{"id":"https://openalex.org/W2151045536","doi":"https://doi.org/10.1109/soli.2012.6273573","title":"Does selection bias blind performance diagnostics of business decision models? A case study in salesforce optimization","display_name":"Does selection bias blind performance diagnostics of business decision models? A case study in salesforce optimization","publication_year":2012,"publication_date":"2012-07-01","ids":{"openalex":"https://openalex.org/W2151045536","doi":"https://doi.org/10.1109/soli.2012.6273573","mag":"2151045536"},"language":"en","primary_location":{"id":"doi:10.1109/soli.2012.6273573","is_oa":false,"landing_page_url":"https://doi.org/10.1109/soli.2012.6273573","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of 2012 IEEE International Conference on Service Operations and Logistics, and Informatics","raw_type":"proceedings-article"},"type":"article","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/A5067091850","display_name":"Jun Wang","orcid":"https://orcid.org/0000-0002-6547-3576"},"institutions":[{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jun Wang","raw_affiliation_strings":["Business Analytics and Mathematical Sciences Department, IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA"],"affiliations":[{"raw_affiliation_string":"Business Analytics and Mathematical Sciences Department, IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I4210114115"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110922516","display_name":"Moninder Singh","orcid":null},"institutions":[{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Moninder Singh","raw_affiliation_strings":["Business Analytics and Mathematical Sciences Department, IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA"],"affiliations":[{"raw_affiliation_string":"Business Analytics and Mathematical Sciences Department, IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I4210114115"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015286159","display_name":"Kush R. Varshney","orcid":"https://orcid.org/0000-0002-7376-5536"},"institutions":[{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kush R. Varshney","raw_affiliation_strings":["Business Analytics and Mathematical Sciences Department, IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA"],"affiliations":[{"raw_affiliation_string":"Business Analytics and Mathematical Sciences Department, IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I4210114115"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5067091850"],"corresponding_institution_ids":["https://openalex.org/I4210114115"],"apc_list":null,"apc_paid":null,"fwci":1.0186,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.81745474,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"84","issue":null,"first_page":"416","last_page":"421"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10328","display_name":"Supply Chain and Inventory Management","score":0.9972000122070312,"subfield":{"id":"https://openalex.org/subfields/1404","display_name":"Management Information Systems"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10328","display_name":"Supply Chain and Inventory Management","score":0.9972000122070312,"subfield":{"id":"https://openalex.org/subfields/1404","display_name":"Management Information Systems"},"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9933000206947327,"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/T11918","display_name":"Forecasting Techniques and Applications","score":0.9900000095367432,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7064054608345032},{"id":"https://openalex.org/keywords/selection-bias","display_name":"Selection bias","score":0.6922762393951416},{"id":"https://openalex.org/keywords/multinational-corporation","display_name":"Multinational corporation","score":0.6314283013343811},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.6200901865959167},{"id":"https://openalex.org/keywords/business-analytics","display_name":"Business analytics","score":0.5991198420524597},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.584343433380127},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.5136675238609314},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.4776493310928345},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.46349024772644043},{"id":"https://openalex.org/keywords/decision-support-system","display_name":"Decision support system","score":0.41840681433677673},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3071501851081848},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.22086912393569946},{"id":"https://openalex.org/keywords/business-model","display_name":"Business model","score":0.21491840481758118},{"id":"https://openalex.org/keywords/business-analysis","display_name":"Business analysis","score":0.2092677652835846},{"id":"https://openalex.org/keywords/marketing","display_name":"Marketing","score":0.13610532879829407},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.13564258813858032}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7064054608345032},{"id":"https://openalex.org/C40423286","wikidata":"https://www.wikidata.org/wiki/Q284172","display_name":"Selection