Kundenorientierte Mitarbeiter : ein neuer Ansatz für Führungskräfte

In dem hier vorgestellten Ansatz für Führungskräfte wird ein umfassendes Spektrum von Maßnahmen vorgestellt, die sich im Kern auf zwei Dinge konzentrieren: die Steigerung der kundenorientierten Einstellung und des kundenorientierten Verhaltens einzelner Mitarbeiter

Führungsverhalten als Einflussgröße der Kundenorientierung von Mitarbeitern : ein dreidimensionales Konzept

Kundenorientierung von Mitarbeitern hat in den letzten Jahren zunehmendes Interesse in Wissenschaft und Praxis auf sich gezogen. In diesem Kontext hat man sich mit der Identifikation von Einflussgrößen der Kundenorientierung beschäftigt, die wiederum Ansatzpunkte zur Steigerung der Kundenorientierung der Mitarbeiter darstellten. Eine zentrale Einflussgröße der Kundenorientierung stellt das Führungsverhalten dar. Der vorliegende Beitrag konzentriert sich auf die Konzeptualisierung des Führungsverhaltens. Dabei wird zwischen drei Dimensionen unterschieden: Leistungsorientierung, Mitarbeiterorientierung und Kundenorientierung. Darüber hinaus werden Auswirkungen des Führungsverhaltens untersucht. Die Ergebnisse dieser Studie wurden auf Basis einer Erhebung bei Vertriebsmitarbeitern erzielt, die in zahlreichen Unternehmen des produzierenden Gewerbes sowie des Dienstleistungsbereiches erhoben wurden. Im Ergebnis kann die Präsenz von drei Dimensionen des Führungsverhaltens – Leistungs-, Mitarbeiter- und Kundenorientierung – nachgewiesen werden. Darüber hinaus können unterschiedlich starke Effekte der Führungsverhaltensdimensionen auf die Kundenorientierung festgestellt werden

How to get lost customers back? : a study of antecedents of relationship revival

Most research in the field of customer relationship management has focused on keeping existing customers. However, some companies also systematically address lost customers and try to revive these relationships. This facet of customer relationship management has been largely neglected by academic research. Our study provides a theoretical discussion and an empirical analysis of factors driving the success of relationship revival activities. Drawing on equity theory we find that the customer’s perceived interactional, procedural, and distributive justice with respect to revival activities positively affect his or her revival-specific satisfaction which in turn has a strong impact on revival performance. Furthermore, revival performance depends on customer characteristics (variety seeking, involvement, age), and the overall customer satisfaction with the relationship

Social Influence in Customer-Robot Interactions

This paper focuses on social influence in customer-robot interactions. Drawing on social impact theory and the computers-are-social-actors (CASA) paradigm, we argue that customers\u27 reluctance to provide information to a service robot decreases when other customers exhibit high information disclosure. The effect of demonstrated information disclosure on customers\u27 reluctance to provide information is enhanced by the application of social norms. The results also show that social influence is stronger in customer-robot interactions than in customer-employee interactions. This article demonstrates the potential of social influence to reduce reluctance towards service robots, which has both theoretical and managerial implications. We extend existing research on the imitation of robot behavior with the imitation of user behavior, and discuss the ethical implications of customers mindlessly following other customers in customer-robot interactions

Facial emotion expressions in human-robot interaction: A survey

Facial expressions are an ideal means of communicating one's emotions or intentions to others. This overview will focus on human facial expression recognition as well as robotic facial expression generation. In case of human facial expression recognition, both facial expression recognition on predefined datasets as well as in real time will be covered. For robotic facial expression generation, hand coded and automated methods i.e., facial expressions of a robot are generated by moving the features (eyes, mouth) of the robot by hand coding or automatically using machine learning techniques, will also be covered. There are already plenty of studies that achieve high accuracy for emotion expression recognition on predefined datasets, but the accuracy for facial expression recognition in real time is comparatively lower. In case of expression generation in robots, while most of the robots are capable of making basic facial expressions, there are not many studies that enable robots to do so automatically.Comment: Pre-print version. Accepted in International Journal of Social Robotic

Customer Responses to (Im)Moral Behavior of Service Robots - Online Experiments in a Retail Setting

