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TRUVACE RECORD VERSION
record: TRV-2026-1250
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-02T14:18:41.743053Z
status: published
lens: g_space
sector: science
headline: Beyond replacement: human-machine collaboration in the age of AI
dek: Purpose The purpose is to advance the understanding of human-machine (H-M) collaboration in service industries, conceptualizing a framework that structures the research space and proposing a research agenda to guide future studies on optimizing collaboration dynamics, outcomes and ethical governance. Design/methodology/approach The authors use an artificial intelligence (AI)-based systematic literature based on the SERVSIG Literature Alert database to identify articles related to H-M collaboration. Insights from…
gain_title: AI and humans combining capabilities in service settings can achieve joint outcomes through teamwork rather than replacement.
problem_title: (none)
trace_subject: (none)
gain_reading: AI and humans combining capabilities in service settings can achieve joint outcomes through teamwork rather than replacement.
gain_evidence: showing how AI and humans are working together and combining capabilities to achieve outcomes together
problem_reading: (none)
problem_evidence: (none)
quick_read: On 2025-09-17, a peer-reviewed article in Journal of Service Management reported an AI-based systematic review of the SERVSIG Literature Alert database to map human-machine collaboration in service industries. The authors traced the evolution of the field and developed an integrative framework describing foundation, process, and outcomes of H-M teamwork, and mapped special-issue empirical studies to it.

The work matters because it shifts the narrative from human replacement or narrow task automation to a teamwork perspective where capabilities are combined, while foregrounding ethics as cross-cutting. What remains uncertain is how the proposed framework performs when applied to specific service contexts, as the supplied text presents the framework and research agenda without reporting measured implementation outcomes.
limitation: 
tag: Evidence-backed gain
key_points: AI-based systematic literature review used SERVSIG Literature Alert database to identify H-M collaboration articles. | Authors traced evolution of H-M collaboration research and formulated integrative framework spanning foundation through outcomes. | Paper introduces new articles from special issue to demonstrate framework applicability.
rundown: The authors conducted an AI-based systematic literature review of the SERVSIG Literature Alert database to collect studies on human-machine collaboration.

From those studies they traced research evolution and built an integrative framework covering foundational resources, process, and outcomes of H-M teamwork in service settings.

The paper positions ethics as a cross-cutting theme across product, consumer and societal levels, rather than siloed discussions.
sources:
- peer_reviewed | Journal of Service Management | https://doi.org/10.1108/josm-04-2025-0194 | 2025-09-17
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429f87e88fa4e7dfa58702d30248fcb048f0ef44148783d3ee5dbe6af3e64139

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