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TRUVACE RECORD VERSION
record: TRV-2026-2689
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:01:15.990638Z
status: published
lens: g_space
sector: business
headline: Human–machine collaboration and algorithmic decision-making: The role of organizational incentives and AI capabilities
dek: Despite growing investment in artificial intelligence (AI), organizations have yet to realize its potential for complex decision-making. We draw on the absorptive capacity (ACAP) view to conceptualize AI systems as organizational knowledge structures and incentive policies as knowledge transfer mechanisms, examining their effects on human–machine collaboration over time. We conduct a randomized controlled experiment wherein financially incentivized participants evaluate 100 loan applications with AI assistance o…
gain_title: In a randomized controlled experiment where paid participants evaluated 100 loan applications with AI assistance over 10 decision rounds, human-AI joint performance improved as experience accumulated.
problem_title: (none)
trace_subject: (none)
gain_reading: In a randomized controlled experiment where paid participants evaluated 100 loan applications with AI assistance over 10 decision rounds, human-AI joint performance improved as experience accumulated.
gain_evidence: human–AI joint performance improves as experience accumulates
problem_reading: (none)
problem_evidence: (none)
quick_read: A randomized controlled experiment asked financially incentivized participants to evaluate 100 loan applications with AI assistance across 10 decision rounds. Joint human-AI performance improved as participants gained experience.

The study varied two factors: how accurate the AI's predictions were and how well incentives were aligned with organizational goals. Both affected outcomes on their own and in combination. Incentive alignment mattered most when the AI's predictive power was low.

The authors read this as a sign that aligned incentives may cushion organizations when AI performance degrades. They also report that cognitive capabilities, trust in AI, algorithm integration and decision quality shaped how individuals engaged with organizational learning processes.
limitation: 
tag: Evidence-backed gain
key_points: Participants were financially incentivized and evaluated 100 loan applications with AI assistance over 10 rounds. | The design was a randomized controlled experiment testing AI predictive power and incentive alignment. | Both factors influenced outcomes independently and interactively. | Incentive alignment mattered most when AI predictive power was low. | Human-AI joint performance improved as experience accumulated over the rounds. | Cognitive capabilities, AI trust, algorithm integration and decision quality shaped individual engagement with organizational learning processes.
rundown: The researchers draw on the absorptive capacity view, treating AI systems as organizational knowledge structures and incentive policies as mechanisms that transfer knowledge between people and algorithms. They examine how collaboration changes over time rather than at a single point.

In the experiment, financially incentivized participants assessed loan applications with AI help. The setup let the team vary AI predictive power and incentive alignment and observe their separate and combined effects on organizational outcomes.

The interaction result is that incentive alignment mattered most when the AI's predictive power was low. The authors suggest aligned incentives may buffer organizations against the negative effects of AI degradation, and argue human-machine collaboration could become a new source of organizational learning.
sources:
- peer_reviewed | Information & Management | https://doi.org/10.1016/j.im.2026.104384 | 2026-06-01
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