Why Study Risk-Taking?

We all take risks every day - from choosing a new restaurant to changing careers, investing money, or even crossing the street. But people approach risk very differently. Some carefully weigh pros and cons before making any risky decision, while others go with their gut and leap before they look. Understanding these differences matters because risk-taking affects everything from our health and relationships to our career success and financial wellbeing.

The Problem with Previous Research

Most research on risk-taking has treated it as a single trait - you're either a "risk-taker" or you're not. Scientists have also typically studied risk-taking in specific areas (like financial decisions or health choices) rather than looking at general patterns. This approach has led to confusing findings: sometimes risk-taking seems good (linked to creativity and entrepreneurship), other times it seems bad (linked to accidents and poor decisions).

This has created what researchers call the "double-edged" nature of risk-taking. Previous studies found that risk-takers were more likely to engage in both positive behaviors (like starting businesses and being creative at work) and negative ones (like criminal activity and dangerous driving). This left an important question unanswered: are all risk-takers the same, or are there different types who behave in fundamentally different ways?

A New Approach: Two Styles of Risk-Taking

Drawing from decades of research on how people think and make decisions (called "dual-process theory"), we proposed that there are actually two distinct styles of risk-taking:

• Calculated Risk-Taking: Taking risks after careful thought and analysis, preferring situations where the potential benefits clearly outweigh the costs. These individuals consider long-term consequences and use deliberate reasoning.
• Spontaneous Risk-Taking: Taking risks based on feelings, emotions, and impulses, often driven by the excitement of uncertainty rather than careful analysis of outcomes.

Our Research Goal

We wanted to create a scientific tool that could measure these two different risk-taking styles and test whether they predict different life outcomes. If calculated and spontaneous risk-takers really do behave differently, this could help explain why risk-taking sometimes leads to success and other times to problems.

What We Found

Across seven studies with over 2,100 people - including college students, working adults, and general population samples - we made several key discoveries:

• The Two Styles Are Related But Distinct: While both types of risk-takers share a general attraction to risk (they're moderately correlated), they show very different patterns in how they approach decisions. Calculated risk-takers actually show more variability in their risk-taking across different situations - they're more selective about when to take risks based on the specific circumstances.
• Different Personality Profiles: Calculated risk-takers tend to be more conscientious (r = .12), have a higher need for thinking things through (r = .24), and are better with numbers (r = .19). Spontaneous risk-takers tend to be less conscientious (r = -.26) and less inclined toward rational decision-making (r = -.36).
• Dramatically Different Life Outcomes: Our research revealed striking differences in what these two styles predict:

Calculated risk-takers were more likely to engage in adaptive risks:

• Workplace creativity (r = .28)
• Entrepreneurial intentions (r = .39)
• Better general health (r = .19)
• Eudaimonic wellbeing (r = .28)
• Employee voice behaviors (r = .17 to .23)

Spontaneous risk-takers were more likely to engage in problematic risks:

• Criminal charges (r = .27)
• Shoplifting frequency (r = .22)
• Traffic tickets (r = .21)
• Safety violations at work (r = -.13 to -.17 for safety compliance/motivation)
• Relationship infidelity (r = .16)

The Key Finding: Calculated risk-takers essentially get the benefits of risk-taking without most of the costs. The CASPRT explained significant additional variance beyond the Big Five personality traits in predicting important life outcomes, with incremental R² values ranging from .017 to .111 across different behaviors.

Why This Matters

This research suggests that not all risk-taking is created equal. The key isn't whether you take risks, but how you take them. For parents, educators, and managers, this means that rather than trying to eliminate risk-taking entirely, the goal should be helping people develop more calculated approaches to risk. For individuals, understanding your own risk-taking style can help you make better decisions about everything from career moves to investment choices.

Our findings also help resolve the puzzle of why previous research found risk-taking to be both beneficial and harmful - it depends entirely on which type of risk-taking we're measuring.

Factor Loadings

Item	General	Calculated	Spontaneous	h²	I-ECV
I tend to take calculated risks in my everyday life	0.592	0.503		0.603	0.580
I am willing to take risks after careful consideration of the potential outcomes	0.501	0.592		0.602	0.417
I consider myself a calculated risk taker	0.637	0.607		0.774	0.524
I believe taking calculated risks is important to achieve your goals	0.547	0.480		0.530	0.566
I enjoy taking risks that involve calculation	0.675	0.408		0.622	0.732
My friends would consider me as someone that takes calculated risks	0.711	0.435		0.695	0.728
I enjoy the thrill of taking risks without thinking	0.635		0.526	0.680	0.593
I am attracted to risky activities where I don't know what will happen	0.621		0.538	0.675	0.572
I don't think much about the consequences of risky decisions. I just make them	0.397		0.738	0.702	0.225
I find it difficult to resist the urge to take risks	0.586		0.584	0.684	0.502
I am a spontaneous risk taker	0.596		0.645	0.771	0.461
I often jump into risky situations without thinking through them	0.476		0.745	0.781	0.290

