Taking Scientific Inventions to Market: Mapping the Academic Entrepreneurship Ecosystem
Technological Forecasting and Social Change(2021)SCI 2区SCI 1区
Oswaldo Cruz Fdn Fiocruz | RMIT Univ | Univ Queensland
Abstract
The active contribution of academic institutions to the technological, social and economic development of societies is of increasing importance. To better understand this contribution, we present a systematic review, together with bibliometric and network analyses of the academic entrepreneurship literature. This provides a map of the main topics approached by scholars, thereby illustrating the scientific scenario of the field. Our findings identify three highly interconnected research activity domains that characterize the multidimensional features of entrepreneurship in the academic setting, as well as a significant gap in the literature regarding studies evaluating approaches to support the navigation of potential scientific discoveries to the market. We organize our findings into a four stages framework consisting of: idea inception; the recognition of how this idea unlocks value for customers and other stakeholders; development of an innovative business model; and a commercialization strategy that creates real impact. We discuss the relevance of each stage for the establishment of a more innovation-friendly environment and conclude by offering perspectives into future research opportunities and by encouraging studies that consider the academic entrepreneurship process from a systemic perspective, to support a greater contribution of academic institutions to the economic and social development of the nations and societies.
MoreTranslated text
Key words
Academic entrepreneurship,Innovation,University,Commercialization,Lean startup,Lean LaunchPad
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
2011
被引用275 | 浏览
2005
被引用571 | 浏览
2013
被引用590 | 浏览
2013
被引用134 | 浏览
2014
被引用46 | 浏览
2012
被引用405 | 浏览
2012
被引用3320 | 浏览
2014
被引用1776 | 浏览
2012
被引用1105 | 浏览
2011
被引用813 | 浏览
2014
被引用73 | 浏览
2018
被引用90 | 浏览
2017
被引用51 | 浏览
2017
被引用861 | 浏览
2016
被引用153 | 浏览
2018
被引用72 | 浏览
2018
被引用173 | 浏览
2019
被引用20 | 浏览
2019
被引用87 | 浏览
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper