Evaluation of the operational efficiency of China’s intellectual property public information service platform

Abstract

Purpose

The purpose of this paper is to measure the operational efficiency of China’s intellectual property public information service (IPPIS) platform, discuss the current problems of the operational efficiency of China’s IPPIS platform and put forward suggestions for improvement.

Design/methodology/approach

First, the paper chose 36 Chinese IPPIS platforms as the research object and established an assessment indicator system for the operational efficiency of IPPIS platforms that includes six input indicators and three output indicators. Second, the authors used Data Envelopment Analysis (DEA) method to calculate the operational efficiency of 36 Chinese IPPIS platforms. Finally, the authors analyze the problems that exist in the operational efficiency of China’s IPPIS platform and put forward suggestions for improvement.

Findings

Of the 36 platforms measured, only 9 are in DEA effective status, which means operating well. In addition, this study finds that the following problems exist in China’s IPPIS platforms: the overall operational efficiency is low, and there are large geographic differences; a small number of platforms have low scale efficiency; and half of the platforms have high input redundancy and output insufficiency in different indexes.

Originality/value

This paper aims to help the platform constructor to provide suggestions on the direction and extent of improvement. In addition, this study is expected to raise the attention of the management and practice departments of other external countries (especially developing countries) to the operational efficiency of IPPIS platforms and to provide experiences for other external countries to improve their platform construction.

Citation

Wanyan, D. and Liu, C. (2025), "Evaluation of the operational efficiency of China’s intellectual property public information service platform", Digital Library Perspectives, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/DLP-05-2024-0076