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This study aims to investigate how firms of different sizes respond to competitive pressure from peers.

This study employs machine learning techniques to measure competitive pressure based on management discussion and analysis (MD&A) documents and then utilises the constructed pressure indicator to explore the relationship between competitive pressure and corporate risk-taking behaviours amongst firms of different sizes.

We find that firm sizes are positively associated with their risk-taking behaviours when firms respond to competitive pressure. Large firms are inclined to exhibit a high level of risk-taking behaviours, whereas small firms tend to make conservative decisions. Regional growth potential and institutional ownership moderate the relationships.

Utilising text mining techniques, this study constructs a novel quantitative indicator to measure competitive pressure perceived by focal firms and demonstrates the heterogeneous behaviour of firms of different sizes in response to competitive pressure from peers, advancing research on competitive market pressures.

To gain a deeper understanding of the determinants impacting the intention of Chinese young consumers to utilize virtual try-on apps (VTOs), this study adopts a modified version of the electronic technology acceptance model (e-TAM). The primary objective is to investigate how consumers' characteristics influence their post-use feelings, subsequently affecting their attitude towards VTOs and their intention to use them. Additionally, this research aims to explore potential gender differences within this process.

In this study, a sample of 243 college students from a university in China was recruited to participate in on-site software use. A total of 227 valid questionnaires were collected and used for data analysis. Structural equation modeling (SEM) was employed to empirically evaluate the data and test the research hypotheses. Additionally, multiple group comparisons were conducted based on gender to examine potential differences in the acceptance process.

The findings of this study reveal a significant association between users' individual characteristics and post-use feelings. Moreover, there are notable differences between male and female students in terms of their perceptions of innovativeness, knowledge and understanding regarding post-use feelings. Notably, four variables within post-use feelings, except for perceived compatibility, are found to be predictive of attitude towards VTOs. Additionally, it is observed that attitude towards VTOs directly influences users' intention to utilize them.

This study contributes to the research on consumers' intention to use VTOs by examining the antecedents of post-use feelings, specifically four consumers’ individual characteristics. The findings of this study offer managerial insights for professionals to formulate marketing positioning and communication strategies.

The paper aims to establish a quality assurance framework for Chinese private higher education (PHE) in particular and for any newly-established higher education sector in general.

This research relied on published qualitative and quantitative data, existing literature, historical documents, and the authors' observations. It described the PHE growth context, analyzed the quality issue from three dimensions (academic, administrative, and relationship quality), and examined its quality assurance practice through three players.

The paper provides empirical insights about how quality and quality assurance issues have impacted on PHE development. To approach the issues, it suggests a “quality assurance triangle” framework that is composed of three PHE players (government, market, institution) and their joint association.

Because of the relative position of each player in current quality assurance, the success of its implementation in China depends on how the government establishes the triangle system and balances its dominating roles to ensure each of the players act independently and collaboratively.

This framework provides directions for establishing an appropriate quality assurance system for PHE in China.

This paper fulfills an identified need to setup a sound quality assurance system for PHE in China and contributes to quality assurance literature with a new framework.

Online hotel ratings, a form of electronic word of mouth (eWOM), are becoming increasingly important to tourism and hospitality management. Using sentiment analysis based on the big data technique, this paper aims to investigate the relationship between customer sentiment and online hotel ratings from the perspective of customers’ motives in the context of eWOM, and to further identify the moderating effects of review characteristics.

The authors first retrieve 273,457 customer-generated reviews from a well-known online travel agency in China using automated data crawlers. Next, they exploit two different sentiment analysis methods to obtain sentiment scores. Finally, empirical studies based on threshold regressions are conducted to establish the asymmetric relationship between customer sentiment and online hotel ratings.

The results suggest that the relationship between customer sentiment and online hotel ratings is asymmetric, and a negative sentiment score will exert a larger decline in online hotel ratings, compared to a positive sentiment score. Meanwhile, the reviewer level and reviews with pictures have moderating effects on the relationship between customer sentiment and online hotel ratings. Moreover, two different types of sentiment scores output by different sentiment analysis methods verify the results of this study.

The moderating effects of reviewer level and reviews with pictures offer new insights for hotel managers to make different customer service policies and for customers to select a hotel based on reviews from the online travel agency.

This paper contributes to the literature by applying big data analysis to the issues in hotel management. Based on the eWOM communication theories, this study extends previous study by providing an analysis framework for the relationship between customer sentiment and online hotel ratings from the perspective of customers’ motives in the context of eWOM.

Bankruptcy is a financial failure of a business or an organization. Different kinds of bankruptcy prediction techniques are proposed to predict it. But, they are restricted as techniques in predicting the bankruptcy and not addressing the associated activities like acquiring the suitable data and delivering the results to the user after processing it. This situation demands to look for a comprehensive solution for predicting bankruptcy with intelligence. The paper aims to discuss these issues.

To model Business Intelligence (BI) solution for BP the concept of reference model is used. A Reference Model for Business Intelligence to Predict Bankruptcy (RMBIPB) is designed by applying unit operations as hierarchical structure with abstract components. The layers of RMBIPB are constructed from the hierarchical structure of the model and the components, which are part of the reference model. In this model, each layer is designed based on the functional requirements of the Business Intelligence System (BIS).

This reference model exhibits the non functional software qualities intended for the appropriate unit operations. It has flexible design in which techniques are selected with minimal effort to conduct the bankruptcy prediction. The same reference model for another domain can be implemented with different kinds of techniques for bankruptcy prediction.

This model is designed using unit operations and the software qualities exhibited by RMBIPB are limited by unit operations. The data set which is applied in RMBIPB is limited to Indian banks.

A comprehensive bankruptcy prediction model using BI with customized reporting.