Beyond googly eyes: stakeholder perceptions of robots in construction

Author:

Walzer Alexander N.ORCID,Kahlert Aniko,Baumann Micha,Uhlmann Melanie,Vasey Lauren,Hall Daniel M.

Abstract

AbstractThe interest in advanced robotic equipment in construction has increased in recent years. However, actual industry adoption lags behind—and fundamental considerations might be at fault. To date, little scholarship in Architecture, Engineering and Construction (AEC) addresses the stakeholder perception of construction robot design. Therefore, we ask, “How do visual attributes of a construction robot influence the perception of AEC stakeholders?” To conduct our study, we performed a bibliometric analysis on a corpus of 59 scholarly research articles, 5 expert interviews and created and pre-validated a robot database of 50 robot pictures classified on their visual attributes of morphology, color and material. As a result, we present a study with 161 construction professionals who judged these robots based on three visual main criteria: ease of use, work task adaptability and risk of job loss. In total, more than 6500 data points are collected and analyzed using binary logistic regression. The five key findings are that construction professionals perceive that: (1) Zoomorphic (animal-like) robots are easier to use than anthropomorphic (human-like) or mechanomorphic (machine-like) robots, (2) Bright robots are easier to use than dark robots, (3) Zoomorphic and anthropomorphic robots are more multifunctional than mechanomorphic robots, (4) Anthropomorphic and mechanomorphic robots are more of a risk to job loss than zoomorphic robots, and (5) Dark robots are more of a risk to job loss than bright ones. These results are important for academics and practitioners that aim to increase the likelihood of positive stakeholder perception of robots in construction. The findings can further help to develop specific user-centered design principles. Such implementation can reduce the risk of construction professionals rejecting future robots when they are introduced at the AEC job site.

Funder

Swiss Federal Institute of Technology Zurich

Publisher

Springer Science and Business Media LLC

Subject

General Medicine

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