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In a sample of 70 leader-follower dyads, this study examines the separate and interactive effects of the leaders’ implicit needs for power, achievement, and affiliation on leadership behaviors and outcomes. Results show that whereas the need for achievement was marginally associated with follower-rated passive leadership, the need for affiliation was significantly related to ratings of the leaders’ concern for the needs of their followers. Analyzing motive combinations in terms of interactive effects and accounting for the growing evidence on the value of affiliative concerns in leadership, we assumed the need for affiliation would channel the interplay among the needs for power and achievement in such a way that the leaders would become more effective in leading others. As expected, based on high need for achievement, the followers were more satisfied with their jobs and with their leaders and perceived more transformational leadership behavior if power-motivated leaders equally had a high need for affiliation. Moreover, the leaders indicated higher career success when this was the case. However, in indicators of followers’ performance, the three-way interaction among the needs for power, achievement, and affiliation did not account for additional variance.

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Objective This meta-analysis reviews robot design features of interface, controller, and appearance and statistically summarizes their effect on successful human–robot interaction (HRI) at work (that is, task performance, cooperation, satisfaction, acceptance, trust, mental workload, and situation awareness). Background Robots are becoming an integral part of many workplaces. As interactions with employees increase, ensuring success becomes ever more vital. Even though many studies investigated robot design features, an overview on general and specific effects is missing. Method Systematic selection of literature and structured coding led to 81 included experimental studies containing 380 effect sizes. Mean effects were calculated using a three-level meta-analysis to handle dependencies of multiple effect sizes in one study. Results Sufficient feedback through the interface, clear visibility of affordances, and adaptability and autonomy of the controller significantly affect successful HRI, whereas appearance does not. The features of the interface and controller affect performance and satisfaction but do not affect situation awareness and trust. Specific effects of adaptability on cooperation and acceptance, as well as autonomy on mental workload, could be shown. Conclusion Robot design at work needs to cover multiple features of interface and controller to achieve successful HRI that covers not only performance and satisfaction, but also cooperation, acceptance, and mental workload. More empirical research is needed to investigate mediating mechanisms and underrepresented design features’ effects. Application Robot designers should carefully choose design features to balance specific effects and implementation costs with regard to tasks, work design aims, and employee needs in the specific work context.

An ideal physical human-robot interaction (pHRI) should offer the users robotic systems that are easy to handle, intuitive to use, ergonomic and adaptive to human habits and preferences. But the variance in the user behavior is often high and rather unpredictable, which hinders the development of such systems. This article introduces a Personalized Adaptive Stiffness controller for pHRI that is calibrated for the user's force profile and validates its performance in an extensive user study with 49 participants on two different tasks. The user study compares the new scheme to conventional fixed stiffness or gravi tation compensation controllers on the 7DOF KUKA LWR IVb by employing two typical jointmanipulation tasks. The results clearly point out the importance of considering task specific parameters and human specific parameters while designing control modes for pHRI. The analysis shows that for simpler tasks a standard fixed controller may perform sufficiently well and that respective task dependency strongly prevails over individual differences. In the more complex task, quantitative and qualitative results reveal differ ences between the respective control modes, where the Personalized Adaptive Stiffness controller excels in terms of both performance gain and user preference. Further analysis shows that human and task parameters can be combined and quantified by considering the manipulability of a simplified human arm model. The analysis of user's interaction force profiles confirms this finding.