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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.

In an environment in which there has been a reduction in consumer trust of both organizations and advertising, as well as a decrease in television advertising, word of mouth (WOM) offers a way to obtain a significant competitive advantage. WOM is especially important in the hospitality and tourism industry, whose intangible products are difficult to evaluate prior to their consumption. When WOM becomes digital, the large‐scale, anonymous, ephemeral nature of the Internet induces new ways of capturing, analyzing, interpreting, and managing the influence that one consumer may have on another. The purpose of this paper is to investigate the impact of electronic WOM (eWOM) on a tourism destination choice, using the theory of planned behaviour (TPB). Further, an effort was employed to determine the effect of past travel experience on using eWOM and TPB constructs.

A structural equation modeling procedure was applied to the examination of the influences of eWOM on attitudes towards visiting Isfahan, subjective norms, perceived behavioral control, and travel intention. Analysis of variance (ANOVA) was utilised in order to analysing the relations between these constructs and past travel experience. The research model was tested empirically using a sample of 296 inbound tourists who had experience within the online communities and travelled to Isfahan during the period of research.

Findings indicate that online WOM communications have a significant impact on attitudes toward visiting Isfahan, subjective norms, perceived behavioral control, and intention to travel. In addition, travel experience has a significant impact on using eWOM and TPB constructs.

The paper suggests that eWOM activity is more complex than previous research has argued. Managers should consider various eWOM facets and try to motivate tourists for participating in online travel communities and to build online travel communities with characteristics such usefulness and ease of use.

There has been a lot of marketing research on eWOM but none has focused on covering eWOM in tourism industry and its impact on tourism destination choice by adding new dimension of eWOM to the theory of planned behavior. This paper seeks to fill this gap.