Balancing between dynamics and stability for innovation alliance synergy based on evolutionary game model

IF 1.8 Q3 MANAGEMENT

Journal of Modelling in Management Pub Date : 2024-04-02 DOI:10.1108/jm2-02-2023-0042

Hongmei Qi, Kailin Yang, Sibin Wu, Joo Jung

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引用次数: 0

Abstract

Purpose

Research on strategic alliances is concerned with two issues: continuation and reconfiguration. Building on prior research that examines the two issues separately, the paper studies them simultaneously. This paper aims to investigate how strategic alliances may exert the synergetic effect between dynamics and stability as well as to discuss the dynamic evolution process and influence factors of strategic alliances.

Design/methodology/approach

This paper describes the construction of a two-party evolutionary game model of alliance and partners. The model is used to analyze the evolution process of synergetic mechanism to determine when to terminate and when to continue with a partnership. Further, numerical simulation is used to quantify the results and to gain insight into the effects of various factors on the dynamic evolution of the synergetic mechanism.

Findings

This paper reveals several synergetic states of dynamics and stability in the alliances. The results show that synergy states are positively affected by the collaborative innovation benefits, alliance management capability, the intensity of intellectual property protection, liquidated damages and reputation losses, and negatively affected by the absorptive capacity of partners.

Practical implications

The study helps the alliance to achieve long-term development as well as to balance the paradoxical relationship. The results suggest that managers of strategic alliances should focus on building strong and long-term relationships in order to achieve high performance innovations. Managers should also pay close attention to their partners’ behaviors in previous alliances.

Originality/value

This paper provides new insights into the paradoxical relationship in alliance by revealing the evolution of synergetic mechanism between dynamics and stability. The results remind alliances to understand the relationship between dynamics and stability and to notice the influence factors of synergistic effects when they are making decisions.

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基于演化博弈模型的创新联盟协同效应的动态性与稳定性平衡

目的关于战略联盟的研究关注两个问题：延续和重组。在以往研究分别探讨这两个问题的基础上，本文对它们同时进行了研究。本文旨在研究战略联盟如何在动态性和稳定性之间发挥协同效应，并探讨战略联盟的动态演化过程和影响因素。该模型用于分析协同机制的演化过程，以确定何时终止、何时继续合作关系。本文揭示了联盟中动态和稳定的几种协同状态。研究结果表明，协同状态受协同创新收益、联盟管理能力、知识产权保护强度、违约金和声誉损失的正向影响，受合作伙伴吸收能力的负向影响。研究结果表明，战略联盟的管理者应注重建立稳固而长期的关系，以实现高绩效创新。本文通过揭示动态性与稳定性之间协同机制的演变，对联盟中的悖论关系提出了新的见解。研究结果提醒联盟了解动态性与稳定性之间的关系，并在决策时注意协同效应的影响因素。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Modelling in Management MANAGEMENT-

CiteScore

5.50

自引率

12.50%

发文量

期刊介绍： Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.