Note. h² = communality (proportion of item variance explained by all factors); I-ECV = Item Explained Common Variance (proportion of common variance explained by the general factor). Factor loadings marked with asterisks indicate statistical significance. All factor loadings are significant at p < .001

Measurement Invariance Testing

Model	χ²	df	RMSEA	SRMR	Δ SRMR	CFI	ΔCFI
Sex
Configural	279.31**	106	0.042	0.046		0.991
Metric	318.97**	116	0.044	0.049	0.003	0.990	-0.001
Scalar	334.02**	126	0.042	0.050	0.001	0.989	-0.001
Strict	353.56**	138	0.041	0.053	0.003	0.989	0.000
Age
Configural	312.99**	159	0.040	0.050		0.992
Metric	419.23**	179	0.047	0.055	0.005	0.988	-0.004
Scalar	457.43**	199	0.046	0.057	0.002	0.987	-0.001
Strict	535.71**	223	0.048	0.063	0.006	0.984	-0.003
Sample
Configural	325.46**	212	0.034	0.050		0.994
Metric	421.65**	242	0.040	0.055	0.005	0.991	0.003
Scalar	484.15**	272	0.041	0.058	0.003	0.990	0.001
Strict	565.34**	308	0.042	0.064	0.006	0.987	0.003

Notes. RMSEA = root mean square error of approximation; SRMR = standardized root mean squared residual; CFI = comparative fit index; TLI = Tucker–Lewis index, ** = p < .001

Sample Demographics (N = 1,860)

Variable	N	Calculated M (SD)	Spontaneous M (SD)
Total sample	1,860	3.34 (0.85)	1.93 (0.91)
Female	1,037	3.23 (0.86)	1.88 (0.91)
Male	807	3.47 (0.82)	1.99 (0.91)
Age 18-29	760	3.43 (0.76)	2.10 (0.94)
Age 30-49	782	3.35 (0.85)	1.90 (0.90)
Age 50-69	293	3.11 (0.95)	1.63 (0.78)
Age 70+	22	2.35 (1.05)	1.27 (0.53)
Race: Caucasian	1,474	3.29 (0.87)	1.92 (0.92)
Race: Black/African American	128	3.63 (0.69)	2.04 (0.95)
Race: Asian American	150	3.55 (0.77)	1.95 (0.85)
Race: Other	107	3.26 (0.76)	1.94 (0.87)

Outcome / Trait	Calculated	Spontaneous
Correlations with Risk Measures
Social	0.33	0.17
Recreational	0.38	0.45
Financial	0.45	0.45
Health	0.30	0.48
Ethical	0.29	0.46
Gambling	0.26	0.52
Investment	0.47	0.24
DOSPERT Sum	0.50	0.58
GRiPS	0.69	0.79
Correlations with Non-Risk Traits
Neuroticism	-0.20	0.06
Extraversion	0.45	0.31
Openness	0.16	0.08
Agreeableness	-0.16	-0.22
Conscientiousness	0.12	-0.26
Promotion	0.35	0.14
Prevention	-0.03	0.07
SNS	0.19	0.04
CRT	-0.03	-0.13
Berlin	-0.04	-0.09
NFC	0.24	0.05
Maximize	0.29	0.11
Rational	0.00	-0.36
Intuitive	0.15	0.40
Student Sample Correlations
Dropout	-0.01	0.07
Alcohol	0.19	0.21
CWB	0.04	0.03
Tardiness	0.00	0.18
Absenteeism	0.00	0.13
Working Sample Correlations
Voice Challenge	0.17	0.14
Voice Prohibitive	0.23	0.15
Creativity	0.28	0.20
CWBC	0.04	0.12
Status	0.18	0.09
PSRB Total	0.11	0.09
Safety Motivation	-0.07	-0.17
Income	0.17	-0.04
Adult Sample Correlations
Traffic Tickets	0.03	0.21
Speeding	0.17	0.19
Credit Debt	0.01	-0.02
Broken Bone	0.01	0.03
Romance	0.11	0.23
Cheat	0.01	0.16
Move	0.18	0.20
Job Change	0.11	0.10
Shoplifting	0.08	0.22
Car Accident	0.13	0.24
Crime Charge	0.10	0.27
Financial Stress	-0.13	-0.03
Credit Score	0.05	-0.17
Extreme Sport	0.15	0.21
Travel Insurance	0.14	-0.05
Drinks	-0.01	0.06
Entrepreneurship	0.39	0.29
Wellbeing	0.28	0.01
Gambling Behavior	0.15	0.25
Gambling Outcome	0.12	0.15

Incremental Validity Beyond Big Five

Outcome	R² Big Five	ΔR² CASPRT	Stronger Predictor
Creativity	0.142	0.067**	Calculated
Entrepreneurial Intent	0.095	0.111**	Calculated
Safety Violations	0.047	0.022*	Spontaneous
Criminal Charges	0.027	0.050**	Spontaneous
General Health	0.162	0.020*	Calculated
Wellbeing	0.412	0.017*	Calculated

*p < .05, **p < .01. CASPRT explains unique variance beyond Big Five personality traits.