{"id":"https://openalex.org/W7119004092","doi":"https://doi.org/10.48550/arxiv.2601.00869","title":"Cultural Encoding in Large Language Models: The Existence Gap in AI-Mediated Brand Discovery","display_name":"Cultural Encoding in Large Language Models: The Existence Gap in AI-Mediated Brand Discovery","publication_year":2025,"publication_date":"2025-12-30","ids":{"openalex":"https://openalex.org/W7119004092","doi":"https://doi.org/10.48550/arxiv.2601.00869"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.00869","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.00869","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2601.00869","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5122230363","display_name":"Huang Junyao","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Junyao, Huang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122193993","display_name":"Situ Ruimin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ruimin, Situ","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5122135331","display_name":"Ye Renqin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Renqin, Ye","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5122230363"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13910","display_name":"Computational and Text Analysis Methods","score":0.11919999867677689,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T13910","display_name":"Computational and Text Analysis Methods","score":0.11919999867677689,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12128","display_name":"AI in Service Interactions","score":0.08049999922513962,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10609","display_name":"Digital Marketing and Social Media","score":0.0729999989271164,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/operationalization","display_name":"Operationalization","score":0.6488999724388123},{"id":"https://openalex.org/keywords/omnipresence","display_name":"Omnipresence","score":0.6434999704360962},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.45590001344680786},{"id":"https://openalex.org/keywords/competitive-advantage","display_name":"Competitive advantage","score":0.36579999327659607},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.3555999994277954},{"id":"https://openalex.org/keywords/semiotics","display_name":"Semiotics","score":0.3538999855518341},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.32899999618530273},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.32190001010894775}],"concepts":[{"id":"https://openalex.org/C9354725","wikidata":"https://www.wikidata.org/wiki/Q286017","display_name":"Operationalization","level":2,"score":0.6488999724388123},{"id":"https://openalex.org/C2778612188","wikidata":"https://www.wikidata.org/wiki/Q1062803","display_name":"Omnipresence","level":2,"score":0.6434999704360962},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.45590001344680786},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.4214000105857849},{"id":"https://openalex.org/C112698675","wikidata":"https://www.wikidata.org/wiki/Q37038","display_name":"Advertising","level":1,"score":0.382099986076355},{"id":"https://openalex.org/C58546491","wikidata":"https://www.wikidata.org/wiki/Q1150207","display_name":"Competitive advantage","level":2,"score":0.36579999327659607},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3571000099182129},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.3555999994277954},{"id":"https://openalex.org/C139997677","wikidata":"https://www.wikidata.org/wiki/Q60195","display_name":"Semiotics","level":2,"score":0.3538999855518341},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.3296999931335449},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.32899999618530273},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.32190001010894775},{"id":"https://openalex.org/C2777680339","wikidata":"https://www.wikidata.org/wiki/Q218202","display_name":"Nike","level":2,"score":0.29109999537467957},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.28859999775886536},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.28380000591278076},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.2705000042915344},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.2702000141143799},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.26989999413490295},{"id":"https://openalex.org/C125209646","wikidata":"https://www.wikidata.org/wiki/Q1338878","display_name":"Cultural diversity","level":2,"score":0.26989999413490295},{"id":"https://openalex.org/C23213687","wikidata":"https://www.wikidata.org/wiki/Q301468","display_name":"Consumer behaviour","level":2,"score":0.267300009727478},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.26579999923706055},{"id":"https://openalex.org/C57375061","wikidata":"https://www.wikidata.org/wiki/Q453181","display_name":"Brand management","level":2,"score":0.263700008392334},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.257999986410141},{"id":"https://openalex.org/C27564746","wikidata":"https://www.wikidata.org/wiki/Q913709","display_name":"Market research","level":2,"score":0.2540999948978424}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.00869","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.00869","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2601.00869","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.00869","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"As":[0],"artificial":[1],"intelligence":[2],"systems":[3],"increasingly":[4],"mediate":[5],"consumer":[6],"information":[7],"discovery,":[8],"brands":[9,92,184],"face":[10],"algorithmic":[11],"invisibility.":[12],"This":[13,69],"study":[14,109],"investigates":[15],"Cultural":[16],"Encoding":[17],"in":[18,26,72,100,120,125,201],"Large":[19],"Language":[20],"Models":[21],"(LLMs)":[22],"--":[23,80,83,159,167],"systematic":[24],"differences":[25],"brand":[27,59,161],"recommendations":[28],"arising":[29],"from":[30,94],"training":[31,77,96],"data":[32,78],"composition.":[33],"Analyzing":[34],"1,909":[35],"pure-English":[36],"queries":[37],"across":[38,163],"6":[39],"LLMs":[40,53,64,122],"(GPT-4o,":[41],"Claude,":[42],"Gemini,":[43],"Qwen3,":[44],"DeepSeek,":[45],"Doubao)":[46],"and":[47,194],"30":[48],"brands,":[49],"we":[50,129,141,178],"find":[51],"Chinese":[52,121],"exhibit":[54],"30.6":[55],"percentage":[56],"points":[57],"higher":[58],"mention":[60,118],"rates":[61],"than":[62],"International":[63,126],"(88.9%":[65],"vs.":[66],"58.3%,":[67],"p<.001).":[68],"disparity":[70],"persists":[71],"identical":[73],"English":[74],"queries,":[75],"indicating":[76],"geography":[79],"not":[81],"language":[82],"drives":[84],"the":[85,89,143,169,204,212],"effect.":[86],"We":[87,155],"introduce":[88],"Existence":[90],"Gap:":[91],"absent":[93],"LLM":[95,164],"corpora":[97],"lack":[98],"\"existence\"":[99],"AI":[101],"responses":[102],"regardless":[103],"of":[104,110,206,214],"quality.":[105],"Through":[106],"a":[107,113,151,207],"case":[108],"Zhizibianjie":[111],"(OmniEdge),":[112],"collaboration":[114],"platform":[115],"with":[116],"65.6%":[117],"rate":[119],"but":[123],"0%":[124],"models":[127],"(p<.001),":[128],"demonstrate":[130],"how":[131],"Linguistic":[132],"Boundary":[133],"Barriers":[134],"create":[135],"invisible":[136],"market":[137],"entry":[138],"obstacles.":[139],"Theoretically,":[140],"contribute":[142],"Data":[144,187],"Moat":[145],"Framework,":[146],"conceptualizing":[147],"AI-visible":[148],"content":[149],"as":[150,168],"VRIN":[152],"strategic":[153,170],"resource.":[154],"operationalize":[156],"Algorithmic":[157],"Omnipresence":[158],"comprehensive":[160],"visibility":[162],"knowledge":[165],"bases":[166],"objective":[171],"for":[172,183],"Generative":[173],"Engine":[174],"Optimization":[175],"(GEO).":[176],"Managerially,":[177],"provide":[179],"an":[180],"18-month":[181],"roadmap":[182],"to":[185],"build":[186],"Moats":[188],"through":[189],"semantic":[190],"coverage,":[191],"technical":[192],"depth,":[193],"cultural":[195],"localization.":[196],"Our":[197],"findings":[198],"reveal":[199],"that":[200],"AI-mediated":[202],"markets,":[203],"limits":[205,213],"brand's":[208],"\"Data":[209],"Boundaries\"":[210],"define":[211],"its":[215],"\"Market":[216],"Frontiers.\"":[217]},"counts_by_year":[],"updated_date":"2026-01-08T20:10:11.968330","created_date":"2026-01-08T00:00:00"}