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Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that incorporating online review data can enhance the performance of tourism demand forecasting models, the reliability of online review data and consumers’ decision-making process have not been given adequate attention. To address the aforementioned problem, the purpose of this study is to forecast tourism demand using online review data derived from the analysis of review helpfulness.

The authors propose a novel “identification-first, forecasting-second” framework. This framework prioritizes the identification of helpful reviews through a comprehensive analysis of review helpfulness, followed by the integration of helpful online review data into the forecasting system. Using the SARIMAX model with helpful online review data sourced from TripAdvisor, this study forecasts tourist arrivals in Hong Kong during the period from August 2012 to June 2019. The SNAÏVE/SARIMA model was used as the benchmark model. Additionally, artificial intelligence models including long short-term memory, back propagation neural network, extreme learning machine and random forest models were used to assess the robustness of the results.

The results demonstrate that online review data are subject to noise and bias, which can adversely affect the accuracy of predictions when used directly. However, by identifying helpful online reviews beforehand and incorporating them into the forecasting process, a notable enhancement in predictive performance can be realized.

First, to the best of the authors’ knowledge, this study is one of the first to focus on the data issue of online reviews on tourism arrivals forecasting. Second, this study pioneers the integration of the consumer decision-making process into the domain of tourism demand forecasting, marking one of the earliest endeavors in this area. Third, this study makes a novel attempt to identify helpful online reviews based on reviews helpfulness analysis.

This study clarifies the history and significance of cryptocurrencies. It explores the underlying decentralisation and trustlessness concepts that set these digital assets apart from conventional fiat currencies. It clarifies how blockchain technology functions as the core component of decentralised money. The mechanics of mining, its function in creating and validating Bitcoin transactions, and the emergence of substitute consensus mechanisms to solve environmental issues are all covered in this study. An in-depth analysis of blockchain technology covers its advantages, such as immutability and transparency, as well as its architecture and consensus processes. This study continues with a focus on the future by examining the development of decentralised finance (DeFi) and showcasing numerous DeFi applications, including yield farming, lending protocols, and decentralised exchanges (DEXs). As a result of the development of cryptocurrencies and blockchain technology, DeFi has become possible, ushering in a new era of financial independence and inclusivity. This study emphasises the significance of striking a balance between innovation and suitable regulatory measures as the globe embraces this revolution in order to enable the proper integration of DeFi into the global financial environment. The revolutionary potential of DeFi, particularly in increasing financial inclusion and empowerment for marginalised groups globally, is one of the major themes discussed. To negotiate legal frameworks while maintaining DeFi's decentralised nature, this study looks at the regulatory problems that come with this potential.

The increasing popularity of live-streaming commerce has provided a new opportunity for e-retailers to boost sales. This study integrated signaling theory and social exchange theory to investigate how streamer- and product-centered signals influence customers’ likelihood of making an impulsive purchase in the live-streaming commerce context.

An online survey was designed and distributed to the target respondents in China using purposive sampling. A total of 735 valid responses were analyzed with partial least square structural equation modeling (PLS-SEM).

Both streamer-centered signals, i.e. streamer credibility and streamer interaction quality, were discovered to significantly influence product-centered signal, i.e. product information quality. Additionally, streamer interaction quality was found to have a significant impact on streamer credibility. Furthermore, it was observed that customer engagement played a significant mediating role in the relationship between product information quality and impulsive buying tendency. Moreover, the paths between product information quality and customer engagement, as well as the connection between engagement and impulsive buying tendency, were found to be moderated by guanxi orientation.

Despite the prevalence of impulsive purchases in live-streaming commerce, few studies have empirically investigated the impact of streamer and product signals on influencing customers’ impulsive purchase decisions. Consequently, to the best of our knowledge, this study distinguishes itself by offering empirical insights into how streamers use reciprocating relationship mechanisms to communicate signals that facilitate impulsive purchase decisions.

Information filtering systems serve as robust tools in the ongoing difficulties associated with overwhelming volumes of data. With constant generation and accumulation of reviews in online communities, the ability to distill and provide valuable insights to assist customers in their search for relevant information is of considerable significance. This study devised an effective review filtering system for a popular online physical experience review site.

This study entailed an investigation of a hybrid approach for a review filtering system augmented with various text mining-based operational variables to extract the linguistic signals of online reviews. Moreover, we devised three ensemble models based on multiple machine learning and deep learning algorithms to build a high-performance review filtering system.

The main findings confirm the effectiveness of using the derived operational variables when reviewing filtering systems. We found that the reviewer’s tendency and history macros, as well as the readability and sentiment of the reviews, contribute significantly to the filtering performance. Furthermore, the proposed three ensemble frameworks demonstrated good efficiency with an average accuracy of 89.39%.

