Brave new world: service robots in the frontline
Open accessAbstract
Purpose: The service sector is at an inflection point with regard to productivity gains and service industrialization similar to the industrial revolution in manufacturing that started in the eighteenth century. Robotics in combination with rapidly improving technologies like artificial intelligence (AI), mobile, cloud, big data and biometrics will bring opportunities for a wide range of innovations that have the potential to dramatically change service industries. The purpose of this paper is to explore the potential role service robots will play in the future and to advance a research agenda for service researchers. Design/methodology/approach: This paper uses a conceptual approach that is rooted in the service, robotics and AI literature. Findings: The contribution of this paper is threefold. First, it provides a definition of service robots, describes their key attributes, contrasts their features and capabilities with those of frontline employees, and provides an understanding for which types of service tasks robots will dominate and where humans will dominate. Second, this paper examines consumer perceptions, beliefs and behaviors as related to service robots, and advances the service robot acceptance model. Third, it provides an overview of the ethical questions surrounding robot-delivered services at the individual, market and societal level. Practical implications: This paper helps service organizations and their management, service robot innovators, programmers and developers, and policymakers better understand the implications of a ubiquitous deployment of service robots. Originality/value: This is the first conceptual paper that systematically examines key dimensions of robot-delivered frontline service and explores how these will differ in the future.
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- Authors
- Wirtz, J., Patterson, P. G., Kunz, W. H., Gruber, T., Lu, V. N., Paluch, S., & Martins, A.
- Affiliations
- National University of Singapore (SG)
- UNSW Sydney (AU)
- University of Massachusetts Boston (US)
- Loughborough University (GB)
- Australian National University (AU)
- RWTH Aachen University (DE)
- The University of Queensland (AU)
- Corresponding author
- Wirtz, Jochen
- ORCID
- Venue
- Journal of Service Management
- Publisher
- Emerald
- Published
- 2018-09-26
- Volume
- 29
- Issue
- 5
- Pages
- 907-931
- ISSN
- 1757-5818 (ISSN-L)
- OpenAlex
- W2894528744
- Cited by
- 2,315
- References
- 77
- Citations by year
- 2026: 374 · 2025: 430 · 2024: 393 · 2023: 359 · 2022: 313 · 2021: 193 · 2020: 179 · 2019: 68 · 2018: 6
- FWCI
- 62.76
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of service management (publishedVersion, cc-by)
- UNSWorks (University of New South Wales, Sydney, Australia) (submittedVersion, cc-by-nc-nd)
- Loughborough University Institutional Repository (Loughborough University) (submittedVersion, cc-by)
- Loughborough University Research Repository (Loughborough University) (submittedVersion, cc-by)
- ANU Open Research (Australian National University) (submittedVersion, cc-by)
- RWTH Publications (RWTH Aachen) (submittedVersion)
- National University of Singapore (submittedVersion)
- Funders
- University of Nottingham
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Physical Sciences › Engineering › Control and Systems Engineering › Robotics and Automated Systems
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Robotic Process Automation Applications
- Keywords
- service robots, frontline services, industrialization of services, artificial intelligence, human-robot comparison, service innovation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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