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• W4308805987 abstract "With the diffusion of information systems and new technologies for the real-time capturing of data, especially in rapid technological and managerial innovation contexts such as the automotive industry, data-driven decision-making (DDM) has now the potential to generate dramatic improvements in the performance of manufacturing firms. However, there is still a lack of evidence in literature on whether these technologies can actually enhance the effectiveness of data-driven approaches. The aim of this article is to investigate the impact of DDM on operational performance moderated by two main dimensions of digitalization: data integration and the breadth of new digitization technologies. The results of a cross-country survey of 138 Italian and U.S. auto-supplier firms, which was supported by plant visits and interviews, suggest that an interplay between the two dimensions exists. Higher degrees of data integration in information systems increase the positive effect of DDM on the probability of cost reductions. On the other hand, introducing multiple emerging digitization technologies leads to worse DDM results, in terms of cost performance. The conclusion that can be drawn is that the operational employees of auto-supplier firms are now facing difficulties in successfully combining real-time operational data from various sources and in exploiting them for decision-making. Managers and workers need to align their intuition, experience and analytical capabilities to initiate the digitalization process. The challenge, in the medium term, is to limit the difficulties of implementing new digitization technologies and integrating their data, to embrace DDM and fully grasp the potential of data analytics in operations." @default.
• W4308805987 created "2022-11-15" @default.
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• W4308805987 date "2023-01-01" @default.
• W4308805987 modified "2023-10-17" @default.
• W4308805987 title "The interplay between data-driven decision-making and digitalization: A firm-level survey of the Italian and U.S. automotive industries" @default.
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• W4308805987 doi "https://doi.org/10.1016/j.ijpe.2022.108718" @default.
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