Research Article

Innovation Efficiency Evaluation of China’s High-Tech Industry considering Subindustry with a Parallel Slack-Based Measure Approach

Table 1

Notation definition.

SymbolDescription

R&D personnel
R&D capital
R&D expenditure
Patents in force
Sales revenue of new products
R&D personnel of MPP, EECE, COE, and MEMI (hereinafter referred to as four subindustries), respectively
R&D capital of four subindustries, respectively
R&D expenditure of four subindustries, respectively
Patents in force of four subindustries, respectively
Sales revenue of new products of four subindustries, respectively
The specific subindustry ()
The th decision-making unit
Innovation efficiency for DMU in model (1)
Slack variable for R&D personnel
Slack variable for R&D capital
Slack variable for R&D expenditure
Slack variable for patents in force
Slack variable for sales revenue of new products
The participation intensity of each evaluated DMU in constructing the production frontier
Innovation efficiency for DMU in model (2)
Slack variable for R&D personnel of four subindustries, respectively
Slack variable for R&D capital of four subindustries, respectively
Slack variable for R&D expenditure of four subindustries, respectively
Slack variable for patents in force of four subindustries, respectively
Slack variable for sales revenue of new products of four subindustries, respectively
The weights of four subindustries, respectively
The participation intensity of each evaluated DMU in constructing the production frontier corresponding to four subindustries, respectively
Innovation efficiency of MPP for DMU in model (2)
Innovation efficiency of EECE for DMU in model (2)
Innovation efficiency of COE for DMU in model (2)
Innovation efficiency of MEMI for DMU in model (2)