Eastern Ease and Western Worries? How collectivism and belief in magic increase user acceptance of operationally autonomous blackbox technologies
Bibliographic record
Abstract
New products and services increasingly require users to relinquish control to so-called “Blackbox” technologies, wherein connections between inputs and outputs appear opaque and uncontrollable. No research has yet investigated whether cultural factors moderate the effect of perceived technological controllability on user acceptance. Drawing on the Theory of Planned Behavior and Bandura's Theory of Human Agency, we examine how Individualism versus Collectivism influences the acceptance of products and services that require users to surrender control to the operationally autonomous Blackbox technologies of Artificial Intelligence and Blockchain. We furthermore investigate the impact of belief in the magical controllability of the world through, e.g., lucky numbers and charms (“belief in magic”). Through three survey-based experiments employing Scenario Techniques featuring fictitious products and services with a total sample size of n = 1750 respondents across the US, India, and Canada, our findings reveal that collectivism and belief in magic increase acceptance of operationally autonomous Blackbox technologies. Conversely, users endorsing the Western cultural norm of individualism and rejecting the magical controllability of the world exhibit heightened skepticism toward such innovations. The moderating effect of Individualism-Collectivism is mediated by a heightened Need for Controllability among individualistic users. Moreover, activating magical thinking amplifies the influence of belief in magic among users predisposed toward such beliefs. Our results stress the importance of considering cultural factors when designing and marketing operationally autonomous applications of Blackbox technologies. They also underscore the need for a broad societal debate and future research on the role of perceived controllability of a technology in user acceptance. • We study user acceptance of operationally autonomous applications of AI and Blockchain. • Individualism reduces user acceptance, due to a higher need for controllability. • Users with collectivist values also prefer high levels of control. • But they are more open to the convenience of autonomous Blackbox technologies. • A strong belief in magic (e.g., lucky numbers) increases user acceptance.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".