The Psychology of Entrepreneurship

This is a narrative meta-analytic review, not a single experiment. The authors synthesised findings from 151 studies across entrepreneurship and organisational psychology to test how psychological constructs relate to two distinct entrepreneurial outcomes:

**Business creation** (starting a new venture, becoming self-employed, founding a company)

**Business success** (revenue growth, profit, employment growth, survival)

The "interventions" being tested are not experimental treatments but rather stable psychological characteristics and cognitive processes. The authors grouped these into three categories:

1. **Personality traits:** General self-efficacy (GSE), need for achievement (nAch), risk-taking propensity, locus of control, Big Five traits (especially conscientiousness and openness)

2. **Entrepreneurial-specific constructs:** Entrepreneurial alertness (the ability to notice opportunities others miss), business planning, entrepreneurial orientation (a firm-level construct combining innovativeness, proactiveness, and risk-taking)

3. **Cognitive and affective processes:** Practical intelligence, cognitive biases (e.g., overconfidence, illusion of control), goals and visions, personal initiative, passion, positive and negative affect

The comparator is implicit: entrepreneurs versus non-entrepreneurs, or successful versus less successful entrepreneurs. Outcome measures include business creation (binary: did they start a business?) and business success (continuous: revenue, profit, employment growth, survival duration).

The review covers studies published up to 2013, drawing from multiple samples across Western and some Asian contexts. Specific sample sizes for individual studies ranged from approximately 100 to over 1,000 participants. Populations included:

Nascent entrepreneurs (people actively trying to start a business)

Existing small business owners

Corporate managers (as comparison groups)

Students in entrepreneurship programmes

Samples from the US, Germany, UK, Netherlands, Singapore, and China

The authors note that most studies used convenience samples of small business owners or students, and that few studies tracked participants longitudinally. No single demographic profile is reported because this is a synthesis, but the typical entrepreneur in these studies was male (roughly 60–70%), aged 30–50, with at least some college education.

The review reports on multiple measurement approaches across the included studies:

**General self-efficacy:** Typically measured with the 8-item General Self-Efficacy Scale (Schwarzer & Jerusalem, 1995), scored 1–4, higher = more confidence in coping with challenges

**Need for achievement:** Jackson's Personality Research Form or the Thematic Apperception Test (TAT), with reliability coefficients around 0.70–0.80

**Entrepreneurial orientation:** The 9-item scale by Covin & Slevin (1989), measuring innovativeness, proactiveness, and risk-taking on 1–7 Likert scales

**Business success:** Self-reported revenue growth, profit growth, and employment growth over 1–5 years; some studies used objective tax records or registry data

**Business creation:** Self-reported founding status, confirmed by business registration records where available

**Cognitive biases:** Overconfidence measured by comparing self-assessed probability of success to actual base rates; illusion of control measured by scenario-based questionnaires

**Passion:** The 13-item Entrepreneurial Passion Scale (Cardon et al., 2009), measuring intense positive feelings toward entrepreneurial activities

The authors note a major methodological concern: most studies relied on self-report for both the psychological predictors and the outcome measures, creating potential for common method bias (where correlations are inflated because the same person reports both variables).

**Design:** This is a narrative meta-analytic review with systematic literature search. The authors searched PsycINFO, ABI/INFORM, and Google Scholar for studies published up to 2013, using keywords like "entrepreneurship," "entrepreneur," "personality," "cognition," "affect," and "motivation." They included both quantitative empirical studies and qualitative reviews. They did not perform a formal meta-analysis with pooled effect sizes (no forest plots, no heterogeneity statistics), but instead provided narrative synthesis with reported effect sizes from individual studies.

**Why this design matters:** A narrative review allows the authors to integrate findings across diverse methodologies—longitudinal studies, cross-sectional surveys, and a few experiments. However, it cannot provide a single, precise estimate of effect magnitude the way a formal meta-analysis can. The authors compensate by being explicit about which findings are robust (replicated across multiple studies) versus preliminary (single studies or conflicting results).

