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The purpose of this study is to examine the association between revenue-based earnings management in the periods immediately before and after firms’ initial public offerings (IPOs) and regulatory scrutiny by the United States Securities and Exchange Commission (SEC) during review of IPO firms’ registration statements.

This paper uses conditional discretionary revenues (Stubben, 2010) as its measure of earnings management, and revenue recognition comments delivered by the SEC as its measure of regulatory scrutiny. The authors use ordinary least squares regression (OLS) models, as well as a supplemental count model, to assess the association between conditional discretionary revenues and revenue recognition comments delivered by the SEC.

This study finds evidence of a positive association between earnings management measures in the pre-IPO period and the number of revenue recognition comments received by those firms during the SEC’s review. Furthermore, this study provides evidence that greater numbers of comments are associated with declining earnings management measures in the post-IPO period. However, the evidence suggests that these associations apply only to income-decreasing earnings management.

This paper extends the IPO earnings management literature by using conditional discretionary revenues as the measure of earnings management, and contributes to a nascent research stream in the accounting literature by investigating the SEC’s comment letter process and its association with, and impact upon, earnings management in the IPO process.

This study aims to examine the factors influencing hotel employee satisfaction and explores the different sentiments expressed in these factors in online reviews by hotel type (premium versus economy) and employment status (current versus former).

A total of 78,535 online reviews by employees of 29 hotel companies for the period of 2011-2019 were scraped from Indeed.com. Structural topic modeling (STM) and sentiment analysis were used to extract topics influencing employee satisfaction and examine differences in sentiments in each topic.

Results showed that employees of premium hotels expressed more positive sentiments in their reviews than employees of economy hotels. The STM results demonstrated that 20 topics influenced employee satisfaction, the top three of which were workplace bullying and dirty work (18.01%), organizational support (16.29%) and career advancement (8.88%). The results indicated that the sentiments in each topic differed by employment status and hotel type.

Rather than relying on survey data to explore employee satisfaction, hotel industry practitioners can analyze employees’ online reviews to design action plans.

This study is one of only a few to use online reviews from an employment search engine to explore hotel employee satisfaction. This study found that workplace bullying and dirty work heavily influenced employee satisfaction. Moreover, analysis of the comments from previous employees identified antecedents of employees’ actual turnover behavior but not their turnover intention.

The study aims to provide an analytical overview of current innovations in peer review and their potential impacts on scholarly communication.

The authors created a survey that was disseminated among publishers, academic journal editors and other organizations in the scholarly communication ecosystem, resulting in a data set of 95 self-defined innovations. The authors ordered the material using a taxonomy that compares innovation projects according to five dimensions. For example, what is the object of review? How are reviewers recruited, and does the innovation entail specific review foci?

Peer review innovations partly pull in mutually opposed directions. Several initiatives aim to make peer review more efficient and less costly, while other initiatives aim to promote its rigor, which is likely to increase costs; innovations based on a singular notion of “good scientific practice” are at odds with more pluralistic understandings of scientific quality; and the idea of transparency in peer review is the antithesis to the notion that objectivity requires anonymization. These fault lines suggest a need for better coordination.

This paper presents original data that were analyzed using a novel, inductively developed, taxonomy. Contrary to earlier research, the authors do not attempt to gauge the extent to which peer review innovations increase the “reliability” or “quality” of reviews (as defined according to often implicit normative criteria), nor are they trying to measure the uptake of innovations in the routines of academic journals. Instead, they focus on peer review innovation activities as a distinct object of analysis.

The purpose of this study is to analyze online hotel reviews produced by customers to identify and compare factors known as satisfiers and dissatisfiers based on Herzberg’s two-factor theory. This approach was applied to compare full-service and limited-service hotels, which can show different levels of customer expectation.

A content analysis of 919 satisfaction- and dissatisfaction-indicating reviews of 100 hotels in both full-service and limited-service hotel segments in New York City on Trip Advisor was conducted.

