Considerable attention has been given to the identification of key forms of reward and its linkage to employee engagement. For this purpose following study aims to uncover the influence of extrinsic and intrinsic rewards on employee engagement in the public sector of Uganda. A sample of 184 public sector employees was randomly selected and taken from Gulu district. A quantita...

BACKGROUND Brain regions that track value (including the ventral striatum) respond more during the anticipation of immediate than delayed rewards, even when the delayed rewards are larger and equally preferred to the immediate. The anticipatory response to immediate vs. delayed rewards has not previously been examined in association with cigarette smoking. METHODS Smokers (n=35) and nonsmoker...

This study aims to explore howdifferently social and economic rewards of a hotel loyalty program impact program loyalty and further examine how the differential impact produces relational behaviors. Findings suggest that economic rewards drive program loyalty more strongly than social rewards because members tend to stay with a loyalty program due to its economic reward. However, social rewards...

Previous research has shown that the value of large future rewards is discounted less steeply than is the value of small future rewards. These experiments extended this line of research to probabilistic rewards. Two experiments replicated the standard findings for delayed rewards but demonstrated that amount has an opposite effect on the discounting of probabilistic rewards. That is, large prob...

Laboratory studies of choice and decision making among real monetary rewards typically use smaller real rewards than those common in real life. When laboratory rewards are large, they are almost always hypothetical. In applying laboratory results meaningfully to real-life situations, it is important to know the extent to which choices among hypothetical rewards correspond to choices among real ...

We present a new bandit algorithm, SAO (Stochastic and Adversarial Optimal) whose regret is (essentially) optimal both for adversarial rewards and for stochastic rewards. Specifically, SAO combines the O( √ n) worst-case regret of Exp3 (Auer et al., 2002b) and the (poly)logarithmic regret of UCB1 (Auer et al., 2002a) for stochastic rewards. Adversarial rewards and stochastic rewards are the two...