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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
business, management and accounting
A tailored-fit model evaluation strategy for better decisions about structural equation models
Technological Forecasting and Social Change, Volume 173, Article 121142, Year 2021
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Description
Proper measurement of technology knowledge and social change enables managers to advance strategies in technology management. Structural equation modeling is the ideal method in Technological Forecasting and Social Change (TFSC) and other leading journals to assess the measurement quality of the relevant decision variables and understand how they are related. However, a myriad of indicators are now available to judge how suitable these measurements are (i.e., how well they fit). Despite a consensus that fit indicators are highly context-dependent and no “one-fits-all approach” emerges, a more contingent perspective is surprisingly missing. To fill this gap, we advocate for a “tailored-fit model evaluation strategy” that is specific to the situation at hand to exploit the particular strengths of fit indicators. Motivated by a synthesis of structural equation modeling in TFSC, our simulation study finds that three critical distinctions regarding (a) model novelty, (b) focus on measurement or structural models, and (c) sample size are vital. The proposed strategy demonstrates that, in many contexts, only a few indicators are recommended to avoid artificially inflated Type I/II errors. We provide a decision tree to reach more accurate decisions in model evaluation in order to better theorize and forecast technological and social challenges. © 2021 The Author(s)
Authors & Co-Authors
Mai, Robert
France, Grenoble
Grenoble Ecole de Management
Niemand, Thomas
Germany, Clausthal-zellerfeld
Technische Universität Clausthal
Kraus, Sascha
Italy, Bolzano
Free University of Bozen-bolzano
Statistics
Citations: 34
Authors: 3
Affiliations: 3
Identifiers
Doi:
10.1016/j.techfore.2021.121142
ISSN:
00401625