bias","level":2,"score":0.6922762393951416},{"id":"https://openalex.org/C158016649","wikidata":"https://www.wikidata.org/wiki/Q161726","display_name":"Multinational corporation","level":2,"score":0.6314283013343811},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.6200901865959167},{"id":"https://openalex.org/C37952496","wikidata":"https://www.wikidata.org/wiki/Q5001829","display_name":"Business analytics","level":4,"score":0.5991198420524597},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.584343433380127},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.5136675238609314},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.4776493310928345},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.46349024772644043},{"id":"https://openalex.org/C107327155","wikidata":"https://www.wikidata.org/wiki/Q330268","display_name":"Decision support system","level":2,"score":0.41840681433677673},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3071501851081848},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.22086912393569946},{"id":"https://openalex.org/C4216890","wikidata":"https://www.wikidata.org/wiki/Q815823","display_name":"Business model","level":2,"score":0.21491840481758118},{"id":"https://openalex.org/C189076506","wikidata":"https://www.wikidata.org/wiki/Q1518232","display_name":"Business analysis","level":3,"score":0.2092677652835846},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.13610532879829407},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.13564258813858032},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/soli.2012.6273573","is_oa":false,"landing_page_url":"https://doi.org/10.1109/soli.2012.6273573","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of 2012 IEEE International Conference on Service Operations and Logistics, and Informatics","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W74272320","https://openalex.org/W1493044792","https://openalex.org/W1506399260","https://openalex.org/W1969261616","https://openalex.org/W2149454242","https://openalex.org/W2154053567","https://openalex.org/W2997674406","https://openalex.org/W3144510026","https://openalex.org/W6602969359","https://openalex.org/W6682496738"],"related_works":["https://openalex.org/W4367856707","https://openalex.org/W2725836824","https://openalex.org/W3041288053","https://openalex.org/W3033964479","https://openalex.org/W3068949829","https://openalex.org/W2081084322","https://openalex.org/W3005175923","https://openalex.org/W4308701384","https://openalex.org/W3188416226","https://openalex.org/W3023376933"],"abstract_inverted_index":{"Modern":[0],"business":[1,57,82,101,109,176],"decision":[2,19],"models":[3,20],"are":[4],"often":[5],"very":[6,145],"complicated":[7],"due":[8,24],"to":[9,25,44,73,173],"a":[10,46,71,80,129,174,180,194],"deluge":[11],"of":[12,17,33,56,91,105,108,113,128,179,192],"information.":[13],"Evaluation":[14],"and":[15,31,54,133,147],"diagnostics":[16,55,162],"such":[18,153,193],"is":[21,39],"extremely":[22],"challenging":[23],"many":[26],"factors,":[27],"including":[28],"the":[29,92,100,106,114,124,137,142,185,190],"complexity":[30],"volume":[32],"data.":[34],"In":[35,66,84],"addition,":[36],"since":[37],"there":[38],"no":[40],"ideal":[41],"data":[42,127],"sample":[43,76,155],"construct":[45],"control":[47],"group":[48],"for":[49,199],"comparison":[50],"studies,":[51],"performance":[52,107],"evaluation":[53,104,144,197],"actions":[58,110],"can":[59,159],"easily":[60],"be":[61],"distorted":[62],"by":[63,151],"selection":[64,97,116,138],"bias.":[65,117],"this":[67,75],"paper,":[68],"we":[69,86],"design":[70],"framework":[72,158,198],"analyze":[74],"bias":[77,98,139,195],"issue":[78,140],"under":[79],"practical":[81],"scenario.":[83],"particular,":[85],"focus":[87],"on:":[88],"a)":[89],"identification":[90],"key":[93],"factors":[94],"which":[95],"drive":[96],"during":[99],"decision;":[102],"b)":[103],"with":[111],"consideration":[112],"identified":[115],"We":[118],"evaluate":[119],"baseline":[120],"analytics":[121,186],"tools":[122],"on":[123],"worldwide":[125,175],"sales-force":[126,200],"large":[130],"global":[131],"corporation":[132],"clearly":[134,188],"demonstrate":[135],"that":[136],"makes":[141],"usual":[143],"unstable":[146],"not":[148],"trustable.":[149],"However,":[150],"removing":[152],"detected":[154],"bias,":[156],"our":[157],"generate":[160],"reasonable":[161],"results":[163,187],"across":[164],"different":[165],"dimensions.":[166],"The":[167],"implemented":[168],"analysis":[169],"tool":[170],"was":[171],"applied":[172],"opportunity":[177],"dataset":[178],"multinational":[181],"Fortune":[182],"500":[183],"corporation;":[184],"show":[189],"significance":[191],"detection-based":[196],"optimization.":[201]},"counts_by_year":[{"year":2013,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
