官员
业务
供应链
运营管理
供应链管理
营销
过程管理
管理
经济
政治学
法学
作者
Jukka Sihvonen,Katri Kauppi
标识
DOI:10.1108/ijopm-11-2024-0985
摘要
Purpose This study investigates the impact of chief supply chain officers (CSCOs) on firm performance, particularly during periods of heightened uncertainty. Using organizational information processing theory (OIPT), we argue that the CSCO role creates information processing capabilities needed to perform under uncertainty. Design/methodology/approach We use a triple-differences approach to evaluate the financial performance of North American firms in the manufacturing, wholesale and retail sectors. We estimate the CSCO effect by jointly analyzing performance differentials (1) between matched firms with and without a CSCO, (2) across industries with varying levels of supply and demand risk exposure and (3) before and after the outbreak of the COVID-19 pandemic. Findings CSCO presence enhances firm sales and profitability during COVID-19 in industries with high supply and demand risk exposure. This effect is mainly achieved by reducing the cost of sales, shortening the cash conversion cycle and improving capacity utilization. During the pandemic, CSCO presence also helped firms mitigate negative supply shocks, respond to positive demand shocks and dampen extreme stock price volatility. Practical implications CSCO performance impacts during COVID-19 were contingent on the industry being previously exposed to high risks. This suggests a learning effect: firms should exercise patience when evaluating CSCO effectiveness. Given today’s multi-risk environment, we argue there is no better time than the present to appoint a CSCO who can develop organizational information processing capabilities in preparation for the next major turbulence. Originality/value Our results suggest that information processing capabilities for uncertainty can thus be achieved not only through vertical information systems, as per previous SCM literature but also from the elevation of SCM in functional hierarchy. We utilize a newly introduced text-mining methodology to assess the exposure to supply and demand risks and COVID-19-induced shocks.
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