This study provides a methodological blueprint for operationalizing variables in online reviews, covering both structured and unstructured datasets. Incorporating different variables enhances the efficiency of the algorithm and provides a more comprehensive understanding of user-generated content. Furthermore, the study affords a strategic perspective and integrated guidelines for developers seeking to create advanced review filtering systems.

We adopt an information risk view and argue that higher levels of pledge risk incurred by insiders incentivize opportunistic financial disclosure and impair the quality of information available to analysts to forecast firm performance.

We sample Chinese listed companies from 2010 to 2022. Following the literature, we apply established models to measure and test analysts’ forecasting accuracy/dispersion related to controlling shareholders pledging equity and the amount of margin call pressure. Analyst characteristics and nonfinancial disclosures proxied by CSR reports are also examined as factors likely to influence the relationship between pledge risk and analysts’ forecast quality.

We find that analysts’ earnings predictions are less accurate and more dispersed as the proportion of shares pledged (pledge ratio) increases and in combination with greater margin call pressure. Pledge ratios are significantly associated with several information risk proxies (i.e. earnings permanence, accruals quality, audit quality, financial restatements, related party transactions and internal control weaknesses), validating the channel through which equity pledges undermine analysts’ forecast quality. The results also demonstrate that forecast quality declines for a wide variety of analysts’ attributes, including high- and low-quality analysts and analysts from small and large brokerage firms. Importantly, nonfinancial disclosures, as proxied by CSR reporting, improve analysts’ forecasts.

We extend the literature by demonstrating that incremental pledge risk increases non-diversifiable information risk; all non-pledging shareholders pay a premium through more diverse and less accurate earnings forecasts. Our study provides important policy implications with economically significant costs to investors associated with insider equity pledges. Our results highlight the benefits of nonfinancial disclosures in China, which has implications for the current debate on the global convergence of CSR reporting.

This study aims to explore the synthesis and tribological performances of di-n-octyl sebacate (DOS) synthesized with spherical nano-MoS₂/sericite (SMS) and carboxylated SMS (CSMS) as catalysts.

SMS and CSMS were used as esterification catalysts to synthesize DOS from sebacic acid and n-octanol. The two catalysts were in situ dispersed in the synthesized DOS after the reaction to form suspensions. The tribological performances of the two suspensions after 20 days of storage were studied.

CSMS was more stably dispersed in DOS than SMS, and they reduced friction by 55.6% and 22.2% and wear by 51.3% and 56.5%, respectively. Such results were mainly caused by the COOH on CSMS, which was more conducive to improving the dispersion and friction reduction of CSMS than wear resistance. Another possible reason was the difference between the dispersion amounts of CSMS and SMS in DOS. The sericite of SMS was converted into SiO₂ to enhance wear resistance, while that of CSMS only partially generated SiO₂, and the rest still remained on the surface to reduce friction.

This work provides a more effective SMS catalytical way for DOS synthesis than the traditional inorganic acid catalytical method. SMS does not need to be separated after reaction and can be dispersed directly in DOS as a lubricant additive. Replacing SMS with CSMS can produce a more stable suspension and reduce friction significantly. This work combined the advantages of surface carboxylation modification and in situ catalytic dispersion and provided alternatives for the synthesis of DOS and the dispersion of MoS₂-based lubricant additives.

Aficionados of wine festivals, a component of wine tourism experience, engage in vigorous online discussions that influence fellow travelers’ purchase behaviors. This study aims to delve into these overlooked discussions, identifying emotions, topics and assessing their usefulness in TripAdvisor’s Travel Forums for two US wine festivals: Taste of Yountville and Epcot International Food and Wine Festival, located in traditional and nontraditional wine tourism destinations.

The study uses state-of-art sentiment analysis and topic modeling methods to extract emotions and underlying latent topics in travel forum discussions. Drawing from information theory, two regression analyses are performed on 10,677 forum posts to examine how the extracted Ekman’s emotions and key underlying topics influence the helpfulness of wine forum posts for each festival.

While three topics were identified in Epcot and four in Yountville, both festival platforms highlight travelers’ common preferences for “culinary experience” and “planning” attributes but reveal notable differences in their utility. Other shared novel findings include the importance of “anger” and “surprise” emotions on the helpfulness of forum posts.

These findings enhance wine festival managers’ and destination planners’ understanding of online travelers’ preferences and cognitive evaluation of user-generated contents’ usefulness. This marketing intelligence informs strategies for boosting the wine destination’s economic development.

This research offers a novel comparative analysis of social media on wine festival tourism experiences in diverse regions. Unlike hotel reviews, typically posted after consumption, forums offer unique and broader perspectives on discussions before, during, and after experiencing the wine festival.

