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Expectancy Theory of Motivation

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Expectancy Theory of Motivation
The expectancy theory of motivation, which was first produced by Victor Vroom, has become a generally accepted theory for explaining how individuals make decisions concerning different behavioural alternatives.
According to Vroom to motivate someone mere offer a person something to satisfy his important needs will not be adequate. In order for the person to be motivated, he must also be convincingly sure that he has the ability to obtain the reward. An employee’s motivation increases when he values a particular outcome greatly and when he feels a reasonably good chance of achieving the desired goal. This definition states that: Any individual acts in a way to reach a maximal effect with a minimal effort.

The first major expectancy theory was put forward by Victor Harold Vroom. The expectancy theory works on the basis that to achieve high motivation, hard productive work must gain a valued goal or reward for example in a workplace if you want more money, and more money will come if you work hard then we can predict that you will work hard. IF you still want more money, and all you think working hard will get you is smiles from the boss, an predict that you will chose not to work hard, unless you put a high value on smiles from the boss’(D. Buchanan & A.Huczynski., 2004). Victor Harold Vroom formed the expectancy theory using three concepts: Expectancy, Instrumentality and valence. The equation that he made is:

F (force motivation) =å(V (Valence) x I (instrumentality) x E (expectancy))

The expectancy is the belief that one’s effort (E) will result in attainment of the desired performance goals. This belief, or perception, is generally based on an individual’s past experience, self confidence and the perceived difficulty of the performance standard or goal. Studies has suggested that the expectancy theory must be extended in order to consider the effects of the time between when the individual intended to act and when the actual behaviour took place;

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