Gaining competitive intelligence from social media data. Evidence from two largest retail chains in the world
| Pages | 1622-1636 |
| DOI | https://doi.org/10.1108/IMDS-03-2015-0098 |
| Date | 19 October 2015 |
| Published date | 19 October 2015 |
| Author | Wu He,Jiancheng Shen,Xin Tian,Yaohang Li,Vasudeva Akula,Gongjun Yan,Ran Tao |
| Subject Matter | Information & knowledge management,Information systems,Data management systems |
Gaining competitive intelligence
from social media data
Evidence from two largest retail
chains in the world
Wu He
Department of Information Technology & Decision Sciences,
Old Dominion University, Norfolk, Virginia, USA
Jiancheng Shen
Department of Finance, Old Dominion University, Norfolk, Virginia, USA
Xin Tian
Department of Information Technology & Decision Sciences,
Old Dominion University, Norfolk, Virginia, USA
Yaohang Li
Department of Computer Science,
Old Dominion University, Norfolk, Virginia, USA
Vasudeva Akula
VOZIQ Company, Reston, Virginia, USA
Gongjun Yan
Romain College of Business,
University of Southern Indiana, Evansville, Indiana, USA, and
Ran Tao
School of Computer Science & Technology,
Donghua University, Shanghai, China
Abstract
Purpose –Social media analytics uses data mining platforms, tools and analytics techniques to
collect, monitor and analyze massive amounts of social media data to extract useful patterns,
gain insight into market requirements and enhance business intelligence. The purpose of this paper
is to propose a framework for social media competitive intelligence to enhance business value and
market intelligence.
Design/methodology/approach –The authors conducted a case study to collect and analyze a data
set with nearly half million tweets related to two largest retail chains in the world: Walmart and Costco
in the past three months during December 1, 2014-February 28, 2015.
Findings –The results of the case study revealed the value of analyzing social media mentions and
conducting sentiment analysis and comparison on individual product level. In addition to analyzing
the social media data-at-rest, the proposed framework and the case study results also indicate that
there is a strong need for creating a social media data application that can conduct real-time social
media competitive intelligence for social media data-in-motion.
Originality/value –So far there is little research to guide businesses for social media competitive
intelligence. This paper proposes a novel framework for social media competitive intelligence to
illustrate how organizations can leverage social media analytics to enhance business value through a
case study.
Keywords Competitive intelligence, Social media analytics
Paper type Research paper
Industrial Management & Data
Systems
Vol. 115 No. 9, 2015
pp. 1622-1636
©Emerald Group Publishing Limited
0263-5577
DOI 10.1108/IMDS-03-2015-0098
Received 26 March 2015
Revised 22 August 2015
7 September 2015
Accepted 13 September 2015
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0263-5577.htm
1622
IMDS
115,9
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