Business networks and organizational resilience capacity in the digital age during COVID-19: A perspective utilizing organizational information processing theory

Xuemei Xie, Yonghui Wu, Daniel Palacios-Marqués and Samuel Ribeiro-Navarrete

Technological Forecasting and Social Change, 2022, vol. 177, issue C

Abstract: Based on organizational information processing theory (OIPT), this study examines how and when business networks exert a positive influence on firms’ organizational resilience capacity. Using data collected from 409 Chinese manufacturing firms during the COVID-19 pandemic, and by disaggregating business networks into two dimensions—network breadth and network depth—our findings show, firstly, that both network breadth and network depth are positively correlated to the organizational resilience capacity of firms; secondly, that these relationships are mediated by firms’ ambidextrous learning; and thirdly, that the positive effects of network breadth and network depth on organizational resilience capacity are stronger when the firms’ digital technology levels are higher. Furthermore, through additional analysis, we find that the positive impact of business networks on organizational resilience capacity is stronger for non-state-owned enterprises (non-SOEs) than it is for SOEs, and also that the moderating effect of digital technology on the relationship between business networks and organizational resilience capacity is greater for non-SOEs than it is for SOEs. These findings provide new insight into how a firm's business network, in combination with its ambidextrous learning and level of digital technology, affects its organizational resilience capacity development, which helps it survive a crisis for future sustainable development.

Keywords: Business networks; Organizational resilience capacity; Ambidextrous learning; Digital technology; Organizational information processing theory (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (24)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:177:y:2022:i:c:s0040162522000804

DOI: 10.1016/j.techfore.2022.121548

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