Digital M&As, knowledge distance, and labor productivity: Technical and organizational perspectives

IF 4.2 2区 经济学 Q1 ECONOMICS
Yiming Zhao , Haitong Li , Zicong Miao , Keyang Li
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Abstract

This study investigates the impact of digital mergers and acquisitions (M&As) on labor productivity, focusing on the influence of the knowledge distance between merging parties. Using a sample of firms that underwent M&As between 2007 and 2022, we employ the difference-in-differences method to analyze whether digital M&As lead to higher labor productivity than non-digital M&As. Our results show that a longer knowledge distance between merging parties strengthens the positive relationship between digital M&As and labor productivity. Channel tests reveal that digital M&As improve labor productivity through enhanced technological innovation efficiency when knowledge distance is closer and reduce organizational instability when knowledge distance is longer. Moreover, the effects are more pronounced in larger, younger firms and those with higher labor intensity and better talent pools. These findings provide new insights into the outcomes of digital M&As and highlight the critical role of knowledge distance in shaping labor productivity.

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来源期刊
Economic Modelling
Economic Modelling ECONOMICS-
CiteScore
8.00
自引率
10.60%
发文量
295
期刊介绍: Economic Modelling fills a major gap in the economics literature, providing a single source of both theoretical and applied papers on economic modelling. The journal prime objective is to provide an international review of the state-of-the-art in economic modelling. Economic Modelling publishes the complete versions of many large-scale models of industrially advanced economies which have been developed for policy analysis. Examples are the Bank of England Model and the US Federal Reserve Board Model which had hitherto been unpublished. As individual models are revised and updated, the journal publishes subsequent papers dealing with these revisions, so keeping its readers as up to date as possible.
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