Search results

This study aims to investigate whether and to what extent the characteristics of headlines impact the attraction of online financial articles by using data collected from WeChat, a popular social app in China.

By integrating the methods of econometric and text mining, this study analyzed the content of 113,917 headlines published by 126 official accounts from the day account being created to May 12, 2016. Hierarchical regression was used to investigate the effects of headline features, account ownership type and stock market volatility on the attraction of online financial articles.

The empirical results show that sentiment, length, domain specificity and language intensity in a headline are significantly associated with the attraction of an online financial article. In addition, the relative and moderating roles of stock market volatility and account ownership type were also explored, showing significant moderating effects on the relationship between sentiment and online article attraction.

This study had several limitations. First, the sample data for this research were collected from one social media platform. While WeChat is the most popular social media application in China, it is just one of the many social media applications that can be used to publish online financial articles, and it differs from other social media applications greatly. This makes it hard to generalize the conclusions of the study. Future studies could compare the different features of headlines and their effects on the attraction of financial articles on different platforms. Second, in mining the characteristics of headline, this study only analyzed the influence of the sentiment, domain specificity, length and language intensity of the headline on article attraction. In future studies, in-depth analysis of the headline content could be conducted, such as the similarity between the body text and the headline, the theme and the sense of humor. However, the authors believe that these limitations do not have major negative implications for the results and contributions of this study.

From a practical perspective, this work could help official WeChat accounts to write better headlines for the articles they publish to attract more readers and fans and thus improve the value of their accounts, which would enable them to maximize the tangible benefits through differential pricing on advertisement placement.

The contributions of this study are as follows. First, the paper explored how headline sentiment influences article attraction and found that positive sentiment is negatively related to article attraction, while negative sentiment is positively related to article attraction. In addition, there is an inverted U-shaped relationship between the extent of negative sentiment and article attraction. Second, the paper investigates how headline domain specificity affects article attraction and there is an inverted U-shaped relationship between headline domain specificity and article attraction. Third, to the best of the authors’ knowledge, this is the first large-scale case study that explores the association between stock market volatility and the attraction of an online financial article.

A composite sentiment index (CSI) from quantitative proxy sentiment indicators is likely to be a lag sentiment measure as it reflects only the information absorbed in the market. Information theories and behavioral finance research suggest that market prices may not adjust to all the available information at a point in time. This study hypothesizes that the sentiment from the unincorporated information may provide possible market leads. Thus, this paper aims to discuss a method to identify the un-incorporated qualitative Sentiment from information unadjusted in the market price to test whether sentiment polarity from the information can impact stock returns. Factoring market sentiment extracted from unincorporated information (residual sentiment or sentiment backlog) in CSI is an essential step for developing an integrated sentiment index to explain deviation in asset prices from their intrinsic value. Identifying the unincorporated Sentiment also helps in text analytics to distinguish between current and future market sentiment.

Initially, this study collects the news from various textual sources and runs the NVivo tool to compute the corpus data’s sentiment polarity. Subsequently, using the predictability horizon technique, this paper mines the unincorporated component of the news’s sentiment polarity. This study regresses three months’ sentiment polarity (the current period and its lags for two months) on the NIFTY50 index of the National Stock Exchange of India. If the three-month lags are significant, it indicates that news sentiment from the three months is unabsorbed and is likely to impact the future NIFTY50 index. The sentiment is also conditionally tested for firm size, volatility and specific industry sector-dependence. This paper discusses the implications of the results.

Based on information theories and empirical findings, the paper demonstrates that it is possible to identify unincorporated information and extract the sentiment polarity to predict future market direction. The sentiment polarity variables are significant for the current period and two-month lags. The magnitude of the sentiment polarity coefficient has decreased from the current period to lag one and lag two. This study finds that the unabsorbed component or backlog of news consisted of mainly negative market news or unconfirmed news of the previous period, as illustrated in Tables 1 and 2 and Figure 2. The findings on unadjusted news effects vary with firm size, volatility and sectoral indices as depicted in Figures 3, 4, 5 and 6.

The related literature on sentiment index describes top-down/ bottom-up models using quantitative proxy sentiment indicators and natural language processing (NLP)/machine learning approaches to compute the sentiment from qualitative information to explain variance in market returns. NLP approaches use current period sentiment to understand market trends ignoring the unadjusted sentiment carried from the previous period. The underlying assumption here is that the market adjusts to all available information instantly, which is proved false in various empirical studies backed by information theories. The paper discusses a novel approach to identify and extract sentiment from unincorporated information, which is a critical sentiment measure for developing a holistic sentiment index, both in text analytics and in top-down quantitative models. Practitioners may use the methodology in the algorithmic trading models and conduct stock market research.

Free knowledge sharing in the online health community has been widely documented. However, whether free knowledge sharing can help physicians accumulate popularity and further the accumulated popularity can help physicians attract patients remain unclear. To unveil these gaps, this study aims to examine how physicians' popularity are affected by their free knowledge sharing, how the relationship between free knowledge sharing and popularity is moderated by professional capital, and how the popularity finally impacts patients' attraction.

The authors collect a panel dataset from Hepatitis B within an online health community platform with 10,888 observations from April 2020 to August 2020. The authors develop a model that integrates free knowledge sharing, popularity, professional capital, and patients' attraction. The hierarchical regression model is used to for examining the impact of free knowledge sharing on physicians' popularity and further investigating the impact of popularity on patients' attraction.

