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ABSTRACTTraditional sentiment analysis methods do not perform well when applied to social media data. In this study, we propose an approach to improve sentiment analysis performance in the context of social media. Our approach utilizes three types of additional information that can be collected from social media platforms—personal preference, friend influence, and herding effect—to enrich the input features of a supervised sentiment classification model. We implement the approach on data sets collected from Twitter across two industries (airlines and wireless service providers) and present the performance improvement attained by combining social features with pure text‐based features. To further investigate the operational implications of this improvement, we develop a stylized service recovery model for customer relationship management in social media. Our work has implications for automating social media monitoring and, more broadly, for improving customer relationship management in organizations.

Blockchain has become a new frontier of venture capitals that has attracted the attention of banks, governments, and other business corporations. The recent blockchain related attempts included legal blockchains by Fadada.com and Microsoft and pork tracking blockchains by Walmart and IBM. Blockchain is poised to become the most exciting invention after the Internet; while the latter connects the world to enable new business models based on online business processes, the former will help resolve the trust issue more efficiently via network computing. In this paper, we give an overview on blockchain research and development as well as introduce the papers in this special issue. We show that while blockchain has enabled Bitcoin, the most successful digital currency, its widespread adoption in finance and other business sectors will lead to many business innovations as well as many research opportunities.