**What this design can prove:**

It can identify consistent patterns across multiple studies (e.g., self-efficacy predicts business creation in 8 out of 10 studies)

It can highlight moderators (e.g., the relationship between risk-taking and entrepreneurship depends on whether you measure business creation vs. success)

It can reveal gaps in the literature (e.g., few studies examine affect and emotions longitudinally)

**What this design cannot prove:**

It cannot establish causality. Most included studies are cross-sectional, so we cannot tell whether self-efficacy causes entrepreneurship or entrepreneurship increases self-efficacy (likely both, in a reciprocal loop)

It cannot provide precise effect sizes with confidence intervals for the overall relationships

It cannot rule out publication bias (the tendency for journals to publish positive results over null results)

**Major methodological weaknesses:**

**Cross-sectional dominance:** The majority of studies measured personality and outcomes at the same time point, making direction of causality impossible to determine

**Self-report bias:** Both predictors and outcomes often came from the same person, inflating correlations

**Survivor bias:** Studies of "successful entrepreneurs" only include those who haven't failed yet, potentially overestimating the importance of positive traits

**Cultural narrowness:** Most studies were conducted in Western, educated, industrialised, rich, democratic (WEIRD) societies

**No formal meta-analysis:** Without pooled effect sizes, the review cannot quantify the overall strength of relationships

**Personality traits and business creation:**

General self-efficacy showed one of the strongest and most consistent relationships with business creation. Across multiple studies, entrepreneurs scored approximately 0.5–0.8 standard deviations higher on GSE than non-entrepreneurs (Cohen's d ≈ 0.6–0.8, a medium-to-large effect)

Need for achievement was positively associated with business creation in approximately 70% of studies reviewed, with effect sizes ranging from d = 0.3 to 0.5 (small-to-medium)

Risk-taking propensity showed a more complex pattern: entrepreneurs were moderately higher in risk tolerance than managers (d ≈ 0.3–0.4), but the relationship was weaker when comparing entrepreneurs to the general population

Locus of control: entrepreneurs scored more internal (believing they control outcomes) than non-entrepreneurs, with effect sizes around d = 0.4–0.6

Big Five: Conscientiousness (d ≈ 0.3) and openness to experience (d ≈ 0.3) were positively associated with business creation; neuroticism was negatively associated (d ≈ -0.2)

**Personality traits and business success:**

The relationships were generally weaker for success than for creation. General self-efficacy still predicted success (r ≈ 0.20–0.30, small-to-medium), but need for achievement showed inconsistent results

Entrepreneurial orientation (at the firm level) showed stronger relationships with business success: firms scoring high on innovativeness, proactiveness, and risk-taking had 15–25% higher revenue growth on average, though the effect varied by industry

**Cognitive factors:**

Business planning: Studies were mixed. Some found that formal business planning predicted survival (20–30% higher survival rates at 3-year follow-up), while others found no effect or even negative effects in highly uncertain environments

Cognitive biases: Entrepreneurs showed higher overconfidence than non-entrepreneurs. In one study, 68% of entrepreneurs rated their chance of success as "very likely" (>80%), whereas actual 5-year survival rates for new businesses were approximately 40–50%

Practical intelligence (tacit knowledge about running a business) predicted business success in several studies, with correlations around r = 0.25–0.35

**Motivational and affective factors:**

Personal initiative (self-starting, proactive behaviour) predicted business success in longitudinal studies: entrepreneurs high in personal initiative had 30–40% higher revenue growth over 2 years compared to those low in initiative

Passion: Intense positive feelings toward entrepreneurship were associated with higher persistence (entrepreneurs with high passion were 1.5–2 times less likely to quit after 1 year), but the relationship with objective success was weaker (r ≈ 0.15–0.20)

Positive affect was associated with creativity and opportunity recognition (r ≈ 0.20–0.30), while negative affect was associated with more careful decision-making but lower overall performance

**Goals and visions:**

Entrepreneurs with specific, challenging goals showed 20–30% higher business growth compared to those with vague goals (e.g., "do my best")

Vision communication (articulating a compelling future state) predicted employee commitment and venture growth, with effect sizes around d = 0.4–0.6

To translate these findings into plain language:

**Self-efficacy:** An entrepreneur scoring at the 70th percentile on self-efficacy is roughly 1.5–2 times more likely to start a business than someone at the 30th percentile. This is comparable to the difference in physical health between someone who exercises 3x/week versus someone who never exercises.

**Need for achievement:** The effect is moderate. Think of it like the difference in exam scores between a student who studies 2 hours per day versus 1 hour per day—noticeable but not transformative.

**Overconfidence bias:** Entrepreneurs are about as overconfident as the average person is about their driving ability. Most people think they're above-average drivers; most entrepreneurs think they'll beat the 50% failure rate. This bias may help them take the leap but may also lead to underpreparation.

**Business planning:** The effect of formal planning on survival is roughly equivalent to the effect of a seatbelt on surviving a car crash—it helps, but it's not a guarantee, and in some situations (like driving on ice) it may not matter as much.

**Personal initiative:** The 30–40% revenue growth difference between high and low initiative entrepreneurs is roughly the difference between a small business growing from $100,000 to $130,000 versus staying flat over 2 years.

