Profiling Employee Time Bandits: Weasels, Mercenaries, Sandbaggers, and Parasites

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8562416557_4eb71bbab7_zNothing is more counterproductive for organizations than when employees use work time to engage in non-task-related activities. That said, time banditry is widespread and sometimes difficult for organizations prevent. A recent article published in Journal of Leadership & Organizational Studies, entitled “Time Banditry and Impression Management Behavior: Prediction and Profiling of Time Bandit Types,” authors Meagan E. Brock Baskin, Victoria McKee, and M. Ronald Buckley investigate the characteristics of time bandits. Their paper outlines situational and dispositional variables that can help predict the four time bandit types–the weasel, the mercenary, the sandbagger, and the parasite. The abstract for the article:

Time banditry recently has been introduced as a distinct construct in the JLOcounterproductive work behavior literature. Employees are engaged in time banditry when they pursue non–task-related activities during work time. We posit that they capitalize on the ambiguity in most work environments to manage impressions that their time banditry behavior really is productive and not counterproductive work behavior. In this investigation, two studies were conducted to explore variables that can be used to classify time bandits into four different categories. Discriminant function analysis was used to determine individual-level and job-level factors that classify time bandits. Results revealed that both situational and dispositional variables can be used to predict time bandit type. Suggestions for future research and implications for managing, reducing, and changing time banditry behaviors are discussed.

Interested in reading more and finding out what kind of time bandit you are? You can read “Time Banditry and Impression Management Behavior: Prediction and Profiling Time Bandit Types” from Journal of Leadership and Organizational Studies free for the next two weeks by clicking here.

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*Post-it prank image attributed to Michael Arrighi (CC)

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