{"id":603,"date":"2020-11-11T10:32:51","date_gmt":"2020-11-11T10:32:51","guid":{"rendered":"https:\/\/www.myecole.it\/biblio\/?p=603"},"modified":"2020-11-11T10:33:33","modified_gmt":"2020-11-11T10:33:33","slug":"approccio-data-driven-manufacturing-usa","status":"publish","type":"post","link":"https:\/\/www.myecole.it\/biblio\/approccio-data-driven-manufacturing-usa\/","title":{"rendered":"L\u2019approccio data-driven nelle decisioni analizzato nel settore del manufacturing in USA"},"content":{"rendered":"\n

Abstract: <\/em><\/strong><\/p>\n\n\n\n

Management in America has become significantly more data-intensive, yet the economic, organizational, and strategic implications of this shift are poorly understood. Working with the U.S. Census Bureau, we developed measures of how manufacturing firms have used data to guide decision making over the past decade. In our large and representative sample, data-driven decision making (DDD) is strongly associated with increased productivity. The benefits attributable to DDD are distinct from those associated with other structured management practices or investment in IT, though the latter is an important complement.<\/p>\n\n\n\n

Moreover, instrumental variables estimates and timing falsification tests suggest a causal relationship. Implications for firm strategy, however, are nuanced; we find evidence of significant advantages for early adopters of DDD, particularly in the 2005-2010 window, when adoption rates in the sector were lower.<\/p>\n\n\n\n

Yet we also observe timing-dependent complementarities. The frontier of data-centric practices shifts during our study period, with increased use of predictive analytics becoming the key driver of productivity gains from 2010 to 2015.<\/p>\n\n\n\n

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Scarica pdf<\/a><\/div>\n<\/div>\n\n\n\n

L’articolo \u00e8 accessibile tramite ResearchGate<\/a><\/p>\n\n\n\n

\n","protected":false},"excerpt":{"rendered":"

Abstract: Management in America has become significantly more data-intensive, yet the economic, organizational, and strategic implications of this shift are poorly understood. 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