Beyond rationality

Our most revered models of business processes are highly rational. But is this how business really works? Only in part. Many nonrational considerations are factors in promotions, client relations, or decision making. Managers and educational institutions should be aware of this fact.     

I’m Number One! Does Narcissism Impair Ethical Judgment Even for the Highly Religious?

Can an assessment of individuals’ narcissism help explain the quality of a respondent’s ethical judgment? How is the relationship between religiosity and ethical judgment moderated by the effects of narcissism? With a sample of 385 undergraduate business majors, this study uses a taxonomic approach to examine the effects of intrinsic and extrinsic religiosity as well as orthodox Christian beliefs on ethical judgment. Three distinct clusters were identified: Skeptics, Nominals, and Devouts. Surprisingly, of the three clusters, Nominals and Devouts were the only groups impacted by narcissism, although Skeptics overall demonstrate the worst ethical judgment.     

Exploring Ambiguity and Familiarity Effects in The “Earnings Game” Between Managers and Investors

Prior evidence suggests that managers and investors play an earnings game in which managers bias their earnings forecasts downward as the earnings announcement date approaches. Knowing managers’ incentives to provide biased guidance, investors still revise their expectations downward helping to create “positive earnings surprises.” Using a 2 (ambiguity) × 2 (familiarity) between subject randomized experimental design where MBA students playing the roles of manager and investor answer a series of questions related to earnings guidance, we investigate whether earnings environment ambiguity and manager-investor familiarity influence behavior during the “earnings game.” In general, results from this study suggest that ambiguity contributes to managers’ propensity to mislead and investors’ propensity to follow, and a false sense of familiarity may amplify investors’ reliance on managers’ guidance.     

Integrated analysis of content and construct validity of psychometric instruments

Establishing adequacy of psychometric properties of an instrument involves acquisition and evaluation of evidence based on item content and internal structure. Content validity evidence consists of subject matter experts providing quantitative ratings of the extent to which items are a representative sample of targeted domain. Evidence of internal structure includes factor analytic studies and examination of item interrelationships based on item responses from participants. Although subject matter expert ratings and participant response data are traditionally analyzed separately, each serves to inform the other in important ways. We propose integrating subject matter experts’ and participants’ data seamlessly to establish a unified model of validity evidence. The approach is applied to an instrument designed to measure nursing home culture change (i.e., resident-centered care). The proposed method has been demonstrated to be useful with a posterior distribution resulting in stable estimates of psychometric parameters superior to traditional analytic approaches. To illustrate the efficacy of the methodology, we present a simulation study and discuss its place in psychometric methods.     

The Effects of Disclosure and Analyst Regulations on the Relevance of Analyst Characteristics for Explaining Analyst Forecast Accuracy

We posit and find an effect of disclosure and analyst reporting regulations implemented from 2000 through 2003 (including Regulation Fair Disclosure, the Sarbanes‐Oxley Act and the Global Settlement Act) on the importance of analyst and forecast characteristics for analyst forecast accuracy. Following the enactment of these regulations, more experienced analysts and All‐Star analysts do not maintain their superior forecast accuracy, and analysts employed by large brokerage houses perform worse than other analysts. In addition, we find a decrease in the importance of analyst effort, the number of industries and firms followed, days elapsed since the last forecast, and forecast horizon. While the importance of bold upward forecast revisions does not change, bold downward revisions lose their relevance for forecast accuracy after 2003. Finally, we find an increase in the importance of prior forecast accuracy. We find that the importance of these characteristics varies with the precision of publicly available information. Specifically, the decrease in the importance of most analyst and forecast characteristics and the increase in the importance of prior forecast accuracy are greater when the precision of publicly available information is low. Overall, our results suggest that the positive effects of experience, effort, brokerage house size and All‐Star status on forecast accuracy in the pre‐regulation period were because of the information advantages that these analysts enjoyed (rather than their ability to generate private information). In contrast, our results suggest that prior forecast accuracy is related to analysts’ ability to generate private information.     

A Bayesian Approach to Understanding Time Series Data

This paper explores the use of Bayesian models to analyze time series data. The Bayesian approach produces output that can be readily understood by actuaries and included in their own experience studies. We illustrate this Bayesian approach by analyzing U.S. unemployment rates, a macroeconomic time series. Understanding time series of macroeconomic variables can help actuaries in pricing and reserving their products. For example, a change in the level and/or variance of the unemployment series is of interest to actuaries, because its movement can explain a changing pattern of lapse rates of incidence rates. Our Bayesian analysis, based on models developed by McCulloch and Tsay (1993, 1994), allows for shifts in the level and in the error variance of a process. We develop a measure of model fit, based on the Akaike Information Criterion, that can be used in choosing between alternative models. Posterior prediction intervals for the fitted values are also created to pictorially show the range of paths that could result from the choice of a particular model.