Results show that satisfiers and dissatisfiers in full-service hotels were distinct, with the exception of two common service-related factors, namely, “staff and their attitude” and “service”. On the other hand, “staff and their attitude” and four room facilities-related factors, “room cleanliness/dirtiness”, “bed”, “bathroom” and “room size”, were revealed as common satisfiers and dissatisfiers in limited-service hotels. To fulfill customer satisfaction and resolve dissatisfaction in both full-service and limited-service hotels, satisfiers and dissatisfiers should be highlighted according to the hotel class; the most critical factor is “staff and their attitude”.

Analysis of online hotel reviews provides understanding of customers’ satisfiers and dissatisfiers, and the results are very useful to hotel management. Therefore, hotel operators should monitor electronic word-of-mouth, recognizing and acting upon previous and current customers’ satisfactory and unsatisfactory reactions.

As technologies such as social media develop, customers are increasingly sharing their satisfactory and unsatisfactory experiences on consumer-generated online review sites. These have become a major source of information not only for customers deciding on a hotel stay but also for hotel managers trying to understand their customers and competitors.

The dynamic yet volatile nature of tourism and travel industry in a competitive environment calls for enhanced marketing intelligence and analytics, especially for those entities with limited marketing budgets. The past decade has witnessed an increased use of user-generated content (UGC) analysis as a marketing tool to make better informed decisions. Likewise, textual data analysis of UGC has gained much attention among tourism and hospitality scholars. Nonetheless, most of the scholarly works have focused on the singular application of an existing method or technique rather than using a multi-method approach. The purpose of this study is to propose a novel Web analytics methodology to examine online reviews posted by tourists in real time and assist decision-makers tasked with marketing strategy and intelligence.

For illustration, the case of tourism campaign in India was undertaken. A total of 305,298 reviews were collected, and after filtering, 276,154 reviews were qualified for analysis using a string of models. Descriptive charts, sentiment analysis, clustering, topic modeling and machine learning algorithms for real-time classification were applied.

Using big data from TripAdvisor, a total of 145 tourist destinations were clustered based on tourists’ perceptions. Further exploration of each cluster through topic modeling was conducted, which revealed interesting insights into satisfiers and dissatisfiers of different clusters of destinations. The results supported the use of the proposed multi-method Web-analytics approach.

The proposed machine learning model demonstrated that it could provide real-time information on the sentiments in each incoming review about a destination. This information might be useful for taking timely action for improvisation or controlling a service situation.

In terms of Web-analytics and UGC, a comprehensive analytical model to perform an end-to-end understanding of tourist behavior patterns and offer the potential for real-time interpretation is rarely proposed. The current study not only proposes such a model but also offers empirical evidence for a successful application. It contributes to the literature by providing scholars interested in textual analytics a step-by-step guide to implement a multi-method approach.

The purpose of this paper is to examine the significance of TripAdvisor on reputation within the hotel industry. TripAdvisor encapsulates key themes in establishing an online reputation strategy in an evolving digital landscape.

Through the use of an exploratory case study, data were gathered primarily by means of a series of expert interviews within the hotel industry in Cyprus, today a mature holiday destination in Europe. Further data collection included a document search of presentations, annual reports, past surveys and sales and marketing literature from the examined industry.

Hotel communication practitioners are fully aware of the impact of social media in managing reputation. Constant monitoring, prompt responses, training and transparency were identified as key factors. Online reputation management needs to be taken into consideration when designing a comprehensive integrated communication strategy.

Congruence amongst interviewees in certain areas could be on account of the homogeneity of practitioners, of their background and training and of similar organisational cultures across the locale of study. This leads to limits in the generalisations from this study’s findings.

Encouragement and training of employees were amongst the primary suggestions that emerged. An internal and external environmental scan, recognising possible strengths, weaknesses, opportunities and threats, which could assist in the effective engagement and monitoring of the organisation’s online presence, were also suggested.

The uniqueness of the study lies in its exploration of reputation management of a well-known traveller’s platform by addressing social media content in both a proactive and reactive manner.

