The exact text we saved

This is the version exactly as it was saved, character for character. The SHA-256 hash below is a 64-character code worked out from that text. Run the same text through SHA-256 and you get the same code; change a single letter and the code comes out completely different. Each version also stores the previous version’s hash, which chains them together so no version can be swapped out quietly.

TRUVACE RECORD VERSION
record: TRV-2026-2726
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:15:22.600886Z
status: published
lens: p_space
sector: business
headline: Dark Side of Artificial Intelligence: A Meta‐Analytic Review
dek: ABSTRACT The rapid integration of artificial intelligence (AI) into consumer interactions has sparked concerns about its darker implications. When AI‐enabled service encounters are perceived as error‐prone, socially disruptive, emotionally misattuned, privacy‐invasive, or ethically questionable, consumers may develop distrust and resistance towards adoption. Despite growing scholarly attention, research on AI‐related risks and consumer resistance remains fragmented and empirically inconsistent, limiting clarity…
gain_title: (none)
problem_title: A meta-analysis of 30 studies with 109,758 participants found that privacy and security risk and ethical risk were the strongest predictors of consumer distrust of AI services, and that distrust in turn fuelled resistance to using them.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: A meta-analysis of 30 studies with 109,758 participants found that privacy and security risk and ethical risk were the strongest predictors of consumer distrust of AI services, and that distrust in turn fuelled resistance to using them.
problem_evidence: privacy and security risk and ethical risk emerge as the strongest predictors of distrust, which in turn fuels resistance behavior
quick_read: A meta-analysis pooled 302 effect sizes from 30 empirical studies covering 109,758 participants to test why consumers distrust and resist AI-enabled services. It found privacy and security risk and ethical risk were the strongest predictors of distrust, and that distrust fuels resistance behavior.

The authors compared five risk types: performance, social, emotional, privacy and security, and ethical. Using meta-analytic structural equation modeling, they tested the path from distrust to resistance, which they say earlier research had left fragmented and inconsistent.

The size of these effects was not uniform. It varied with gender composition, economic development, cultural orientation and digital readiness, which the authors say helps explain why earlier studies disagreed.
limitation: 
tag: Evidence-backed problem
key_points: The analysis combined 30 empirical studies, 302 independent effect sizes and 109,758 participants. | It was grounded in innovation resistance theory and the theory of distrust. | Privacy and security risk and ethical risk were the strongest predictors of consumer distrust of AI. | Distrust in turn fuelled resistance to adopting AI in services, tested with meta-analytic structural equation modeling. | Social and emotional risks mattered more in developing economies and collectivist cultures. | Ethical concerns were more salient in digitally mature regions. | Gender composition, economic development, cultural orientation and digital readiness moderated the relationships.
rundown: The review addresses AI-enabled service encounters that consumers see as error-prone, socially disruptive, emotionally misattuned, privacy-invasive or ethically questionable. The authors say such perceptions can produce distrust and resistance to adoption.

Pooling the evidence, the authors estimated the effects of five risk dimensions on distrust and then tested whether distrust translates into resistance. Privacy and security risk and ethical risk came out strongest.

Moderator analyses showed the patterns differ by context: social and emotional risks weighed more in developing economies and collectivist cultures, while ethical concerns weighed more in digitally mature regions. The authors offer guidance for AI developers, policymakers and marketers on trust-centered adoption and context-sensitive design.
sources:
- peer_reviewed | Journal of Consumer Behaviour | https://doi.org/10.1002/cb.70144 | 2026-03-17
prev: 0000000000000000000000000000000000000000000000000000000000000000

SHA-256 hash

ce7ecf3f6b690d278d5e23d170dd0e2ccae059bbe91b02277b15dbd17760fd91

Previous version’s hash

0000000000000000000000000000000000000000000000000000000000000000