{"id":"https://openalex.org/W7125925324","doi":"https://doi.org/10.1109/smc58881.2025.11343282","title":"A framework for designing explainable micro-level customer behavior models using causal discovery for retail management","display_name":"A framework for designing explainable micro-level customer behavior models using causal discovery for retail management","publication_year":2025,"publication_date":"2025-10-05","ids":{"openalex":"https://openalex.org/W7125925324","doi":"https://doi.org/10.1109/smc58881.2025.11343282"},"language":null,"primary_location":{"id":"doi:10.1109/smc58881.2025.11343282","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11343282","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101402171","display_name":"Shuang Chang","orcid":"https://orcid.org/0000-0003-4250-5106"},"institutions":[{"id":"https://openalex.org/I2252096349","display_name":"Fujitsu (Japan)","ror":"https://ror.org/038e2g226","country_code":"JP","type":"company","lineage":["https://openalex.org/I2252096349"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shuang Chang","raw_affiliation_strings":["Fujitsu Research, Fujitsu Ltd.,Kawasaki,Japan,211-8588"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fujitsu Research, Fujitsu Ltd.,Kawasaki,Japan,211-8588","institution_ids":["https://openalex.org/I2252096349"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112541296","display_name":"Shohei Yamane","orcid":null},"institutions":[{"id":"https://openalex.org/I2252096349","display_name":"Fujitsu (Japan)","ror":"https://ror.org/038e2g226","country_code":"JP","type":"company","lineage":["https://openalex.org/I2252096349"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shohei Yamane","raw_affiliation_strings":["Fujitsu Research, Fujitsu Ltd.,Kawasaki,Japan,211-8588"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fujitsu Research, Fujitsu Ltd.,Kawasaki,Japan,211-8588","institution_ids":["https://openalex.org/I2252096349"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066599523","display_name":"Koji Maruhashi","orcid":null},"institutions":[{"id":"https://openalex.org/I2252096349","display_name":"Fujitsu (Japan)","ror":"https://ror.org/038e2g226","country_code":"JP","type":"company","lineage":["https://openalex.org/I2252096349"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Koji Maruhashi","raw_affiliation_strings":["Fujitsu Research, Fujitsu Ltd.,Kawasaki,Japan,211-8588"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fujitsu Research, Fujitsu Ltd.,Kawasaki,Japan,211-8588","institution_ids":["https://openalex.org/I2252096349"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I2252096349"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.69448975,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"660","last_page":"663"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11814","display_name":"Advanced Manufacturing and Logistics Optimization","score":0.20589999854564667,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11814","display_name":"Advanced Manufacturing and Logistics Optimization","score":0.20589999854564667,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.2021999955177307,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11536","display_name":"Consumer Retail Behavior Studies","score":0.08959999680519104,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/realm","display_name":"Realm","score":0.49779999256134033},{"id":"https://openalex.org/keywords/consumer-behaviour","display_name":"Consumer behaviour","score":0.421099990606308},{"id":"https://openalex.org/keywords/causal-model","display_name":"Causal model","score":0.41359999775886536},{"id":"https://openalex.org/keywords/page-layout","display_name":"Page layout","score":0.37700000405311584},{"id":"https://openalex.org/keywords/design-science","display_name":"Design science","score":0.3328000009059906}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6534000039100647},{"id":"https://openalex.org/C2778757428","wikidata":"https://www.wikidata.org/wiki/Q1250464","display_name":"Realm","level":2,"score":0.49779999256134033},{"id":"https://openalex.org/C23213687","wikidata":"https://www.wikidata.org/wiki/Q301468","display_name":"Consumer behaviour","level":2,"score":0.421099990606308},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.41359999775886536},{"id":"https://openalex.org/C188985296","wikidata":"https://www.wikidata.org/wiki/Q868954","display_name":"Page layout","level":2,"score":0.37700000405311584},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3646000027656555},{"id":"https://openalex.org/C539667460","wikidata":"https://www.wikidata.org/wiki/Q2414942","display_name":"Management science","level":1,"score":0.3637000024318695},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.3546999990940094},{"id":"https://openalex.org/C2780103759","wikidata":"https://www.wikidata.org/wiki/Q5264375","display_name":"Design science","level":2,"score":0.3328000009059906},{"id":"https://openalex.org/C2986470052","wikidata":"https://www.wikidata.org/wiki/Q126793","display_name":"Retail industry","level":2,"score":0.31839999556541443},{"id":"https://openalex.org/C195094911","wikidata":"https://www.wikidata.org/wiki/Q14167904","display_name":"Process management","level":1,"score":0.3084000051021576},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.2863999903202057},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2766999900341034},{"id":"https://openalex.org/C13736549","wikidata":"https://www.wikidata.org/wiki/Q4489420","display_name":"Industrial engineering","level":1,"score":0.2718999981880188},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.27160000801086426},{"id":"https://openalex.org/C98825075","wikidata":"https://www.wikidata.org/wiki/Q485643","display_name":"Customer relationship management","level":2,"score":0.26989999413490295},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25839999318122864},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.25679999589920044}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc58881.2025.11343282","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11343282","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W2110130947","https://openalex.org/W2557632452","https://openalex.org/W2581172906","https://openalex.org/W2948579453","https://openalex.org/W3007289092","https://openalex.org/W3164330174","https://openalex.org/W4205467735","https://openalex.org/W4253172700","https://openalex.org/W4318688346","https://openalex.org/W4406612805"],"related_works":[],"abstract_inverted_index":{"Store":[0],"layout":[1,16,34,62,90,114],"design":[2,17,98],"is":[3,36],"of":[4,10,88,101],"paramount":[5],"importance":[6],"in":[7,141,146],"the":[8,15,33,67,85,118,135,155],"realm":[9],"retail":[11,81,124],"management.":[12],"To":[13,70],"enhance":[14],"from":[18],"a":[19,77,122],"micro-level":[20],"perspective,":[21],"building":[22],"accurate":[23],"and":[24,61,99,104,145],"explainable":[25,97,149],"agent-based":[26,48],"models":[27,49],"on":[28,106,113,121],"customers\u2019":[29,59,93,156],"behaviors":[30,60],"accounting":[31],"for":[32,80],"characteristics":[35,63,91],"critical":[37],"yet":[38,50],"challenging.":[39],"Various":[40],"methods":[41,140],"have":[42],"been":[43],"developed":[44],"to":[45,54,64,83,95,108],"build":[46],"data-driven":[47],"they":[51],"are":[52],"incapable":[53],"elucidate":[55],"causal":[56,86],"relations":[57],"between":[58],"explicitly":[65],"explaining":[66],"constructed":[68],"model.":[69],"fill":[71],"this":[72],"research":[73],"gap,":[74],"we":[75,132],"develop":[76],"framework":[78,120],"specialized":[79],"management":[82],"refine":[84],"understanding":[87],"how":[89],"influence":[92],"behaviors,":[94,103],"support":[96],"modeling":[100],"such":[102],"based":[105],"which":[107],"enable":[109],"retailers\u2019":[110],"informed":[111],"decisions":[112],"design.":[115],"By":[116],"applying":[117],"proposed":[119],"small":[123],"store":[125],"case":[126],"with":[127],"only":[128],"limited":[129],"real":[130],"data,":[131],"demonstrated":[133],"that":[134,151],"method":[136],"outperforms":[137],"conventional":[138],"optimization":[139],"saving":[142],"simulation":[143],"costs":[144],"constructing":[147],"an":[148],"model":[150],"more":[152],"accurately":[153],"replicates":[154],"in-store":[157],"traffic.":[158]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-01-29T00:00:00"}