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    AI based decision making: combining strategies to improve operational performance

    Al-Surmi, Abdulrahman and Bashiri, M. and Koliousis, I. (2021) AI based decision making: combining strategies to improve operational performance. International Journal of Production Research , pp. 1-23. ISSN 0020-7543. (In Press)

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    Abstract

    This study investigates the strategic alignment between marketing and information technology (IT) strategies and provides production and operations decision makers a model for improving operational performance. Based on a comprehensive literature review, the combined strategies were used to develop a novel decision-making framework. The hypothesised relationships of an SEM model are validated with data collected from 242 managers from various industries. An artificial intelligence (AI)–based method is developed using artificial neural networks (ANN) feeding into a decision-making framework which explores the optimality of the combined strategies. The results indicate that (a) IT strategy is positively mediated by marketing strategy on performance and (b) the organisational structure moderates the mediation of marketing strategy on performance. The analysis confirms that the extracted strategies based on the proposed framework have superior performance compared to existing strategies. This paper contributes to the literature by conceptualising and empirically testing the mediation role of marketing strategy on IT strategy, performance and operational decision-making. The use of a novel three-phase decision-making framework which uses AI processes improves operational efficiency, increases insights and enhances the decision accuracy of complex problems at the strategic level in industries such as manufacturing. It could help operations executives to apply effective decisions.

    Metadata

    Item Type: Article
    School: School of Business, Economics & Informatics > Management
    Depositing User: Abdulrahman Al-Surmi
    Date Deposited: 16 May 2022 13:15
    Last Modified: 17 May 2022 02:28
    URI: https://eprints.bbk.ac.uk/id/eprint/48044

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