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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