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Report . 2025
License: CC BY
Data sources: Datacite
ZENODO
Report . 2025
License: CC BY
Data sources: Datacite
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The Entrepreneurial Mindset: A Data-Driven Analysis of Success and Failure Patterns in 500+ Startup Journeys

Authors: Aynstyn Technologies Pvt Ltd;

The Entrepreneurial Mindset: A Data-Driven Analysis of Success and Failure Patterns in 500+ Startup Journeys

Abstract

Entrepreneurship is often characterized by extreme uncertainty,with failure rates for startups reportedly exceeding 90%. While traditional literature relies on retrospective biographies or surveys, this study leverages a novel dataset: the unfiltered, real-time narratives of entrepreneurs shared on anonymous community forums. This research analyzes 501 authentic posts collected from top Reddit entrepreneurship communities (r/Entrepreneur, r/startups, r/smallbusiness) to decode the behavioral and psychological differentiators between founders who successfully scale and those who fail. Key findings indicate that the primary predictor of success is not the novelty of the business idea, but the founder's resilience. (grit) and execution discipline. The study identifies a distinct "Stoic" mindset among successful founders during periods of uncertainty characterized by radical acceptance of reality combined with unwavering faith in the ultimate outcome. Furthermore, the analysis reveals a critical attitudinal shift in high-performing entrepreneurs: moving from an "extraction mindset" (how to make money) to a "service mindset" (how to solve problems). This dataset and report provide a practical, evidence-based framework for aspiring entrepreneurs to navigate the "Valley of Death" in early-stage ventures. Methodology 1. Data Collection Data was harvested using a custom Python script utilizing the Reddit API (PRAW wrapper). The script targeted the three most active entrepreneurship subreddits: r/Entrepreneur, r/startups, and r/smallbusiness. Collection focused on "Top" posts to ensure high community engagement and validation. 2. Data Filtering and Cleaning A total of 550 raw posts were initially fetched. A keyword-based filtering algorithm was applied to isolate narratives relevant to specific entrepreneurial stages. Keywords included terms related to: * Success indicators: "scale", "revenue", "profit", "exit", "acquisition". * Failure indicators: "bankrupt", "shut down", "burnout", "mistake", "quit". * Operational metrics: "bootstrapping", "funding", "cash flow", "marketing". This process resulted in a final dataset of 501 relevant posts comprising over 640,000 upvotes and 141,000 comments. 3. Analysis Framework The study employed a mixed-methods approach: * Quantitative Analysis: Analyzing engagement metrics to determine community consensus on specific advice and strategies. * Qualitative Thematic Analysis: Categorizing narratives into "Success" vs. "Failure" cohorts to identify recurring behavioral patterns. * Cohort A (Success): Founders reporting >$1M ARR, profitable exits, or sustainable growth. * Cohort B (Failure): Founders reporting business closure, burnout, or financial ruin. 4. Privacy and Ethics All data analyzed was publicly available on the Reddit platform. The analysis focuses on aggregated trends and insights rather than individual identification. Direct quotes used in the qualitative analysis serve as representative examples of broader themes.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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