Abstract
This paper analyzes the relationship between efficiency and innovation activity in Spanish industrial and service sectors by introducing a new methodology framework. A new model combining principal component analysis (PCA) and data envelopment analysis (DEA) is applied in order to obtain an efficiency score. To achieve a more comprehensive evaluation, a large dataset is included, but a large number of variables compared with the number of decision-making units (DMUs) may diminish the discriminatory power of DEA. To avoid this effect, we first apply PCA to separately obtain the input and output main factors. We then apply DEA to the new variables. The PCA–DEA model allows us to identify 5 efficient sectors out of 42. If only DEA were applied, 16 sectors would turn out to be efficient. This shows that the model improves the discriminatory capability of DEA. Methodologically, this work contributes to the literature by proposing an efficiency measurement using a large number of inputs and outputs that could be applied in different fields. Likewise, this analysis allows for the evaluation and interpretation of innovation activity in the different sectors, which can be taken into account in the management and allocation of resources by institutions.
Highlights
By carrying out the analysis, 5 out of 42 decision-making units (DMUs) were identified as innovation-efficient industrial and service sectors, and there were 37 nonefficient industries whose efficiency score ranged from 93.58% to 20.04%
It should be noted that when applying data envelopment analysis (DEA) on the original dataset, without using the principal component analysis (PCA)–DEA model, 16 efficient DMUs were obtained
Observing the total of efficient DMUs, four of them belonged to the service sector, and the other one belonged to the industrial sector
Summary
R&D expenditure is an input used in most of the innovation efficiency revised works: Bin [46]; Díaz-Blateiro et al [47]; Guan et al [48]; Guan and Chen [8]; Hong et al [49]; Lee et al [50]; Liu and Wang [51]; Revilla, Sarkis, and Modrego [52]; Zhang, Zhang, and Zhao [53]; and Zhong et al [54] All of these authors took into account the firm’s annual total expenditure on internal R&D activities. This input was used by Guan and Chen [8], Lee et al [50], Zhang et al [53], and
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