Although group project concepts and skills have become a major component in most information systems (IS) academic programs, very little research has attempted to examine factors that may improve or undermine effectiveness of IS group projects. Accordingly, based on relevant literatures, this study develops and empirically tests a model of factors affecting IS group project effectiveness. The research model posits that group cohesion and group efficacy will have positive effects on group effectiveness (project success and expected impact), whereas perceived loafing is expected to have a negative effect on IS group effectiveness. Data collected from 104 students working in 29 groups to complete semester-long projects in two IS courses revealed that group efficacy had positive impact on group effectiveness and perceived loafing demonstrated a partial effect. Contrary to expectations, the impact of group cohesion was nonsignificant. These results could be useful in evaluating groups' potential for success and creating conditions conducive to enhancing effectiveness and success of IS student group projects.

Purpose -Aims to demonstrate the importance of reporting IS management constructs rather than reporting and ranking the individual management issues; determine whether the ratings of IS management factors differ across organizational and personal variables; and benchmark the position of Kuwait's results on dimensionality and determinants of IS management issues with that of other previous studies. Design/methodology/approach -This field study considered only the opinions of the highest ranked executives of the IS functions within their organizations. A seven-page structured interview guide was used for data collection. Principal component factor analysis was performed on the issue ratings in order to determine underlying IS management factors. Confirmatory factor analysis was performed to further assess how well the factors fit the issue data and to test the fit of the resulting factor model. Finally, t-tests were performed to test whether the differences between factors were significant in order to demonstrate the discriminatory value of reporting IS management factor areas rather than individual issues. Findings -The key IS management factors identified by IS managers are the effective management of IS resources such as data, networks and applications; and managers' knowledge of IS. This study also found that most situational variables including nationality are not associated with differences in IS management factor ratings. Thus, the survey results are consistent across different types of organizations and respondents. The exception is organization size and IS department size. Size differences can lead to different opinions on the relative importance of various IS management factors. Originality/value -To demonstrate the importance of reporting IS management factors (constructs) as a benchmarking framework rather than reporting and ranking the individual management issues, and to use the derived conceptual benchmarking model to determine whether the ratings of IS management factors differ across organizational and personal variables.

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The authors present a new method of recognizing different human facial gestures through their neural activities and muscle movements, which can be used in machine-interfacing applications. Human–machine interface (HMI) technology utilizes human neural activities as input controllers for the machine. Recently, much work has been done on the specific application of facial electromyography (EMG)-based HMI, which have used limited and fixed numbers of facial gestures. In this work, a multipurpose interface is suggested that can support 2–11 control commands that can be applied to various HMI systems. The significance of this work is finding the most accurate facial gestures for any application with a maximum of eleven control commands. Eleven facial gesture EMGs are recorded from ten volunteers. Detected EMGs are passed through a band-pass filter and root mean square features are extracted. Various combinations of gestures with a different number of gestures in each group are made from the existing facial gestures. Finally, all combinations are trained and classified by a Fuzzy c-means classifier. In conclusion, combinations with the highest recognition accuracy in each group are chosen. An average accuracy >90% of chosen combinations proved their ability to be used as command controllers.

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