Purpose: Although there are a number of studies on the factors affecting the continuance usage intention for social network services (SNS), the studies on the integrated model encompassing SNS qualities (information, system, and service quality, and hedonic value, which are gratifications for SNS usage), flow, relationship quality (which are trust and satisfaction), and continuance intention are almost non-existent. Based on Bagozzi’s self-regulation framework, the IS success model, uses and gratification theory, and flow theory, the purpose of this paper is to examine the qualities and hedonic value of SNS, which have an influence on flow experience and relationship quality which are posited to affect the continuance usage intention.Design/methodology/approach: This study used an e-mail survey method to collect 460 questionnaires for the final sample. The sample of the study includes college students or job workers who have experience in using SNS. Various user categories in terms of gender, school degree, occupation, usage years, and daily time of usage were included.Findings: The study results show that system, service quality, and hedonic value have an impact on flow experience, information and service quality, and hedonic value influence the relationship quality of SNS. The flow experience and relationship quality while using SNS affect the continuance usage intention of SNS. This results show that the consideration of various SNS qualities, hedonic value, flow experience, and relationship quality is necessary to actively explore the factors for continuance usage intention for SNS.Practical implications: IS managers trying to understand reuse intent can use surveys on information, system, and service quality, and hedonic value. Practitioners should constantly incorporate and develop various activities or useful applications to allow people to reach out to each other, to reinforce users’ attention, interaction, and increase social connections, trust, satisfaction, and further intensify users’ intention to use, increasing SNS value. The findings explain a mechanism through which SNS users’ continuance intention is built. Understanding this mechanism provides valuable information for practitioners to understand the qualities driving SNS users’ intention to revisit SNS.Originality/value: This study sheds light on the existing literature on website usage that focuses on the design qualities as this paper has combined website quality perspective, i.e., information, system, and service quality, and hedonic value, and the flow and relationship perspective to investigate how these constructs would affect users’ intention to continuously use SNS. On the basis of four theories, Bagozzi’s self-regulation framework, IS success model, users and gratification theory, and flow theory, this study extended the previous studies on SNS by adding information, system, and service quality, hedonic value, flow and relationship quality as second-order constructs affecting continuance intention.

More and more people are gravitating to reading online product reviews prior to making purchasing decisions. Because a number of reviews that vary in usefulness are posted every day, much attention is being paid to measuring their helpfulness. The goal of this paper is to investigate the various determinants of the helpfulness of reviews, and it also intends to examine the moderating effect of product type, that is, the experience or search goods in relation to the helpfulness of online reviews. The study results show that reviewer reputation, the disclosure of reviewer identity, and review depth positively affect the helpfulness of an online review. The moderating effects of product type exist for these determinants on helpfulness. That is, the number of reviews for a product and the disclosure of reviewer identity have a greater influence on the helpfulness for experience goods, while reviewer reputation, review extremity, and review depth are more important for helpfulness in relation to search goods. The interaction effects exist for average review rating and average review depth for a product with review helpfulness on product sales. The results of the study will identify helpful online reviews and assist in designing review sites effectively.

Purpose: While a number of studies examined the eWOM (online word-of-mouth) factors affecting box office, the studies on the impact of review helpfulness on box office are lacking. The purpose of this paper is to fill the void in previous studies and further extend prior work regarding eWOM and box office. In order to explain the interaction effect of helpfulness with other variables on product sales, this study posits that review characteristics such as number of reviews, review rating, review length interact with review helpfulness to have an influence on box office. Further, as the studies that have examined whether eWOM factors are significant in box office performances for the international markets other than US are lacking, this study is targeting Korean markets to validate the effect of eWOM on box office. Design/methodology/approach: This study used publicly available data from www.naver.com to build a sample of online review data concerning box office. The final sample of the study included 2090 movies. Findings: The results indicated that in cases when the review is helpful, the number of reviews and review length are more greatly influencing box office. Review rating, review extremity, and helpfulness for reviewer are important determinants for review helpfulness. Practical implications: Managers can concentrate on the review rating and review extremity of online customer reviews in the design of online sites for movies. The design of user review systems can follow the direction that promotes more helpfulness for online user reviews based on an enhanced understanding of what drives helpfulness voting. Originality/value: Given that previous studies on the effect of review helpfulness on box office are lacking, it contributes to eWOM literature by investigating the impact of review helpfulness on box office revenue.

The social engagement of eWOM (electronic word-of-mouth) can reduce the threat of adverse selection in e-commerce. As studies that examine the social influence of eWOM are rare, the present work suggests the moderating effect of review or reviewer helpfulness and product type (experience or search goods) on the relationship between eWOM and product sales. The volume of eWOM, which is defined as the multiplication of the average length by the number of reviews, is shown to be moderated by review and reviewer helpfulness and search goods to affect product sales. Review ratings are moderated by reviewer helpfulness, and review extremity is positively (negatively) moderated by search (experience) goods and review helpfulness to affect product sales. As previous studies of differentiated sampling strategies that consider review helpfulness for predicting product sales using eWOM are lacking, this study compares the prediction power of business intelligence methods for different subsamples of products created according to high or low review and reviewer helpfulness levels. The subsample with high review or reviewer helpfulness demonstrates greater prediction performance than the subsample with low review or reviewer helpfulness when eWOM variables are used as predictors of product sales. Hence, preliminary filtering data preprocessing should consider review or reviewer helpfulness as a crucial criterion of the data quality. This will contribute to the sampling or preprocessing strategy used to predict product sales using eWOM.

While many business intelligence methods have been applied to predict movie box office revenue, the studies using an ensemble approach to predict box office revenue are almost nonexistent. In this study, we propose decision trees, k-nearest-neighbors (k-NN), and linear regression using ensemble methods and the prediction performance of decision trees based on random forests, bagging and boosting are compared with that of k-NN and linear regression based on bagging and boosting using the sample of 1439 movies. The results indicate that ensemble methods based on decision trees (random forests, bagging, boosting) outperform ensemble methods based on k-NN (bagging, boosting) in predicting box office at week 1, 2, 3 after release. Decision trees using ensemble methods provide better prediction performance than ensemble methods based on linear regression analysis in the box office at week 1 after release. This is explained by the results that after comparing the prediction performance between ensemble methods and non-ensemble methods. For decision tree methods, unlike the other methods, the prediction performance of ensemble methods is greater than that of non-ensemble methods. This shows that decision trees using ensemble methods provide better application effectiveness of ensemble methods than k-NN and linear regression analysis.