- Title
- The dark side of artificial intelligence in marketing: meta-analytics review
- Creator
- Barari, Mojtaba; Ferm, Lars-Erik Casper; Quach, Sara; Thaichon, Park; Ngo, Liem
- Relation
- Marketing Intelligence & Planning Vol. 42, Issue 7, p. 1234-1256
- Publisher Link
- http://dx.doi.org/10.1108/MIP-09-2023-0494
- Publisher
- Emerald
- Resource Type
- journal article
- Date
- 2024
- Description
- 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, customer alienation, and uniqueness neglect, have a negative and significant effect on customers' cognitive (perceived benefit, trust), affective (attitude and satisfaction) and behavioural responses (purchase, loyalty, well-being). Additionally, moderators in AI (online versus offline), customer (age, male vs. female), product (hedonic vs. utilitarian, high vs. low involvement), and firm level (service vs. manufacturing) and national level (individualism, power distance, masculinity, uncertainty avoidance, long-term orientation) moderate these relationships. Practical implications: Our findings inform marketing managers about the drawbacks of utilising AI as part of their value proposition and provide recommendations on how to minimise these effects in different contexts. Additionally, policymakers need to consider the dark side of AI, especially among the vulnerable groups. Originality/value: This paper is among the first research studies that synthesise previous research on the dark side of AI, providing a comprehensive view of its diminishing impact on customer responses.
- Subject
- artificial intelligence; privacy; perceived risk; customer alienation; uniqueness neglect; meta-analysis
- Identifier
- http://hdl.handle.net/1959.13/1511878
- Identifier
- uon:56568
- Identifier
- ISSN:0263-4503
- Language
- eng
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