Service robots play an increasingly important role in the service sector. Drawing on moral psychology research, moral foundations theory as well as the computers-as-social-actors (CASA) paradigm, this experimental study containing of four online experiments examines the extent to which the moral or immoral behavior of a service robot affects customer responses during a service interaction. This study contributes to design science by defining, conceptualizing and operationalizing morality of service robots and developing a corresponding vignette as basis to manipulate (im)moral robotic behavior in a retail setting. To investigate possible effects of the robot’s appearance, we tested our hypotheses with two different robots, i.e., a humanoid robot and an android robot. Results from the online experiment indicate that the (im)moral behavior of service robots at the customer interface has a significant effect on customers’ trust and customers’ ethical concerns towards the robot

Facial Emotion Expressions in Human-Robot Interaction: A Survey

Facial expressions are an ideal means of communicating one's emotions or intentions to others. This overview will focus on human facial expression recognition as well as robotic facial expression generation. In the case of human facial expression recognition, both facial expression recognition on predefined datasets as well as in real-time will be covered. For robotic facial expression generation, hand-coded and automated methods i.e., facial expressions of a robot are generated by moving the features (eyes, mouth) of the robot by hand-coding or automatically using machine learning techniques, will also be covered. There are already plenty of studies that achieve high accuracy for emotion expression recognition on predefined datasets, but the accuracy for facial expression recognition in real-time is comparatively lower. In the case of expression generation in robots, while most of the robots are capable of making basic facial expressions, there are not many studies that enable robots to do so automatically. In this overview, state-of-the-art research in facial emotion expressions during human-robot interaction has been discussed leading to several possible directions for future research

See No Evil, Hear No Evil: How Users Blindly Overrely on Robots with Automation Bias

Recent developments in generative artificial intelligence show how quickly users carelessly adhere to intelligent systems, ignoring systems\u27 vulnerabilities and focusing on their superior capabilities. This is detrimental when system failures are ignored. This paper investigates this mindless overreliance on systems, defined as automation bias (AB), in human-robot interaction. We conducted two experimental studies (N1 = 210, N2 = 438) with social robots in a corporate setting to investigate psychological mechanisms and influencing factors of AB. Particularly, users experience perceptual and behavioral AB with the robot that is enhanced by robot competence depending on task complexity and is even stronger for emotional than analytical tasks. Surprisingly, robot reliability negatively affected AB. We also found a negative indirect-only mediation of AB on robot satisfaction. Finally, we provide implications for the appropriate use of robots to prevent employees from using them as a self-sufficient system instead of a supporting system

Good Robot, Bad Robot: Customer Responses to Norm-Compliant and Norm-Violating Service Robots

Service robots that interact with customers have penetrated various industries. With a basis in social identity theory, this study examines how customers respond to frontline service robots (FSRs) by investigating norm-compliant versus norm-violating behaviors compared with similar behaviors by human frontline employees (FLEs). In experimental studies, a black sheep effect occurs, such that customers downgrade norm-violating FLE behaviors more than similar behaviors by FSRs. They also upgrade norm-compliant behaviors by human FLEs more than those of FSRs. In service failures, this effect manifests as greater anger and frustration toward the FLE. We establish the underlying mechanism driving the black sheep effect: customers assign FSRs to an outgroup but categorize FLEs to their social ingroup, across different service encounters and independent of interaction frequency

How does technology readiness affect the transfer of innovation between the professional domain and the private domain? A longitudinal study

Many researchers have studied innovation in terms of either employee innovation or user innovation, but much less is known about the transfer of innovative behavior between the professional and private domains. This quantitative empirical study investigates the relationship between the development of innovations by user innovators in the private domain and the transfer of the innovation to the professional domain and vice versa. Focusing on technology readiness as a moderator on the relationship between the transfer of innovation and innovative behavior in the other domain, we address the importance of digitization for innovations. The study is relied on the spillover theory and conducted as a longitudinal online survey consists of three consecutive waves over a period of four months. The result of the study demonstrates that employee innovation in the professional domain has an impact on innovative behavior in the private domain and, in reverse causality, user innovation in the private domain has an impact on innovative behavior in the professional domain. The relationship between these spillover effects and innovative behavior is strengthened by the technology readiness of innovators