Introducing Neural Computing in Governance Research: Applying Self‐Organizing Maps to Configurational Studies
本文介绍自组织映射（SOM）这一神经计算方法，用于增强组态治理研究，通过分析金融服务业公司行为准则的语言和意义，揭示企业对制度治理逻辑的接受模式。
Abstract Manuscript Type Empirical Research Issue/Question Self‐organizing maps (SOMs), a neural computing paradigm, were introduced as a methodology to enhance and extend configurational governance research. The capabilities of SOMs include assessment of nonlinear relationships among study variables and projection of firms and clusters in two‐dimensional space based on their relative similarity. Research Findings/Insights To demonstrate their application to governance research, SOMs were used to study patterns of immunity to institutional governance logics in the financial services industry. Firm sensemaking and governance logics were assessed by analyzing the language and meaning of corporate codes of conduct. Content analysis was guided by the DICTION software program. DICTION uses data dictionaries to analyze the meaning of text documents based on word usage. Our results supported a configurational model characterized by distinct groupings of firms with varying degrees of acceptance of prevailing institutional governance logics. Practitioner/Policy Implications SOM analysis demonstrated that context influences firm governance logics. Specifically, different interpretations of environmental pressures led to different adaptive responses suggesting reconsideration of the notion of universal or best governance practices.