No abstract

Online customer reviews (OCRs) are increasingly used by travelers to inform their purchase decisions. However, the vast amount of reviews available nowadays may increase travellers' effort in information processing. In order to facilitate traveller's decisions, social commerce organizations must help travellers rapidly identify the most helpful reviews to reduce their cognitive effort. Academic literature has often documented that negative reviews are judged as helpful by consumers. However, extremely negative reviews are not always perceived as such. This study is the first that unveils what factors moderate the influence of extremely negative reviews on review helpfulness. The study has adopted a sample of 7,455 online customer reviews of hotels to test hypotheses. Findings show that reviews with extremely negative ratings are more likely to be helpful when the review is long and easy to read and when the reviewer is an expert or discloses his identity (geographical origin).

Online customer reviews (OCRs) have become increasingly important in travelers' decisionmaking. However, the proliferation of OCRs requires e-commerce organizations to identify the characteristics of the most helpful reviews to reduce information overload. This study focuses on OCRs of hotels and particularly on the moderating factors in the relationship between extreme ratings and review helpfulness. The study has adopted 11,358 OCRs of 90 French hotels from TripAdvisor.com. Findings highlight that large hotels are more affected by extreme reviews than small hotels. Extreme reviews are more helpful to consumers when reviews are long and accompanied by the reviewers' photos.

Big data has recently been recognized as one of the most important areas of future technology. It has attracted the attention of many industries, since it has the potential to provide companies with high business value. This paper examines the forms of business value that companies can create from big data analytics investments, the direct impacts it has on the financial performance of a firm, and the mediating effects of market performance and customer satisfaction. Drawing on the resource-based view theory, this study demonstrates that the business value achieved from investments in big data analytics leads to advantages in terms of the financial performance of a firm. The results offer evidence of the existence of a customer satisfaction mediation effect and of the absence of a market performance mediation effect. Theoretical and practical implications are discussed at the end of the paper.

Contrasting findings about the role of extremely negative ratings (ENRR) are found in the literature, thus suggesting that not all ENRR are perceived as helpful by consumers. In order to shed light on the most helpful ENRR, we have drawn on negativity bias and signaling theory, and we have analyzed the moderating role of product quality signals in the relationship between ENRR and review helpfulness. The study is based on the analysis of 9,479 online reviews, posted on TripAdvisor.com, pertaining to 220 French hotels. The findings highlight that ENRR is judged as being more helpful when the hotel has been awarded a certificate of excellence, and when the average rating score and the hotel classification are higher. On the basis of these results, we recommend that managers of higher category hotels, with a certificate of excellence and with higher average score ratings, pay more attention to extremely negative judgments.