Algorithms for operational decision-making: An absorptive capacity perspective on the process of converting data into relevant knowledge

Paolo Neirotti, Danilo Pesce and Daniele Battaglia

Technological Forecasting and Social Change, 2021, vol. 173, issue C

Abstract: The organisational mechanisms through which algorithms can be exploited in the process of converting data into relevant knowledge for operational decision-making have not yet been fully investigated from an absorptive capacity perspective. Previous studies underlined a rise in new digital specialised roles, but they said little about how the organisational knowledge and structures should be redesigned to take advantage of these data-rich operational environments. In this article, we present the findings of a case study on the way algorithms can be exploited in the electrical sector to shed light on these issues. We then develop a framework to theorise how the organisational mechanisms associated with absorptive capacity influence the way algorithms can be exploited to convert data into relevant knowledge for operational decision-making. Our emerging framework reveals that to convert data into relevant knowledge for operational decision-making, the involvement of line employees and liaison roles are required to introduce system-level knowledge that algorithms are able to capture less effectively. Additionally, more formalisation is needed in operational work to ensure the quality of the data that feed such algorithms. Finally, socialisation tactics facilitate the convergence between the knowledge produced from algorithms and the experiential knowledge of line employees.

Keywords: Data-driven decision-making; Digital transformation; Algorithms; Absorptive capacity; Knowledge creation; Organisational mechanisms (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:173:y:2021:i:c:s0040162521005217

DOI: 10.1016/j.techfore.2021.121088

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