Introducing Neural Computing in Governance Research: Applying Self-Organizing Maps to Configurational Studies

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.