Virtual humans as co-workers: A novel methodology to study peer effects

https://doi.org/10.1016/j.socec.2018.11.003Get rights and content

Highlights

  • In an immersive virtual environment, we embed a virtual human (VH) as co-worker.

  • Human subjects perform a sorting task observing a low or highly productive VH.

  • Competitive subjects perform better when working with a highly productive peer.

  • Using hand and head-tracking data, we define a measure of careful work.

  • We investigate the extent to which careful work affects performance.

Abstract

We introduce a novel methodology to study peer effects. Using virtual reality technology, we create a naturalistic work setting in an immersive virtual environment where we embed a computer-generated virtual human as the co-worker of a human subject, both performing a sorting task at a conveyor belt. In our setup, subjects observe the virtual peer, while the virtual human is not observing them. In two treatments, human subjects observe either a low productive or a highly productive virtual peer. We find that human subjects rate their presence feeling of “being there” in the immersive virtual environment as natural. Subjects also recognize that virtual peers in our two treatments showed different productivities. We do not find a general treatment effect on productivity. However, we find that competitive subjects display higher performance when they are in the presence of a highly productive peer - compared to when they observe a low productive peer. We use tracking data to learn about the subjects’ body movements. Analyzing hand and head data, we show that competitive subjects are more careful in the sorting task than non-competitive subjects. We also discuss some VR related methodological issues.

Introduction

Peer effects are observed in a plethora of domains, such as financial decision making (Bursztyn et al., 2014), academic success of students (Zimmerman, 2003), or in health-related choices (Trogdon et al., 2008). One important domain where peer effects can occur is the workplace. Workers can influence each other in their work performance when they receive information about or directly observe the respective peer’s performance. How strong is the influence of a “rotten apple” on her peers’ performances? Does a top performer induce fellow workers to high effort levels? Understanding the size and nature of such peer effects allows for a more effective organization of labor by answering important questions, such as whether shifts should be composed of workers with homogeneous or heterogeneous productivities/potentials.

We analyze peer effects in an organizational framework where workers work, side by side, on identical tasks but do not interact in any way, except that one of the (two) workers can observe the other worker’s activity. In particular, workers neither complement nor substitute each other in the production process, and receive a fixed and performance independent wage. Spillovers that may occur in such situations are referred to as “pure” peer effects (Charness, Kuhn, 2011, Falk, Ichino, 2006).

In this study, we introduce a new experimental methodology for testing (pure) peer effects. We create a naturalistic work setting in an immersive virtual environment (abbrev. as IVE), where human subjects work together with peers who are computer-generated virtual humans (abbrev. as VH). Subjects perform a real-effort sorting task with their dominant hand, while at the same time the experimenter has control over the actions of the VH via an algorithm. This control over the VH eliminates the reflection problem Manski (1993): while the VH may influence the human subject, the virtual peer cannot be influenced by the human subject.1

Specifically, we address the question whether and to what extent a subject’s work performance is affected by a peer whose performance can be observed, but where the subject’s performance is unobserved by the peer. To control for heterogeneity in the performance of the specific task at hand, we design an experiment consisting of two phases. In the first phase (the initial phase), subjects perform the sorting task alone. In the second phase (the peer phase), they perform the task in the presence of a peer, working independently from each other, and a worker’s payoff is fixed. In two treatments, we model the subject’s virtual peer either as a low productive or a highly productive worker. We refer to these treatments as the SLOW and the FAST treatment, respectively.

Based on a post-experimental survey, we find that most subjects perceive the virtual peer as human-like and the IVE as naturalistic. Subjects evaluate the VH they observe in the FAST treatment indeed as the more productive one – compared to the VH they watch in the SLOW treatment. This means subjects perceive the difference in the performances of both virtual peers exactly as intended. Furthermore, a significant share of subjects, to which we refer as the “competitive” subjects, reports that they wanted to be more successful than the virtual peer, which we interpret as further evidence that subjects acknowledge and react to the presence of VHs.

