Understanding organizational resilience in a platform-based sharing business: The role of absorptive capacity

Yuan, Ruizhi; Luo, Jun; Liu, Martin; Jiang, Yu

doi:10.1016/j.jbusres.2021.11.012

Cited by 62 publications

(30 citation statements)

References 116 publications

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“…Large enterprises and enterprises with a long history have abundant resources for learning and innovation and thus can absorb external knowledge more effectively than small enterprises [79]. A commonly used enterprise scale index in the literature is the number of enterprise employees [4,[79][80][81][82]. Based on the literature, we used the logarithm of the number of employees to represent the enterprise scale and the number of years of the establishment of the enterprise to represent the enterprise age.…”

Section: Control Variablesmentioning

confidence: 99%

Substitution or Complementarity: Influence of Industry–University–Research-Institute Cooperation Governance Mechanism on Knowledge Transfer—An Empirical Analysis from China

Wan

Lan

2022

Sustainability

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China’s economic growth is transforming from being traditional and factor-driven to being innovation-driven. How to improve the independent innovation ability and build the sustainable competitiveness of enterprises through knowledge transfer in industry–university–research-institute (IUR) cooperation has become an urgent problem to be solved. The obstacles to knowledge transfer in an IUR cooperation include internal and external cooperation risk factors. Improving the governance mechanism of an IUR cooperation, reducing cooperation risks, and promoting knowledge transfer are effective means for overcoming such obstacles in the short term. Interorganizational governance mechanisms include contractual governance and relationship governance. Obvious differences exist in the target functions of the different types of governance mechanisms, and their effects on knowledge transfer also differ. Based on a questionnaire survey on the IUR cooperation innovation of 364 enterprises, this study discusses the impact of the contractual governance mechanism and relationship governance mechanism on knowledge transfer in the IUR cooperation. Different types of contractual governance mechanisms have different effects on knowledge transfer. The contractual coordination mechanism significantly promotes explicit knowledge transfer in an IUR cooperation but has no significant impact on tacit knowledge transfer, whereas the contractual control mechanism significantly promotes explicit knowledge transfer but hinders tacit knowledge transfer. Meanwhile, the relationship governance mechanism has a significant positive impact on explicit knowledge transfer and tacit knowledge transfer. The joint use of the contractual coordination mechanism and relationship governance mechanism can significantly promote explicit knowledge transfer and tacit knowledge transfer, whereas the joint use of the contractual control mechanism and relationship governance mechanism can significantly weaken the two types of knowledge transfer. This study provides not only a theoretical explanation for the dispute over the “complementarity” or “substitution” relationship between the contractual governance mechanism and relationship governance mechanism in knowledge transfer in an IUR cooperation, thereby enriching relationship governance theory and knowledge management theory, but also a reference to the government, enterprises, and universities/scientific institutions participating in an IUR cooperation.

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Section: Control Variablesmentioning

confidence: 99%

Substitution or Complementarity: Influence of Industry–University–Research-Institute Cooperation Governance Mechanism on Knowledge Transfer—An Empirical Analysis from China

Wan

Lan

2022

Sustainability

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“…If hope and self-efficacy are positive motivators, then resilience is a coping mechanism in difficult situations, and resilience is a dimension of PC ( Han et al, 2022 ). The ability to replicate and adapt in the face of loss, hardship, or adversity is defined as resilience ( Yuan et al, 2022 ), and it has an impact on entrepreneurial outcomes. As a component of PC, optimism cannot be directly classified as a mediator because it can, in some cases, negatively impact EI ( Mahfud et al, 2020 ).…”

Section: Literature Review and Hypothesis Developmentmentioning

confidence: 99%

Entrepreneurial Intention and Delayed Job Satisfaction From the Perspective of Emotional Interaction: The Mediating of Psychological Capital

Ahmad²,

Zhang³

et al. 2022

Front. Psychol.

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The coronavirus disease 2019 (COVID-19) pandemic has exacerbated the labour shortage, and promoting entrepreneurship to spur job creation is one of the most effective strategies to address this problem. Entrepreneurs must lengthen their employment or start-up cycles due to COVID-19 normalisation. Consequently, the impact of career willingness to delay satisfaction on entrepreneurial ambition is investigated in this research via an online survey in Jiangsu Province, China. The findings show that students with a high level of career delayed contentment has a higher level of entrepreneurial intention (EI), implying that career delayed contentment intention influences EI positively. Psychological capital (PC) modifies this process, increasing the influence of job delayed satisfaction on EI by strengthening PC. PC’s significant components are self-efficacy, hope, optimism, and flexibility. This study combines the willingness to wait for satisfaction with the willingness to start a business, providing a valuable reference for reducing the work condition caused by the COVID-19 epidemic.

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“…A wide range of real-world systems such as power grids [1], financial transaction networks [2], communication networks (e.g., the Internet) [3], and command and control systems [4] have been modeled as complex networks. Among other characteristics, the resiliency of networked systems has received growing research attention from diverse application areas including economic systems [5], organizational management [6], and multiple engineering systems [7,8]. Generally speaking, resiliency can be viewed as the ability of a system to bounce back from high-impact disruptions to achieve partial or full recovery [9].…”

Section: Introductionmentioning

confidence: 99%

Dynamics and resiliency of networks with concurrent cascading failure and self-healing

2022

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Local attacks in networked systems can often propagate and trigger cascading failures. Designing effective healing mechanisms to counter cascading failures is critical to enhance system resiliency. This work proposes a self-healing algorithm for networks undergoing load-based cascading failure. To advance understanding of the dynamics of networks with concurrent cascading failure and self-healing, a general discrete-time simulation framework is developed, and the resiliency is evaluated using two metrics, i.e., the system impact and the recovery time. This work further explores the effects of the multiple model parameters on the resiliency metrics. It is found that two parameters (reactivated node load parameter and node healing certainty level) span a phase plane for network dynamics where three regimes exist. To ensure full network recovery, the two parameters need to be moderate. This work lays the foundation for subsequent studies on optimization of model parameters to maximize resiliency, which will have implications to many real-world scenarios.

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Understanding organizational resilience in a platform-based sharing business: The role of absorptive capacity

Cited by 62 publications

References 116 publications

Substitution or Complementarity: Influence of Industry–University–Research-Institute Cooperation Governance Mechanism on Knowledge Transfer—An Empirical Analysis from China

Substitution or Complementarity: Influence of Industry–University–Research-Institute Cooperation Governance Mechanism on Knowledge Transfer—An Empirical Analysis from China

Entrepreneurial Intention and Delayed Job Satisfaction From the Perspective of Emotional Interaction: The Mediating of Psychological Capital

Dynamics and resiliency of networks with concurrent cascading failure and self-healing

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