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Online employee reviews have a substantial impact on employee recruitment, retention and the overall perception of a company’s image, making them a crucial element of its online reputation. Consequently, these reviews play a vital role in shaping the company’s competitiveness in the talent market. This study aims to investigate the role of employee loyalty in online reputation in the tourism and hospitality sector.

This study collected online reviews posted by 334,428 employees across 173 companies in the tourism and hospitality sector. Then, this study proposed a method for measuring employee loyalty toward the company through text comments. Furthermore, the role of employee loyalty in online reputation through regression models was analyzed.

Employee loyalty is positively associated with the closed-form evaluations, and the length and readability of open-ended comments is directly and positively associated with review helpfulness and is indirectly associated with review helpfulness through employee online reviews. Employees’ perception of job instability has a significant moderating effect on the above relationships.

This study contributes to the literature on loyalty in the tourism and hospitality industry, online reputation and employee risk perception. These findings offer a more profound understanding of the extra-role behaviors demonstrated by loyal employees, provide a theoretical foundation for the formation of a company’s online reputation and contribute to helping the tourism and service industry better address risk events. These conclusions provide valuable insights for companies in the fields of human resource management and online reputation management.

To the best of the authors’ knowledge, this study is the first to reveal the impact of employee loyalty on the company’s online reputation and provides important theoretical and practical implications for management.

Financial reports are the basis of economic decisions that affect organizational interests and shareholders. However, there is a severe research gap concerning the factors affecting the quality of financial information (such as audit report readability and tone). Therefore, considering the importance of presenting high-quality financial information, this study aims to investigate the impact of intellectual capital (IC) and its components on the audit report's readability and tone.

The multivariate regression model tests research hypotheses. Then, hypotheses are tested via a sample of 824 observations of the listed companies on the Tehran Stock Exchange (103 companies) from 2014 to 2021, using the multivariate regression model based on pooled data and fixed effects.

Results determine that customer capital (CC) and structural capital (SC) are likely to influence the audit report tone positively. In general, the IC and human capital (HC) negatively impact auditors' tone. More analyses also document that IC and its CC, HC and SC components positively and significantly affect audit report readability based on two readability indices, including FOG and text length. Finally, findings pertaining to the third readability index (Flesch index) reveal that only HC and SC are robust based on this measurement, whereas the IC and CC have a negative and significant impact on the readability of auditors’ reports.

To the best of the authors’ knowledge, this study is the first to address this issue in emerging markets, and it provides helpful insights for users, analysts and legal institutions regarding IC, which significantly affects audit report readability and tone.

This paper aims to examine the relationship between the readability of annual reports and corporate performance in Chinese listed firms.

This research examined the annual report readability factors of Chinese listed companies by using a textual analysis method using Python to extract the text from the annual reports, convert it into numerical form to facilitate statistical analysis and then merge the results with data from the Chinese stock market to explain the impact on corporate performance and predict future earnings in the Chinese financial markets from 2008 to 2021.

Study findings indicate that firms with better financial reporting readability are more profitable, incur lower agency costs and have low earnings in the Chinese stock markets when readability is low (i.e. more complexity and length of annual reports). It was also found that when a listed company has a good performance, it prefers to use a short space to explain its operating and financial status. More generally, the means of the report length are short, and accounting terms are used less frequently; in the case of a poor company, the annual report is particularly long and accounting terms are more frequently used. In the context of the COVID-19 crisis, this study served as a proxy measure of returns prior to the announcement of the COVID-19 pandemic. In addition, an instrumental variable approach is used, which helps results to remain robust and control for fixed effects and potential endogeneity problems.

Although this study’s results cannot be generalised globally because of their limited scope, they can still be generalised across non-English speaking countries. Thus, future cross-country research is encouraged to examine the textual analysis of financial reports across those countries.

This study conveys two messages to investors and policymakers within the Chinese market. First, investors ought to pay greater attention to the nonfinancial information contained in annual reports to improve the accuracy of their predictions regarding future firm performance. Second, Chinese policymakers are encouraged to instate a policy for the use of plain English in annual reports to make them more readable by international investors.

This study contributes to the paucity of research that examines English-written annual reports in non-English speaking countries by examining the readability of annual reports in the Chinese market.

This study aims to examine the influence of reward personalization on financing outcomes in the Industry 5.0 era, where reward-based crowdfunding meets the personalized needs of individuals.

The study utilizes a corpus of 218,822 crowdfunding projects and 1,276,786 reward options on Kickstarter to investigate the effect of reward personalization on investors’ willingness to participate in crowdfunding. The research draws on expectancy theory and employs quantitative and qualitative approaches to measure reward personalization. Quantitatively, the number of reward options is calculated by frequency; whereas text-mining techniques are implemented qualitatively to extract novelty, which serves as a proxy for innovation.