**What the authors acknowledge:**

Most studies are cross-sectional, preventing causal conclusions

Self-report bias is pervasive

The field lacks standardised definitions of "entrepreneur" and "success"

Few studies examine failure or business closure as an outcome

The review is narrative rather than a formal meta-analysis, so effect sizes are approximate

**What a critical reader would note:**

**Survivor bias is severe:** Studies of "successful entrepreneurs" only include those who haven't failed. If failure is random or driven by factors outside personality (e.g., market conditions), then the observed personality-success correlations may be spurious

**Publication bias is likely:** Journals tend to publish positive findings. The true effect sizes for personality traits may be 20–40% smaller than reported here

**Cultural limits:** Nearly all studies come from Western, individualistic cultures. In collectivist cultures (e.g., Japan, China), traits like need for achievement may manifest differently (e.g., as team-oriented rather than individualistic achievement)

**No control for intelligence:** General cognitive ability was rarely measured or controlled for. Some of the "personality" effects may actually reflect intelligence differences

**The "entrepreneur" definition problem:** Studies mix together lifestyle entrepreneurs (e.g., running a small café), high-growth tech founders, and freelancers. These groups likely have different psychological profiles

**Temporal instability:** Personality traits like self-efficacy change over time, especially during the entrepreneurial process. A single measurement may not capture the dynamic relationship

**Industry effects ignored:** The psychological profile that predicts success in tech (high openness, risk tolerance) may differ from retail (high conscientiousness, low risk tolerance)

For someone running their own n=1 experiment to test whether psychological factors affect your entrepreneurial outcomes:

### What to test (specific intervention and dose)

1. **Self-efficacy intervention:** Practice "enactive mastery"—set and achieve small, concrete business goals weekly. For example, "This week I will contact 5 potential customers and get feedback on my product." Track whether completing these goals increases your confidence and subsequent action.

2. **Goal-setting intervention:** Write down specific, challenging, time-bound goals for your business each week. Example: "Increase revenue by 15% in the next 3 months by acquiring 10 new clients." Compare weeks with written goals to weeks without.

3. **Personal initiative training:** Each morning, ask yourself: "What is one thing I can do today that I wasn't planning to do, that would move my business forward?" Do it. Track whether this proactive behaviour leads to more opportunities or revenue.

### Minimum meaningful duration

**For self-efficacy and goal-setting:** At least 8–12 weeks. Personality changes slowly, and business outcomes (revenue, customers) take time to materialise

**For personal initiative:** 4–6 weeks may show initial effects on daily behaviour, but business outcomes need 3–6 months

**For passion/affect:** Track daily for 2–4 weeks to establish baseline, then introduce intervention and track for another 4–8 weeks

### What to measure (specific metrics)

**Primary outcomes (weekly):**

**Business creation progress:** Number of actions taken toward starting (e.g., registrations filed, customers acquired, funding secured)

**Business success:** Weekly revenue, number of new customers, hours billed

**Self-efficacy:** Use the 8-item General Self-Efficacy Scale (free online). Score 8–32. Track weekly.

**Personal initiative:** Rate yourself 1–7 on "This week, I took initiative to solve problems before they became urgent"

**Secondary outcomes (daily):**

**Positive/negative affect:** Use the Positive and Negative Affect Schedule (PANAS, 20 items). Track daily mood.

**Hours worked:** Track total hours and focused work hours

**Risk-taking behaviour:** Count instances of "calculated risks" (e.g., cold-calling a big client, investing in marketing)

### Key confounds to control for

**Seasonal effects:** Business revenue varies by season. Run your experiment for at least one full business cycle (e.g., 3 months) or compare same periods year-over-year

**Life events:** Major life changes (moving, illness, relationship changes) will swamp any psychological intervention. Note these in a log

**Sleep and health:** Poor sleep reduces self-efficacy and increases negative affect. Track sleep quality (e.g., 1–10 scale each morning)

**Financial runway:** If you're running out of money, no psychological intervention will help. Track your cash position weekly

**Industry shocks:** A new competitor or regulatory change can destroy your results. Note external events in a log

**Regression to the mean:** If you start the experiment during a particularly good or bad week, your results will naturally move toward average. Take a 2-week baseline before starting any intervention

### What a positive result would look like

**Self-efficacy:** Your GSE score increases by at least 3–5 points (on the 8–32 scale) over 8 weeks, AND your weekly revenue or customer count increases by at least 15–20% compared to baseline

**Goal-setting:** On weeks when you write specific goals, you complete 30–50% more business-related actions than on weeks without written goals

**Personal initiative:** Your daily initiative rating increases from an average of 3/7 to 5/7 over 6 weeks, AND you can point