Is more always better? On the relevance of decreasing returns to scale on innovation

Barbero, Javier; Zabala‐Iturriagagoitia, Jon Mikel; Zofío, José L.

doi:10.1016/j.technovation.2021.102314

Cited by 24 publications

(14 citation statements)

References 51 publications

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“…In the R&D innovation perfomance model, R&D investments are conbined with other firm input factors that may be fixed at a point in time and therefore could explain why firms may experience slow growth in innovation in spite of increased R&D investment. In a study by Barbero, et al (2021), the authors used data envelopment analysis to investigate the efficiency in the use of innovation inputs to translate into innovation outcomes. The findings of the study showed that in most countries the transmission of innovation inputs into to innovation outputs have not always shown evidence of increasing returns to innovation inputs or constant returns, but rather decreasing returns to innovation inputs is a common phenomenon in most countries.…”

Section: Randd and Innovation Performancementioning

confidence: 99%

Revisiting the Effect of R&D intensity on innovation Performance: An Instrument Variable Approach

Kwaame Adane Darfo-Oduro

2023

ECIE

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The literature on innovation performance makes clear the important role of R&D in innovation performance. Studies that have investigated this relationship have generally assessed the effect of R&D investment on innovation performance. It is important to note that innovation performance and R&D investment are known to be closely associated with the possibility of reverse causality. Regressing innovation performance on R&D expenditure therefore poses an important statistical challenge of endogeneity. In the presence of endogeneity regression parameter estimates are biased and inconsistent and therefore hypothesis testing may be misleading. Using data from different sectors of OECD economies sourced from the OECD data base, instrumental variable analysis is conducted through a two stage least square using the number of R&D personnel as instrument. Again, the literature assumes that innovation is an increasing function of R&D. However, considering R&D is combined with other factors, some of which are fixed, the possibility that R&D will experience diminishing returns cannot be overlooked. This means that the dominant linear relationship authors specify in these studies may not be wholly accurate. We captured this effect by modeling a quadratic relationship to reflect the diminishing returns to innovation performance. The findings of the study show that the number of R&D persons exhibit a nonlinear inverted U-shaped relationship with innovation performance. The policy implication of the findings of the study is that R&D activities must be commensurate with the size of other organizational factors to ensure that changes in R&D activities solicit a favourable response from innovation performance.

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Section: Randd and Innovation Performancementioning

confidence: 99%

Revisiting the Effect of R&D intensity on innovation Performance: An Instrument Variable Approach

Kwaame Adane Darfo-Oduro

2023

ECIE

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“…Studies on national innovation efficiency mainly measure the level of innovation efficiency of countries. Zabala-Iturriagagoitia et al (2007), Hollanders and Celikel-Esser (2007), Matei and Aldea (2012), Barbero et al (2021) and Zabala-Iturriagagoitia et al (2021) measured and analyzed the national innovation efficiency of European countries using data from EIS. EIS contains various indicators to measure the innovation performance of European countries based on input and output variables (European Commission, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Researchers proposed different perspectives and used different DEA models such as the CCR model (Charnes, Cooper and Rhodes, 1978), which is used to measure technical efficiency under the assumption of constant return to scale (CRS) (Nasierowski and Arcelus, 2003; Hollanders and Celikel-Esser, 2007; Fritsch and Slavtchev, 2011; Chen et al , 2018; Zabala-Iturriagagoitia et al , 2021), BCC model (Banker, Charnes and Cooper, 1984), which is used to measure pure technical efficiency assuming variable return to scale (VRS) (Zhang and Wang, 2019), CRS and VRS models together (Sharma and Thomas, 2008; Afzal, 2014), two-stage DEA model (Carayannis et al , 2016), network DEA model (Alnafrah, 2021; Chen and Guan, 2012; Choi and Zo, 2019; Guan and Chen, 2012; Guan and Zuo, 2014; Kaihua and Mingting, 2014; Kou et al , 2016), dynamic DEA (Chen et al , 2018), global Malmquist productivity index (Zabala-Iturriagagoitia et al , 2021) and fuzzy-set qualitative comparative analysis DEA (Prokop et al , 2021). Barbero et al (2021) estimated national innovation efficiency by frontier DEA-TOPSIS method. In addition to DEA, stochastic frontier analysis (SFA) is also used in some studies (Bai and Li, 2011; Fritsch and Slavtchev, 2011).…”

