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      Venture creation patterns in academic entrepreneurship: the role of founder motivations

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          Abstract

          The path to academic entrepreneurship is characterized by a sequence of venture creation activities, which can be classified into operational-, financing- and commercialization activities. Academic entrepreneurship research is concerned with the question how different motives of scientists affect the patterns of these venture creation activities. Using a longitudinal two-period dataset of 165 academic entrepreneurs from 73 universities in Germany, we propose and test a multi-activity-based model that links different types of entrepreneurial motives to venture creation activities. The findings show that founder motives related to self-realization, necessity and an increased financial income increase the likelihood of completing venture creation activities, whereas work-life balance motivations and the drive to make better use of one’s professional knowledge decrease that likelihood. The desire to translate research ideas into practice has no effect. Our results further show that the positive effects of seeking self-realization and an increased financial income are more pronounced for completing commercialization activities than for operational activities. Our study contributes to research on academic entrepreneurship and entrepreneurial motivations and helps university administrators and policymakers to design their entrepreneurship support programs more effectively.

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          Common method biases in behavioral research: A critical review of the literature and recommended remedies.

          Interest in the problem of method biases has a long history in the behavioral sciences. Despite this, a comprehensive summary of the potential sources of method biases and how to control for them does not exist. Therefore, the purpose of this article is to examine the extent to which method biases influence behavioral research results, identify potential sources of method biases, discuss the cognitive processes through which method biases influence responses to measures, evaluate the many different procedural and statistical techniques that can be used to control method biases, and provide recommendations for how to select appropriate procedural and statistical remedies for different types of research settings.
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            Evaluating Structural Equation Models with Unobservable Variables and Measurement Error

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              On the evaluation of structural equation models

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                Author and article information

                Contributors
                Journal
                The Journal of Technology Transfer
                J Technol Transf
                Springer Science and Business Media LLC
                0892-9912
                1573-7047
                February 2023
                November 23 2021
                February 2023
                : 48
                : 1
                : 68-125
                Article
                10.1007/s10961-021-09904-y
                111c8de0-302e-4b3e-b69b-36bddcc43751
                © 2023

                https://creativecommons.org/licenses/by/4.0

                https://creativecommons.org/licenses/by/4.0

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