The purpose of this paper is to explore the role of storytelling in data interpretation, decision-making and individual-level adoption of business analytics (BA).
Existing theory is extended by introducing the concept of BA data-driven storytelling and by synthesizing insights from BA, storytelling, behavioral research, linguistics, psychology and neuroscience. Using theory-building methodology, a model with propositions is introduced to demonstrate the relationship between storytelling, data interpretation quality, decision-making quality, intention to use BA and actual BA use.
BA data-driven storytelling is a narrative sensemaking heuristic positively influencing human behavior towards BA use. Organizations deliberately disseminating BA data-driven stories can improve the quality of individual data interpretation and decision-making, resulting in increased individual utilization of BA on a daily basis.
To acquire a deeper understanding of BA data-driven storytelling in behavioral operational research (BOR), future studies should test the theoretical model of this study and focus on exploring the complexity and diversity in individual attitudes toward BA.
This study provides practical guidance for business practitioners who struggle with interpreting vast amounts of complex data, making data-driven decisions and incorporating BA into daily operations.
This cross-disciplinary study develops existing BOR, storytelling and BA literature by showing how a novel BA data-driven storytelling approach can facilitate BA adoption in organizations.
Boldosova, V. and Luoto, S. (2019), "Storytelling, business analytics and big data interpretation: Literature review and theoretical propositions", Management Research Review, Vol. 43 No. 2, pp. 204-222. https://doi.org/10.1108/MRR-03-2019-0106
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