Date of Award

8-2020

Document Type

Campus Access Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Business Administration

First Advisor

Pratyush Bharati

Second Advisor

Jeffrey M. Keisler

Third Advisor

Wei Zhang

Abstract

This dissertation investigates the phenomena of the role of social media and big data in organizations and communities and examines the impact of social media on community empowerment, decision making, and sustainability via three essays. The first essay examines the role of social media in communities of place in community empowerment. Using an iterative and inductive data analysis approach, social media messages from an online community outreach platform, Twitter and interviews were analyzed with natural language processing (NLP). The first essay contributes to literature by elaborating on the mechanisms of community discourse and ideation for community empowerment over social media. The second essay examines how the fusion of multiple big data sources and expert elicitation can improve community decision support systems. A new process and architecture were developed that integrates big data sources, expert elicitation, and multi-attribute utility techniques for decision support systems. A big data transformation layer was developed to extract, preprocess, analysis, evaluate, and map multiple big data sources to community decision making objectives. With a design science research (DSR) approach, a case study in the context of community development and planning was employed as the design evaluation with big data sources and informants. A multi-attribute utility model and geographical information system (GIS) techniques were applied to evaluate test sites for community siting decisions. The theoretical contribution of the second essay is the development of a flexible community decision making model with a big data transformation layer that can leverage decision analysis techniques to improve GIS-based decision support systems. The third essay investigates how social media-enabled dynamics influence movement outcomes for climate-focused social movement organizations (SMOs). An iterative inductive data analysis approach with NLP was employed to develop categories and sub-codes with Twitter data. The analysis led to a new research model illustrating the social media-enabled dynamics of association and contention, influenced by social media affordances, norms, and awareness in the context of sustainability. The third essay contributes to the understanding of boundary spanning in social media-enabled social dynamics and the impact on digital activism for SMOs.

Comments

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