Findings indicate that reward personalization has an inverted U-shaped effect on investors’ willingness to participate, with investors in life-related projects having a stronger need for reward personalization than those interested in art-related projects. The pledge goal and reward text readability have an inverted U-shaped moderating effect on reward personalization from the perspective of reward expectations and reward instrumentality.

This study refines the application of expectancy theory to online financing, providing theoretical insight and practical guidance for crowdfunding platforms and financiers seeking to promote sustainable development through personalized innovation.

The growing business complexity has caused many risks (e.g. operational, financial, reputational, cybersecurity, regulatory and compliance) that threaten companies' sustainability and have received attention from regulators, investors, and businesses. The authors present a model for assessing and reporting corporate risk by examining the indicators underlying corporate risk reporting.

A thorough review of the literature and semi-structured interviews with experts were conducted and the fuzzy Delphi technique was used to obtain consensus and screening of risks. The relationships between these risk indicators were recognized, weighted and prioritized by employing a hybrid Decision Making Trial and Evaluation Laboratory Model (DEMATEL) method integrated with Analytic Network Process (ANP) (DEMATEL-ANP [DANP]) approach. Finally, using the Iranian setting of corporate risk reporting, a model was developed to calculate the risk-reporting scores.

The results indicate that risk disclosure quality is more important than risk disclosures' textual properties and quantity. According to the experts, reporting the key risks that the company faces, management's approach to dealing with these risks and quantifying their impact are more important than the other indicators. The results also show that risk reporting in Iran lacks quantitative and specific information, and most risk disclosures are sticky.

The data have been prepared and analyzed according to the unique Iranian reporting environment, which should be considered when interpreting the results.

The results of this research can be used by the regulators of the Stock Exchange Organizations (SEO) to evaluate corporate risk reports and rank companies. Results are also relevant to investors and policymakers to identify companies with poor risk disclosure and to take necessary measures to improve their reporting practices.

This paper contributes to the social and governance literature by presenting the importance of risk reporting in corporate disclosures.

The unique Iranian setting of corporate risk reporting furthers the understanding of risk reporting and thus provides education, policy, practice and research implications for other emerging economies like Iran. Many prior studies focus mainly on the quality of risk disclosure, and other aspects of corporate risk disclosure presented in the study have remained largely overlooked. The corporate risk reporting attributes identified in the study are relevant to the rise of non-financial risks, the textual and qualitative nature of risk reporting and textual risk disclosures.

Scientific impact is traditionally assessed with citation-based metrics. Recently, altmetric indices have been introduced to measure scientific impact both within academia and among the general public. However, little research has investigated the association between the linguistic features of research article titles and received online attention. To address this issue, the authors examined in the present study the relationship between a series of title features and altmetric attention scores.

The data included 8,658 titles of Science articles. The authors extracted six features from the title corpus (i.e. mean word length, lexical sophistication, lexical density, title length, syntactic dependency length and sentiment score). The authors performed Spearman’s rank analyses to analyze the correlations between these features and online impact. The authors then conducted a stepwise backward multiple regression to identify predictors for the articles' online impact.

The correlation analyses revealed weak but significant correlations between all six title features and the altmetric attention scores. The regression analysis showed that four linguistic features of titles (mean word length, lexical sophistication, title length and sentiment score) have modest predictive effects on the online impact of research articles.

In the internet era with the widespread use of social media and online platforms, it is becoming increasingly important for researchers to adapt to the changing context of research evaluation. This study identifies several linguistic features that deserve scholars’ attention in the writing of article titles. It also has practical implications for academic administrators and pedagogical implications for instructors of academic writing courses.

This research proposes to organise and distil this massive amount of data, making it easier to understand. Using data mining, machine learning techniques and visual approaches, researchers and managers can extract valuable insights (on guests' preferences) and convert them into strategic thinking based on exploration and predictive analysis. Consequently, this research aims to assist hotel managers in making informed decisions, thus improving the overall guest experience and increasing competitiveness.

This research employs natural language processing techniques, data visualisation proposals and machine learning methodologies to analyse unstructured guest service experience content. In particular, this research (1) applies data mining to evaluate the role and significance of critical terms and semantic structures in hotel assessments; (2) identifies salient tokens to depict guests' narratives based on term frequency and the information quantity they convey; and (3) tackles the challenge of managing extensive document repositories through automated identification of latent topics in reviews by using machine learning methods for semantic grouping and pattern visualisation.

This study’s findings (1) aim to identify critical features and topics that guests highlight during their hotel stays, (2) visually explore the relationships between these features and differences among diverse types of travellers through online hotel reviews and (3) determine predictive power. Their implications are crucial for the hospitality domain, as they provide real-time insights into guests' perceptions and business performance and are essential for making informed decisions and staying competitive.

