Persona Migration and Expectation Recalibration in Generative AI Adoption: A Longitudinal Study at a State Department of Transportation

2026-07-15

AI Summary

This study addresses the empirical gap in understanding how public-sector employees’ acceptance of generative AI dynamically evolves following actual usage. Through an eight-week pilot deployment of Microsoft 365 Copilot, complemented by pre- and post-intervention surveys and longitudinal tracking, the research innovatively constructs a transition analysis framework grounded in baseline personas—Skeptics, Cautiously Positive, and Champions—to uncover mechanisms of expectation recalibration and attitudinal change. Employing a mixed-methods approach integrating k-means clustering, fixed-centroid assignment, non-parametric tests, and keyword mapping, the findings reveal a significant decline in perceived usefulness: 40% of Skeptics shifted toward cautious acceptance, while 68% of Champions exhibited waning enthusiasm. Usage predominantly centered on communication and summarization tasks, with reduced engagement in data-related applications. Although concerns about accuracy diminished, anxieties regarding skill displacement intensified.

Abstract

Generative AI tools are increasingly being piloted in public agencies, but limited evidence explains how employee acceptance changes after hands-on use. This study examines Microsoft 365 Copilot adoption during an eight-week pilot at a state Department of Transportation. A matched two-wave survey measured perceived usefulness, perceived ease of use, behavioral intention, and trust before and after participation. After matching and response-quality screening, the sample included 124 employees. Nonparametric tests assessed aggregate changes, k-means clustering identified baseline acceptance personas, and fixed-centroid assignment tracked migration. Open-ended responses were examined using keyword-based content mapping. Perceived usefulness declined significantly after use, suggesting recalibration of expectations, while perceived ease of use, behavioral intention, and trust showed only small, nonsignificant changes. Three baseline personas emerged: Skeptics, Cautiously Positive users, and Champions. Although persona counts changed modestly, individual movement was substantial: 40 percent of Skeptics moved to Cautiously Positive, while 68 percent of Champions moved to less enthusiastic personas. Upward movement was associated with gains in usefulness, behavioral intention, and trust; downward movement was associated with declines in usefulness and trust. Communication and summarization remained stable use cases, while data, chart, and presentation tasks declined. Accuracy and privacy concerns decreased, but job and skills concerns increased. Public-sector AI adoption should be monitored dynamically and supported through persona-specific training, workflow examples, verification routines, and trust-calibration safeguards. The study offers a framework for tracking workforce heterogeneity during enterprise generative AI implementation.

Problem

• Generative AI adoption
• User acceptance
• Expectation recalibration
• Persona migration
• Public sector

Innovation

• persona migration
• expectation recalibration
• longitudinal AI adoption
• acceptance heterogeneity
• generative AI implementation