/method-engine

name: method-engine
description: >
  Activate when the user needs to select, justify, describe, or execute a research
  methodology. Provides method selection guidance, complete method section templates,
  quality criteria, and tool recommendations. Covers SLR, qualitative (case study,
  Gioia, Mayring, Grounded Theory), quantitative (SEM, regression, survey, experiment),
  DSR, mixed methods, action research, ethnography, Delphi study, and simulation.
  Also includes Research Data Management (RDM) guidance for FAIR-compliant data handling.

> **Orchestration Log**: When this skill is activated, append a log entry to `outputs/orchestration_log.md`: > ``` > ### Skill Activation: Method Engine > **Timestamp:** [current date/time] > **Actor:** AI Agent (method-engine) > **Input:** [brief description of the methodology request] > **Output:** [brief description of what was produced — e.g., "DSR method section drafted with 3 evaluation criteria"] > ```

Method Engine

Method Selection Guide

Decision Tree

What is your primary research goal?
│
├─ "I want to map what the literature says" 
│   → Systematic Literature Review (Section A)
│
├─ "I want to understand a phenomenon in depth"
│   → Qualitative Study (Section B)
│   ├─ Single context, deep → Single Case Study
│   ├─ Multiple contexts, comparison → Multiple Case Study  
│   ├─ Build new theory from data → Grounded Theory / Gioia
│   └─ Analyze text/documents systematically → Content Analysis (Mayring)
│
├─ "I want to test hypotheses / measure relationships"
│   → Quantitative Study (Section C)
│   ├─ Complex model with latent variables → SEM (PLS or CB)
│   ├─ Simpler relationships → Regression
│   └─ Experimental comparison → Experiment / RCT (Section F)
│
├─ "I want to build something (tool, framework, model)"
│   → Design Science Research (Section D)
│
├─ "I want to combine approaches"
│   → Mixed Methods (Section E)
│
├─ "I want to improve practice through iterative intervention"
│   → Action Research (Section G)
│
├─ "I want to understand culture, practices, or lived experience"
│   → Ethnography (Section H)
│
├─ "I want structured expert consensus on a complex issue"
│   → Delphi Study (Section I)
│
└─ "I want to model and test scenarios computationally"
    → Simulation (Section J)

---

Section A: Systematic Literature Review

Method Section Template (ready to adapt):

3. Research Methodology

We conducted a systematic literature review following the guidelines of 
[vom Brocke et al. (2009, 2015) / Webster & Watson (2002) / Kitchenham & 
Charters (2007) / PRISMA 2020 (Page et al., 2021)]. This approach is 
appropriate because [justification: need to synthesize a growing but 
fragmented body of knowledge / field is maturing and needs stock-taking / 
practical guidance requires evidence synthesis].

3.1 Search Strategy

We searched [N] electronic databases: Semantic Scholar, OpenAlex, CrossRef, 
[and arXiv for preprints / and AIS eLibrary for IS-specific venues]. 
The search was conducted in [month/year] using the following query terms:

  [("generative AI" OR "generative artificial intelligence" OR "large language 
  model*" OR "LLM" OR "GPT" OR "foundation model*") AND ("enterprise" OR 
  "organization*" OR "business" OR "implementation" OR "adoption")]

  [("AI agent*" OR "autonomous agent*" OR "agentic AI") AND ("organization*" 
  OR "enterprise" OR "business process" OR "implementation")]

The search was limited to publications from [year] to [year], in 
[English / English and German].

3.2 Selection Criteria

Table [N] summarizes our inclusion and exclusion criteria.

| ID | Criterion | Rationale |
|----|-----------|-----------|
| IC1 | Peer-reviewed journal article or conference paper | Quality assurance |
| IC2 | Focuses on [topic] in organizational context | Scope alignment |
| IC3 | Published between [year] and [year] | Recency |
| IC4 | Available in English [or German] | Accessibility |
| EC1 | Purely technical (no organizational dimension) | Out of scope |
| EC2 | Editorial, book review, or abstract-only | Insufficient depth |
| EC3 | Duplicate publication | Avoid double-counting |

3.3 Search and Screening Process

Figure [N] presents the PRISMA flow diagram of our search and selection process. 
The initial search yielded [N] records across all databases. After removing 
[N] duplicates, [N] records were screened based on title and abstract, of 
which [N] were excluded. The remaining [N] articles were assessed in full text, 
resulting in [N] studies included in the final synthesis.

[Forward and backward citation tracking (snowballing) on the [N] most-cited 
included studies identified an additional [N] relevant papers, bringing the 
total to [N] studies.]

3.4 Data Extraction and Analysis

From each included study, we extracted: [list categories: research question, 
theoretical lens, methodology, sample/context, key findings, limitations, 
and contribution type].

We synthesized findings using a concept-centric approach (Webster & Watson, 2002), 
organizing results in a concept matrix that maps studies against key themes 
identified through iterative reading and coding.

PRISMA Flow Diagram (text version):

Identification:
  Records from Semantic Scholar:     [n]
  Records from OpenAlex:             [n]  
  Records from CrossRef:             [n]
  Records from arXiv:                [n]
  Records from manual/snowballing:   [n]
  ─────────────────────────────────
  Total identified:                  [N]
  Duplicates removed:               -[n]
  Records after deduplication:       [N]

Screening:
  Title/abstract screened:           [N]
  Excluded:                         -[n]
  Full-text assessed:                [N]

Eligibility:
  Full-text excluded (with reasons): -[n]
    - Not organizational context:    [n]
    - Not empirical/conceptual:      [n]  
    - Not accessible:                [n]

Included:
  Studies in final synthesis:        [N]

---

Section B: Qualit