# Domain Identification Prompt for Strategic Ambiguity Research ## Context I am developing a research paper on strategic ambiguity in entrepreneurial promises, examining the optimal variance/precision of promises entrepreneurs make to stakeholders. The core hypothesis is that there's a trade-off between: - **Mobilization** (precise promises attract resources but create rigidity) - **Adaptability** (ambiguous promises preserve flexibility but hinder credibility) ## Your Task Based on the attached literature review and recent trends in entrepreneurship research, please complete three tasks: ### Task 1: Identify Three "Era of Ferment" Domains Identify three specific domains/industries that satisfy ALL of Scott Stern's criteria: **Required Criteria:** 1. **Cluster of competing companies** (ideally 20-30+ firms) 2. **Available pitch deck data or comparable founding documents** from around time of founding 3. **Era of ferment characteristics** (Jim Utterback style): - Multiple technological or business model approaches being tried simultaneously - High uncertainty about which approach will dominate - Active experimentation and iteration - Industry standards not yet crystallized 4. **Observable funding outcomes** in Crunchbase, PitchBook, or similar databases 5. **Appropriate temporal window**: - Series A funding rounds: 2021-2022 (or comparable early-stage funding) - Series B outcomes observable by 2025 - This allows ~3-4 year window to observe success/failure **Scott's Specific Examples (use as inspiration, not limits):** - Clean energy boom of mid-2000s (windmills, solar, etc.) - AI drug discovery - Y Combinator cohorts with pitch day data - Creative Destruction Lab verticals (clean energy, quantum, etc.) **Additional Considerations:** - Must have textual data analyzable for precision/vagueness (descriptions, pitch decks, founder statements) - Should include mix of high and low "integration cost" ventures (hardware vs. software) - Prefer domains where precision vs. ambiguity trade-offs are salient ### Task 2: Prioritize the Three Domains For each domain identified, evaluate and rank based on: **Data Accessibility (40% weight):** - Availability of pitch decks or founding descriptions - Coverage in PitchBook/Crunchbase - Public versus proprietary data requirements - Sample size achievable (target: 60-80 firms minimum) **Era of Ferment Strength (30% weight):** - Degree of technological/business model uncertainty - Number of competing approaches - Rate of pivoting and experimentation - Heterogeneity in promises made **Research Interest & Impact (30% weight):** - Current academic and practitioner attention - Policy relevance - Generalizability to other contexts - Potential for journal publication impact **Provide ranking with detailed justification for each criterion.** ### Task 3: Evidence from Literature and Data Landscape For each of the three domains, provide: **From Attached Literature Review:** - Which papers have studied similar domains or adjacent questions? - What methodologies have proven successful in these contexts? - Are there existing datasets we can build upon? **From Recent Trends (2023-2025):** - What recent papers or working papers touch on these domains? - Which top-tier journals (Management Science, Strategic Management Journal, Organization Science, Academy of Management Journal) have shown interest? - Are there special issues or calls for papers relevant to these domains? **Data Landscape Assessment:** - Known public datasets available (e.g., Y Combinator public data, specific accelerator portfolios) - Industry reports or market analyses documenting the ferment - Potential collaborators who have relevant data access ## Reference Materials ### From Literature Review (Attached) **Most Relevant Papers for Domain Selection:** 1. **El-Zayaty et al. (2025)** - Used TechCrunch Disrupt pitches, validated LIWC-based vagueness measures 2. **Novelli et al. (2024)** - Large RCT with 759 ventures, business model crystallization tracked over time 3. **Kleinert (2024)** - Crowdcube equity crowdfunding (482 campaigns), numerical forecast precision 4. **Yang & Hahn (2015)** - Airline industry (9,456 annual reports), vagueness tracked longitudinally 5. **McDonnell et al. (2017)** - 40,871 management forecasts, precision measurement methodology 6. **Camuffo et al. (2020, 2024)** - Scientific approach RCTs with repeated measurements 7. **Cong et al. (2019)** - Accelerator