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Amr Contribution Framing

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franklee16
amr-contribution-framing

Use when articulating and differentiating the theoretical contribution of an Academy of Management Review (AMR) manuscript — showing precisely what is NEW versus prior theory and why it matters. Frames the contribution; it does NOT build the theory (amr-theory-development) or check its internal logic (amr-data-analysis).

Overview

Publisherfranklee16
Repositoryacademic-research-skills
Skill nameamr-contribution-framing
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 Amr Contribution Framing 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-Review-Skills/skills/amr-contribution-framing
  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-Review-Skills/skills/amr-contribution-framing .claude/skills/amr-contribution-framing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Amr Contribution Framing 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 Amr Contribution Framing 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 Amr Contribution Framing 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.

Contribution Framing: What Is New, and So What (amr-contribution-framing)

When to trigger

  • The theory is built and logic-checked, but the "contribution" is vague
  • A reviewer would write "I don't see what's new here"
  • Your contribution paragraph restates the propositions instead of differentiating them
  • You cannot say, in one sentence, what scholars can now explain that they could not

The AMR contribution bar

AMR seeks "novel, insightful, and carefully crafted conceptual articles that challenge conventional wisdom." Whetten's classic AMR editorial, "What Constitutes a Theoretical Contribution?" (1989, DOI 10.5465/amr.1989.4308371), is the rubric: a complete contribution specifies the What (constructs), How (relationships), Why (the underlying logic), and Who/Where/When (boundary conditions). The Why is the centerpiece — it is what separates theory from description. AMR's scope frames the four legitimate contribution paths: develop new theory; significantly challenge or clarify existing theory; synthesize advances into fresh theory; or identify and delineate a novel theoretical problem.

The two questions AMR contributions must answer

  1. What is new? Not "we studied X" — AMR has no studies. What theoretical machinery did not exist before this paper? A new construct, a new mechanism, a new process model, a re-specified relationship, a dissolved debate.
  2. So what? Why does the new machinery matter to the conversation? What does it let scholars do, see, or explain that the prior theory did not?

A contribution claim that cannot be stated as "Before this paper, theory could not X; after it, theory can X" is not yet a contribution.

Differentiation: contribution vs. prior work

The single most common AMR rejection reason is "contribution not differentiated from prior work." Defend against it explicitly:

  • Name the closest prior theory and state, concretely, how yours differs — not "we go further" but in what specific respect.
  • Distinguish from relabeling: if a reviewer could map your construct one-to-one onto an existing one, you have a synonym, not a contribution (cf. Suddaby on construct clarity, AMR 2010, DOI 10.5465/amr.2010.0419).
  • Distinguish from incremental moderation: "we add a moderator" is usually an empirical contribution, not a theoretical one. AMR wants a change in the theory, not a new contingency to test.

Sizing the contribution

Contribution typeStrength at AMR
New theory / reconceptualization that reorients a conversationStrongest
New construct + mechanism that explains a persistent puzzleStrong
Bridging two conversations to generate new propositionsStrong
Bounding an existing theory in a newly theorized waySolid
Adding a contingency/moderator to existing theoryUsually too thin for AMR alone
Reviewing/organizing a literatureNot a theoretical contribution

Exemplar

Dyer & Singh's "The Relational View" (AMR 1998, DOI 10.5465/amr.1998.1255632) states its "before → after" crisply: before, competitive advantage was theorized at the firm or industry level; after, relational rents can accrue to dyads and networks, with four specified sources. That is new theoretical machinery, differentiated from the nearest prior theories — not a relabel and not an added moderator.

Implications (part of the contribution)

A complete AMR contribution closes with implications:

  • For theory: which conversations are changed; what new questions open.
  • For future research: how the propositions could eventually be examined (you point the way; you do not test).
  • For practice: what managers/organizations would do differently if the theory holds — stated as reasoned implication, not as empirically proven advice.

Checklist

  • The "what's new" is stated as theoretical machinery that did not exist before
  • The contribution is framed as "before → after" (what theory can now explain)
  • The closest prior theory is named and the specific difference stated
  • The contribution is not a relabel and not merely an added moderator
  • Implications for theory, future research, and practice are present and proportional
  • The contribution claim matches what the propositions actually deliver (no over-claim)

Anti-patterns

  • "Contribution" paragraph that just restates the propositions
  • Claiming novelty without naming or engaging the nearest prior theory
  • Over-claiming: a modest extension sold as a paradigm shift
  • A new label for an old construct presented as new theory
  • Practice implications written as proven prescriptions ("managers should...") rather than reasoned consequences of the theory
  • Listing three "contributions" that are really one restated three ways

Output format

【What's new】the theoretical machinery that did not exist before
【Before → after】theory could not X; now it can X
【Closest prior theory】name + the specific difference
【Contribution type】reconceptualization / new construct+mechanism / bridge / bounding
【Implications】theory / future research / practice
【Over-claim risk】none / trim to: ...
【Next step】amr-tables-figures

Frequently asked questions

What does the Amr Contribution Framing AI skill do?

Use when articulating and differentiating the theoretical contribution of an Academy of Management Review (AMR) manuscript — showing precisely what is NEW versus prior theory and why it matters. Frames the contribution; it does NOT build the theory (amr-theory-development) or check its internal logic (amr-data-analysis).

Why use Amr Contribution Framing on TypingMind?

Because you install it once and use it with any model. Amr Contribution Framing 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 Amr Contribution Framing 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-Review-Skills/skills/amr-contribution-framing. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Amr Contribution Framing?

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 Amr Contribution Framing?

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

Is the Amr Contribution Framing 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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