The Conceptualization of Data-driven Decision Making Capability
2015
- 1,522Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage1,522
- Abstract Views854
- Downloads668
Article Description
We are in the era of big data, and businesses are trying to become more data-driven and increase their decision making efficacy. However, there is little research that explores the dimensions of data-driven decision making (DDDM) capability. The authors analyze existing literature and summarizes four approaches to conceptualizing capabilities: unrelated, unidirectional, entanglement, and mixed. We then discuss the DDDM process and propose a multi-dimension construct of DDDM capability by following the process-based entanglement approach. The DDDM capability is conceptualized as a combination of data governance capability, data analytics capability, insight exploitation capability, performance management capability, and integration capability. Academic and managerial implications are also discussed._x000D_ _x000D_ Keywords: data-driven decision making capability, data-driven decision making process, data governance capability, data analytics capability, insight exploitation capability, performance management capability, integration capability. _x000D_
Bibliographic Details
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