Exploring the Innovative Efficiency of Big Multinational Firms through Conditional Efficiency Analysis

Abstract

This paper addresses the issue of the efficiency of innovation of multinational firms applying nonparametric efficiency models which include, in a flexible multi-input multi-output Data Envelopment Analysis (DEA) framework, observed factors of heterogeneity and dynamics. Several models (a basic model, a quantitative model, a qualitative model and a mixed quantitative/qualitative model) were developed to explore the innovative activity of large multinational firms and explain the effects of R&D expenditures. They were tested using data on large and R&D intensive companies from the Industrial R&D Investment Scoreboard (economic and financial data, R&D investment) and a set of patent indicators characterizing the companies' inventive production built using the CIB (Corporate Invention Board) patent database. The analysis carried out on the different models of innovation has shown that partial models that consider only one aspect of the production process at a time can mask important non-linear interactions and complementarities. In fact, there may be trade-offs between quantity and quality and these can escape from one-dimensional analyses based on linear innovation models. Further investigations will include the application of a robust two stage nonparametric approach to estimate the “managerial” efficiency of the firms in order to allow the comparison between heterogeneous firms, taking out the effect of heterogeneity factors.