A1 Journal article (refereed), original research

A recipe for big data value creation


Publication Details

Authors: Ylijoki Ossi, Porras Jari

Publisher: Emerald: 24 month embargo

Publication year: 2018

Language: English

Related journal or series: Business Process Management Journal

Journal name in source: Business Process Management Journal

ISSN: 1463-7154

eISSN: 1758-4116

JUFO level of this publication: 1

Digital Object Identifier (DOI): http://dx.doi.org/10.1108/BPMJ-03-2018-0082

Permanent website address: https://www.emeraldinsight.com/doi/abs/10.1108/BPMJ-03-2018-0082

Social media address: www.researchgate.net/publication/328700710_A_recipe_for_big_data_value_creation

Open Access: Not an Open Access publication


Abstract

Purpose The purpose of this paper is to present a process-theory-based model of big data value creation in a business context. The authors approach the topic from the viewpoint of a single firm. Design/methodology/approach The authors reflect current big data literature in two widely used value creation frameworks and arrange the results according to a process theory perspective. Findings The model, consisting of four probabilistic processes, provides a “recipe” for converting big data investments into firm performance. The provided recipe helps practitioners to understand the ingredients and complexities that may promote or demote the performance impact of big data in a business context. Practical implications The model acts as a framework which helps to understand the necessary conditions and their relationships in the conversion process. This helps to focus on success factors which promote positive performance. Originality/value Using well-established frameworks and process components, the authors synthetize big data value creation-related papers into a holistic model which explains how big data investments translate into economic performance, and why the conversion sometimes fails. While the authors rely on existing theories and frameworks, the authors claim that the arrangement and application of the elements to the big data context is novel.


Last updated on 2019-13-03 at 12:00