Although prior research has employed various variables to predict player churn, the dynamic evolution of the behavioral patterns of players has received limited attention. In this study, churn prediction models are developed by incorporating the progress level, in-game purchase, social interaction, behavioral pattern and behavioral variability (BV) of players in social casino games (SCGs). The study distinguishes churn prediction between two player groups: monetizers and non-monetizers.

This study employs three machine learning techniques—logistic regression, decision trees and random forests—using real-world player data from an SCG company to construct churn prediction models. Two experiments were conducted. In Experiment 1, BV was combined with four other variable categories to effectively predict churn behaviors across all players (n = 52,246). In Experiment 2, churn prediction models were developed separately for monetizers (n = 16,628) and non-monetizers (n = 35,618).

The findings from Experiment 1 indicate that incorporating BV significantly improves the overall performance of churn prediction models. Experiment 2 demonstrates that churn prediction models achieve better performance and predictive accuracy for monetizers and non-monetizers when BV is calculated over the 3-day to 7-day and 7-day to 14-day windows, respectively.

This study introduces BV as a novel variable category for churn prediction, emphasizing within-person variability and demonstrating its effectiveness in enhancing model performance. Churn prediction models were independently constructed for monetizers and non-monetizers, utilizing different time windows for variable extraction. This approach improves predictive performance and highlights key differences in critical variables influencing churn across the two player groups. The findings provide valuable insights into churn management strategies tailored for monetizers and non-monetizers.

The purpose of this study is to assess the current level of knowledge on the application of text mining in tourism and hospitality (T&H) research and provide a novel comprehensive framework for the field. This study also identifies gaps and proposes future research directions.

This bibliometric study analyzes 814 journal articles, sourced from Scopus between 2004 and 2024, and uses performance analysis and science mapping using Biblioshiny and VOSviewer software.

Over 50% of the articles were published between 2022 and 2024, reflecting a surge in text-mining applications in T&H research. These studies primarily focus on topics such as customer satisfaction, sustainability, destination image and COVID-19 effects, with sentiment analysis and topic modeling being the predominant techniques. The primary data sources are online reviews and microblogs. The review also highlights recent research trends (e.g. long–short-term memory, support vector machines and crisis) and classifies them into four conceptual categories concerning the application of text mining in T&H research: How? Where? Why? When?

This study comprehensively explores the evolution of T&H, contributions from research constituents and the intellectual structure of the field, providing a novel comprehensive framework while also assessing the field and highlighting its challenges.

本研究评估了文本挖掘在旅游与酒店管理（T&H）研究中的应用现状, 并提供了该领域的全新综合框架。同时, 研究还识别了当前存在的研究空白并提出了未来的研究方向。

本研究通过文献计量分析方法, 对2004年至2024年间Scopus数据库中814篇期刊文章进行分析, 采用Biblioshiny和VOSviewer软件进行绩效分析和科学映射。

超过50%的文章发表在2022年至2024年间, 反映了文本挖掘在T&H研究中的应用激增。这些研究主要集中在顾客满意度、可持续性、目的地形象以及COVID-19影响等主题, 情感分析和主题建模是主要使用的技术。主要数据来源为在线评论和微博。该综述还揭示了近期的研究趋势（如长短期记忆LSTM、支持向量机SVM、危机管理）, 并将其归纳为四个文本挖掘在T&H研究中的概念类别：如何？在哪里？为什么？以及何时？

本研究全面探索了T&H领域的演变、研究贡献者的作用以及该领域的知识结构, 提供了一个新的综合框架, 评估了该领域的发展并突出了其挑战。

Purpose: The purpose of the current chapter is to emphasize the importance of renewable energy sources and their impact on society’s desire to accept them in light of the substantial energy consumption that determines economic growth and its sustainability.

Need for the study: The ideals of sustainable development are the cornerstones of the green economy. This calls for the creation of new technologies and the adoption of eco-friendly practices that could spur economic growth and boosting efficiency. The chapter tries to measure total efficiency improvement after the adoption of green fuel.

Methodology: This is a qualitative research that uses secondary data sources. It is proposed to include a variety of online databases, e-journals, printed journals, periodicals, newspapers, etc., for the chapter. The scope of the chapter encompasses the discussion about the economic and environmental benefits of green energy resources.

Findings: The results show that the biggest advantages of employing green energy sources are typically acknowledged for their capacity to gradually lower the cost of electricity service, such as the avoided expenses of electricity generation or the avoidance of the need to build new power plants. These advantages may materialize in the short term, the long term, or both.

Practical implications: The result of the chapter implies improvements in life standards and environmental effects. This chapter put forth that renewable energy projects require complicated installation and are sensitive to the local ecology.