Modelling Efficiency in Regional Innovation Systems: A Two-Stage Data Envelopment Analysis Problem with Shared Outputs within Groups of Decision-Making Units

Highlights

• A methodology is developed for evaluating efficiencies in a regional innovation system (RIS)

• The RIS is viewed as a two-stage process where R&D projects are proposed in stage 1, and then implemented in stage 2

• In conventional efficiency situations firms are assumed to operate independently of one another

• In our situation there are shared outputs among the firms in each of a set of industries

Abstract

Regional Innovation Systems (RISs) literature usually focuses on the comparative performance of different regions and analyzes how each region is utilizing its own dedicated resources. The available resources can be shared by many firms which are grouped by industry, just as universities collaborate with many firms in many industries. This paper studies a region in Mexico and the firms within that region with the aim being to identify which of those firms are using the available resources in the best way. We use Data Envelopment Analysis (DEA) as a methodology for evaluating the relative efficiencies of the firms, based on their multiple inputs and outputs, and considering their processes as being divided into two stages. An important problem in this setting is that the two-stage process exhibits the characteristic of having outputs being shared among the firms in each industry; this makes it more challenging to determine independent efficiency scores for each firm in each industry, where we need to cater for this phenomenon. To address this, the current article presents a methodology for measuring efficiency in situations where Decision Making Units (DMUs) share outputs with other units within the same group. By solving this problem, we can identify the best-performers and their strategies regarding how they use the available resources in the region.

Introduction

In the current paper, we examine the dynamics of a Regional Innovation System (RIS) in one state in Mexico. A RIS can be defined as “a system of interconnected institutions that create, store and transfer knowledge, skills and artifacts which define new technologies” (Metcalfe, 1995a). Due to their economic importance, RISs have been actively studied in the past few decades and have been identified as the development engines in the new knowledge economies. The various perspectives from which RISs have been studied, including comparing the roles of the actors, identifying best practices, examining the roles of the demand in the system, across political systems, among others, have been aimed at identifying the key elements of the system. Other studies that have attempted to uncover the performance of each of the participating members of a RIS are those based on Data Envelopment Analysis (DEA), first presented in Charnes, Cooper & Rhodes (1978). DEA is a method for identifying best practices among peer Decision Making Units (DMUs) in the presence of multiple inputs and outputs (Cook & Zhu, 2014). Cruz-Cázares, Bayona-Sáez, & García-Marco (2013) investigated previous studies that analyzed innovation systems using DEA. (Charnes, Cooper, & Rhodes, 1978) (Cruz-Cázares, Bayona-Sáez, & García-Marco, 2013)

The current research examines a particular RIS and its constituent elements, where industries and their respective firms are using the region's available resources (universities, R&D Centres, and capital) to be more productive. DEA is used to examine the dynamics of the RIS, at the firm level, to guide strategies for improvement. One interesting feature of previous studies of RISs is that most compare regions and try to identify the best performers among the chosen set of regions; in contrast, our research looks at industries and their respective firms within one region of a country. As well, unlike previous applications that utilize conventional DEA structures wherein each DMU has its own independent set of input/output factors, the current application involves a situation where some of the inputs and outputs in an industry are shared among the firms that make up that industry. That being the case, DMUs are thus not completely independent of one another. This latter feature means that the conventional DEA model must be modified.

The structure of the current paper is as follows. Section 2 presents a brief literature review of RIS and DEA and discusses the pertinent factors and data that have been used previously to study RISs. Section 3 presents the new model structure discussed above and specifically caters to the presence of shared factors among the firms within each industry. We emphasize that we restrict our investigation herein to the firm level, as opposed to the level of the industries containing those firms. This is because, for some industries, only a subset of participating firms is available. In Section 4, we use this new methodology to investigate the performance of each of the corresponding firms within the selected region. Conclusions, limitations, and recommended future research are presented in Section 5.