Identifying enablers for the successful deployment of prescriptive analytics – a multiple case study

Marjut Hirvonen*, Katri Kauppi, Juuso Liesiö

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

Purpose: Although it is commonly agreed that prescriptive analytics can benefit organizations by enabling better decision-making, the deployment of prescriptive analytics tools can be challenging. Previous studies have primarily focused on methodological issues rather than the organizational deployment of analytics. However, successful deployment is key to achieving the intended benefits of prescriptive analytics tools. Therefore, this study aims to identify the enablers of successful deployment of prescriptive analytics. Design/methodology/approach: The authors examine the enablers for the successful deployment of prescriptive analytics through five organizational case studies. To provide a comprehensive view of the deployment process, each case includes interviews with users, managers and top management. Findings: The findings suggest the key enablers for successful analytics deployment are strong leadership and management support, sufficient resources, user participation in development and a common dialogue between users, managers and top management. However, contrary to the existing literature, the authors found little evidence of external pressures to develop and deploy analytics. Importantly, the success of deployment in each case was related to the similarity with which different actors within the organization viewed the deployment process. Furthermore, end users tended to highlight user participation, skills and training, whereas managers and top management placed greater emphasis on the importance of organizational changes. Originality/value: The results will help practitioners ensure that key enablers are in place to increase the likelihood of the successful deployment of prescriptive analytics.

Original languageEnglish
Pages (from-to)245-277
Number of pages33
JournalEuropean Business Review
Volume37
Issue number2
Early online date29 Aug 2024
DOIs
Publication statusPublished - 22 Jan 2025
MoE publication typeA1 Journal article-refereed

Keywords

  • Case study
  • Decision support system implementation
  • Interviews
  • Prescriptive analytics

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