Objective

Artificial intelligence (AI) has become a key driver of competitive advantage in the startup ecosystem. While many firms struggle to capture its potential, startups with AI-literate executive teams may be better positioned to develop strategic orientations that attract investor capital. This project investigates how the AI capabilities and strategic focus of top management teams influence funding outcomes. Students will analyze firm, company and leadership data to examine how AI literacy and AI orientation acts as a signal to investors. They are encouraged to develop their own empirical approaches to identify mechanisms through which executive AI focus drives funding performance.

Conceptual Framework

The project builds on the intersection of Upper Echelons Theory and Signaling Theory:

• Upper Echelons Theory posits that firm outcomes reflect the cognitive and experiential characteristics of their top executives.
• Signaling Theory suggests that observable actions or characteristics (e.g., AI-related capabilities, digital orientation) can act as signals of quality to investors under information asymmetry.

The conceptual model assumes that:

1. TMT AI Literacy enhances a firm’s AI Orientation (strategic focus on AI).

2. AI Orientation acts as a signal to external investors, improving funding success.

3. AI Literacy and AI Orientation jointly explain funding performance through both capability and perception mechanisms.

Tasks

• Collect startup and executive-level data from PitchBook, Crunchbase, LinkedIn, or open datasets.
• Identify AI-related indicators from TMT profiles (e.g., AI skills, experience, education).
• Quantify startup-level characteristics (e.g., AI orientation in company descriptions, industry, or technology focus).
• Construct and preprocess a dataset linking AI-related managerial attributes to funding success metrics (e.g., total funding raised, number of rounds, valuation, patents, growth of employees).
• Apply econometric methods and optionally machine learning models (e.g., regression models, random forest, XGBoost) to predict funding success based on managerial AI focus.
• Interpret and visualize key factors driving funding outcomes
• Optionally: Explore text-based AI orientation indicators e.g. using NLP to explain central keywords

Methods & Tools

• Python or other helpful tools for data preprocessing
• Regression and classification models for analysis and prediction (Logit, OLS, Random Forest, XGBoost,…) using R and Python or other helpful tools
• Optionally: NLP toolkits (spaCy, HuggingFace) for AI keyword detection
• Data visualization and interpretation tools

Expected Outcome

A data-driven empirical model quantifying how AI literacy in executive teams and AI orientation influences startup funding success.

Students will produce:

• A structured dataset combining startup and leadership information.
• Empirical insights into how AI-related leadership and AI orientation capabilities signal quality to investors.
• A visualization which executive AI factors drive startup funding outcomes.