Research On Online Hotel Review Helpfulness From The Perspective Of Reviewers And Managers

With the rapid development of the Internet,the network has become the main channel of consumer shopping.On shopping platform,consumers can freely post reviews on the consumption experience of products or services,which can help potential consumers make consumption decisions.However,the sharp rise in the number of online reviews has increased the pressure on consumers to read them.In order to reduce the pressure on consumers to read reviews,Amazon first added a helpful vote function,which helps consumers filter out helpful reviews and avoid reading unnecessary reviews.In recent years,many scholars focused on the influencing factors of the helpfulness of reviews from the aspects of reviewers and the content of reviews.The research on reviewer factors is mainly carried out from the aspects of reviewer's identity information disclosure,expertise and reputation.From the perspective of the content of reviews,the research mainly considers the characteristics of review length,review sentiment,review star rating and review readability.Most research methods are multiple regression analysis and machine learning.However,there are still some deficiencies in the research on the influencing factors of review helpfulness,the influencing factors have not been fully explored,and the research methods are relatively simple.Based on the above shortcomings,this study mainly focuses on the following three research questions:(1)Will the disclosure of reviewer's identity information affect the voting of review helpfulness?(2)Will the expertise exhibition of reviewers affect the voting of review helpfulness?(3)Will the factors related to the response content of the manager have moderated effect on the voting of review helpfulness?The first part of this paper is mainly starting from the perspective of the reviewers,including the disclosure of reviewer's identity information and expertise exhibition,and studies the influencing factors of review helpfulness from two dimensions.By combining Tobit regression model and neural network analysis method,significant influencing factors are analyzed,and different degrees of influence produced by significant influencing factors are studied.The study found that identity disclosure(age,gender)and expertise exhibition(Level,Rge age,Number of photos,and vote rate)have a significant impact on online review helpfulness.In addition,the research results of artificial neural network show that the expertise exhibitions of reviewers are superior to the identity disclosure,which ultimately dominates the first four most important factors of review helpfulness.Based on the research results,it is suggested that hotel managers should adopt a variety of incentive strategies to guide reviewers to disclose personal information.At the same time,rational consumers should pay more attention to these significant factors and quickly identify reviewers with expertise.This result is helpful for consumers to filter out helpful reviews quickly from a large number of reviews and release the pressure of cognitive load.From the perspective of manager's response,the second study studied the moderating effect of the response length and response timeliness of managers on review sentiment,review rating and review helpfulness.According to the data of 21,197 valid reviews of 83 hotels in New York and Los Angeles,the negative binomial regression model was adopted to study the moderating effect of manager's response on the relationship between review sentiment,review rating and review helpfulness.This study found that negative review sentiment and low review star rating had significant effects on review helpfulness.Meanwhile,the response length negatively moderates the effect of review sentiment on review helpfulness vote,and the response timeliness positively moderates the effect of review star ratings on review helpfulness vote.For potential consumers,paying attention to the response content and response speed of managers can help them better understand the service attitude of hotels,obtain more information related to products or services,and reduce the uncertainty in their hearts.The innovation of this paper is reflected in the following two aspects:First of all,this paper studies the helpfulness of reviews from the perspective of reviewers and managers' responses,providing a new research content for the future study of the helpfulness of online reviews.It tries to fill in the gap and conduct a comprehensive study on the impact of the reviewer's attributes from the two aspects of identity disclosure and expertise exhibition.In addition,we combine Tobit regression and neural network to provide a new method to effectively discover the complex relationship between the reviewer factors and the helpfulness of online reviews.Second,this paper considers the effect of manager response length and response timeliness on reviews helpfulness-voting mechanism,especially the moderating effects of response length and response speed on review sentiment and review star ratings of review helpfulness,makes up for the past only focused on the reviewers perspective on reviews helpfulness,perspective to expand it to managers,to enrich the content of research on the reviews helpfulness.