When AI advice backfires: Field experimental evidence on personalization, intrusiveness, and purchase behavior

As conversational artificial intelligence (AI) systems increasingly serve as frontline interfaces in retail, understanding how specific AI recommendation message styles shape consumer behavior has become a central concern. This study examines two alternative AI recommendation response styles - personalized and humorous message framing - and identifies the psychological mechanisms through which these styles influence purchasing decisions. Drawing on persuasion theory and the heuristic-systematic model, we propose a parallel dual-pathway framework in which response style affects purchase behavior through perceived helpfulness attribution and perceived intrusiveness, with shopping goal urgency and customer-retailer relationship strength acting as key boundary conditions. We employ a multi-method research design consisting of an exploratory qualitative study (46 semi-structured interviews) and a randomized field experiment conducted in a U.S. retail context (N = 409), in which purchase behavior is measured using secondary transaction data. The results show that personalized AI communication significantly increases the likelihood of purchase relative to humorous communication. This effect is driven by stronger attributions of helping intent, but is partially offset by heightened perceptions of intrusiveness, revealing personalized message framing as a double-edged strategy. Further analyses indicate that urgency amplifies both pathways, whereas stronger customer-retailer relationships enhance helpfulness attribution and attenuate intrusiveness concerns. Robustness checks confirm the stability of the findings across alternative specifications and outcome operationalizations. Overall, this study advances theory on human-AI interaction by linking AI recommendation message framing to observed consumer behavior and clarifying when personalization enhances or undermines behavioral effectiveness. • Personalized AI communication increases purchase behavior relative to humorous AI communication in a retail field experiment. • Personalization operates through competing mechanisms of perceived helpfulness attribution and perceived intrusiveness. • Shopping goal urgency amplifies both the positive and negative effects of personalized AI communication. • Customer–retailer relationship strength enhances helpfulness attribution while attenuating intrusiveness concerns.

Authors

Institutions

Publication Details

Journal
Journal of Retailing and Consumer Services
Published
2026-04-27
DOI
https://doi.org/10.1016/j.jretconser.2026.104844
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

When AI advice backfires: Field experimental evidence on personalization, intrusiveness, and purchase behavior

Gomaa Agag, Salisu Mohammed, Alwia Saeed Osman Zobair, Osman Alipour et al.
Journal of Retailing and Consumer Services
AI in Service Interactions
article

When AI advice backfires: Field experimental evidence on personalization, intrusiveness, and purchase behavior

Gomaa Agag, Salisu Mohammed, Alwia Saeed Osman Zobair, Osman Alipour, Mohammed Abdulaziz Almansour, Zainab Mohammed Osman Kodai
article en

Abstract

As conversational artificial intelligence (AI) systems increasingly serve as frontline interfaces in retail, understanding how specific AI recommendation message styles shape consumer behavior has become a central concern. This study examines two alternative AI recommendation response styles - personalized and humorous message framing - and identifies the psychological mechanisms through which these styles influence purchasing decisions. Drawing on persuasion theory and the heuristic-systematic model, we propose a parallel dual-pathway framework in which response style affects purchase behavior through perceived helpfulness attribution and perceived intrusiveness, with shopping goal urgency and customer-retailer relationship strength acting as key boundary conditions. We employ a multi-method research design consisting of an exploratory qualitative study (46 semi-structured interviews) and a randomized field experiment conducted in a U.S. retail context (N = 409), in which purchase behavior is measured using secondary transaction data. The results show that personalized AI communication significantly increases the likelihood of purchase relative to humorous communication. This effect is driven by stronger attributions of helping intent, but is partially offset by heightened perceptions of intrusiveness, revealing personalized message framing as a double-edged strategy. Further analyses indicate that urgency amplifies both pathways, whereas stronger customer-retailer relationships enhance helpfulness attribution and attenuate intrusiveness concerns. Robustness checks confirm the stability of the findings across alternative specifications and outcome operationalizations. Overall, this study advances theory on human-AI interaction by linking AI recommendation message framing to observed consumer behavior and clarifying when personalization enhances or undermines behavioral effectiveness. • Personalized AI communication increases purchase behavior relative to humorous AI communication in a retail field experiment. • Personalization operates through competing mechanisms of perceived helpfulness attribution and perceived intrusiveness. • Shopping goal urgency amplifies both the positive and negative effects of personalized AI communication. • Customer–retailer relationship strength enhances helpfulness attribution while attenuating intrusiveness concerns.

Journal of Retailing and Consumer ServicesVol. 92
Islamic Azad University, Tehran (IR), University of Nottingham (GB), Islamic Azad University, Science and Research Branch (IR), Imam Mohammad ibn Saud Islamic University (SA), Nottingham Trent University (GB), King Khalid University (SA)
Openalex Percentile: Top 5%
AI in Service Interactions
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.