Overall, we find no statistically significant peer effect between treatments. Interestingly, competitive subjects display significantly higher work performance in the presence of a high productive virtual peer, compared to when they work in the presence of a low productive peer.

Our methodology allows us to collect tracking data in the IVE, in particular, the position and orientation of a subject’s head and hand. These data allow us to define novel performance measures and refine our findings. Using tracking data, we find that competitive subjects display more careful sorting behavior than non-competitive subjects.

We make two important contributions to the literature. First, we introduce a novel experimental methodology to investigate peer effects in a naturalistic setting, but without losing any experimental control. We refine our findings using the tracking possibilities of VR technology. Second, we contribute to the literature on peer effects, by reporting a peer effect in competitive subjects.

Section snippets

Related literature on peer effects

Peer effects have been attributed to peer pressure which may cause a positive productivity effect, due to the workers’ general dislike of providing lower effort than the peer(s).2 Peer pressure, however, may also induce negative productivity effects, in particular, due to discouragement (Georganas et al., 2015). Peer effects have also been attributed to knowledge spillovers causing a positive peer effect (

Hypotheses

We investigate whether and how a worker’s performance is influenced by the physical presence of another worker, referred to as co-worker/peer, whom the worker can observe performing an identical but otherwise unrelated task. In particular, we ask whether the observable productivity of a peer affects a worker’s own work performance in a setup without any interdependency between co-worker and worker.

History and methodology of VR experiments

In this section, we provide a brief history of experiments conducted in IVEs and some introductory methodological remarks. Following some other scholars, we also refer to them as VR experiments. Behavioral research using VR experiments started in the 1990s in domains such as visual perception, spatial cognition, psychotherapy, education, and learning. For a comprehensive overview of early VR experiments in the 2000s and before, we refer to the book by Blascovich and Bailenson (2011).

The virtual environment

We conduct our experiment in the surround-projection room aixCAVE at the RWTH Aachen University (see Fig. 2). The aixCAVE provides a five-sided IVE with a size of 5.25m × 5.25m × 3.30m (width  ×  length  ×  height). The five projection displays, i.e., the four walls and the floor, enclose the user giving her a 360 ° field of regard in the horizontal plane in the computer-generated environment (Kuhlen, 2014). In our setting, when a human subject enters the aixCAVE, she perceives the virtual

Results: Virtual humans and peer effects

We organize our results in two sections. In Section 6, we analyze performance data similar to those collected in related experimental studies. In Section 7, we perform additional analyses based on performance measures that use tracking data that is easily generated in IVEs.

Performance analysis using tracking data

One of the advantages of conducting experiments in IVEs is that one can collect tracking data. Such data can help to identify determinants of superior performance and might measure the exerted effort more directly than a potentially noisy output measure. In our experiment, we collected data about the subject’s hand position, and about the position and the orientation of the subject’s head. In addition, we obtained data about the exact position of each cube when grabbed, and whether and how it

Conclusion and discussion

We present a novel methodology to study peer effects in the workplace. By incorporating a virtual human (VH) in an immersive virtual environment (IVE) as a peer, we avoid the reflection problem: while the VH is immune to be influenced by the human subject, it may affect the human subject. We find that human subjects rate the IVE and the VH as natural.

We find that competitive subjects perform well in our setting, in particular in the FAST treatment. Whether and to what extent this is due to

Acknowledgments

We thank Jonathan Wendt for his assistance in conducting the experiment, and Lucas Braun for preparing the raw data for the analysis. We also thank Almut Balleer, Robert Böhm, Bernd Irlenbusch, the participants of the seminars in Bamberg, Erfurt, Göttingen, Karlsruhe; the conference participants at the 2015 Economic Science Association Meetings in Sydney, Heidelberg, and Dallas; and the participants of the 15th TIBER Symposium in Tilburg, for their valuable comments. We acknowledge the

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