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Abstract

Review helpfulness is attracting increasing attention of practitioners and academics. It helps in reducing risks and uncertainty faced by users in online shopping. This study examines uninvestigated variables by looking at not only the review characteristics but also important indicators of reviewers. Several significant review content and two reviewer variables are proposed and an effective review helpfulness prediction model is built using stochastic gradient boosting learning method. This study derived a mechanism to extract novel review content variables from review text. Six popular machine learning models and three real-life Amazon review data sets are used for analysis. Our results are robust to several product categories and along three Amazon review data sets. The results show that review content variables deliver the best performance as compared to the reviewer and state-of-the-art baseline as a standalone model. This study finds that reviewer helpfulness per day and syllables in review text strongly relates to review helpfulness. Moreover, the number of space, aux verb, drives words in review text and productivity score of a reviewer are also effective predictors of review helpfulness. The findings will help customers to write better reviews, help retailers to manage their websites intelligently and aid customers in their product purchasing decisions.

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Abstract

This paper reviews the worldwide growth of open-access (OA) repositories, 2005 to 2012, using data collected by the OpenDOAR project. Initial repository development was focused on North America, Western Europe, and Australasia, particularly the United States, United Kingdom, Germany, and Australia, followed by Japan. Since 2010, there has been repository growth in East Asia, South America, and Eastern Europe, especially in Taiwan, Brazil, and Poland. During the period, some countries, including France, Italy, and Spain, have maintained steady growth, whereas other countries, notably China and Russia, have experienced limited growth. Globally, repositories are predominantly institutional, multidisciplinary and English-language based. They typically use open-source OAI-compliant software but have immature licensing arrangements. Although the size of repositories is difficult to assess accurately, available data indicate that a small number of large repositories and a large number of small repositories make up the repository landscape. These trends are analyzed using innovation diffusion theory, which is shown to provide a useful explanatory framework for repository adoption at global, national, organizational, and individual levels. Major factors affecting both the initial development of repositories and their take-up include IT infrastructure, cultural factors, policy initiatives, awareness-raising activity, and usage mandates. Mandates are likely to be crucial in determining future repository development.

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Abstract

Conclusions of research articles depend on bodies of data that cannot be included in articles themselves. To share this data is important for reasons of both transparency and reuse. Science, Technology, and Medicine journals have a role in facilitating sharing, but by what mechanism is not yet clear. The Journal Research Data (JoRD) Project was a JISC (Joint Information Systems Committee)-funded feasibility study on the potential for a central service on journal research data policies. The objectives of the study included identifying the current state of journal data sharing policies and investigating stakeholders' views and practices. The project confirmed that a large percentage of journals have no data sharing policy and that there are inconsistencies between those that are traceable. This state leaves authors unsure of whether they should share article related data and where and how to deposit those data. In the absence of a consolidated infrastructure to share data easily, a model journal data sharing policy was developed by comparing quantitative information from analyzing existing journal data policies with qualitative data collected from stakeholders. This article summarizes and outlines the process by which the model was developed and presents the model journal data sharing policy.