Cleaner Production Quality Regulation Strategy of Pharmaceutical with Collusive Behavior and Patient Feedback. (17th August 2020)

• Record Type: Journal Article
• Authors: Rong, Junmei; Zhu, Lilong
• Other Names: Volchenkov Dimitri Academic Editor.
• Is Part Of: Complexity. Volume 2020(2020)
• Publication Date: 2020-08-17
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2020/1920523
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : The quality of pharmaceuticals has always been a hot issue in the world, and it involves public health, economic development of countries, social stability, and national security. Cleaner production is a prerequisite for ensuring the quality of medicines. However, the various types of counterfeit pharmaceuticals and fake vaccines exposed in the recent years have revealed many problems, such as lacking government regulation, loose quality management of companies, illegal profit of medical service agencies, and failure of patient complaints. This paper's two innovations are as follows: first, it not only considers the collusion between pharmaceutical companies and medical service agencies, but also introduces patient feedback to study drug quality regulation strategies from a microperspective; second, this paper constructs a tripartite evolutionary game model involving cleaner production pharmaceutical companies, medical service agencies, and the governments to analyze the evolutionary stability using the Lyapunov first rule. The results of the research show that, first, improvement of patient complaint rates can effectively curb collusive behavior and promote the stable improvement of cleaner production drug quality; second, the governments must impose sufficient fines on pharmaceutical companies to avoid a stable strategic combination of collusion; third, enhancing patient feedback can speed up the evolution of the stable choice of legitimate strategies by pharmaceutical companies and the medical service agencies; finally, the government reducing the strict regulation costs can increase the strict regulation rate employed in the evolution process and slow down the pace of evolution to loose regulation. In addition, the simulation analysis was carried out using Matlab 2017b, which verified the validity of the model and proved the practical meaning of countermeasures and suggestions for improving government quality regulation.