Companies have increasingly deployed artificial intelligence (AI) systems to analyze vast amounts of data and make product and service recommendations to consumers. Despite the global prevalence of algorithmic recommendation systems, surprisingly little research explores the distinct tendencies of diverse populations and their cultural identity to accept algorithmic recommendations. This research addresses this gap by proposing that consumers with an interdependent cultural identity, commonly found among Eastern populations, are more receptive to algorithmic recommendations than consumers with an independent cultural identity, more prevalent in Western societies. Using a multi-method approach and building upon consumers’ belief that AI-generated algorithmic recommendations reflect group consensus and shared preferences derived from aggregated data, we theorize and demonstrate that the higher acceptance of algorithmic recommendations among interdependent consumers is driven by their greater desire to follow consensus, where the recommendations serve as social information (i.e., perceived social truth) that guide their choices. This research further identifies three theoretically driven and substantively relevant conditions demonstrating that this effect is contingent on whether the recommendation is perceived as a valuable source of information, whether the product category relies on subjective experience evaluation, and whether the objective of the recommendation is for self or other.
","abstract_html":"<p>Companies have increasingly deployed artificial intelligence (AI) systems to analyze vast amounts of data and make product and service recommendations to consumers. Despite the global prevalence of algorithmic recommendation systems, surprisingly little research explores the distinct tendencies of diverse populations and their cultural identity to accept algorithmic recommendations. This research addresses this gap by proposing that consumers with an interdependent cultural identity, commonly found among Eastern populations, are more receptive to algorithmic recommendations than consumers with an independent cultural identity, more prevalent in Western societies. Using a multi-method approach and building upon consumers’ belief that AI-generated algorithmic recommendations reflect group consensus and shared preferences derived from aggregated data, we theorize and demonstrate that the higher acceptance of algorithmic recommendations among interdependent consumers is driven by their greater desire to follow consensus, where the recommendations serve as social information (i.e., perceived social truth) that guide their choices. This research further identifies three theoretically driven and substantively relevant conditions demonstrating that this effect is contingent on whether the recommendation is perceived as a valuable source of information, whether the product category relies on subjective experience evaluation, and whether the objective of the recommendation is for self or other.</p>","abstract_has_math":false,"creators":["Zhang, Yuanyuan"],"institution":"The Graduate School and University Center of The City University of New York","degree_name":"Doctor of Philosophy","degree_level":"Doctoral","degree_discipline":"Business","degree_department":null,"school":null,"contributors":[],"advisors":["Diogo Hildebrand"],"committee_chairs":[],"committee_members":["Ana Valenzuela","Pragya Mathur","Stefano Puntoni"],"year":2025,"date_issued":"2025-06-01T07:00:00Z","date_published":"2025-06-01T07:00:00Z","updated_at":"2026-07-24T01:59:30Z","subjects":["Marketing","Algorithmic recommendations","cultural identity","human-technology interaction","cross-cultural research","consumer behavior","artificial intelligence"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://academicworks.cuny.edu/gc_etds/6210","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Diogo Hildebrand"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Ana Valenzuela","Pragya Mathur","Stefano Puntoni"]},{"key":"dc:creator","label":"Author","values":["Zhang, Yuanyuan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2027-06-01T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Business"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["The Graduate School and University Center of The City University of New York"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Marketing","Algorithmic recommendations","cultural identity","human-technology interaction","cross-cultural research","consumer behavior","artificial intelligence"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://academicworks.cuny.edu/gc_etds/6210"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Companies have increasingly deployed artificial intelligence (AI) systems to analyze vast amounts of data and make product and service recommendations to consumers. Despite the global prevalence of algorithmic recommendation systems, surprisingly little research explores the distinct tendencies of diverse populations and their cultural identity to accept algorithmic recommendations. This research addresses this gap by proposing that consumers with an interdependent cultural identity, commonly found among Eastern populations, are more receptive to algorithmic recommendations than consumers with an independent cultural identity, more prevalent in Western societies. Using a multi-method approach and building upon consumers’ belief that AI-generated algorithmic recommendations reflect group consensus and shared preferences derived from aggregated data, we theorize and demonstrate that the higher acceptance of algorithmic recommendations among interdependent consumers is driven by their greater desire to follow consensus, where the recommendations serve as social information (i.e., perceived social truth) that guide their choices. This research further identifies three theoretically driven and substantively relevant conditions demonstrating that this effect is contingent on whether the recommendation is perceived as a valuable source of information, whether the product category relies on subjective experience evaluation, and whether the objective of the recommendation is for self or other.
"]},{"key":"dc:title","label":"Title","values":["“We” Listen to Algorithms: How Cultural Identity Influences Acceptance of Algorithmic Recommendations"]}]}],"canonical_facts":{"dc:contributor.advisor":["Diogo Hildebrand"],"dc:contributor.committeemember":["Ana Valenzuela","Pragya Mathur","Stefano Puntoni"],"dc:creator":["Zhang, Yuanyuan"],"dc:date.available":["2027-06-01T07:00:00Z"],"dc:description.abstract":["Companies have increasingly deployed artificial intelligence (AI) systems to analyze vast amounts of data and make product and service recommendations to consumers. Despite the global prevalence of algorithmic recommendation systems, surprisingly little research explores the distinct tendencies of diverse populations and their cultural identity to accept algorithmic recommendations. This research addresses this gap by proposing that consumers with an interdependent cultural identity, commonly found among Eastern populations, are more receptive to algorithmic recommendations than consumers with an independent cultural identity, more prevalent in Western societies. Using a multi-method approach and building upon consumers’ belief that AI-generated algorithmic recommendations reflect group consensus and shared preferences derived from aggregated data, we theorize and demonstrate that the higher acceptance of algorithmic recommendations among interdependent consumers is driven by their greater desire to follow consensus, where the recommendations serve as social information (i.e., perceived social truth) that guide their choices. This research further identifies three theoretically driven and substantively relevant conditions demonstrating that this effect is contingent on whether the recommendation is perceived as a valuable source of information, whether the product category relies on subjective experience evaluation, and whether the objective of the recommendation is for self or other.
"],"dc:identifier":["https://academicworks.cuny.edu/gc_etds/6210"],"dc:subject":["Marketing","Algorithmic recommendations","cultural identity","human-technology interaction","cross-cultural research","consumer behavior","artificial intelligence"],"dc:title":["“We” Listen to Algorithms: How Cultural Identity Influences Acceptance of Algorithmic Recommendations"],"thesis:degree_discipline":["Business"],"thesis:degree_level":["Doctoral"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["The Graduate School and University Center of The City University of New York"]},"updated_at":"2026-07-24T01:59:30Z"}