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Developing a Big Data Success Model in Organizations: A Grounded Theory Method [Abstract]

Aim/Purpose: In spite of the insights in paving solid grounds and avenues for meaningful studies, the predicament of the literature in lacking fruitful understanding of the critical success factors and models of Big Data remain elusive and unexplored. A systematic literature review of research topics, perspectives, and substantial findings of Big Data is needed, so an overarching framework of Big Data success can be developed to integrate findings and systematically guide future research for advancing IS theoretical and practical progressing. 

Background: This study (1) uses the grounded theory as a literature review method to search and collect Big Data studies in the AIS “Senior Scholars’ Basket of Journals” over the period of twenty years from 2000 to 2020, (2) employs data coding and content analysis of the grounded theory to conduct a systematic literature review of research concepts, categories, topics, methodologies, and models and paradigms of Big Data in IS discipline, and (3) up-on synthesis of theoretical perspectives and empirical findings, develops a Big Data success theory with a research agenda to enrich the cumulative knowledge of critical success factors and interrelationships of Big Data in the organizational contexts.

Methodology: A grounded theory-based review of Big Data literature helps investigate the emerging and evolving theoretical foundations of the subject, and create a roadmap for advancing IS theory and business relevance.
Contribution The research in critical success factors and models of Big Data presents a novel opportunity for advancing IS theory across different IS traditions and paradigms. 

Findings: While this study is still in progress, currently we report preliminary findings in research methodologies, topics, and abstractions of open coding. Re-search of next steps toward a Big Data success theory is also reported in the submitted abstract. As the study proceeds, we expect more in-depth findings to be reported in the conference presentation in July, 2021. 

Recommendations for Practitioners: The findings of this study shall enrich our understanding of how organizations transform Big Data potentials into organizational performance and economic value.

Recommendations for Researchers: The research in critical success factors and models of Big Data presents a novel opportunity for advancing IS theory across different IS traditions and paradigms.

Impact on Society: The findings of this study shall enrich the cumulative knowledge of critical success factors and interrelationships of Big Data in the organizational contexts. 

Future Research: Future research may consider collecting the literature data from a wider variety of journal outlets and capture more relevant critical success factors and interrelationships of Big Data for the theory development.