Designing a multi-division model of national innovation capability promotion based on social network analysis

Purpose This study aims to design a national innovation capability appraisal model. This would indicate Iran’s competitiveness situation among regional countries and suggest factors influencing Iran’s performance promotion. Design/methodology/approach The methodology included four sections: bibliometric analysis to discover intellectual evolution of innovation capability and related concepts; construction of a multi-division structure of national innovation capability according to the clusters extracted from bibliometric results, and experts’ opinions; creating dynamic network data envelope analysis (DEA) according to designed structure, and analysis of Iran’s performance among regional countries; identification and prioritization of the factors extracted from experts’ opinions that improve Iran’s performance in created network using Fuzzy decision-making trial and evaluation laboratory method. Findings The contemporary bibliometric analysis by its extracted clusters proved the necessity of a multi-division for measuring national innovation capability performance that each division and indicators of each step were designed according to clusters concepts. In designed structure, dynamic network DEA results revealed the weakness of Iran’s performance in the third division in the transformation of patents and high-tech imports to high-tech and creative exports, which led to proposing improving factors by getting experts’ opinions to enhance Iran’s performance in this division by prioritizing them. Research limitations/implications One of the limitations of this research was that the indicators used in three phases of national innovation capability in DEA were extracted only from the World Intellectual Property Organization database. Another limitation was the number of experts in focus group sessions because due to the difficulty of gathering them in one session, the researchers lost the participation of some of these recognized experts. Also, the research concentration was on the improvement of the third phase of national innovation capability, in which Iran’s performance was weak. Originality/value Creating a relationship between the divisions of dynamic network DEA, as a national innovation capability appraisal model, and the concepts of clusters extracted from reviewing the intellectual structure and evolution of innovation capability and related concepts.

A Novel Enhanced Russell Graph Efficiency Measure in Network DEA

Hitherto, the presented models for measuring the efficiency score of multi-stage decision-making units (DMUs) either are nonlinear or require to specify the weights for combining their divisional efficiencies. The nonlinearity leads to high computational complexity for these models, especially when used for problems with enormous dimensions, and also assigning various weights to the divisional efficiencies causes to obtain different efficiency scores for the multi-stage network system. To tackle these problems, this study contributes to network DEA by introducing a novel enhanced Russell graph (ERG) efficiency measure for evaluating the general two-stage series network structures. Then, the proposed model is extended into the general multi-stage series network structures. This study also describes the managerial and economic implications of measuring the efficiency score of the multi-stage DMUs and provides two numerical and empirical examples for illustrating the use of our proposed model.

Efficiency and productivity of the Brazilian banks: A new approach based on two-stage network DEA

This article creates a conceptual model, called a network system, to represent the Brazilian banking production system, based on its internal operational processes. The first, called the intermediation process, measures a bank's efficiency in extending loans from its available resources. The second, called the revenue process, measures a bank's efficiency in earning profit, mainly from loans granted. We adopt a two-stage DEA model. In the first stage, a relational network DEA model measures both the network system efficiency scores and internal processes. This technique, associated with the Malmquist Index, assesses performance changes over time. In the second stage, these efficiency scores are considered dependent variables, such that Tobit models can determine how the Brazilian credit market's characteristics can explain the network system and internal processes' efficiency. Results show not only a growing trend toward greater efficiency in the revenue process, but also an increase in productivity accompanied by a decline in the intermediation process technology. Given the high banking spreads in Brazil, these results indicate deterioration in the quality of the credit portfolio and the prospect of future insolvency. We discuss implications of this scenario for domestic banks and collateral policy.

Network DEA based on DEA-ratio

Data envelopment analysis (DEA) is a technique to measure the performance of decision-making units (DMUs). Conventional DEA treats DMUs as black boxes and the internal structure of DMUs is ignored. Two-stage DEA models are special case network DEA models that explore the internal structures of DMUs. Most often, one output cannot be produced by certain input data and/or the data may be expressed as ratio output/input. In these cases, traditional two-stage DEA models can no longer be used. To deal with these situations, we applied DEA-Ratio (DEA-R) to evaluate two-stage DMUs instead of traditional DEA. To this end, we developed two novel DEA-R models, namely, range directional DEA-R (RDD-R) and (weighted) Tchebycheff norm DEA-R (TND-R). The validity and reliability of our proposed approaches are shown by some examples. The Taiwanese non-life insurance companies are revisited using these proposed approaches and the results from the proposed methods are compared with those from some other methods.

Combined with two-stage network DEA and DEa-Malmquist analysis of unemployment insurance efficiency.

Throughout history, unemployment has been a major national concern. Unemployment insurance is an important means to relieve unemployment pressure and promote re-employment and social stability, so it is very necessary to study the operating efficiency of unemployment insurance. As the number of unemployed continues to increase since the COVID-19 outbreak, it is urgent to give full play to the role of unemployment insurance and improve the efficiency of unemployment insurance. This paper intends to use two-stage network DEA and DEA- Malmquist to study the efficiency of unemployment insurance in Hunan Province and 8 provinces with different geographical locations and levels of economic development from 2015 to 2019. The results show that the efficiency of unemployment insurance in Hunan Province from 2015 to 2019 shows a declining trend in the M index model. The main reason is the lack of technological progress. In the two-stage network DEA model, it is the first stage that affects the overall efficiency. Therefore, to improve the efficiency of unemployment insurance, it is necessary to develop technological progress first, attract more insured people, and improve the relevant system of unemployment insurance.

Two-Stage Data Envelopment Analysis Efficiency of Indian General Insurance Companies

The present study compares efficiency-related performance of 15 Indian general insurance companies using a two-stage efficiency evaluation model. Efficiency evaluation has been made for the span 2009–2010 to 2017–2018 using network DEA (data envelopment analysis). The results indicate that the in-sample private sector general insurance companies outcompeted the public sector insurers with regard to first-stage activity (premium mobilization), while the reverse was observed in terms of the second-stage activity (asset management and provision of claim benefits). The study also carried out regression of efficiency scores on several contextual variables. The results indicate that ownership is an influential contextual variable in both stages of productivity while solvency significantly impacts efficiency in the second stage.