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• W2901787613 abstract "Purpose The purpose of this paper is to apply experimentalist framework to understand self-optimizing efforts within German manufacturing multinationals. Benefits and characteristic obstacles to diffusion are discussed. Mechanisms for combatting obstacles are outlined. Design/methodology/approach Qualitative case studies, interview-based research, processual and reflexive action theory are applied to the governance of manufacturing-based multinational enterprises. Findings Uncertainty is an ineradicable element in multinational companies (MNC) FDI operations. Self-optimizing systems, many with an experimentalist character, are a pervasive form of response to this uncertainty. Obstacles to the diffusion and effective operation of self-optimization are chronic and, indeed, endogenously generated. But as a result, so are superordinate efforts to undercut the continuous emergence of obstacles. MNC development is, thus, characterized by continuous self-recomposition. Research limitations/implications Implication is that managers and management theorists should focus as much on the management of dynamic process and learning that results in the recomposition of institutional rules as they do on the constraining and enabling effects of those rules. Practical implications Superordinate mechanisms for the disruption of incipient insulation and exclusion are crucial for the implementation of successful experimentalist (learning) systems. Social implications Transparency, stakeholder involvement in MNC governance processes has positive implications for learning, innovation and competitiveness. Originality/value This paper presents the application of experimentalist learning theory to MNC global governance." @default.
• W2901787613 created "2018-11-29" @default.
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• W2901787613 date "2018-10-01" @default.
• W2901787613 modified "2023-09-26" @default.
• W2901787613 title "Experimentalist systems in manufacturing multinationals" @default.
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• W2901787613 doi "https://doi.org/10.1108/cpoib-10-2017-0063" @default.
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