- 27 Feb 2013
- Research & Ideas
Sidetracked: Why Can’t We Stick to the Plan?
In her new book, Sidetracked, behavioral scientist and professor Francesca Gino explores the unexpected forces that often keep people from following through with their plans, both professional and personal. Closed for comment; 12 Comment(s) posted.
- 07 Oct 2009
- Working Paper Summaries
Specific Knowledge and Divisional Performance Measurement
Performance measurement is one of the critical factors that determine how individuals in an organization behave. It includes subjective as well as objective assessments of the performance of both individuals and subunits of an organization such as divisions or departments. Besides the choice of the performance measures themselves, performance evaluation involves the process of attaching value weights to the different measures to represent the importance of achievement on each dimension. This paper examines five common divisional performance measurement methods: cost centers, revenue centers, profit centers, investment centers, and expense centers. The authors furnish the outlines of a theory that attempts to explain when each of these five methods is likely to be the most efficient. Key concepts include: Each of these methods can be seen as providing an alternative way of aligning corporate decision-making authority with valuable "specific knowledge" inside the organization. Jensen and Meckling's theory suggests that cost and revenue centers work best in cases where headquarters has (or can readily obtain) good information about cost and demand functions, product quality, and investment opportunities. Decentralized profit and investment centers tend to supplant revenue and cost centers when managers of business units have a significant informational advantage over headquarters. Closed for comment; 0 Comment(s) posted.
Designing an Agile Software Portfolio Architecture: The Impact of Coupling on Performance
This study deepens our understanding of how firms can better design software portfolio architectures to improve their agility. The authors examined data from over 1,000 different software applications and 3,000 dependencies between them. They found that indirect measures of coupling and dependency have more power in predicting IT agility than direct measures.