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28th International Congress of Psychology August 8 ... - U-netSURF

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Katherine Selgrade, Old Dominion University, USA<br />

We describe the results <strong>of</strong> a five-year study conducted for NASA in the United States. We<br />

examined the performance <strong>of</strong> 150 Chinese and American teams working in a flight simulation<br />

experiment. Teams consisted <strong>of</strong> single nationality (all Chinese or all American) or mixed<br />

nationality (Chinese and American) members counterbalanced for national origin <strong>of</strong> leader. The<br />

impact <strong>of</strong> national differences on nine components <strong>of</strong> team process was examined. Significant<br />

differences were found for decision making, situational awareness, communication, and team<br />

coordination. We discuss the influence <strong>of</strong> cultural values and personality traits as mediating and<br />

moderating variables.<br />

4159.2 A study on the relationships between managers conscientiousness and their job<br />

performance, Zhao Guoxiang 1 , Wang Minghui 2 , 1 <strong>Psychology</strong> Department <strong>of</strong> Henan University<br />

Kaifeng, China, 2 Institute <strong>of</strong> Human Resources Management Jinan University, Guangzhou, China<br />

The purposes <strong>of</strong> study are to investigate the relationships between managers' conscientiousness<br />

and job performance by using the regression analysis. After surveying 236 managers, the<br />

conclusions are as follows: (1) Managers' conscientiousness do better to the prediction <strong>of</strong><br />

contextual performance than to that <strong>of</strong> task performance, (2) Service tendency and dependability<br />

do good to the predictive effects <strong>of</strong> interpersonal facilitation, (3) Service tendency, individual<br />

morality and planning do good to the predictive effects <strong>of</strong> task performance, (4) Individual<br />

morality, planning and achievement tendency are helped for the predictive effects <strong>of</strong> job<br />

performance.<br />

4159.3 An analysis instrument for diagnosing the project steering competences <strong>of</strong> the leader <strong>of</strong><br />

a project group, Patricia Simon, University <strong>of</strong> Regensburg, Germany<br />

In this lecture an analysis instrument is presented with which it is possible to diagnose the project<br />

steering competences <strong>of</strong> the leader <strong>of</strong> a project group. The instrument is based on a multiple<br />

regression analysis which shows that the leadership functions are in curve linear relationship to the<br />

effectiveness <strong>of</strong> a group. Therefore, a leader has to avoid to over- or understeer the<br />

problem-solving process. According to this model the effectiveness <strong>of</strong> a group is at 72%<br />

dependent on the behavior <strong>of</strong> the leader. Currently, this high variance explanation is tested in a<br />

cross-validation study. First results are referred.<br />

4159.4 Psychological characteristics <strong>of</strong> dispersed and mobile work, Matti Vartiainen, Marko<br />

Hakonen, Niina Kokko, Helsinki University <strong>of</strong> Technology, Finland<br />

The number <strong>of</strong> mobile employees and dispersed organizational units is increasing rapidly. Work in<br />

these units can be compared to co-located work by using seven job requirement categories: task<br />

complexity, distance, mobility, synchronity, temporariness, diversity and mode <strong>of</strong> interaction.<br />

Working in extreme ends <strong>of</strong> these dimensions creates new challenges to the theories and models <strong>of</strong><br />

organizational behaviour. New challenges are studied by interview and questionnaire data<br />

collected from about sixteen dispersed teams and projects. Differences in internal processes and<br />

group climate <strong>of</strong> these units are compared based on their differences in the virtual work index<br />

made by combining seven categories.<br />

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