Agent skill · Data & Analytics

jom-theory-development

Use when building the theoretical argument and hypotheses for a Journal of Operations Management (JOM) empirical study — deriving operations/behavioral mechanisms a priori, borrowing and adapting reference theory to an OM phenomenon, and specifying mediation/moderation so they are testable against observed operations data.

brycew6m4,252★ · +31/wk · 3 repos on radarProfile →
claude-codeMIT
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jom-theory-development --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 6 KB
Bundled scripts: none
Path: Journal-of-Operations-Management-Skills/skills/jom-theory-development/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 909 · +31 this week
Language: Stata
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Theory Development for Empirical OM (jom-theory-development) ## When to trigger - Your hypotheses describe what happens but not *why*, operationally - You are importing a reference theory (TCE, RBV, agency, institutional, behavioral decision theory, contingency, queueing/coordination logic) and must adapt it to an operations setting - A reviewer says "this is a correlation, not a mechanism" or "the theory could apply to any context" - You need to specify mediators/moderators that operations data can actually identify ## JOM's theory bar JOM is empirical, but empirical strength without theory reads as a technical report. The argument must give an **operations mechanism** — a causal logic rooted in how work, capacity, inventory, information, incentives, or human behavior in operations actually function — not a generic management story bolted onto an OM dataset. Because JOM explicitly excludes purely analytical/optimization work, your theory is *verbal and falsifiable*, developed to be tested against **observation**, not derived as an optimization proof. ## Build the mechanism a priori 1. **Name the operations phenomenon and its level** (task, shift, line, plant, project, dyad, supp

What's inside
Steps it walks through
  1. When to trigger
  2. JOM's theory bar
  3. Build the mechanism a priori
  4. Behavioral vs. operational vs. organizational logic
  5. Mediation / moderation
  6. Anti-patterns
  7. Matching the mechanism engine to the evidence
  8. Desk-reject and return triggers on theory
  9. Worked vignette: building an operations mechanism a priori
  10. Theory objections and the mechanism-grounded fix
  11. Output format
More from Awesome-Journal-Skills
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About this skill
What does the jom-theory-development skill do?

Use when building the theoretical argument and hypotheses for a Journal of Operations Management (JOM) empirical study — deriving operations/behavioral mechanisms a priori, borrowing and adapting reference theory to an OM phenomenon, and specifying mediation/moderation so they are testable against observed operations data.

How do I install it?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jom-theory-development --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going