Social media use has increased in recent years, and businesses are looking to capitalize on the plethora of marketing opportunities afforded by this digital shift by paying attention to user-generated content (UGC) posted on review websites. Leveraging UGC can help small businesses gain a competitive advantage over late-adopters. At the same time, there is evidence to suggest that small businesses do not have the time, resources or skill level to properly use social media to create a competitive advantage. This paper aims to explore how wine tourism businesses can analyze consumer feedback on online review websites to evaluate customer perceptions and expectations and generate more effective ways to improve customer satisfaction.

The qualitative method of thematic analysis was used to map out consumer reviews online to assess service satisfaction and dissatisfaction. A total of 848 reviews were gathered and qualitatively analyzed from two online review websites (TripAdvisor.com and Yelp.com) using open and axial coding and thematic analysis.

The results show that wine consumers are interested in the hedonic aspects of their experience, are most often attracted to wineries as a special outing and focus on factors such as scenery and atmosphere, service quality and products in their reviews. Hence, service and sales personnel have a key opportunity to capitalize on generating better service experiences through social media analysis.

The present study fills a gap by providing a more in-depth, qualitative exploration of the wine consumers’ psychology and experience, including factors such as atmosphere and special occasions. Furthermore, this study uses interpretive, manual coding to pick up on nuanced themes that are often missed by using automated qualitative analysis software or by looking at frequency counts in isolation.

This study aims to investigate the relationships between service quality, perceived value and hotel guest satisfaction, drawing upon data from TripAdvisor – an online travel agent (OTA) platform. The study also investigates the mediating role of perceived value on the relationship between service quality and satisfaction, as well as the moderating role of hotel star ratings on all direct and indirect relationships.

Data for this study were collected via Web scraping from August–October 2018. Data were collected from 192 three- to five star-rated hotels in Kuala Lumpur, Malaysia. Partial least squares – structural equation modeling was used for data analysis. Furthermore, importance-performance map analysis (IPMA) was performed to identify the most important items of service quality and perceived value in improving customer satisfaction.

The findings of this study provide support for all direct and indirect relationships for three-star and four- and five-star hotels. Moreover, the results indicate that perceived value mediates the relationship between service quality and customer satisfaction. These results support the moderating role of hotel star ratings for the relationship between service quality and perceived value. The results also show that after perceived value, three-star hotels looking to improve customer satisfaction should prioritize improving the quality of their services, sleep quality, cleanliness and rooms. Four- and five-star hotels, on the other hand, should prioritize service, cleanliness, room and sleep quality.

OTA platforms collect a wealth of data pertaining to large number of hotels; nevertheless, few studies to date have drawn on this data to examine a pre-determined conceptual framework developed based on the literature. As such, this study makes a valuable methodological contribution to the tourism and hospitality literature. In terms of theoretical contributions, this study examines the mediating role of perceived value between service quality and satisfaction using OTA data. In addition, this study assesses the moderating role of hotel star ratings for the direct and indirect effects of service quality on satisfaction. Using IPMA, this study compares the importance and performance of service quality indicators to generate satisfaction between three-star and four- and five-star hotels.

本论文检测了服务质量、价值感知、和酒店顾客满意度之间的关系, 使用TripAdvisor的数据—OTA。本论文还检测了价值感知对服务质量和满意度之间的中介作用, 以及酒店星级评价对其中直接和间接关系的调节作用。.

本论文采样通过网络爬虫技术, 截取了2018年八月至十月之间的数据。研究样本为192家马来西亚Kuala Lumpur地区的三星-五星酒店。样本分析方法为PLS-SEM。此外, 本论文采样IPMA分析法来找出提高顾客满意度中的服务质量和价值感知中最重要的因子。.

研究结果指出了三星、四星、五星酒店的直接和间接关系。此外, 研究还显示了服务质量和顾客满意度关系的价值感知中介作用。研究结果还指出了酒店星级评价对服务质量和价值感知关系的调节作用。此外, 研究还指出, 除了价值感知, 如果三星酒店想提高顾客满意度, 那么他们应该优先提高其服务质量、睡眠质量、清洁度、和房间。另一方面, 四星和五星酒店应该优先提高其服务质量、清洁度、房间、和睡眠质量。.

