The role of leadership in shared mental model convergence and team performance improvement: An agent-based computational model

Research in shared mental models has immeasurably aided our understanding of effective teamwork and taskwork. However, little research has focused on the role that leaders play, if any, in influencing, developing and/or fostering shared mental models and thereby improving team performance. We developed an agent-based computational model based on McComb's theory of three-phase mental model development, where agents repeatedly share individual opinions (orientation phase), evaluate and respond to the opinions expressed by others (differentiation phase), and modify their understanding of the team based on the responses (integration phase). Leadership and team properties are represented in three experimental parameters: social network structure, heterogeneity of agents' domains of expertise, and level of their mutual interest. Participative leadership is represented by a fully connected network, while Leader–Member eXchange (LMX) is represented by a fully connected network of in-group members and several out-group members connected only to the leader. Our simulation results show that, in general, participative leadership promotes mental model convergence better than LMX. In the meantime, the team performance improvement is achieved by participative leadership only when members have both heterogeneous domains of expertise and strong mutual interest. In all other conditions, participative leadership causes the worst degradation of team performance through team development processes, while LMX is the best to minimize such team degradation. Implications and suggestions for future research are provided.     

Salvage therapy trial for erectile dysfunction using phosphodiesterase type 5 inhibitors and vitamin E: Preliminary report

We report our initial experience with salvage therapy for low responders to PDE-5 inhibitors by adding vitamin E. Of 89 patients with ED who visited our clinic between January 2004 to August 2006, 9 were unable to obtain a full response to a PDE-5 inhibitor and included in the present study. After providing informed consent, each was given 300 mg per day of α-tocophenol at least 1 month and completed IIEF-5 questionnaires to assess its efficacy while also taking a PDE-5 inhibitor. With α-tocophenol administration, the average IIEF-5 score increased from 13.8 ± 3.2 to 17.1 ± 3.6. Four of seven patients who completed the questionnaire each time showed improved IIEF-5 scores, with a maximum elevation of 9 points. Further, eight of the nine patients experienced favourable subjective changes, the majority being increased penile rigidity. The present clinical trial results are, to our knowledge, the first known to show the effects of vitamin E for enhancing the efficacy of a PDE-5 inhibitor.     

Ratio tests of a unit root

Based on the maximal invariant principle, we derive two ratio tests (locally best invariant test and point optimal test) for a unit root and compare them with previously proposed ratio tests. We also show that our ratio tests tend to have better powers than the Dickey-Fuller test and the modified Dickey-Fuller test.     

Probit and Logit Model Selection

Monte Carlo experiments are conducted to compare the Bayesian and sample theory model selection criteria in choosing the univariate probit and logit models. We use five criteria: the deviance information criterion (DIC), predictive deviance information criterion (PDIC), Akaike information criterion (AIC), weighted, and unweighted sums of squared errors. The first two criteria are Bayesian while the others are sample theory criteria. The results show that if data are balanced none of the model selection criteria considered in this article can distinguish the probit and logit models. If data are unbalanced and the sample size is large the DIC and AIC choose the correct models better than the other criteria. We show that if unbalanced binary data are generated by a leptokurtic distribution the logit model is preferred over the probit model. The probit model is preferred if unbalanced data are generated by a platykurtic distribution. We apply the model selection criteria to the probit and logit models that link the ups and downs of the returns on S&P500 to the crude oil price.     

Comparison of Bayesian Model Selection Criteria and Conditional Kolmogorov Test as Applied to Spot Asset Pricing Models

We compare Bayesian and sample theory model specification criteria. For the Bayesian criteria we use the deviance information criterion and the cumulative density of the mean squared errors of forecast. For the sample theory criterion we use the conditional Kolmogorov test. We use Markov chain Monte Carlo methods to obtain the Bayesian criteria and bootstrap sampling to obtain the conditional Kolmogorov test. Two non nested models we consider are the CIR and Vasicek models for spot asset prices. Monte Carlo experiments show that the DIC performs better than the cumulative density of the mean squared errors of forecast and the CKT. According to the DIC and the mean squared errors of forecast, the CIR model explains the daily data on uncollateralized Japanese call rate from January 1, 1990 to April 18, 1996; but according to the CKT, neither the CIR nor Vasicek models explains the daily data.     

Bayesian tests of a parameter shift under heteroscedasticity:weighted-t vs. double-t approaches

A highest posterior density interval for examining shifts in individual regression coefficients between subsamples with different error variances is derived from a weighted-t posterior density. Compared to a double-t approach, the weighted-t approach is computationally more efficient and allows a direct comparison to a sampling theory approach. In a numerical example the weighted-t, double-t and confidence interval approaches are compared.     

Aggression exhibited by older dementia clients toward staff in Japanese long-term care

This study examined the experiences of staff members from seven Japanese hospitals who had been treated aggressively by older dementia clients. Altogether, 170 questionnaires were analyzed. In the past year, 75.3% and 63.5% of staff members had experienced physical and verbal aggression, respectively. Working numerous night shifts, working shifts other than 3-shifts, and being allotted assignments with clients who had a lower average level of physical capacity were the factors associated with recurrent client aggression. Those staff members who spent adequate time caring for their clients, who gained client consent before providing care, and who tried to build a trusting relationship with their clients were found to have experienced less aggression. Burnout, which is likely to enhance the risk of the staff mistreatment and neglect of older clients, was found to be higher among those who experienced aggression.