W7171701948 Working papers AI × management and organizations

Human-AI Teaming on the Factory Floor: Cognitive Load, Trust Calibration, and Productivity Outcomes

Ali Sadhik Shaik

Also recorded as: doi:10.2139/ssrn.6638418 · ssrn:6638418

ssrn abstract page · 2026-05-06

Recorded claims
1
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Evidence classes
1
ssrn-abstract-page
Recorded limitations
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Recorded claims

Each claim states what the inspected source says, at the recorded location—bounded by its scope and graded by its confidence. Nothing here is a synthesis across works.

Drawing on human-factors literature, manufacturing field evidence, and practitioner experience, the paper identifies over-reliance and under-reliance as a principal failure mode of shop-floor AI and proposes seven interface-design principles intended to calibrate operator trust.

confidence: moderate ssrn-abstract-page apw-e1-ssrn-backfill

Scope: Manufacturing shop-floor decision support in automotive and electronics settings; the abstract provides no study universe, sample size, observation period, comparison denominator, or effect estimate

Structured relationships

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Boundaries

Limitations & independence

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Recorded limitations

  • Abstract-level evidence only; full text and PDFs were not inspected.
  • The abstract does not define the field-evidence sources, cases, sample, period, measures, or analytic method.
  • Its statement that implementation design correlates more strongly with miscalibration than model accuracy lacks a reported coefficient, denominator, or statistical test on the abstract page.

Source independence

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