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Innovation, which helps maintain and strengthen firms’ competitive ability under
challenging environments, has been the key to business success. The creation of business
innovation is always fertilized by various sources of information or data, coming from
internal and external environments. With widespread information technologies and systems
that firms develop or adopt to support organizational processes, decisions, and
communications, as well as with contemporary social media platforms in which user-
generated content is created, shared, and disseminated via virtual communities and social
networks, a massive amount of data accumulated and maintained by firms are expanding at
an increasing rate. Such “big” data, characterized by high volume, high velocity, high
variety, and low veracity, has posted many challenges on how to efficiently and effectively
manage and analyze these data (referred to as big data analytics). Moreover, these “big data”
not only facilitate firms’ decision making, but also prompt opportunities for business
innovation. Thus, this special issue is devoted to big data analytics and big data-enabled
business innovations. In this special issue, we have selected four peer-reviewed research
articles, which are summarized as follows.
The paper by
Chin-Sheng Yang
,
Pei-Yun Xie
, and
Hsiao-Ping Shih
discusses how to
extract knowledge from social media to potentially support the development of novel
services or products tailored to customers’ needs. Their study addresses the issue of opinion
mining by analyzing voluminous online consumer reviews, an important type of social
media, to identify and summarize the sentiments of consumers toward the services or
products of interest. Specifically, they develop a rule-based opinion sentence identification
(R-OSI) technique, a semi-supervised learning approach, to retrieve sentences relevant to a
user-specific product feature from a large volume of consumer reviews. By providing the
Introduction to Special Issue
Big Data Analytics and Business Innovation
Guest Editors
Chih-Ping Wei
, Professor, Department of Information Management, National Taiwan University
Ching-Chin Chern
, Professor, Department of Information Management, National Taiwan University
Anthony J. T. Lee
, Professor, Department of Information Management, National Taiwan University
Yen-Hsien Lee
, Associate Professor, Department of Management Information Systems, National Chiayi
University