Global vision (formerly Vantage 2000): Recent advances in research on decision making and innovation and an often overlooked resource--University research centers

1998 ◽  
Author(s):  
Charmine E. J. Hartel
Keyword(s):  
Decision Making ◽  
University Research ◽  
Research Centers ◽  
Recent Advances ◽  
Global Vision

Role Strain in University Research Centers

2007 ◽  
Vol 78 (4) ◽  
pp. 430-463 ◽  
Author(s):  
Craig Boardman ◽  
Barry Bozeman
Keyword(s):  
Role Strain ◽  
University Research ◽  
Research Centers

scholarly journals Algorithm for Assessment of Students’ Individual Choice of Sports Sections

2016 ◽  
pp. 21-26
Author(s):  
Ж. О. Цимбалюк ◽  
Л. В. Коник ◽  
В. А. Тихонова
Keyword(s):  
Decision Making ◽  
Factor Analysis ◽  
Hierarchical Model ◽  
University Research ◽  
First Year ◽  
Sports Facilities ◽  
Individual Choice ◽  
First Year Students ◽  
Language And Literature ◽  
Hierarchy Analysis

Research objective. To develop a model of students’ decision-making when individually choosing a sports section. Research methods: The study relies on the analysis of scientific and methodological literature, questionnaires, and factor analysis. The participants in the study were 55 first-year students (2015 enrollment year) of the School of Ukrainian Language and Literature of H.S. Skovoroda Kharkiv National Pedagogical University. Research results. The analysis of the questionnaires showed diverse motivational choices. The factor analysis revealed the prevailing motifs. The study comes up with a hierarchical model of students’ choice of a training section by a particular sport, and determines the system of subjective values of each student in points. Conclusions. The model developed by the hierarchy analysis allows to take into account all the variants of priorities in the students’ motivational choice. The method of hierarchy analysis allows to objectively process the subjective advantages seen by the students (who do not have any sports experience) when choosing a training section in the opted-for sport. This, in its turn, cultivates a steady motivation for physical education, allows to determine the required number of sports sections, and to distribute the educational and sports facilities at the higher educational university.

Innovation Dynamics Through the Encouragement of Knowledge Spin-Off From Touristic Destinations

2022 ◽  
pp. 170-190
Author(s):  
Sofia Vairinho
Keyword(s):  
New Technology ◽  
University Research ◽  
Research Centers ◽  
Regional Innovation ◽  
Innovation Ecosystem ◽  
The Public ◽  
Regional Cluster ◽  
Innovation Dynamics ◽  
Innovation Clusters ◽  
Innovation Processes

The present approach aims to explore the innovation dynamic that may lead to knowledge opportunities in a specific regional cluster characterized by a strong touristic positioning. The new technology-based companies, namely the spin-out created from university research, represent a possible and reliable approach to the economy stimulation. This said, it is mandatory to explore the topics that will allow a reflection on the networks associated with innovation processes, developed from the relations between the public universe (including universities and research centers), and the new technology or humanistic based companies. This chapter intends to be a contribution to the discussion of innovation clusters and sets the preliminary issues to discuss and implement an innovation ecosystem. This chapter explores and reflects the importance of regional innovation clusters dynamics, setting and describing the steps and specific strategical procedures in order to implement an innovation ecosystem, using as example a specific touristic territory.

scholarly journals Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction

2019 ◽  
Vol 33 (2) ◽  
pp. 31-50 ◽  
Author(s):  
Ajay Agrawal ◽  
Joshua S. Gans ◽  
Avi Goldfarb
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Labor Market ◽  
Market Impact ◽  
Recent Advances ◽  
The Impact ◽  
Decision Tasks

Recent advances in artificial intelligence are primarily driven by machine learning, a prediction technology. Prediction is useful because it is an input into decision-making. In order to appreciate the impact of artificial intelligence on jobs, it is important to understand the relative roles of prediction and decision tasks. We describe and provide examples of how artificial intelligence will affect labor, emphasizing differences between when the automation of prediction leads to automating decisions versus enhancing decision-making by humans.

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