Orchestration Log: When this skill is activated, append a log entry to outputs/orchestration_log.md:
```
### Skill Activation: Literature Engine
Timestamp: [current date/time]
Actor: AI Agent (literature-engine)
Input: [brief description of the search/synthesis request]
Output: [brief description of results — e.g., "47 papers found across 4 databases, deduplicated to 38"]
```
Literature Engine
Data Source Priority — STRICT
- Academic APIs via
scripts/academic_search.py— ALWAYS first - Semantic Scholar (200M+ papers): citation graphs, TLDR, snowballing
- OpenAlex (474M+ works): broadest coverage, German publications, CC0
- CrossRef (150M+ works): DOI resolution, journal metadata
- arXiv (2.4M+ preprints): CS/AI/ML cutting-edge
- Web search — ONLY for: VHB-JOURQUAL rankings, specific CFPs, conference info
- Firecrawl — LAST RESORT for sources without APIs (AIS eLibrary, specific repositories)
Search Strategy
Step 1: Query Construction
For any research topic, construct 4-6 search queries:
| Query Type | Purpose | Example (GenAI/Agents paper) | |-----------|---------|------------------------------| | Core English | Main topic | "generative AI enterprise implementation" | | Synonym English | Alternative terms | "large language models organizational adoption" | | Narrow English | Specific aspect | "autonomous AI agents business process" | | Adjacent English | Related field | "AI transformation strategy organizational change" | | German | German publications | "generative KI Unternehmen Implementierung" | | Theoretical | Theory-specific | "sociotechnical systems artificial intelligence" |
Step 2: Execute Search
from scripts.academic_search import search_all, search_semantic_scholar, search_openalex, snowball, deduplicate_papers, papers_to_csv, papers_to_bibtex_file
# Round 1: Broad multi-API search
papers = search_all("generative AI enterprise implementation",
max_results_per_source=20, year_from=2020)
papers += search_all("autonomous AI agents organizational",
max_results_per_source=20, year_from=2020)
papers += search_all("LLM adoption business strategy",
max_results_per_source=15, year_from=2022)
# Round 2: German sources via OpenAlex
papers += search_openalex("generative KI Implementierung Unternehmen",
max_results=15, year_from=2020)
# Deduplicate
papers = deduplicate_papers(papers)
print(f"After dedup: {len(papers)} unique papers")Step 3: Snowball Key Papers
# Sort by citations, snowball top 5
top = sorted(papers, key=lambda p: -(p.get("citation_count") or 0))[:5]
for p in top:
if p.get("doi"):
result = snowball(p["doi"], direction="both", limit=15)
papers.extend(result.get("forward", []))
papers.extend(result.get("backward", []))
# Also snowball seminal papers you know are relevant
seminal_dois = [
# Add known seminal papers here, e.g.:
# "10.2307/25148667", # DeLone & McLean IS Success
]
for doi in seminal_dois:
result = snowball(doi, direction="forward", limit=20)
papers.extend(result.get("forward", []))
papers = deduplicate_papers(papers)Step 4: Export & Save
# Save for further analysis
papers_to_csv(papers, "literature_base.csv")
papers_to_bibtex_file(papers, "references.bib")Screening
Quick Screening (for building a paper's literature base)
Not a full SLR — just filter the most relevant papers:
- Year range appropriate?
- Published in a reputable venue?
- Title/abstract clearly relevant to the research questions?
- Methodology aligned with what we're looking for?
Formal SLR Screening
Use scripts/screening.py for systematic reviews:
from scripts.screening import screen_title_abstract, save_screening_results, generate_prisma_counts
results = screen_title_abstract(
papers=papers,
include_keywords=["generative AI", "LLM", "AI agent", "implementation", "adoption", "organization"],
exclude_keywords=["medical imaging", "drug discovery", "protein folding"],
min_year=2020,
require_abstract=True
)
save_screening_results(results, "screening/")
print(generate_prisma_counts(results))SLR Protocol Template (PRISMA-aligned)
## Systematic Literature Review Protocol
### Research Questions
- RQ1: [Primary question]
- RQ2: [Secondary question]
### Search Strategy
- Databases: Semantic Scholar, OpenAlex, CrossRef, arXiv [+ manual: AIS eLibrary, Google Scholar]
- Search terms: [list all queries]
- Date range: [YYYY] to [YYYY]
- Language: English [+ German if applicable]
### Inclusion Criteria
- IC1: [criterion]
- IC2: [criterion]
- IC3: [criterion]
### Exclusion Criteria
- EC1: [criterion]
- EC2: [criterion]
### Quality Assessment
- [criteria: VHB B+, peer-reviewed, minimum citation threshold for older papers]
### Data Extraction Categories
- [list what to extract from each paper]
### Synthesis Method
- [Webster & Watson concept matrix / thematic synthesis / meta-analysis / vote counting]Organizing Literature: The Concept Matrix
The single most important tool for turning a pile of papers into a structured literature review (Webster & Watson, 2002).
How to Build One
| Source | [Concept A] | [Concept B] | [Concept C] | [Concept D] | Method | Context |
|--------|------------|------------|------------|------------|--------|---------|
| Author1 (Year) | ✓ | ✓ | | | Survey | Enterprise |
| Author2 (Year) | | ✓ | ✓ | | Case Study | SME |
| Author3 (Year) | ✓ | | | ✓ | DSR | Healthcare |Example for the GenAI/Agents paper:
| Source | Implementation Strategy | Org. Change | Technical Architecture | Governance & Ethics | Human-AI Interaction | Method | Context | |--------|----------------------|-------------|----------------------|-------------------|---------------------|--------|---------| | [Author1] | ✓ | ✓ | | | | Case Study | Large Corp | | [Author2] | | | ✓ | ✓ | | DSR | Enterprise | | [Author3] | ✓ | | | | ✓ | Survey | Multi-industry |
What the Matrix Tells You
- Columns with many ✓: Well-researched concepts → summarize, don't deep-dive
- Columns with few ✓: Gaps → opportunity for contribution
- Row patterns: Which studies are most comprehensive?
- Method column: What methods dominate? What's underrepresented?
- Context column: Which contexts are studied? Which are missing?
Narrative Synthesis
Turning the Concept Matrix into a Literature Review Section
For each concept (column), write one subsection:
[Concept Name] has received [considerable/growing/limited] attention in the literature.
[Dominant finding:] The majority of studies [find/suggest/demonstrate] that
[main finding] (Author1, Year; Author2, Year; Author3, Year).
[Nuance/contradiction:] However, [Author4] (Year) argues that [alternative view],
particularly in [context]. This is echoed by [Author5] (Year) who found [supporting
evidence for the alternative].
[Your position:] These mixed findings suggest that [your interpretation].
In particular, [specific aspect] warrants further investigation, especially
in the context of [your study's context].Quality Indicators for Paper Selection
Journal Rankings (German academic context)
- VHB-JOURQUAL 3: A+, A, B (minimum for good lit review), C (selective)
- AIS Senior Scholars' Basket of Eight: EJIS, ISJ, ISR, JAIS, JIT, JMIS, JSIS, MISQ
- ABS/AJG: 4*, 4, 3 (secondary reference)
For AI/GenAI Topics (fast-moving field)
- Preprints acceptable for very recent developments (arXiv, SSRN)
- Industry reports (McKinsey, Gartner, Deloitte) useful for motivation, not for theoretical claims
- Conference papers from ICIS, ECIS, AAAI, NeurIPS useful for cutting-edge
- Recency premium: For GenAI topics, 2023+ papers carry extra weight