Open AccessStatistical Research of Intersection of Technology and Entrepreneurship to Modern Practices, Policies and Promises
Author(s) -
Peter O. Emereje,
C. U. Okolie,
Tunde B. Adeleke
Publication year - 2020
Publication title -
asian journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2321-2462
DOI - 10.24203/ajet.v8i3.6365
Subject(s) - principal component analysis , entrepreneurship , varimax rotation , ranking (information retrieval) , factor analysis , statistic , intersection (aeronautics) , statistics , mathematics , correspondence analysis , rank (graph theory) , biplot , econometrics , descriptive statistics , computer science , geography , political science , combinatorics , artificial intelligence , cartography , biochemistry , chemistry , cronbach's alpha , genotype , law , gene
There are many factors affecting the relationship between entrepreneurship and technology which has not been examined and studied and this has been a daunting problem for researchers in this area. This paper seeks to identify a number of factors that deal with technology and entrepreneurship with a view to understanding the inter-correlation among the identified factors thereby making us to know the intersection between them. It will help provide an overview of the state of the art in terms of technology and offers fresh insights for entrepreneurship policy for technology.This study employed Kendall’s Coefficient of Concordance (KCC) to rank the 32 identified variables and subsequently apply Principal Component Analysis (PCA. KCC was used to rank 32 identified variables in descending order of importance. Furthermore, the PCA was used to analyze a set of questionnaire crafted with the 32 variables and administered to knowledgeable respondents in the area. The outputs gotten from the statistical software include descriptive statistic, correlation matrix, eigenvalues, eigenvector, varimax rotated factor loadings, explained variance and factor plot, among others and thereafter interpretation was given. Result obtained unveiled five principal factors which were labeled creatively. Results obtained by KCC suggested that judges ranking were consistent. Also, PCA was indicating parsimony in data reduction from 32 variables to just five. The most influential variable by its factor loading of 0.954 is innovation. The import of this is that innovation which has the highest factor loading is the nexus between technology and entrepreneurship and should therefore be embraced.