嵌入性
政治
业务
中国
关系
背景(考古学)
独创性
价值(数学)
构造(python库)
产业组织
经济体制
营销
社会学
经济
政治学
定性研究
生物
社会科学
古生物学
机器学习
人类学
法学
计算机科学
程序设计语言
作者
Eli Gimmon,Teng Ying,Xiaobin He
标识
DOI:10.1108/cms-08-2020-0359
摘要
Purpose This study aims to present multi-layered embeddedness and explore the main and interaction effects of political embeddedness on the performance of private enterprises in China. This study tests multi-layered embeddedness through interaction effects between three layers, namely, political, territorial and inter-firm embeddedness. Political embeddedness is related at the personal and the firm levels. Design/methodology/approach The authors used secondary data of four non-panel waves (2002, 2008, 2010 and 2016) of large samples having identical questions based on the Chinese private enterprises’ survey. The accumulated number of business owners’ responses is a total of 10,686. Findings The main effects of each of the layers of embeddedness showed a positive influence on enterprise performance consistently and unchanged over time which fits the Chinese Guanxi regardless of the immense macro-economic transition. However, unexpectedly some interactions showed negative significant effects on performance. Practical implications First, business owners should be aware of the specific contribution to the performance of political embeddedness at both the firm level and the personal level. Second, the pursuit of exercising simultaneously several layers of embeddedness may be detrimental to company performance. This study provides generalizable lessons regarding different embeddedness layers beyond the context of China’s transition economy. Originality/value First, the authors extend political embeddedness to the firm level whilst in previous research this construct had been mostly related to the personal level. Second, building on the resource-based view and redundant resources the authors present the disadvantage of “over-embeddedness” as related to multi-layered embeddedness which has been understudied.
科研通智能强力驱动
Strongly Powered by AbleSci AI