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The crowdfunding market has experienced rapid growth in recent years. However, not all projects are successfully financed because of information asymmetries between the founder and the providers of external finance. This shortfall in funding has made factors that lead to successful fundraising, a great interest to researchers. This study draws on the social capital theory, human capital theory and level of processing (LOP) theory to predict the success of crowdfunding projects.

A feature set is extracted and correlations between project success and features are utilized to order the features. The artificial neural network (ANN) is popularly applied to analyze the dependencies of the input variables to improve the accuracy of prediction. However, the problem of overfitting may exist in such neural networks. This study proposes a neural network method based on ensemble machine learning and dropout methods to generate several neural networks for preventing the problem of overfitting. Four machine learning techniques are applied and compared for prediction performance.

This study shows that the success of crowdfunding projects can be predicted by measuring and analyzing big data of social media activity, human capital of funders and online project presentation. The ensemble neural network method achieves highest accuracy. The investments rose from early projects and another platform by the funder serve as credible indicators for later investors.

The managerial implication of this study is that the project founders and investors can apply the proposed model to predict the success of crowdfunding projects. This study also identifies the most influential features that affect fundraising outcomes. The project funders can use these features to increase the successful opportunities of crowdfunding project.

This study contributes to apply a new machine learning modeling method to extract features from activity data of crowdfunding platforms and predict crowdfunding project success. In addition, it contributes to the research on the deployment of social capital, human capital and online presentation strategies in a crowdfunding context as well as offers practical implications for project funders and investors.

Enterprise resources planning (ERP) systems often produce intangible benefits and implementation problems related to social and human factors. Implementing an enterprise system in an organization is a complex process. ERP evaluation should treat the human and social effects and the broader organizational consequences. The interpretive approach adopted here by virtue of an in depth case study aims to provide an understanding of the context of ERP‐system implementation, the process over time of mutual influence between the system and its context, how political action takes place, and how cultural attitudes and values changed during the ERP implementation.

The collection of data rested on both interviews with staff at various levels of the subject organization and careful examination of available archived records.

The paper finds that ERP implementation improved business processes, communication, and the interaction between users and customers. Power issues are problematic to the success of the implementation. Understanding the values of individuals and groups, and managing the power balances, are requisite in the ERP implementation. This paper suggests that human‐resource management requires deep consideration in the implementation processes.

The paper makes a contribution by enhancing the understanding of ERP‐implementation context and the context's interaction with the implementation. This paper provides additional lessons that could be useful to organizations in evaluating ERP implementation.