Flare Minimization of Ethylene Plant Start-up via Resource-Task Network Approach

Author(s):  
Guang Song ◽  
Tong Qiu ◽  
Bingzhen Chen
2001 ◽  
Vol 2 (2) ◽  
pp. 103-109 ◽  
Author(s):  
Jan Inge Jenssen

The question to be addressed in this study is how social networks and entrepreneurial resources relate to and impact on entrepreneurship. This question has been answered through an empirical investigation carried out in Norway. The explanatory variables applied in the study capture up to 45.6% of the variability of start-up success. The results show that social networks are important as channels for resources. The introduction of resources as an intervening variable considerably increases the explanatory power of the network approach. The study also indicates that it is useful to distinguish between the network developed before the entrepreneurial process and the network developed through the process.


2014 ◽  
Vol 21 (3) ◽  
pp. 528-544 ◽  
Author(s):  
Andrea Furlan ◽  
Roberto Grandinetti

Purpose – Literature on spin-offs still lacks a thorough understanding of the forces governing spin-off performance. The purpose of this paper is to fill this gap by taking a network perspective. Design/methodology/approach – The paper combines the literature on spin-offs with the network approach to new ventures to proposing a model showing how networking in the pre-entry phases affects a spin-off's survival and early growth. Findings – The intensity and variety of interactions between the future entrepreneur (FE) and other individual actors has a positive impact on spin-off performance in both the incubation and the emergence phases. The degree of overlap between the network of the incubation phase and the network of the emergence phase also reinforces the effects of the intensity and variety of these interactions on performance during the emergence phase. Finally, entrepreneurial innovativeness is an antecedent of spin-off performance in that it requires different degrees of overlap between the network of the incubation phase and the network of the emergence phase. Research limitations/implications – Being a conceptual paper, the study needs the support of empirical research. For example, samples of spin-offs achieving a high and low performance could be compared in relation to their FE's networking activity. Originality/value – The paper creates a bridge between the inherited knowledge approach to spin-offs and the network approach to new ventures to provide a framework for explaining spin-off performance.


2019 ◽  
Vol 3 (1) ◽  
pp. 97-105
Author(s):  
Mary Zuccato ◽  
Dustin Shilling ◽  
David C. Fajgenbaum

Abstract There are ∼7000 rare diseases affecting 30 000 000 individuals in the U.S.A. 95% of these rare diseases do not have a single Food and Drug Administration-approved therapy. Relatively, limited progress has been made to develop new or repurpose existing therapies for these disorders, in part because traditional funding models are not as effective when applied to rare diseases. Due to the suboptimal research infrastructure and treatment options for Castleman disease, the Castleman Disease Collaborative Network (CDCN), founded in 2012, spearheaded a novel strategy for advancing biomedical research, the ‘Collaborative Network Approach’. At its heart, the Collaborative Network Approach leverages and integrates the entire community of stakeholders — patients, physicians and researchers — to identify and prioritize high-impact research questions. It then recruits the most qualified researchers to conduct these studies. In parallel, patients are empowered to fight back by supporting research through fundraising and providing their biospecimens and clinical data. This approach democratizes research, allowing the entire community to identify the most clinically relevant and pressing questions; any idea can be translated into a study rather than limiting research to the ideas proposed by researchers in grant applications. Preliminary results from the CDCN and other organizations that have followed its Collaborative Network Approach suggest that this model is generalizable across rare diseases.


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