OTA平台搜集大量酒店数据, 但是很少作品研究这些数据, 以检测根据文献提出的理论模型。因此, 本论文在方法论上对旅游酒店文献做出宝贵贡献。理论贡献而言, 本论文使用OTA数据检测了价值感知对服务质量和满意度关系之间的中介作用。此外, 本论文检测了酒店星级评价对服务质量和满意度之间直接和间接关系的调节作用。本论文使用IPMA方法, 比较各种服务质量指标的重要性对在三星、四星、五星酒店的提高满意度的不同作用。.

This study argues that online user comments on social media platforms provide feedback and evaluation functions. These functions can provide services for the relevant departments of organizations or institutions to formulate corresponding public opinion response strategies.

This study considers Chinese universities’ public opinion events on the Weibo platform as the research object. It collects online comments on Chinese universities’ network public opinion governance strategy texts on Weibo, constructs the sentiment index based on sentiment analysis and evaluates the effectiveness of the network public opinion governance strategy adopted by university officials.

This study found the following: First, a complete information release process can effectively improve the effect of public opinion governance strategies. Second, the effect of network public opinion governance strategies was significantly influenced by the type of public opinion event. Finally, the effect of public opinion governance strategies is closely related to the severity of punishment for the subjects involved.

The theoretical contribution of this study lies in the application of image repair theory and strategies in the field of network public opinion governance, which further broadens the scope of the application of image repair theory and strategies.

This study expands online user comment research to network public opinion governance and provides a quantitative method for evaluating the effect of governance strategies.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-05-2022-0269

This study aims to analyse online customer experience in the hospitality industry through dynamic topic modelling (DTM) and net promoter score (NPS). A novel model that was used for collecting, pre-processing and analysing online reviews was proposed to understand the hidden information in the corpus and gain customer experience.

A corpus with 259,470 customer comments in English was collected. The researchers experimented and selected the best K parameter (number of topics) by perplexity and coherence score measurements as the input parameter for the model. Finally, the team experimented on the corpus using the Latent Dirichlet allocation (LDA) model and DTM with K coefficient to explore latent topics and trends of topics in the corpus over time.

The results of the topic model show hidden topics with the top high-probability keywords that are concerned with customers and the trends of topics over time. In addition, this study also calculated and analysed the NPS from customer rating scores and presented it on an overview dashboard.

The data used in the experiment are only a part of all user comments; therefore, it may not reflect all of the current customer experience.

The management and business development of companies in the hotel industry can also benefit from the empirical findings from the topic model and NPS analytics, which will support decision-making to help businesses improve products and services, increase existing customer satisfaction and draw in new customers.

This study differs from previous works in that it attempts to fill a gap in research focused on online customer experience in the hospitality industry and uses text analytics and NPS to reach this goal.

本研究旨在通过动态主题建模和净推荐值分析酒店业的在线客户体验。 提出了一种用于收集、预处理和分析在线评论的新模型, 以了解语料库中的隐藏信息并获得客户体验。

收集了一个包含 259,470 条英文客户评论的语料库。 研究人员通过 Perplexity 和 Coherence Score 测量结果进行了实验, 并选择了最佳的 K 参数（主题数量）作为模型的输入参数。 最后, 团队使用 Latent Dirichlet allocation (LDA) 模型和具有 K 系数的 Dynamic Topic Model (DTM) 在语料库上进行实验, 以探索语料库中的潜在主题和主题随时间变化的趋势。

主题模型的结果显示了隐藏的主题, 其中包含与客户相关的顶级高概率关键字以及主题随时间的变化趋势。 此外, 该研究还根据客户评分计算和分析净推荐值 (NPS), 并将其显示在概览仪表板上。

实验中使用的数据只是所有用户评论的一部分; 因此, 它可能无法反映所有当前的客户体验。

酒店业公司的管理和业务发展也可以受益于主题模型和 NPS 分析的实证结果, 这将支持决策制定, 帮助企业改进产品和服务, 提高现有客户满意度, 并吸引新客户 .

本研究不同于以往的研究, 因为它试图填补以酒店业在线客户体验为重点的研究空白, 并使用文本分析和 NPS 来实现这一目标。