Go deep or go wide? The effects of digitalization intensity and digitalization breadth on manufacturers' circular economy performance
Jie Deng et al.
Abstract
Purpose Drawing upon dynamic capability theory (DCT), this study aims to explore the differing effects of digitalization intensity and digitalization breadth on circular economy (CE) performance, as well as the moderating roles of internal corporate governance represented by the chief technology officer (CTO) presence and external public governance formed through exposure to government agencies. Design/methodology/approach Utilizing a sample of 1,030 listed Chinese manufacturing firms between 2011 and 2022, this study constructs a panel dataset and employs the fixed effect model to examine the proposed hypotheses. Various analyses, including self-selection bias correction, endogeneity test and robustness checks, are conducted to verify the reliability of the results. Findings The empirical results indicate that digitalization intensity facilitates firms' CE performance, whereas digitalization breadth does not have a significant impact. Furthermore, CTO presence reinforces the influence of both digitalization intensity and digitalization breadth on CE performance, while government agency exposure only positively moderates the effect of digitalization intensity on CE performance. Originality/value This study extends the technology management literature by identifying two digitalization investment strategies (i.e. digitalization intensity and digitalization breadth) and exploring their distinct impacts on CE performance. It also contributes to the governance literature and expands the theoretical discourse on DCT by highlighting the significant roles of internal and external governance in facilitating the development of dynamic capabilities.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.