Prediction-led prescription: Optimal decision-making in times of turbulence and business performance improvement

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Schäfers, Andreas, Bougioukos, Vasileios, Karamatzanis, Georgios and Nikolopoulos, Konstantinos (2024) Prediction-led prescription: Optimal decision-making in times of turbulence and business performance improvement. Journal of Business Research, 182. ISSN 0148-2963

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Abstract

Can you have prescription without prediction? Most scholars and practitioners would argue that a good forecast drives an optimal decision, thus promoting the concept of prediction-led prescription. In times of turbulence, Special events like promotions and supply chain disruptions are impacting businesses severely. Nevertheless, limited research has been carried out to date to accurately forecast the impact of, and consequentially prescribe in the presence of special events. Nowadays Artificial Intelligence (AI) predictive analytics methods and heuristics imitate and even improve human intelligence, progressively leading towards innovative cognitive analytics solutions. This research aims to contribute to applying advancements in AI-based predictive analytics to improve business performance. We provide empirical evidence that these AI solutions outperform the popular (especially among practitioners) linear regression models. We corroborate the stream of literature arguing that AI predictive analytics could − via a natural path-dependent process − enhance prescriptive analytics solutions, and thus improve business performance.

Item Type:	Article
Additional Information:	Data availability statement: Data will be made available on request.
Uncontrolled Keywords:	ai,forecasting,prediction-led prescription,special events,turbulence,marketing ,/dk/atira/pure/subjectarea/asjc/1400/1406
Faculty \ School:	Faculty of Social Sciences > Norwich Business School
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	08 Jul 2024 11:33
Last Modified:	02 Nov 2024 00:52
URI:	https://ueaeprints.uea.ac.uk/id/eprint/95818
DOI:	10.1016/j.jbusres.2024.114805

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