portfolio disclosure strategies **Key Methodological Insights:** - LIWC certitude dictionary for measuring vagueness/precision (validated) - Text analysis of pitch decks, founding documents, annual reports - Integration with Crunchbase/PitchBook funding data - Panel data structure (firm × multiple time periods) - RCT or quasi-experimental designs where feasible ### Scott Stern's Specific Guidance **From transcript:** > "I would find a setting... could be a domain... there have been a few booms of things. It could be the clean energy boom of your mid, 2000s you know, when you know clean energy talking about windmills. It could be AI drug discovery. It could be where there's a cluster of 20~30 competing companies and they're all have available pitch decks from around the time of founding." **Core Logic:** - Need variance in initial promises (some precise, some vague) - Must observe subsequent funding success (Series A → Series B progression) - Integration cost heterogeneity (hardware vs. software) provides natural moderator - Time window: "pick a time when it could go back to 2020, or something like that" ### Current Working Hypothesis (for context) **Hypothesis 1:** Vagueness at Series A lowers initial funding probability but increases Series B success (because no rigid expectations to disappoint). **Hypothesis 2:** High integration cost firms (hardware, distributed systems) benefit more from initial ambiguity than low integration cost firms (software, APIs). **Hypothesis 3:** Firms with founding teams possessing greater communication/rhetoric skills secure more funding when using vague language than those with lesser endowments. **Variables to Code:** - `Vagueness_i` = 100 - LIWC Certitude score from company descriptions - `High_Integration_Cost_i` = Binary (1=hardware/physical, 0=software) - `Funding_Success_it` = Binary (1=received funding at round t) - Controls: team size, founder credentials, prior exits ## Deliverable Format Please structure your response as: ```markdown # Three Era of Ferment Domains for Strategic Ambiguity Research ## Domain 1: [Name] ### Description [2-3 paragraphs describing the domain, why it's an era of ferment, key characteristics] ### Evaluation Criteria **Data Accessibility:** [Score 1-10 + justification] **Era of Ferment Strength:** [Score 1-10 + justification] **Research Interest & Impact:** [Score 1-10 + justification] **Overall Ranking:** [1st/2nd/3rd with composite score] ### Literature Support - Papers that have studied this domain or adjacent areas - Methodological precedents - Existing datasets to build on ### Data Landscape - Public data sources (with URLs/access methods) - Sample size estimate - Integration cost heterogeneity present? - Temporal window alignment ### Example Companies [List 5-10 example companies in this domain that would fit the study criteria] ## Domain 2: [Name] [Same structure as Domain 1] ## Domain 3: [Name] [Same structure as Domain 1] ## Final Recommendation [1 paragraph explaining which domain you recommend most strongly and why, considering all three evaluation criteria plus feasibility for a Master's thesis timeline] ## Additional Opportunities [Optional: 1-2 paragraphs noting any emerging domains (2024-2025) that might become viable for future research, even if they don't fully meet current temporal requirements] ``` ## Important Notes - **Prioritize practicality:** A domain with 8/10 data accessibility is better than 10/10 theoretical interest if data is proprietary - **Consider timeline:** This is for a Master's thesis, so data collection should be feasible within 3-6 months - **Be specific:** "AI infrastructure" is too broad; "AI chip design startups (2021-2022)" is appropriately specific - **Think creatively:** Scott's examples are starting points, not limits. Recent booms (Web3, space tech, lab-grown foods, etc.) could be excellent if they meet criteria - **Check actual data:** Don't just theorize—verify that PitchBook/Crunchbase actually covers these domains well ## Success Criteria Your response will be most useful if it: 1. Provides three genuinely distinct domains (not three variants of same theme) 2. Gives concrete, actionable data access methods 3. Demonstrates understanding of "era of ferment" characteristics 4. Shows evidence of checking recent literature for domain interest 5. Acknowledges practical constraints while being ambitious Please proceed with your analysis.