Literature Engine

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

1. 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
2. Web search — ONLY for: VHB-JOURQUAL rankings, specific CFPs, conference info
3. 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:

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:

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

Monitoring / Living Literature Review

Purpose

Re-run searches periodically to detect newly published papers since the last search. Essential for fast-moving fields (AI, GenAI) and for papers with long revision cycles where the literature base may become stale.

When to Run

• Before submitting a paper (check for new relevant work published during writing)
• During R&R (check what appeared between submission and revision)
• For living/systematic reviews (quarterly or monthly updates)
• When user runs /monitor-literature

Monitoring Workflow

Step 1: Recover Original Search Parameters

Load the original queries from framing.md or reconstruct from literature_base.csv metadata:

• Extract unique search queries used
• Note the original date range (the date_to becomes the new date_from)
• If no metadata exists, ask the user for the approximate last search date

Step 2: Execute Update Search

import sys
sys.path.insert(0, "scripts")
from academic_search import search_all, deduplicate_papers, papers_to_csv
import csv
from datetime import datetime

# Load existing literature base for deduplication
existing_papers = []
with open("literature_base.csv", "r") as f:
    reader = csv.DictReader(f)
    existing_papers = list(reader)

existing_titles = {p.get("title", "").lower().strip() for p in existing_papers}
existing_dois = {p.get("doi", "").strip() for p in existing_papers if p.get("doi")}

# Re-run queries with date filter
last_search_date = "2025-01-15"  # from metadata or user input
queries = [
    "generative AI enterprise implementation",
    "autonomous AI agents organizational",
    # ... original queries from framing.md
]

new_papers = []
for q in queries:
    results = search_all(q, max_results_per_source=20, year_from=int(last_search_date[:4]))
    new_papers.extend(results)

new_papers = deduplicate_papers(new_papers)

# Filter out papers already in the literature base
truly_new = []
for p in new_papers:
    title_match = p.get("title", "").lower().strip() in existing_titles
    doi_match = p.get("doi", "").strip() in existing_dois if p.get("doi") else False
    if not title_match and not doi_match:
        truly_new.append(p)

print(f"Found {len(truly_new)} new papers since {last_search_date}")

Step 3: Present New Papers for Review

## Literature Monitoring Report

**Last search:** [date]
**Update search:** [today's date]
**Queries re-run:** [N]
**New papers found:** [N] (after deduplication against existing [N] papers)

### Potentially Relevant New Papers

| # | Title | Authors | Year | Venue | Citations | Relevance |
|---|-------|---------|------|-------|-----------|-----------|
| 1 | [Title] | [First author et al.] | [Year] | [Venue] | [N] | [Brief note] |
| 2 | [Title] | [First author et al.] | [Year] | [Venue] | [N] | [Brief note] |
| ... | | | | | | |

### Recommended Actions
- **Add to literature base:** Papers #[list] — directly relevant to RQs
- **Read in full:** Papers #[list] — potentially important, needs assessment
- **Skip:** Papers #[list] — tangential or out of scope

→ Approve to update literature_base.csv and references.bib with selected papers.

Step 4: Update Literature Base (upon approval)

# Add approved new papers to literature base
approved_papers = [truly_new[i] for i in approved_indices]
all_papers = existing_papers + approved_papers
papers_to_csv(all_papers, "literature_base.csv")
papers_to_bibtex_file(approved_papers, "references.bib", append=True)

Monitoring Metadata

After each monitoring run, save metadata for future runs:

<!-- monitoring_metadata (appended to literature_base.csv or framing.md) -->
## Search History
| Date | Queries | Results | New Added | Total |
|------|---------|---------|-----------|-------|
| [initial date] | [N] queries | [N] papers | [N] | [N] |
| [update date] | [N] queries | [N] new | [N] added | [N] |

Living Review Pattern

For papers maintained over months/years:

1. Set a monitoring schedule (monthly for fast fields, quarterly for stable fields)
2. Each run produces a timestamped monitoring report in outputs/monitoring_[date].md
3. Track cumulative additions to show the literature base is current
4. Note in the paper: "The literature search was last updated on [date]"