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Introduction: In today's complex and changing business environment organizations need to learn and adapt to emerging circumstances. Teams can be a preferred vehicle to facilitate solving challenges that require diverse perspectives and expertise, collaboration, and knowledge sharing among members. To support team learning, organizations need to understand and promote an appropriate environment that facilitates learning within teams. By drawing on Fairness Theory and Social Exchange Theory, this study explores the role of leader-induced justice perceptions as a mediator in the relationship of participative leadership and team learning. Methods: Using a split-half team survey methodology with a sample of 211 teams, the study analyzes the role of team justice climate as a mediation mechanism in the relationship between participative leadership behaviors and team learning. Results: Results from structural equation modeling analyses suggest that, at a team level, participative leadership behaviors have both a direct association with team learning and are partially mediated by the team's justice climate. Discussion: This study contributes to existing literature by offering evidence that the perceptions of justice instilled by leaders play a role mediating participatory leadership and team learning. Moreover, the study supports the idea that leader induced justice perceptions can be considered as an aggregated construct at the team level. From a practical standpoint, the findings imply that team leaders can contribute to create an environment conducive to team learning by treating team members with fairness.

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A cross-cultural comparison is made of delay discounting in samples of participants from Chile and China. Comparisons are made based on previous literature that suggests that individuals from an Asian culture should be willing to postpone delayed rewards more than are individuals from a Latin American culture. To test the cross-cultural validity of a hyperbolic discounting model, the model was fitted to both data sets. Additionally, a self-enhancement measure was evaluated as a potential mediator between culture of origin and delay discounting. Seventy-eight college students from China and 120 college students from Chile, with similar demographic backgrounds, discounted hypothetical monetary outcomes using an adjusting-amount titration procedure. Additionally, participants completed a self-enhancement measure. Age, academic major, gender, and grade point average were controlled. Chilean participants discounted much more steeply than Chinese nationals did. No support was obtained for the mediation of self-enhancement between culture of origin and degree of delay discounting. In both samples, delay discounting was better described by a hyperboloid than an exponential function, the only exception being the $10,000 condition in which the medians for Chilean participants' present subjective value were equally well explained by a hyperboloid and an exponential function.

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OBJECTIVES: To develop a model for detecting cases of organized fraud in Chile based on data from the legal forms for medically authorized leave (formulario legal de licencia médica curativa-MAL) and to establish the relevance of this data to fraud detection. METHODS: A binomial logistic regression model was employed using four variables from the MAL form, a national requirement for illness-related work absences: the number of legal absences taken by a single person, the number of days authorized by the prescribing doctor, the total cost per illness, and a dichotic variable reflecting whether or not the diagnosis is one that can be proven. The analysis involved 4,079 MAL forms that had been submitted in 2003 to a private health provider and of which 356 were already identified as fraudulent by a panel of medical fraud experts. RESULTS: The model successfully identified 99.71% of the fraudulent medical authorizations and 99.86% of the non-fraudulent, according to the criteria of the panel of fraud experts. Three of the variables employed had statistically-significant independent predictive power. The positive predictive value of the proposed model was 98.59%, while its negative predictive value was 99.97%. CONCLUSIONS: The binomial logistic model that was developed uses four variables that are common to all MAL forms in use by Chile's public as well as private insurers, permitting separation of fraudulent from non-fraudulent requests to be more accurate, more timely, and at a cost lower that of an expert panel.

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OBJETIVOS: Desarrollar un modelo para detectar casos de fraude planificado en Chile a partir de los datos contenidos en los formularios de licencia médica curativa (LMC) y establecer la contribución relativa de esos datos a su detección. MÉTODOS: SE aplicó un modelo de regresión logística binominal a partir de cuatro variables contenidas en el formulario legal de LMC exigido nacionalmente para justificar las ausencias al trabajo por motivos de enfermedad: el número de licencias médicas asignadas a una misma persona, el número de días de licencia médica otorgados por el médico tratante, el monto total a pagar por la enfermedad y una variable dicotómica que refleja si el diagnóstico es comprobable o no. Se analizaron 4 079 LMC presentadas el año 2003 a una institución privada de salud previsional, de las cuales 356 estaban ya clasificadas como fraudulentas por un panel de médicos expertos en fraude. RESULTADOS: El modelo logró identificar correctamente 99,71 por ciento de las licencias médicas fraudulentas y 99,86 por ciento de las no fraudulentas según el criterio del panel de expertos en fraude. Tres de las variables empleadas presentaron un poder predictivo independiente estadísticamente significativo. El valor predictivo positivo del modelo propuesto fue de 98,59 por ciento, mientras el valor predictivo negativo fue de 99,97 por ciento. CONCLUSIONES: El modelo logístico binomial desarrollado, basado en cuatro variables de uso universal en los formularios de LMC utilizados por todas las entidades aseguradoras de Chile, tanto públicas como privadas, permite discriminar de forma precisa y más rápidamente y con menor costo que los paneles de expertos las solicitudes fraudulentas de las no fraudulentas.

OBJECTIVES: To develop a model for detecting cases of organized fraud in Chile based on data from the legal forms for medically authorized leave (formulario legal de licencia médica curativa-MAL) and to establish the relevance of this data to fraud detection. METHODS: A binomial logistic regression model was employed using four variables from the MAL form, a national requirement for illness-related work absences: the number of legal absences taken by a single person, the number of days authorized by the prescribing doctor, the total cost per illness, and a dichotic variable reflecting whether or not the diagnosis is one that can be proven. The analysis involved 4 079 MAL forms that had been submitted in 2003 to a private health provider and of which 356 were already identified as fraudulent by a panel of medical fraud experts. RESULTS: The model successfully identified 99.71 percent of the fraudulent medical authorizations and 99.86 percent of the non-fraudulent, according to the criteria of the panel of fraud experts. Three of the variables employed had statistically-significant independent predictive power. The positive predictive value of the proposed model was 98.59 percent, while its negative predictive value was 99.97 percent. CONCLUSIONS: The binomial logistic model that was developed uses four variables that are common to all MAL forms in use by Chile's public as well as private insurers, permitting separation of fraudulent from non-fraudulent requests to be more accurate, more timely, and at a cost lower that of an expert panel.

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