The authors find that the quantity of articles acted as the heuristic cue and the quality of articles acted as the systematic cue have positive effect on physicians' popularity, and this effect is strengthened by physicians' professional capital. Furthermore, physicians' popularity positively influences their patients' attraction.

This study reveals the aggregation of physicians' popularity and patients' attraction within online health communities and provides practical implications for managers in online health communities.

Drawing upon Baudrillard’s concept of sign-value, this study aims to investigate consumer behavior and sign perception in visitor attractions.

By adopting netnography, 133 customer-to-customer reviews sourced from TripAdvisor were analyzed regarding visitors’ online post-visit impressions.

The findings reveal that netnography contributes to a deeper understanding of sign consumption and sign promotion and examines how visitors attribute symbolic meanings to their experience in Titanic Belfast.

The findings show that the co-creation and reevaluation of the visitor experience through consumers’ online reviews should be taken into account by both managers and marketers. Furthermore, advertising should avoid creating excessive expectations to visitors to decrease the possibility of negative disconfirmation, which can be easily and instantly spread online. Another implication concerns the winning awards of visitor attractions, hotels and restaurants of a destination which may be used as a basis of co-branding marketing campaigns to enhance destination brand image.

This study continues the debate on the commodification of the visitor experience and the commercialization of visitor attractions.

This paper provides better understanding of sign-value, sign consumption and sign promotion in the visitor attraction sector.

This research explores why tourists are dissatisfied in places considered “wonders of the world”. The authors ask if the place does not match visitors' expectations or if other factors spoil the experience.

The authors analysed the lowest-rated reviews of these wonders on TripAdvisor. The authors identified the main causes of complaints and the problems tourists faced. The authors grouped the complaints into categories and used CoDa.

The results indicate that dissatisfaction does not stem from unmet expectations regarding the monument itself, but rather from other factors related to the quality of the tourist service.

The findings of this research can be implemented in those tourist spots that, despite their global popularity, have considerable proportions of unhappy visitors, not due to the attraction itself, but to shortcomings in its administration.

This study provides a deeper insight into the causes of complaints about some of the most renowned monuments, regarded as extraordinary places, where high satisfaction levels would be anticipated. It also contributes theoretically to the literature on customer complaints in tourist places.

Following the hierarchy-of-effects model, this study aims to propose a two-step process framework to investigate social media post efficacy via attraction and likes.

The study analyzes 113,785 social media posts from 126 WeChat official accounts to explore how external (headline features and account type) and internal (content features and media type) features impact social media post attractions and likes, respectively.

The antecedents of post attraction differ from those of post likes. First, headline features (punctuation, length, sentiment and lexical density) and account type significantly influence social media post attraction. Second, content features (depth, tone, domain specificity, lexical density and readability) and media type affect social media post likes.

First, this study considers online user engagement as a two-step process regarding social media posts and explores different influencing factors. Second, the study constructs new variables (account type and domain specificity) in each stage of the two-step process model.

Millions of ratings and reviews about products are available on the Internet for free, and they are used by academic researchers in the tourism sector. Data from websites like TripAdvisor are replacing or complementing traditional questionnaires and interviews. The authors are proposing a methodology to estimate the percentage accounted for by the sample of self-interviewed individuals over the total study population, in order to calculate the reliability of the results obtained. Average percentages obtained for hotels cannot be easily generalized due to the high dispersion in participation rates among hotels, even in the same city. Participation levels for tourist attractions are substantially lower than those for hotels and are likely biased, due to the fact that some tourists evaluate places without actually visiting them, merely after viewing them from the outside.

In the extant research on online knowledge communities (OKCs), little attention has been paid to the influence of membership fluidity on the coevolution of the social and knowledge systems. This article aims to fill this gap.

Based on the attraction-selection-attrition (ASA) framework, this paper constructs a simulation model to study the coevolution of these two systems under different levels of membership fluidity.

By analyzing the evolution of these systems with the vector autoregression (VAR) method, we find that social and knowledge systems become more orderly as the coevolution progresses. Furthermore, in communities with low membership fluidity, the microlevel of the social system (i.e. users) drives the coevolution, whereas in communities with high membership fluidity, the microlevel of the knowledge system (i.e. users' views) drives the coevolution.

This paper extends the application of the ASA framework and enriches the literature on membership fluidity of online communities and the literature on driving factors for coevolution of the social and knowledge systems in OKCs. On a practical level, our work suggests that community administrators should adopt different strategies for different membership fluidity to efficiently promote the coevolution of the social and knowledge systems in OKCs.

The social media (SM) engagement framework consists of dimensions of employee privacy expectations and organizations' social media orientation. Further, the social media privacy orientation model provides better understanding of complexities of selection and retention created by the social media landscape.

Organizations are increasingly seeking talent to support burgeoning social media strategies. Qualified employees may be expected to have related professional experience and an active personal social media presence. In contrast to this evolving demand, prevailing guidelines suggest applicants minimize their social media activity altogether. These restrictive guidelines may be better suited for organizations that prefer or require high levels of discretion on social media given the differing engagement expectations across firms and among individuals.

How the congruence between an employee's expectations of privacy on SM and the organization's expectation of employees' SM usage affects applicant attraction to organizations and employee retention is outlined. Propositions are offered to foster research in this area.

Social media congruence is an important consideration for human resource (HR) policies and associated training.

Public policies toward the use of social media in recruitment and privacy should consider social media congruence.

The model advanced in the paper provides organizations and applicants with a stronger understanding of the complexities surrounding the use of SM in selection and retention decisions.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-05-2021-0260