Paradoxes of Online Investing: Testing the Influence of Technology on User Expectancies*
Bibliographic record
Abstract
ABSTRACT At an increasing rate, individual investors are taking personal control over their financial destinies by investing their money online. Compared to offline do‐it‐yourself approaches, evidence suggests that investors exhibit lofty expectations and perform significantly worse after going online. However, little is understood about the mechanisms fueling expectancies, the role technologies play in their formation, or how technologies shape investment decisions. Therefore, this article explores the paradoxical nature of online investing technologies, which can give rise to a heightened state of conviction in one's capability to invest successfully. Drawing on Social Cognitive Theory, the concepts of encapsulation and combination are introduced to develop a research model describing how functional and technical self‐efficacy judgments independently and collectively shape and influence outcome expectancies. The results suggest that perceptions about what one can accomplish through online investing technologies can lead investors to exaggerate their capabilities, which, in turn, produces elevated expectancies of financial payoffs and nonmonetary rewards. These findings carry important implications. In tasks requiring both computing and functional skills, the principals of encapsulation and combination highlight the importance of comprehensively capturing self‐efficacy beliefs across skill domain boundaries. Moreover, online investing represents a paradoxical case that challenges the traditional assumption that fostering a robust sense of efficacy represents a purely noble enterprise. In fact, strong self‐efficacy beliefs can prove counterproductive, leading to severe, irreversible, and unintended consequences. Going forward, these discoveries provide a solid foundation to enhance systems designs and facilitate a deeper understanding of user psychology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".