Multivariate analysis to research innovation complementarities

It is widely recognized that orthodox economics is obsessed with econometrics tools. However, econometrics techniques have a limited capacity to deal with qualitative variables coming from surveys. This paper presents a defence of the use of statistical methods, in particular multivariate analysis,...

Full description

Saved in:
Bibliographic Details
Published in:African journal of science, technology, innovation and development technology, innovation and development, 2019-06, Vol.11 (4), p.469-484
Main Authors: Morero, Hernán Alejandro, Ortiz, Pablo
Format: Article
Language:English
Subjects:
Innovation complementarities
Multivariate analysis
Plurality
Software sector
Supermodularity
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:It is widely recognized that orthodox economics is obsessed with econometrics tools. However, econometrics techniques have a limited capacity to deal with qualitative variables coming from surveys. This paper presents a defence of the use of statistical methods, in particular multivariate analysis, which is the overall objective of the paper. Multivariate analysis is a set of methods that can be used when the problem that arises implies multiple dependent or interdependent variables of a qualitative nature. We considered an issue in the literature to probe multivariate analysis in a particular topic, namely: the question of innovation complementarities. We analyzed the presence of complementarities between internal and external innovation activities in 257 software firms from Argentina during the period 2008-2010, comparing the consideration of the problem of complementarities with the more modern complementarity econometrical tests, super and sub modularity tests arising from diverse firm-innovation function estimations (OProbit, Tobit and Probit), with the engagement of the same issue with multiple factor analysis and cluster techniques. The results show not only that the same results obtained by the econometrical tools can be reached by multivariate analysis techniques, but also that multiple factor analysis and cluster techniques allow for better exploitation of the richness of qualitative data.
ISSN:2042-1338
2042-1346
DOI:10.1080/20421338.2017.1355586