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TRUVACE RECORD VERSION
record: TRV-2026-2797
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:25:13.377968Z
status: published
lens: p_space
sector: science
headline: The dark side of artificial intelligence in marketing: meta-analytics review
dek: Purpose Artificial intelligence (AI) has become a pivotal technology in both marketing and daily life. Despite extensive research on the benefits of AI, its adverse effects on customers have received limited attention. Design/methodology/approach We employed meta-analysis to synthesise effect sizes from 45 studies encompassing 50 independent samples (N = 19,503) to illuminate the negative facets of AI's impact on customer responses. Findings Adverse effects of AI, including privacy concern, perceived risks, cust…
gain_title: (none)
problem_title: A meta-analysis of 45 studies (N = 19,503) found that privacy concern, perceived risk, customer alienation and uniqueness neglect from AI had significant negative effects on customers' trust, satisfaction, purchasing, loyalty and well-being.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: A meta-analysis of 45 studies (N = 19,503) found that privacy concern, perceived risk, customer alienation and uniqueness neglect from AI had significant negative effects on customers' trust, satisfaction, purchasing, loyalty and well-being.
problem_evidence: have a negative and significant effect on customers' cognitive (perceived benefit, trust), affective (attitude and satisfaction) and behavioural responses (purchase, loyalty, well-being)
quick_read: A meta-analysis pooled effect sizes from 45 studies covering 50 independent samples and 19,503 people to measure how AI's negative aspects shape customer responses in marketing. Privacy concern, perceived risks, customer alienation and uniqueness neglect each had a negative and significant effect.

The harms reached customers' thinking (perceived benefit, trust), feelings (attitude, satisfaction) and behaviour (purchase, loyalty, well-being). The authors describe the work as among the first to synthesise research on the dark side of AI in marketing.

The size of the effects varied with context: whether AI was online or offline, customer age and gender, product type, firm type and national culture all moderated the relationships. The authors say marketing managers should weigh these drawbacks and policymakers should consider vulnerable groups.
limitation: 
tag: Evidence-backed problem
key_points: The meta-analysis combined 45 studies with 50 independent samples and 19,503 participants. | Four adverse AI factors were examined: privacy concern, perceived risks, customer alienation and uniqueness neglect. | Each had a significant negative effect on perceived benefit and trust. | Attitude and satisfaction also fell, and purchase, loyalty and well-being were also affected. | Effects differed by online versus offline AI, customer age and gender, hedonic versus utilitarian products, and high versus low involvement. | Service versus manufacturing firms, and national traits such as individualism, power distance, masculinity, uncertainty avoidance and long-term orientation, also moderated the effects.
rundown: The researchers synthesised effect sizes from earlier studies rather than running a new experiment. The pool covered 45 studies and 50 independent samples with 19,503 participants in total, aimed at the negative side of AI that they say has had limited attention compared with its benefits.

Adverse effects were grouped as privacy concern, perceived risks, customer alienation and uniqueness neglect. These lowered customers' cognitive responses (perceived benefit, trust), affective responses (attitude, satisfaction) and behavioural responses (purchase, loyalty, well-being).

Moderators shaped the strength of these effects, including AI setting (online or offline), customer age and gender, product type, level of involvement, firm sector and national cultural dimensions. The authors recommend that managers find ways to minimise these effects in different contexts and that policymakers pay attention to vulnerable groups.
sources:
- peer_reviewed | Marketing Intelligence & Planning | https://doi.org/10.1108/mip-09-2023-0494 | 2024-06-05
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