Corporate control in the auto industry: owners, shareholders and the domain of financial resources

• Grant number: 17/11338-3
• Support Opportunities: Scholarships abroad - Research
• Start date: February 02, 2018
• End date: August 01, 2018
• Field of knowledge: Applied Social Sciences - Administration - Business Administration
• Principal Investigator: Mário Sacomano Neto
• Grantee: Mário Sacomano Neto
• Host Investigator: Neil Fligstein
• Host Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil
• Institution abroad: University of California, Berkeley (UC Berkeley), United States

Abstract

Corporate control is a central theme for understanding the dynamics of contemporary capitalism. Corporate control evolves the composition of the distribution of power among the different actors (owners and shareholders) of the intra and interorganizational context. A central question of this control perspective is how the composition of power distribution affects the strategic behavior of corporations (FLIGSTEIN, 1992, FLIGSTEIN, 2001). Recent studies suggest that corporate control of automotive firms is increasingly associated with banks and players in the financial system, representing a significant increase in "financialisation" and the role of banks and financial actors in the dynamics of firms (BORGHI, SARTI and CINTRA, 2013; FLIGSTEIN, 2001). In this sense, this project aims to analyze the composition of corporate control of the largest automotive companies, under the guidance of Neil Fligstein of the Department of Sociology of the University of California, Berkeley. Neil Fligstein, author of "The transformation of corporate control", "The architeture of markets" and "Field Theory", is one of the most tenacious and critical American sociologists on the constitution of the market, economy and corporate control of the great American companies. To carry out the research, the project also aims to: 1) analyze the shareholder structure of the companies (institutional shareholders, mutual fund shareholders and corporate boards); 2) to analyze the structural and relational composition of the actors that hold the corporate control of the companies (owners and shareholders); 3) to compare the composition of the corporate control of companies from different institutional contexts (involving American, European and Eastern companies); 4) to analyze the degree of dependence of organizations on financial activities and their influence on strategic behavior; and 5) to evaluate the possible impacts of this composition of corporate control on Brazilian automotive subsidiaries. The study adopts as an analytical framework the concept of "field" and a political-cultural perspective for the study of corporate control, as proposed by Fligstein (1992) Fligstein (2001) and Fligstein and McAdam (2012). From the methodological point of view, the research is exploratory and explanatory. It involves qualitative, quantitative data and case studies. Data on the corporate makeup of companies will be collected from multiple sources, including: Nasdaq, NYSE, Compustat, PricceCoopers, Automotive News. Data will also be collected in class entities and vehicle associations, such as: OICA, ACEA, Japan Automobile Manufacturers Association (JAMA), NAATA (North American Automobile Trade Association), Data will also be collected on official websites of companies through balance sheets, reports, company documents (and groups). Data will also be collected with agents from the financial system and the automotive sector through interviews, field journals and trajectory analysis. The triangulation of the qualitative and quantitative methods contributes to the design of the validity and reliability of the study. Data analysis will be based on exploratory data analysis, content analysis and social network analysis (ARS). The software UCINET and GEPHI will be used to analyze the networks. This project is part of a larger project on "world automotive field analysis". In the future, this step will support a Multiple Correspondence Analysis (MCA) and / or regression analysis for field mapping, including other features such as patents, trade and technology relations, market domains and control variables such as revenue, profitability, etc.