This research seeks to minimise the cognitive processing costs of the enormous amount of content published by the user through a better organisation of hotel service reviews and their visualisation. Likewise, this research aims to propose a methodology and method available to tourism organisations to obtain truly useable knowledge in the design of the hotel offer and its value propositions.

With the proliferation of ideas submitted by users in firm-built online user innovation communities, community managers are faced with the problem of user idea overload. The purpose of this paper is to explore the influencing factors on the idea adoption to identify high quality ideas, and then propose a method to quickly filter high value ideas.

The authors collected more than 110,000 data submitted by Xiaomi community users and analyzed the factors affecting idea adoption using a multinomial logistic regression model. In addition, the authors also used BP neural network to predict the idea adoption process.

The empirical results show that idea semantics, number of likes, number of comments, number of related posts, the existence of pictures and self-presentation have positive impact on idea adoption, while idea length and idea timeliness had negative impact on idea adoption. In addition, this paper calculates the idea evaluation value through the idea adoption process predicted by neural network and the mean value of idea term frequency inverse document frequency (TF-IDF).

This empirical study expands the theoretical perspective of idea adoption research by using dual-process theory and enriches the research methods in the field of idea adoption research through the multinomial logistic regression method. Based on our findings, firms can quickly identify valuable ideas and effectively alleviate the information overload problem of online user innovation communities.

With the rapid development of technology, 360° panorama on mobile as a very convenient way to present virtual reality has brought a new shopping experience to consumers. Usually, consumers get product information through virtual annotations in 360° panorama and then make a series of shopping behaviors. The visual design of virtual annotation significantly influences users' online visual search for product information. This study aims to investigate the influence of the visual design of virtual annotation on consumers' shopping experience in the online shopping interface of 360° panorama.

A 2 × 3 between-subject design was planned to help explore whether different display model of annotation (i.e. negative polarity and positive polarity) and different background transparency of annotation (i.e. 0% transparency, 25% transparency and 50% transparency) may affect users' task performance and their subjective evaluations.

(1) Virtual annotations with different background transparency affect user performance, and transparency has better visual search performance. (2) Virtual annotation background display mode may affect the user operation performance; the positive polarity of the virtual annotation is more convenient for the users' visual searching for product information. (3) When the annotation background transparency is opaque or semi-transparent, the negative polarity display is more favorable to the users' visual search. However, this situation is reversed when the annotation background transparency is 25%. (4) Participants preferred the presentation of positive polarity virtual annotations. (5) Regarding the degree of willingness to use and ease of understanding, participants preferred the negative polarity display for 0% background transparency or 50% background transparency. However, the opposite result was obtained for 25% background transparency.

The findings generated from the research can be a good reference for the development of virtual annotation visual design for mobile shopping applications.

1. Virtual annotation background transparency and background display mode are two essential attributes of 360° panoramas.

2. This study examined how virtual annotation background transparency and background display mode influence user performance and experience.

3. It is recommended to use a translucent or opaque annotation background with a negative polarity display.

4. Virtual annotation presentation with 25% background transparency facilitates consumer searching and comparison of product information.

5. Users prefer a positive polarity annotation display.

Virtual annotation background transparency and background display mode are two essential attributes of 360° panoramas.

This study examined how virtual annotation background transparency and background display mode influence user performance and experience.

It is recommended to use a translucent or opaque annotation background with a negative polarity display.

Virtual annotation presentation with 25% background transparency facilitates consumer searching and comparison of product information.

Users prefer a positive polarity annotation display.

Earthquake stressful events cause many consequences and need for survivors. Housing reconstruction is one of the most urgent needs; due to traumatic experiences, dialectical changes in people–place relationships occur.

The present study uses the Poe method and Q methodology to identify the hidden dimensions of trauma-informed housing reconstruction. A questionnaire with 74 items on the Likert scale was developed based on indicative Poe. It was completed by the purposive sampling method by Bam households. The influential factors in housing reconstruction with a psychological recovery approach were extracted by q-factor analysis in communities with different traumatic experiences.

According to the findings, first, people who had experienced complete home destruction; severe physical injuries; loss of family members and relatives; and were trapped under the earthquake rubble have different place-based needs in housing reconstruction for coping with fears and environmental concerns, protective behaviors, safety perception and as result safety reassurance. Second, regardless of the traumatic experience and losses, reconstruction acceleration and economic-social dignity have a positive effect on the communities’ psychological recovery.

It is noteworthy that housing reconstruction with a psychological recovery approach has two basic aspects. Although some independent factors of traumatic experiences will be efficient in this approach, it was found that the type of earthquake traumatic experiences will also be effective in the survivors’ place-based needs and biases.