Decision Making & Negotiations

Psychological research has repeatedly demonstrated two seemingly irreconcilable human tendencies. People are motivated towards internal consistency, or acting in accordance with stable, self-generated preferences. Simultaneously though, people demonstrate considerable variation in the content of their preferences, often induced by subtle external influences. The current studies test the hypothesis that decision makers resolve this tension by sustaining illusions of preference consistency, which, in turn, confer psychological benefits.

Age differences in intrinsic and extrinsic motivation and the relationships of each to academic outcomes were examined in an ethnically diverse sample of 797 3rd-grade through 8th-grade children. Using independent measures, the authors found intrinsic and extrinsic motivation to be only moderately correlated, suggesting that they may be largely orthogonal dimensions of motivation in school.

What's a customer really worth? Can you find out, without endlessly complex modelling? And once you know, what should you do with that knowledge? Managing Customers as Investments has the answers. You'll learn simple ways to get reliable customer value information - in a form you can use. You'll discover how to use it to measure marketing effectiveness, generate improvements throughout the entire customer relationship lifecycle, and improve decision making. Everyone tells you to manage your business around customers. This book tells you how to do it.

This paper provides a model of investment timing by managers in a decentralized firm in the presence of agency conflicts and information asymmetries. When investment decisions are delegated to managers, contracts must be designed to provide incentives for managers to both extend effort and truthfully reveal private information. Using a real options approach, we show that an underlying option to invest can be decomposed into two components: a manager's option and an owner's option. The implied investment behavior differs significantly from that of the first-best no-agency solution.

Study Objective: Significant variation in emergency department patient arrival rates necessitates the adjustment of staffing patterns to optimize the timely care of patients. This study evaluates the effectiveness of a queueing model in identifying provider staffing patterns to reduce the fraction of patients who leave without being seen.

Objective: To examine the relationship between ambulance diversions and the incidence of myocardial infarction deaths in New York City. Methods: We obtained data for 1999 and 2000 on all 9,743 deaths due to myocardial infarction in New York City, as well as periods of diversion status for 58 New York City area hospitals operating under a central ambulance dispatch by the New York City Fire Department. Negative binomial regressions were used to model the percentage increase in myocardial infarction deaths associated with diversion status.

This article critically examines McGraw and Tetlock's (2005) notion of relational framing and offers directions for future development of the conceptual model. I begin by discussing the inherent limitations of scenario studies and show how the emergence of attribution analysis in real interpersonal interactions may qualify the results obtained in these studies. I then discuss the norm consistency and social identity maintenance mechanisms proposed in the article and advance several alternative mediators of the phenomenon, including affect and anticipated interaction.

Bayesian learning implies decreasing weights on prior beliefs and increasing weights on the accuracy of the analyst?s past forecast record, as the number of forecast errors comprising her forecast record (its length) increases. Consistent with this model of investor learning, empirical tests show that investors? reactions to forecast news are increasing in the product of the accuracy and length of analysts? forecast records. Moreover, the Bayesian learning predicted by our model is more descriptive of investor reactions than is a static model which predicts that investors?

Using both a linear regression method and a probability-based method, we find that on average analysts place larger than efficient weights on (i.e., they over-weight) their private information when they forecast corporate earnings. We also find that analysts over-weight more when issuing forecasts more favorable than the consensus, and over-weight less, and may even under-weight, private information when issuing forecasts less favorable than the consensus.