Need for affiliation as a motivational add-on for leadership behaviors and managerial success

Steinmann B, Ötting SK, Maier GW. Need for affiliation as a motivational add-on for leadership behaviors and managerial success. Frontiers in Psychology. 2016;7: 1972.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

A user study on personalized stiffness control and task specificity in physical Human-Robot Interaction

Gopinathan S, Ötting SK, Steil JJ. A user study on personalized stiffness control and task specificity in physical Human-Robot Interaction. Frontiers in Robotics and AI. 2017;4: 58.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 gravitation compensation controllers on the 7-DOF KUKA LWR IVb by employing two typical joint-manipulation 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 differences 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

Gerechtigkeit in flexiblen Arbeits- und Managementprozessen

Engels G, Maier GW, Ötting SK, Steffen E, Teetz A. Gerechtigkeit in flexiblen Arbeits- und Managementprozessen. In: Wischmann S, Hartmann EA, eds. Zukunft der Arbeit. Eine praxisnahe Betrachtung. Autonomik Industrie 4.0. Berlin: Springer Vieweg; 2018: 221-231

Artificial intelligence as colleague and supervisor: Successful and fair interactions between intelligent technologies and employees at work

Ötting SK. Artificial intelligence as colleague and supervisor: Successful and fair interactions between intelligent technologies and employees at work. Bielefeld: Universität Bielefeld; 2021.Employees increasingly share workplaces and tasks with artificial intelligence (AI). Intelligent technologies have been developing so rapidly that they can take on the role of a co-worker (e.g., a robot that works in a shared workspace) or even a supervisor (e.g., an algorithm that makes decisions). Both types of relations between AI and employee affect employee motivation, well-being, and performance. In three studies, the present work therefore examines AI as robotic co-workers and as supervisors. More specifically, I investigated which robot design features make human-robot interaction (HRI) at work most successful and how and why effects of procedural justice differ depending on whether humans or AI act as decision agent. In Study 1, we focussed on AI as co-worker and meta-analytically integrated 81 studies on the relation of five robot design features (i.e., feedback and visibility of the interface, adaptability and autonomy of the controller, and human likeness of the appearance) with seven indicators of successful HRI (i.e., task performance, cooperation, satisfaction, acceptance, trust, mental workload, and situation awareness). Results showed that the features of interface and controller significantly affected successful HRI, while human likeness did not. Moderation analyses revealed that only design features of the controller had significant specific effects in addition to those on task performance and satisfaction: Adaptability affected cooperation and acceptance, and autonomy affected mental workload. In Studies 2 and 3, we focussed on AI as supervisor and examined and compared procedural justice effects of human and AI decision agents on employee attitudes and behaviour. To this end, we conducted two vignette experiments in each study. In Study 2, we investigated whether the type of decision agent (human vs. AI) influenced the effects of procedural justice on employee attitudes and behaviour. The results showed no differences in effect sizes between humans or AI as decision agent, emphasising the importance of procedural justice for both decision agents. In Study 3, we compared strength and specificity of four mediators of procedural justice effects, investigated differences between decision agents and examined responsibility as explaining mechanism for these differences. The results for both types of decision agents showed trust as strongest mediator for effects on attitudes, and negative affect as strongest mediator for effects on behaviour. When comparing the two types of decision agents, trust as mediator was less pronounced for AI compared to human decisions, whereas no difference between the two types of decision agents was found for negative affect. Additionally, we confirmed the responsibility that is attributed to a decision agent as underlying mechanism for these differences. In summary, the present work extends the understanding of employee interactions with AI as co-worker and supervisor at work by integrating theories from industrial and organisational psychology as well as engineering and information science. The results provide valuable insights for theory development in HRI and organisational justice concerning the integration and investigation of context factors, of effects of robot design characteristics on successful HRI and of characteristics of the decision agents that might influence justice effects. Moreover, the results provide recommendations for engineers, AI designers and human resource practitioners on what to bear in mind when planning to develop and implement AI in the workplace

Organizational justice in the brave new world of work: Developing successful flexible working environments

Ötting SK, Maier GW. Organizational justice in the brave new world of work: Developing successful flexible working environments. Presented at the 9th International Conference on Researching Work & Learning, Singapur

Faire Mensch-Maschine-Interaktion: Intelligente technische Systeme als Entscheidungsquelle und deren Einfluss auf prozedurale Gerechtigkeit und arbeitsrelevante Reaktionen

Ötting SK, Maier GW. Faire Mensch-Maschine-Interaktion: Intelligente technische Systeme als Entscheidungsquelle und deren Einfluss auf prozedurale Gerechtigkeit und arbeitsrelevante Reaktionen. Presented at the 50. Kongress der DGPs, Leipzig

The importance of procedural justice in human-machine interactions: Intelligent systems as new decision agents in organizations

Ötting SK, Maier GW. The importance of procedural justice in human-machine interactions: Intelligent systems as new decision agents in organizations. Computers in Human Behavior. 2018;89:27-39.In the present study, the effects of procedural justice (fair or unfair) and the type of decision agent (human, robot, or computer) on employee behavior and attitudes (e.g., job satisfaction, organizational citizenship behavior, or counterproductive work behaviors) were examined. It was predicted that the type of decision agent (or the source of justice) would moderate the relationship between procedural justice and employee behavior and attitudes, with the relationship being strongest when the decision agent is a human team leader, medium when the decision agent is a humanoid robot, and weakest when the agent is a computer system. This research question was investigated with a between-subjects design in two experiments (N1 = 149 and N2 = 145) that displayed two different decision situations in organizations (allocation of new tasks and allocation of further vocational trainings). Results of both studies showed significant effects of procedural justice on employee behavior and attitudes, confirming the importance of procedural justice at the workplace for both human and system decision agents. Furthermore, both studies failed to verify any interaction effects of procedural justice and the decision agent. This further emphasizes the importance of procedural justice in decision situations because there is no difference in reactions to procedural justice of human or system decisions. Limitations and implications for future research and the integration of justice and human–machine interaction research are discussed

Arbeit 4.0: Faire Gestaltung der digitalen Arbeitswelt

Ötting SK, Maier GW. Arbeit 4.0: Faire Gestaltung der digitalen Arbeitswelt. supervision. 2016;34(4):19-24

The importance of procedural justice in human-technology interactions. Cyber-physical systems as new decision authorities

Ötting SK, Maier GW. The importance of procedural justice in human-technology interactions. Cyber-physical systems as new decision authorities. Presented at the 17th Congress of the European Association of Work and Organizational Psychology, Dublin, Ireland

Affiliation makes the difference! Die Bedeutung des impliziten Anschlussmotivs zur Vorhersage erfolgreicher Führung im Zusammenspiel mit Macht und Leistung

Steinmann B, Ötting SK, Maier GW. Affiliation makes the difference! Die Bedeutung des impliziten Anschlussmotivs zur Vorhersage erfolgreicher Führung im Zusammenspiel mit Macht und Leistung. Presented at the 9. Fachgruppentagung Arbeits-, Organisations- und Wirtschaftspsychologie, Mainz