Section: Literature Reviewmentioning

confidence: 99%

National innovation efficiency: a DEA-based measurement of OECD countries

Erdin

Çağlar

2022

IJIS

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Purpose The purpose of this study is to measure and analyze the national innovation efficiency of organisation for economic co-operation and development (OECD) countries. This is to determine to what extent OECD countries efficiently use the elements that enable innovation activities possible in generating innovation outputs. Design/methodology/approach An input–output model was constructed to measure efficiency. The inputs and outputs in the research model are the input and output sub-indices of the Global Innovation Index. Data envelopment analysis was used to measure the national innovation efficiency levels of OECD countries. Findings The results show that national innovation efficiency is generally high in OECD countries. However, some countries lag behind in innovation efficiency. OECD countries’ ability to create and provide the elements that enable innovation activities is higher than their ability to create innovation outputs. OECD countries have a good innovation environment and a high level of resources, but they should focus on how to create more innovation outputs. Originality/value This study presents a measurement of national innovation efficiency of OECD countries which contributes “Innovation Strategy” agenda. The results empirically show that overall innovation indices cannot be the only indicator of the performance of national innovation systems. In this study, an innovation efficiency/performance matrix is constructed to present the relative positions of the countries to help in examining countries’ strengths, weaknesses and potentials based on innovation efficiency and innovation performance simultaneously. This study contributes to the literature by presenting a broader perspective and measurement of national innovation efficiency by taking an extensive number of indicators into account.

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“…Hence, in order to compare the e ciency of two systems, it is necessary to measure the output that can be generated by utilizing the same input or less (Dutta et al, 2019). In stipulation to this notion, there is always room to improve the GII methodology by using data-driven techniques such as DEA, which seems a very appropriate technique to measure the e ciency of a national innovation system (Omrani et al, 2019;Barbero et al, 2021;Hate & Torabi, 2010;Alnafrah, 2021;Zabala-Iturriagagoitia et al, 2007b), given that the generation of the weights by using the DEA is taking place without prior intervention, and the e ciency score for each country is relative to all the other countries.…”

Section: The Global Innovation Index and The Need For Different Persp...mentioning

confidence: 99%

“…CIs represent the aggregation of a set of individual indicators into a single index, that aims to measure a multi-dimensional concept (OECD, 2004). In the last decades, a growing trend has emerged among policymakers, media, and all areas of research regarding CIs and the rankings derived from them -examples of CIs include education, health, government quality, innovation - (Bandura, 2011;Hate & Torabi, 2010;Greco et al, 2019; Barbero et al, 2021;Crespo & Crespo, 2016). These ranks have gained a signi cant impact on the perceived image of how decision-making units (e.g., countries, regions, cities, universities) are relatively performing (Cherchye et al, 2008;Zabala-Iturriagagoitia et al, 2007a).…”

Section: Introductionmentioning

confidence: 99%

Assessing the robustness of composite indicators: The case of the Global Innovation Index

Alqararah¹

2022

Preprint

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This research paper introduces a methodology to assess the robustness of the Global Innovation Index (GII), by comparing the rankings provided in it with those achieved using alternative data-driven methodologies such as Data Envelopment Analysis (DEA) and Principal Component Analysis (PCA). With it, the paper aims to reduce the level of subjectivity in the construction of composite indicators with regard to weight generation and indicator aggregation. The paper relies on PCA as a weighting-aggregation scheme to reproduce the 21 sub-pillars of the GII before the application of DEA to calculate the relative efficiency score for every country. By using the PCA-DEA model, a final ranking is produced for all countries. The paper uses Random Forests (RF) classification to examine the robustness of the new rank. The comparison between the new rank and that of the GII suggests that the countries positioned at the top or the bottom of the GII rank are less sensitive toward the modification than those in the middle of the GII, the rank of which is not robust against the modification of the construction method. The PCA-DEA model introduced in this paper provides policymakers with an effective tool to monitor the performance of national innovation policies from the perspective of their relative efficiency. Ultimately, this would enhance the innovation performance of their respective countries.

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Is more always better? On the relevance of decreasing returns to scale on innovation

Cited by 24 publications

References 51 publications

Revisiting the Effect of R&D intensity on innovation Performance: An Instrument Variable Approach

Revisiting the Effect of R&D intensity on innovation Performance: An Instrument Variable Approach

National innovation efficiency: a DEA-based measurement of OECD countries

Assessing the robustness of composite indicators: The case of the Global Innovation Index

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