Motivation Mix and Agency Reputation: A Person-Centered Study of Public-Sector Workforce Composition

Research output: Contribution to journalArticlepeer-review

Abstract

Identifying what motivates public servants and how those motives vary across agencies is essential for both theory and practice, yet most existing “types of bureaucrats” remain untested against real workforces. Drawing on reputation theory, which posits that external audiences’ beliefs shape who seeks and retains employment in an organization, we theorize that agency reputation will systematically sort employees into distinct motivational profiles. We analyze survey data from 13,471 U.S. federal employees merged with an externally derived, 40-year measure of agency reputation based on congressional speeches. A multi-level latent class analysis uncovers four robust motivation types—All-rounders (35%), intrinsically focused Job-motivated (25%), Self-interested (24%), and Amotivated (16%)—and two clusters of agencies distinguished by their profile mix. Reputational standing predicts profile membership: employees in highly reputed agencies are significantly more likely to be Job-motivated and less likely to be Self-interested or Amotivated, consistent with self-selection and socialization mechanisms highlighted in the extant literature. These findings validate classic typologies while demonstrating the value of integrating organizational-level reputation into motivation research, and they imply that recruiting and retention strategies should be tailored to the reputational context of each agency.

Original languageEnglish
Article number353
JournalAdministrative Sciences
Volume15
Issue number9
DOIs
StatePublished - Sep 2025

Keywords

  • bureaucratic motivation
  • bureaucratic reputation
  • multi-level latent class analysis
  • person-centered approach
  • public management

Fingerprint

Dive into the research topics of 'Motivation Mix and Agency Reputation: A Person-Centered Study of Public-Sector Workforce Composition'. Together they form a unique fingerprint.

Cite this