TY - GEN
T1 - Harvesting wisdom on social media for business decision making
AU - Yu, Ji
AU - Taskin, Nazim
AU - Pauleen, David J.
AU - Jafarzadeh, Hamed
N1 - Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.
PY - 2022
Y1 - 2022
N2 - The proliferation of social media provides significant opportunities for organizations to obtain wisdom of the crowds (WOC)-type data for decision making. However, critical challenges associated with collecting such data exist. For example, the openness of social media tends to increase the possibility of social influence, which may diminish group diversity, one of the conditions of WOC. In this research-in-progress paper, a new social media data analytics framework is proposed. It is equipped with well-designed mechanisms (e.g., using different discussion processes to overcome social influence issues and boost social learning) to generate data and employs state-of-the-art big data technologies, e.g., Amazon EMR, for data processing and storage. Design science research methodology is used to develop the framework. This paper contributes to the WOC and social media adoption literature by providing a practical approach for organizations to effectively generate WOC-type data from social media to support their decision making.
AB - The proliferation of social media provides significant opportunities for organizations to obtain wisdom of the crowds (WOC)-type data for decision making. However, critical challenges associated with collecting such data exist. For example, the openness of social media tends to increase the possibility of social influence, which may diminish group diversity, one of the conditions of WOC. In this research-in-progress paper, a new social media data analytics framework is proposed. It is equipped with well-designed mechanisms (e.g., using different discussion processes to overcome social influence issues and boost social learning) to generate data and employs state-of-the-art big data technologies, e.g., Amazon EMR, for data processing and storage. Design science research methodology is used to develop the framework. This paper contributes to the WOC and social media adoption literature by providing a practical approach for organizations to effectively generate WOC-type data from social media to support their decision making.
KW - Judgement
KW - Big Data-Analytics
KW - decision making
KW - social media big data analytics
KW - social media data analytics framework
KW - wisdom of the crowds
UR - http://www.scopus.com/inward/record.url?scp=85152232364&partnerID=8YFLogxK
U2 - 10.24251/HICSS.2022.665
DO - 10.24251/HICSS.2022.665
M3 - Conference proceeding contribution
T3 - Proceedings of the Annual Hawaii International Conference on System Sciences
SP - 5462
EP - 5471
BT - Proceedings of the 55th Annual Hawaii International Conference on System Sciences
A2 - Bui, Tung X.
PB - University of Hawaii
CY - Honolulu
T2 - Annual Hawaii International Conference on System Sciences (55th : 2022)
Y2 - 3 January 2022 through 7 January 2022
ER -