Delusions of Success : How Optimism Undermines Executives' Decisions  

Oleh:

Kahneman, Daniel ; Lovallo, Dan

Jenis: Article from Bulletin/Magazine - ilmiah internasional
Dalam koleksi: Harvard Business Review bisa di lihat di link (http://web.b.ebscohost.com/ehost/command/detail?sid=f227f0b4-7315-44a4-a7f7-a7cd8cbad80b%40sessionmgr114&vid=12&hid=105&bdata=JnNpdGU9ZWhvc3QtbGl2ZQ%3d%3d#db=bth&jid=HBR) vol. 81 no. 7 (2003), page 56-63.
Topik: decisions; behavioural sciences; decision analysis; decision making; decision theory; forecasting; psychology; risk assessment; scenario analysis

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Perpustakaan Pusat (Semanggi) Nomor Panggil: HH10.22 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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The evidence is disturbingly clear : Most major business initiatives - mergers and acquisitions, capital investments, market entries - fail to pay off. Economists would argue that the low success rate reflects a rational assessment of risk, with the returns from a few successes outweighing the losses of many failures. But two distinguished scholars of decision making, Dan Lovallo of the University of New South Wales and Nobel laureate Daniel Kahneman of Princeton University, provide a very different explanation. They show that a combination of cognitive biases (including anchoring and competitor neglect) and organizational pressures lead managers to make overly optimistic forecasts in analyzing proposals for major investments. By exaggerating the likely benefits of a project and ignoring the potential pitfalls, they lead their organizations into initiatives that are doomed to fall well short of expectations. The biases and pressures cannot be escaped, the authors argue, but they can be tempered by applying a very different method of forecasting - one that takes a much more objective "outside view" of an initiative's likely outcome. This outside view, also known as reference - class forecasting, completely ignores the details of the project at hand ; instead, it encourages managers to examine the experiences of a class of similar projects, to lay out a rough distribution of outcomes for this reference class, and then to position the current project in that distribution.

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