Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2022 12:00 AM
End Date
7-1-2022 12:00 AM
Description
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.
Harvesting Wisdom on Social Media for Business Decision Making
Online
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.
https://aisel.aisnet.org/hicss-55/ks/big_data_analytics/3