An empirical investigation of the National Innovation System (NIS) using Data Envelopment Analysis (DEA) and the TOBIT model

Afzal, Munshi Naser İbne

doi:10.1080/02692171.2014.896880

Cited by 59 publications

(23 citation statements)

References 19 publications

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“…Some of these studies applied a second level econometric analysis to examine the effect of environmental factors to efficiency scores ( Afzal (2014), Cai (2011), Guan (2010), Cullmann, Schmidt-Ehmcke, andZloczysti (2011), Guan and Chen (2012), Nasierowski and Arcelus (2003) ). Tobit has been mostly used while more recently Matei and Aldea (2012) and Afzal (2014) employed bootstrap for getting bias corrected estimations. Superefficiency that can produce complete country rankings has been also applied by Chen and Guan (2010), Guan and Chen (2012), Pan, Hung, and Lu (2010) ).…”

Section: Evaluating Innovation Systems With Deamentioning

confidence: 98%

A multilevel and multistage efficiency evaluation of innovation systems: A multiobjective DEA approach

Carayannis

Grigoroudis

Goletsis

2016

Expert Systems with Applications

134

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Section: Evaluating Innovation Systems With Deamentioning

confidence: 98%

A multilevel and multistage efficiency evaluation of innovation systems: A multiobjective DEA approach

Carayannis

Grigoroudis

Goletsis

2016

Expert Systems with Applications

134

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“…Most notable of them is (Carayannis et al 2015) who used a multi-stage and multi-level DEA to compute and compare innovation performance of 23 selected countries from around the world. Afzal (Afzal 2014) also selected 20 countries around the world to assess their national innovation system using DEA Tidd (Tidd 2010) opined that, practically, the adoption of innovation is strongly determined by supply-side factors, e.g., the availability of information, relative advantage of the innovation, barriers to adoption and feedback between suppliers and consumers-and demand-side factors-adopters with different perceptions, imitation of early adopters. Hence, the absorptive capacity of regions essentially determined the diffusion model opted for.…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

“…In Table 2, the input and output variables selected for the present research are presented. These variables selected encompass the category of finance, human resource, creativity and non-financial innovation structures as used by (Rickne 2001;Moon and Lee 2005;Afzal 2014;Carayannis et al 2015) and also published by European Commission (2018) in the latest European Innovation Scoreboard. Below is a list of inputs and output variables used for computation.…”

Section: Data Resources and Research Areamentioning

confidence: 99%

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Diffusion Efficiency of Innovation among EU Member States: A Data Envelopment Analysis

Anderson

Stejskal

2019

Economies

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Innovation, in contemporary times, has been established as the lynchpin of growth and national competitive advantage among countries. Supranational and national resources have jointly combined to create sound innovation strategies and diffusion policies for member states in recent times. However, there is the question of whether increased innovation translates to effective diffusion of innovation. With this in mind, the present research aims to comparatively assess and evaluate the efficiency of diffusion of innovation of European Union member states in reference to their European Innovation Survey rankings. Using the Charnes, Cooper and Rhodes (CCR) model of Data Envelopment Analysis (DEA), the present research found contrasting diffusion efficiency scores of member states with different innovation performances as most innovative member states had much lower efficiency scores compared to some supposedly weak innovating member states. We also computed the input-redundancy and output-deficiency of member states, provided recommendations for efficient input-output combinations based on findings of respective member states and innovation groups, and finally, outlined directions for future studies.

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“…However, if the value is less than 1, a DMU has increasing returns to scale and should expand or increase its use of productive resources to achieve the most productive size. This modification in DEA is called the BCC model, named after Banker, Charnes and Cooper (Afzal, 2014).…”

Section: National Systems Of Entrepreneurshipmentioning

confidence: 99%

An empirical investigation of country level efficiency and national systems of entrepreneurship using Data Envelopment Analysis (DEA) and the TOBIT model

Tasnim

Afzal

2018

J Glob Entrepr Res

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The goal of this paper is to investigate effects of national systems of entrepreneurship on the country level efficiency, on addition we find what macro factors affect efficiency as well. From a comprehensive database of 59 countries using GEM, WDI, WCI for 2018 using data envelopment analysis (DEA) we find the results support the theoretical grounding of Global Entrepreneurship Index (GEI) hypothesis. The GEI methodology has been designed to capture the core features of the National Systems of Entrepreneurship theory. It approaches country-level entrepreneurship as a systemic phenomenon, which is driven by the interaction between individual-level actions and country-level framework conditions. While discussing country level framework, we have depicted key macroeconomic indicators in the analysis along with GEI index. The DEA analysis followed this framework to assess the performance of the study countries. Though inefficiency widely varies across countries, while the group of factor-driven countries is the most inefficient while innovation-driven economies are the most efficient ones. Subsequently, we apply the Tobit model to explain efficiency. Based on the Tobit regression model, the DEA VRS technical efficient score could be improved through GDP per capita and social capital. From policy perspective, to promote economic growth policy makers should consider national systems of entrepreneurship as their priority so that entrepreneurs can allocate resources in the economy effectively.

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An empirical investigation of the National Innovation System (NIS) using Data Envelopment Analysis (DEA) and the TOBIT model

Cited by 59 publications

References 19 publications

A multilevel and multistage efficiency evaluation of innovation systems: A multiobjective DEA approach

A multilevel and multistage efficiency evaluation of innovation systems: A multiobjective DEA approach

Diffusion Efficiency of Innovation among EU Member States: A Data Envelopment Analysis

An empirical investigation of country level efficiency and national systems of entrepreneurship using Data Envelopment Analysis (DEA) and the TOBIT model

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