A Model of Competitive Impression Management: Edison versus Westinghouse in the War of the Currents

Organizational impression management theory traditionally explains how firms manage threats from specific events or from campaigns orchestrated by non-competitors, such as activists or regulators, but has not attempted to explain the complex dynamics of impression management campaigns orchestrated by a firm’s competitor. To address this oversight, we analyze one of the bitterest rivalries in corporate history—the war of the currents between Thomas Edison and George Westinghouse, which ended in the triumph of Westinghouse’s alternating current over Edison’s direct current for electric power transmission. We define competitive impression management as activity by a firm or its employees that is intended to alter the perceptions of a competing firm or its offerings in the eyes of a common audience. By combining historical case study and grounded theory methods, our findings reveal that the war of the currents unfolded across distinct chronological stages dependent on the actions and reactions of others that were shaped by audiences’ information filters. We explore the implications of our theory of campaigns and their consequences, expanding the scope of impression management theory, deepening our understanding of how organizations compete, and providing fertile ground for future research on market-based campaigns.

Gas turbine performance monitoring based on extended information fusion filter

Performance monitoring is a critical issue for gas turbine engine for improving the operation safety and reducing the maintenance cost. With regard to this, variants of Kalman-filters-based state estimation have been employed to detect gas turbine performance, but the classical centralized Kalman filters are subject to heavy computational effort and poor fault tolerance. A novel nonlinear fusion filter algorithm using information description with distributed architecture is proposed and applied to gas turbine performance monitoring. This methodology is developed from federated Kalman filter, and a bank of local extended information filters and one information mixer are combined with extended information fusion filter. The local state estimates and covariance calculated in parallel by the local extended information filters are integrated in the information mixer to yield a global state estimate. The global state estimate of nonlinear system is fed back to the local filters with weighted factor for next iteration. The aim of the proposed methodology is to reduce the computational efforts of state estimation and improve robustness to sensor faults in cases of gas turbine performance monitoring. The simulation results on a turbofan engine confirm the extended information fusion filter's effective capabilities in comparison to the general central ones.