Introduction

Since Kahneman and Tversky's seminal work on prospect theory, the influence of message framing on decision making has been extensively documented in laboratory settings. Yet many questions remain about the robustness of framing effects in applied contexts, particularly regarding financial decision making where expertise and prior knowledge may attenuate biases. Furthermore, most framing studies have employed small laboratory samples, raising questions about generalization to internet-based populations and real-world decision contexts.

The advent of web-based experimental methods now permits recruitment of larger, more diverse samples and naturalistic decision scenarios. The present study extended classical framing research into financial domains using an online platform, examining both the magnitude of frame effects and the role of individual differences in financial knowledge in moderating this bias. We predicted that loss framing would increase risk-seeking behaviour relative to gain framing, but that this effect would be reduced among participants with higher financial literacy.

Method

Participants

One hundred fifty-six participants (mean age = 38.7 years, SD = 11.4; 62% female) were recruited through an online participant panel maintained by a North American survey research firm. Participants completed a self-administered questionnaire assessing demographic information and financial literacy (9-item scale assessing knowledge of compound interest, inflation, diversification, and stock market concepts). The final sample was diverse in education (23% with university degrees) and income (range = $25,000-$195,000 CAD annually).

Procedure

Participants completed the experiment asynchronously in their homes via a secure web portal. The study presented 12 hypothetical financial investment scenarios, each describing an initial portfolio of $50,000. Half the scenarios employed a gain frame ("Your portfolio is expected to grow by $X"), while the other half presented identical expected outcomes in a loss frame ("Your portfolio is at risk of declining by $(50,000 - X)"). Within each frame condition, scenarios varied in the expected value of a conservative versus risky investment option. Participants indicated their choice (conservative or risky) on a seven-point Likert scale anchored by "definitely choose conservative" to "definitely choose risky." Session duration averaged 18 minutes. Presentation order of scenarios was randomized across participants. We measured financial literacy as a continuous predictor in the analysis.

Results

A 2 (Frame: gain vs. loss) × 2 (Scenario type: high variance vs. low variance) mixed-model ANOVA revealed a highly significant main effect of frame on risk-seeking responses, F(1, 154) = 24.8, p < .001, partial η² = .14. Participants in the loss-frame condition endorsed significantly more risk-seeking choices (M = 4.63, SD = 1.28) compared to those in the gain-frame condition (M = 3.15, SD = 1.42), a mean difference of 1.48 points on the seven-point scale. This pattern held across all 12 scenarios individually (all ps < .05). Regression analysis incorporating financial literacy as a continuous moderator revealed that financial literacy significantly attenuated the frame effect, β = -.31, t(150) = 3.87, p < .001. For participants at the 75th percentile of financial literacy, the frame effect was reduced by approximately 60%.

Item response analysis showed that the frame effect was strongest for scenarios with intermediate expected values (where the optimal choice was ambiguous) and weakest for scenarios with highly asymmetric risk-return profiles. Time-course analysis revealed that frame effects emerged early in the decision process and did not diminish across the 12 trials, suggesting that participants did not learn the structure of the decision problem over time.

Discussion

These findings extend classical framing effects into applied financial decision contexts and demonstrate their persistence in web-based experiments with large, heterogeneous samples. The robust loss-frame effect is consistent with prospect theory's prediction that loss-framed decisions activate more risk-seeking responses as individuals adopt more aggressive positions to avoid losses. The moderating role of financial knowledge aligns with dual-process theories proposing that deliberative, system-two processing can override heuristic biases when decision makers possess relevant domain expertise.

These results have practical implications for financial institutions and policy makers. Investment firms that present portfolio options using loss frames may unintentionally increase client risk-taking behaviour and subsequent dissatisfaction with outcome volatility. Moreover, the finding that expertise protects against framing effects suggests that financial education and literacy programs may serve as protective factors against frame-induced decision biases. Future work employing think-aloud protocols and eye-tracking methodology could illuminate the cognitive processes mediating frame effects in financial decision making.

References

  • Kahneman, D., & Tversky, A. (1979). Prospect theory: an analysis of decision under risk. Econometrica, 47(2), 263-292.
  • Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211(4481), 453-458.
  • Stanovich, K. E., & West, R. F. (2000). Individual differences in reasoning: implications for the rationality debate? Behavioral and Brain Sciences, 23(5), 645-665.
  • Lusardi, A., & Mitchell, O. S. (2007). Baby boomer retirement security: the roles of planning, financial literacy, and housing wealth. Journal of Monetary Economics, 54(1), 205-224.
  • Shafir, E., Diamond, P., & Tversky, A. (1997). Money illusion. Quarterly Journal of Economics, 112(2), 341-374.
  • Weber, E. U., & Johnson, E. J. (2009). Mindful judgment and decision making. Journal of Consumer Psychology, 19(1), 4-8.