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jom-tables-figures

Use when building or cleaning the exhibits for a Journal of Operations Management (JOM) empirical manuscript — correlation/descriptive tables, regre…

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Tables & Figures for JOM (jom-tables-figures)

When to trigger

  • Tables are cluttered, inconsistent, or not self-explanatory
  • You need the standard empirical-OM exhibit set (descriptives, correlations, models)
  • An interaction/contingency effect needs a readable plot
  • A field/case/intervention study needs a data-structure or process figure
  • You are reconciling exhibits to APA style for Wiley

The empirical-OM exhibit set

A typical JOM empirical paper carries:

  1. Sample/measure table — constructs, items, sources, reliabilities (survey) or variable definitions and data sources (archival).
  2. Descriptives & correlations — means, SDs, full correlation matrix; reliabilities on the diagonal for survey constructs. Define every operational variable.
  3. Results tables — nested regression / FE / SEM / count / survival models in columns, with coefficients, standard errors (note the clustering), fit/diagnostics, and N. Report effect sizes, not just significance.
  4. Interaction/contingency plot — simple slopes with significance regions for any moderation (contingency effects are central to OM).
  5. Mechanism/process figure — the hypothesized model; for field/case/IBR, a Gioia-style data structure or a process/intervention timeline.

House-style rules

  • APA conventions for tables, figures, notes, and references (JOM uses APA; Wiley applies final styling at proof). Keep formatting consistent — at first submission any consistent style is accepted, journal style preferred.
  • Number tables/figures; give each a stand-alone title and a complete note (estimator, SE type, significance thresholds, N, units).
  • Manuscript body is double-spaced, single-column, 12-point, one-inch margins, numbered pages, no running headers/footers; place exhibits per the author guidelines.
  • Every exhibit must be readable without the text and must report the operational units (days, defects per million, inventory turns, on-time %).

Self-explanation test

A reviewer should grasp each table/figure from its title, note, and labels alone. Spell out abbreviations, state the estimator and clustering in the note, and never show a coefficient without an SE and an N.

Execution bridge (StatsPAI / Stata MCP)

Generate exhibits from the fitted result, not by retyping numbers (the usual source of

body-vs-appendix drift). Full map: [execution-with-mcp](../../../shared-resources/empirical-methods/execution-with-mcp.md). JOM is empirical operations / supply-chain — survey and archival panel data; foreground endogeneity of operational choices and clustered / multilevel inference.

  • Tables: etable (multi-model columns) or did_summary_to_latex straight from the

result_id.

  • Figures: plot_from_result / enhanced_event_study_plot / event_study_table

axis units and the SE/clustering note baked in.

  • Every note names the estimator + clustering and states the effect size in

interpretable units.

See a full fitted-result → exhibit chain in the [JF execution walkthrough](../../../Journal-of-Finance-Skills/resources/worked-examples/02-execution-walkthrough.md).

Anti-patterns

  • A correlation table with no reliabilities (survey) or undefined operational variables.
  • Reporting stars but no effect sizes / operational magnitude.
  • An interaction described in text but not plotted.
  • Exhibits that restate the text instead of carrying evidence.
  • Inconsistent citation/number style across exhibits.

Exhibit-completeness table reviewers check

Each empirical-OM exhibit has elements without which a reviewer cannot verify the claim. The map below is a practical completeness check; APA and Wiley styling specifics should be confirmed against current author guidelines.

| Exhibit | Must contain | Common reviewer complaint |

|---------|--------------|----------------------------|

| Measures/sample table | Constructs, items, sources, reliabilities (survey) or variable definitions and data sources (archival) | Operational variables left undefined |

| Descriptives + correlations | Means, SDs, full correlation matrix, reliabilities on the diagonal | No reliabilities; no operational units |

| Results table | Coefficients, SEs with clustering noted, fit/diagnostics, N, effect sizes | Stars without effect sizes or operational magnitude |

| Interaction plot | Simple slopes with significance regions | Moderation described in text but never plotted |

| Mechanism/process figure | Hypothesized model, or Gioia data structure / intervention timeline | Figure restates text instead of carrying evidence |

Desk-reject and return triggers on exhibits

  • A correlation table with no reliabilities (survey) or with undefined operational variables.
  • A coefficient shown without a standard error and an N.
  • Significance reported but no operational magnitude (days, defects per million, inventory turns, on-time percentage).
  • Inconsistent number/citation style across tables, signaling rushed preparation.

Worked vignette: turning a result into a self-explanatory table

A behavioral-OM experiment finds a fatigue manipulation raises order errors, more so under high time pressure (illustrative). The results table reports the main effect (b = 0.42, SE = 0.11) and the interaction (b = 0.27, SE = 0.09), with N = 180, robust SEs noted, and a partial eta-squared column so magnitude is visible. The note states the estimator, the SE type, the thresholds, and that the outcome is errors per 100 orders — an operational unit. A reader who never sees the body can still grasp the story from the title, note, and labels. The companion plot shows simple slopes with significance regions, satisfying the contingency-effect expectation native to OM.

Exhibit objections reviewers raise, with the fix

  • "The table is not self-explanatory." Add a complete note (estimator, SE clustering, thresholds, N, operational units) and stand-alone title so it reads without the text.
  • "You report significance but not magnitude." Add effect sizes and translate them into operational consequences.

Output format

【Exhibit set】measures / descriptives+corr / models / interaction / mechanism
【Result table】estimator, SE clustering, effect sizes, N present? ...
【Interaction plot】simple slopes + regions? ...
【Process/data-structure figure】(field/case/IBR) ...
【APA consistency】...
【Next step】jom-writing-style

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