Accelerating Mission Engineering: Evidence of a Stratified Landscape and a Path Toward Unified Practice

ABSTRACT Mission Engineering is maturing as a Systems Engineering discipline, yet adoption of its common frameworks remains stratified. This study examines whether selective adoption is observable across ME scholarship and develops a framework to address identified barriers. We analyze 42 self‐identifying ME publications (2014–2025) using a 24‐term rubric derived from Department of War guidance, constructing lexical similarity networks, coauthorship maps, and coherence baselines to test predictions from Kuhn's paradigm competition against Rogers’ diffusion of innovations. Results show a binary stratification between papers engaging with Mission Engineering Guide (MEG)‐coined terminology and those that do not (Cohen's = 1.93), with no meaningful community modularity detected across 102 community detection runs ( = 0.212 < 0.30). Non‐engaged papers are terminologically indistinguishable from random subsets, supporting Rogers’ single‐framework diffusion model and indicating that adoption barriers arise from implementation friction rather than competing theoretical alternatives. These findings identify process complexity, limited trialability, and weak interpersonal collaboration as likely barriers. In response, we present a formalized ten‐step ME process with modular, artifact‐producing steps and explicit mathematical specifications spanning operational, functional, capability, and system domains. This enables independent execution, reproducible evaluation, and standardized outputs, increasing trialability, reducing integration complexity, and supporting interoperability and broader adoption.

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Publication Details

Journal
Systems Engineering
Published
2026-06-17
DOI
https://doi.org/10.1002/sys.70076
Primary Topic
Systems Engineering Methodologies and Applications
Type
article
Field-Weighted Citation Impact
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article

Accelerating Mission Engineering: Evidence of a Stratified Landscape and a Path Toward Unified Practice

Casey LaMar, James Moreland, Craig Sawyer, Maegen Nix et al.
Systems Engineering
Systems Engineering Methodologies and Applications
article

Accelerating Mission Engineering: Evidence of a Stratified Landscape and a Path Toward Unified Practice

Casey LaMar, James Moreland, Craig Sawyer, Maegen Nix, Nathaniel Ambler
article en

Abstract

ABSTRACT Mission Engineering is maturing as a Systems Engineering discipline, yet adoption of its common frameworks remains stratified. This study examines whether selective adoption is observable across ME scholarship and develops a framework to address identified barriers. We analyze 42 self‐identifying ME publications (2014–2025) using a 24‐term rubric derived from Department of War guidance, constructing lexical similarity networks, coauthorship maps, and coherence baselines to test predictions from Kuhn's paradigm competition against Rogers’ diffusion of innovations. Results show a binary stratification between papers engaging with Mission Engineering Guide (MEG)‐coined terminology and those that do not (Cohen's = 1.93), with no meaningful community modularity detected across 102 community detection runs ( = 0.212 < 0.30). Non‐engaged papers are terminologically indistinguishable from random subsets, supporting Rogers’ single‐framework diffusion model and indicating that adoption barriers arise from implementation friction rather than competing theoretical alternatives. These findings identify process complexity, limited trialability, and weak interpersonal collaboration as likely barriers. In response, we present a formalized ten‐step ME process with modular, artifact‐producing steps and explicit mathematical specifications spanning operational, functional, capability, and system domains. This enables independent execution, reproducible evaluation, and standardized outputs, increasing trialability, reducing integration complexity, and supporting interoperability and broader adoption.

Systems Engineering
United States Naval Research Laboratory (US), United States Department of the Navy (US), American Research Corporation of Virginia (United States) (US), Virginia Tech (US)
Openalex Percentile: Top 8%
Systems Engineering Methodologies and Applications
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