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Amj Theory Development

Community
franklee16
amj-theory-development

Use when the theoretical argument and hypotheses are the bottleneck for an Academy of Management Journal (AMJ) manuscript — building a mechanism and deriving testable hypotheses a priori. Constructs the theory; it does not run the analysis (amj-data-analysis) or write the final contribution paragraph (amj-contribution-framing).

Overview

Publisherfranklee16
Repositoryacademic-research-skills
Skill nameamj-theory-development
Stars
225
Forks
34
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by franklee16 on GitHub. Read the source before you install it.

Installation

Install the Amj Theory Development AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/franklee16/academic-research-skills/tree/master/Awesome-Journal-Skills-main/Academy-of-Management-Journal-Skills/skills/amj-theory-development
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/franklee16/academic-research-skills.git /tmp/academic-research-skills
mkdir -p .claude/skills
cp -r /tmp/academic-research-skills/Awesome-Journal-Skills-main/Academy-of-Management-Journal-Skills/skills/amj-theory-development .claude/skills/amj-theory-development
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Amj Theory Development in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Amj Theory Development on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Amj Theory Development is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Theory Development & Hypotheses (amj-theory-development)

When to trigger

  • Hypotheses read as bald predictions ("A is positively related to B") with no mechanism
  • You have results and are tempted to write hypotheses around them (HARKing risk)
  • The argument leans on one borrowed citation rather than a developed logic
  • Mediators/moderators are present in the data but not theorized
  • A reviewer says "the theory is thin," "the logic is underdeveloped," or "why would this be true?"

The AMJ theory bar

AMJ does not publish atheoretical work — its mission is "to publish empirical research that tests, extends, or builds management theory." Every hypothesis must be derived from an articulated theoretical mechanism, written before the results are known. The argument should make a reader feel they understand why the effect occurs and under what conditions it strengthens or reverses.

For theory-building (typically qualitative) papers, AMJ does not expect a priori hypotheses; it expects a grounded model with emergent propositions. Eisenhardt and Graebner's AMJ guidance on building theory from cases is the canonical exemplar for how rich data become crisp constructs and propositions — do not force such a study into a hypothetico-deductive template.

Building a hypothesis (the mechanism chain)

For each hypothesis, write the explicit chain — do not skip steps:

  1. Antecedent — the predictor and why it matters in this context.
  2. Mechanism — the theoretical process linking antecedent to outcome (e.g., social exchange, signaling, attention allocation, identity threat, resource dependence). Name the theory and the process verb (motivates, signals, depletes, legitimates).
  3. Outcome — the dependent construct, defined at a stated level of analysis.
  4. Direction & form — sign, and whether linear, curvilinear, or threshold.
  5. Boundary — the moderator(s) and the theoretical reason the mechanism is stronger/weaker.

State the level of analysis explicitly (individual, team, firm) and keep the theory at the level where the mechanism operates; flag cross-level mediation as such.

Mediation and moderation done right

  • Mediation: theorize the process construct before testing it; do not infer the mechanism from a significant indirect effect alone. An indirect effect is evidence for a theorized mechanism, not a substitute for the theory.
  • Moderation: give a substantive reason the slope changes — "it depends on" is not a theory until you say why it depends.
  • Curvilinear: justify the turning point theoretically (two opposing forces), not just by a significant squared term.

Hypothesis hygiene

  • Number hypotheses and keep them to a defensible set (typically a focal effect plus a mechanism and one or two boundaries). Avoid a "kitchen sink" of ten weakly motivated predictions.
  • Each hypothesis must be falsifiable and tied to a specific test you will run.
  • Competing/alternative theoretical explanations should be named and addressed, not ignored.
  • A figure of the theoretical model (boxes and arrows) should map one-to-one to the hypotheses.

Checklist

  • Every hypothesis has an explicit mechanism, not just a predicted sign
  • The driving theory is named and its process clearly invoked (not a one-line citation)
  • Level of analysis is stated and consistent across argument and design
  • Mediators/moderators are theorized a priori with substantive reasons
  • Hypotheses were derived before analyzing the data (no HARKing)
  • A theoretical-model figure matches the hypotheses exactly
  • At least one rival explanation is named and engaged

Anti-patterns

  • HARKing: hypothesizing after results are known; reviewers detect it when every hypothesis is supported and none is surprising.
  • Bald predictions: "We hypothesize A is positively related to B" with no why.
  • Borrowed theory: invoking a famous theory by name without using its actual logic.
  • Mechanism by mediation: claiming a process exists only because the indirect effect is significant.
  • Kitchen-sink hypotheses: many predictions, each thinly argued, that no single theory ties together.
  • Boundary without reason: a moderator with no theoretical account of why the effect should differ.

Output format

【Focal theory】... (process verb: ...)
【H1 (focal effect)】antecedent → mechanism → outcome; direction/form; level
【H2 (mechanism/mediation)】...
【H3+ (boundary/moderation)】... reason the slope changes
【Rival explanation addressed】...
【HARKing check】hypotheses fixed before analysis? yes/no
【Model figure】matches hypotheses one-to-one? yes/no
【Next step】amj-literature-positioning, then amj-methods

Frequently asked questions

What does the Amj Theory Development AI skill do?

Use when the theoretical argument and hypotheses are the bottleneck for an Academy of Management Journal (AMJ) manuscript — building a mechanism and deriving testable hypotheses a priori. Constructs the theory; it does not run the analysis (amj-data-analysis) or write the final contribution paragraph (amj-contribution-framing).

Why use Amj Theory Development on TypingMind?

Because you install it once and use it with any model. Amj Theory Development is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Amj Theory Development in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/franklee16/academic-research-skills/tree/master/Awesome-Journal-Skills-main/Academy-of-Management-Journal-Skills/skills/amj-theory-development. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Amj Theory Development?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Amj Theory Development?

As many as you like. As long as a model supports skills, you can use Amj Theory Development with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Amj Theory Development AI skill free?

It is published on GitHub by franklee16. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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