the information provided by the hotels about services offered, advertisements and guest reviews also help consumers in familiarizing with the hotel [13,12].…”

Section: Introductionmentioning

confidence: 99%

Role of Hotel Servicescape in Satisfaction and Revisit Intention of Consumers: Familiarity as a Moderator

Balroo

2023

JEMT

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Globally the hospitality industry is seen immensely growing because of opening up of tourism in countries worldwide. One such growth of hospitality industry is specifically being witnessed in a middle eastern country namely Saudi Arabia. The expansion of leisure and religious tourism for diversifying the oil-based economy under the kingdom’s vision 2030 is rapidly fueling growth of hotels in Saudi Arabia. However, very little is known about what aspects of these hotels lead to consumer satisfaction and revisit intentions. Moreover, modern internet hotel booking technologies by providing 360-degree images, videos of hotels and hotel rooms are helping consumers to get well familiar with the hotels aforehand their visits. Very little is also known about the effects of this aforehand familiarity of consumers on the relationship of satisfaction with revisit intention. In marketing terminology, the place where the interaction between consumers and hotel service provider takes place is called hotel servicescape. The hotel servicescape has two important aspects namely physical and social. This study examines how these two aspects of hotel servicescape leads to consumer satisfaction and revisit intentions. The study also examines the moderating role of familiarity on the relationship of satisfaction with revisit intention. The study sample comprised of 390 frequent stayers in hotels of Saudi Arabia. The study sample was collected using snowball sampling method. All of the hypothesized relationships of the study were examined rigorously and simultaneously using structural equation modeling technique. The results of the study revealed that both physical and social aspects of hotel’s servicescape shared significant positive relationships with satisfaction. Significant positive relationship was also reported between satisfaction and revisit intention. Familiarity significantly moderated the relationship between satisfaction and revisit intention in this study. The study provided new findings on the role played by familiarity in strengthening the relationship between satisfaction and revisit intention. Some marketing implications, limitations and future research directions are also discussed in the study.

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The value of rating diversity within multidimensional rating system: Evidence from hotel booking platform

Cited by 10 publications

References 43 publications

Noisy Signals: Do Ratings’ Volatility Depend on the Length of the Consumption Span?

Noisy Signals: Do Ratings’ Volatility Depend on the Length of the Consumption Span?

Integrating Social Media Data and Historical Stock Prices for Predictive Analysis: A Reinforcement Learning Approach

Role of Hotel Servicescape in Satisfaction and Revisit Intention of Consumers: Familiarity as a Moderator

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