Knowledge that Transforms

We investigate the epistemology of trust in social networks. We posit trust as a special epistemic state that depends on actors' beliefs about each others' beliefs as well as about states of the wo...

As with diversity, collaboration, co-operation and teamwork havebecome increasingly important issues for management to handle.The purpose of this study is to analyse the level of Emotional Intelligenceand Organisational Citizenship Behaviour among middlemanagement employees in the Malaysian manufacturing sector.A total of 536 employees from different organisations and industriestook part in this survey. Based on the descriptive analysis,employees in some industries tended to have a lower level ofemotional intelligence and organisational citizenship behaviour.

We develop a new goodness-of-fit test for validating the performance of probability forecasts. Our test statistic is particularly powerful under sparseness and dependence in the observed data. To build our test statistic, we start from a formal definition of calibrated forecasts, which we operationalize by introducing two components. The first component tests the level of the estimated probabilities; the second validates the shape, measuring the differentiation between high and low probability events. After constructing test statistics for both level and shape, we provide a global goodness-of-fit statistic, which is asymptotically χ 2 distributed. In a simulation exercise, we find that our approach is correctly sized and more powerful than alternative statistics. In particular, our shape statistic is significantly more powerful than the Kolmogorov–Smirnov test. Under independence, our global test has significantly greater power than the popular Hosmer–Lemeshow's χ 2 test. Moreover, even under dependence, our global test remains correctly sized and consistent. As a timely and important empirical application of our method, we study the validation of a forecasting model for credit default events.

Are the attitudes and beliefs of chief executive officers (CEOs) linked to their firms' innovative performance? This paper uses a measure of overconfidence, based on CEO stock-option exercise, to study the relationship between a CEO's “revealed beliefs” about future performance and standard measures of corporate innovation. We begin by developing a career concern model where CEOs innovate to provide evidence of their ability. The model predicts that overconfident CEOs, who underestimate the probability of failure, are more likely to pursue innovation, and that this effect is larger in more competitive industries. We test these predictions on a panel of large publicly traded firms for the years from 1980 to 1994. We find a robust positive association between overconfidence and citation-weighted patent counts in both cross-sectional and fixed-effect models. This effect is larger in more competitive industries. Our results suggest that overconfident CEOs are more likely to take their firms in a new technological direction.

Websites typically provide several links on each page visited by a user. Whereas some of these links help users easily navigate the site, others are typically used to provide targeted recommendations based on the available user profile. When the user profile is not available (or is inadequate), the site cannot effectively target products, promotions, and advertisements. In those situations, the site can learn the profile of a user as the user traverses the site. Naturally, the faster the site can learn a user's profile, the sooner the site can benefit from personalization. We develop a technique that sites can use to learn the profile as quickly as possible. The technique identifies links for sites to make available that will lead to a more informative profile when the user chooses one of the offered links. Experiments conducted using our approach demonstrate that it enables learning the profiles markedly better after very few user interactions as compared to benchmark approaches. The approach effectively learns multiple attributes simultaneously, can learn well classes that have highly skewed priors, and remains quite effective even when the distribution of link profiles at a site is relatively homogeneous. The approach works particularly well when a user's traversal is influenced by the most recently visited pages on a site. Finally, we show that the approach is robust to noise in the estimates for the probability parameters needed for its implementation.

Consistent with public statements made by sophisticated practitioners, we document that the hedge returns to Sloan's (Sloan, R. G. 1996. Do stock prices fully reflect information in accruals and cash flows about future earnings? Accounting Rev. 71(3) 289–315) accruals anomaly appear to have decayed in U.S. stock markets to the point that they are, on average, no longer reliably positive. We explore some potential reasons why this has happened. Our empirical analyses suggest that the anomaly's demise stems in part from an increase in the amount of capital invested by hedge funds into exploiting it, as measured by hedge fund assets under management and trading volume in extreme accrual firms. A decline in the size of the accrual mispricing signal, as measured by the magnitude of extreme decile accruals and the relative persistence of cash flows and accruals, may also play a (weaker) role.