Variance-Aware LLM Annotation for Strategy Research: Sources, Diagnostics, and a Protocol for Reliable Measurement

• URL: http://arxiv.org/abs/2601.02370v3
• Date: Mon, 19 Jan 2026 11:23:39 GMT
• Title: Variance-Aware LLM Annotation for Strategy Research: Sources, Diagnostics, and a Protocol for Reliable Measurement
• Authors: Arnaldo Camuffo, Alfonso Gambardella, Saeid Kazemi, Jakub Malachowski, Abhinav Pandey,
• Abstract summary: Large language models (LLMs) offer strategy researchers powerful tools for annotating text at scale.<n>But treating LLM-generated labels as deterministic overlooks substantial instability.<n>We diagnose five variance sources: construct specification, interface effects, model preferences, output extraction, and system-level aggregation.
• Score: 0.3228822469249803
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) offer strategy researchers powerful tools for annotating text at scale, but treating LLM-generated labels as deterministic overlooks substantial instability. Grounded in content analysis and generalizability theory, we diagnose five variance sources: construct specification, interface effects, model preferences, output extraction, and system-level aggregation. Empirical demonstrations show that minor design choices-prompt phrasing, model selection-can shift outcomes by 12-85 percentage points. Such variance threatens not only reproducibility but econometric identification: annotation errors correlated with covariates bias parameter estimates regardless of average accuracy. We develop a variance-aware protocol specifying sampling budgets, aggregation rules, and reporting standards, and delineate scope conditions where LLM annotation should not be used. These contributions transform LLM-based annotation from ad hoc practice into auditable measurement infrastructure.

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