989 resultados para RATINGS


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According to career construction theory, continuous adaptation to the work environment is crucial to achieve work and career success. In this study, we examined the relative importance of career adaptability for job performance ratings using an experimental policy-capturing design. Employees (N = 135) from different vocational backgrounds rated the overall job performance of fictitious employees in 40 scenarios based on information about their career adaptability, mental ability, conscientiousness, and job complexity. We used multilevel modeling to investigate the relative importance of each factor. Consistent with expectations, career adaptability positively predicted job performance ratings, and this effect was relatively smaller than the effects of conscientiousness and mental ability. Job complexity did not moderate the effect of career adaptability on job performance ratings, suggesting that career adaptability predicts job performance ratings in high-, medium-, and low-complexity jobs. Consistent with previous research, the effect of mental ability on job performance ratings was stronger in high- compared to low-complexity jobs. Overall, our findings provide initial evidence for the predictive validity of employees' career adaptability with regard to other people's ratings of job performance.

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Recommender systems aggregate individual user ratings into predictions of products or services that might interest visitors. The quality of this aggregation process crucially affects the user experience and hence the effectiveness of recommenders in e-commerce. We present a characterization of nearest-neighbor collaborative filtering that allows us to disaggregate global recommender performance measures into contributions made by each individual rating. In particular, we formulate three roles-scouts, promoters, and connectors-that capture how users receive recommendations, how items get recommended, and how ratings of these two types are themselves connected, respectively. These roles find direct uses in improving recommendations for users, in better targeting of items and, most importantly, in helping monitor the health of the system as a whole. For instance, they can be used to track the evolution of neighborhoods, to identify rating subspaces that do not contribute ( or contribute negatively) to system performance, to enumerate users who are in danger of leaving, and to assess the susceptibility of the system to attacks such as shilling. We argue that the three rating roles presented here provide broad primitives to manage a recommender system and its community.

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In three essays we examine user-generated product ratings with aggregation. While recommendation systems have been studied extensively, this simple type of recommendation system has been neglected, despite its prevalence in the field. We develop a novel theoretical model of user-generated ratings. This model improves upon previous work in three ways: it considers rational agents and allows them to abstain from rating when rating is costly; it incorporates rating aggregation (such as averaging ratings); and it considers the effect on rating strategies of multiple simultaneous raters. In the first essay we provide a partial characterization of equilibrium behavior. In the second essay we test this theoretical model in laboratory, and in the third we apply established behavioral models to the data generated in the lab. This study provides clues to the prevalence of extreme-valued ratings in field implementations. We show theoretically that in equilibrium, ratings distributions do not represent the value distributions of sincere ratings. Indeed, we show that if rating strategies follow a set of regularity conditions, then in equilibrium the rate at which players participate is increasing in the extremity of agents' valuations of the product. This theoretical prediction is realized in the lab. We also find that human subjects show a disproportionate predilection for sincere rating, and that when they do send insincere ratings, they are almost always in the direction of exaggeration. Both sincere and exaggerated ratings occur with great frequency despite the fact that such rating strategies are not in subjects' best interest. We therefore apply the behavioral concepts of quantal response equilibrium (QRE) and cursed equilibrium (CE) to the experimental data. Together, these theories explain the data significantly better than does a theory of rational, Bayesian behavior -- accurately predicting key comparative statics. However, the theories fail to predict the high rates of sincerity, and it is clear that a better theory is needed.

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This paper discusses the inverter ratings of Brushless Doubly-Fed Machine (BDFM) adjustable speed drive (ASD) or generator (ASG) systems. Based on the per phase equivalent circuit model, the ratings of the two inverters in a bidirectional converter are evaluated individually. An approach to minimise the total inverter rating is presented, taking into account power factor constraints of the power grid. The effects of speed deviation and control winding excitation on the inverter ratings are discussed. Predictions of inverter ratings are presented with experimental verification. A design example is also provided in which the total inverter rating is minimised for a practical BDFM based ASG system. © 2005 IEEE.

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Expectations about the magnitude of impending pain exert a substantial effect on subsequent perception. However, the neural mechanisms that underlie the predictive processes that modulate pain are poorly understood. In a combined behavioral and high-density electrophysiological study we measured anticipatory neural responses to heat stimuli to determine how predictions of pain intensity, and certainty about those predictions, modulate brain activity and subjective pain ratings. Prior to receiving randomized laser heat stimuli at different intensities (low, medium or high) subjects (n=15) viewed cues that either accurately informed them of forthcoming intensity (certain expectation) or not (uncertain expectation). Pain ratings were biased towards prior expectations of either high or low intensity. Anticipatory neural responses increased with expectations of painful vs. non-painful heat intensity, suggesting the presence of neural responses that represent predicted heat stimulus intensity. These anticipatory responses also correlated with the amplitude of the Laser-Evoked Potential (LEP) response to painful stimuli when the intensity was predictable. Source analysis (LORETA) revealed that uncertainty about expected heat intensity involves an anticipatory cortical network commonly associated with attention (left dorsolateral prefrontal, posterior cingulate and bilateral inferior parietal cortices). Relative certainty, however, involves cortical areas previously associated with semantic and prospective memory (left inferior frontal and inferior temporal cortex, and right anterior prefrontal cortex). This suggests that biasing of pain reports and LEPs by expectation involves temporally precise activity in specific cortical networks.

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This study estimates default probabilities of 124 emerging countries from 1981 to 2002 as a function of a set of macroeconomic and political variables. The estimated probabilities are then compared with the default rates implied by sovereign credit ratings of three major international credit rating agencies (CRAs) – Moody's Investor's Service, Standard & Poor's and Fitch Ratings. Sovereign debt default probabilities are used by investors in pricing sovereign bonds and loans as well as in determining country risk exposure. The study finds that CRAs usually underestimate the risk of sovereign debt as the sovereign credit ratings from rating agencies are usually too optimistic.