{"id":"https://openalex.org/W2577567775","doi":"https://doi.org/10.1108/imds-12-2015-0509","title":"Predicting customer churn in mobile industry using data mining technology","display_name":"Predicting customer churn in mobile industry using data mining technology","publication_year":2017,"publication_date":"2017-01-13","ids":{"openalex":"https://openalex.org/W2577567775","doi":"https://doi.org/10.1108/imds-12-2015-0509","mag":"2577567775"},"language":"en","primary_location":{"id":"doi:10.1108/imds-12-2015-0509","is_oa":false,"landing_page_url":"https://doi.org/10.1108/imds-12-2015-0509","pdf_url":null,"source":{"id":"https://openalex.org/S37320504","display_name":"Industrial Management & Data Systems","issn_l":"0263-5577","issn":["0263-5577","1758-5783"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Industrial Management & Data Systems","raw_type":"journal-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/A5056058238","display_name":"Eui-Bang Lee","orcid":"https://orcid.org/0000-0003-4762-0541"},"institutions":[{"id":"https://openalex.org/I148751991","display_name":"Sogang University","ror":"https://ror.org/056tn4839","country_code":"KR","type":"education","lineage":["https://openalex.org/I148751991"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Eui-Bang Lee","raw_affiliation_strings":["Department of Business Administration, Sogang University, Seoul, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Business Administration, Sogang University, Seoul, Korea","institution_ids":["https://openalex.org/I148751991"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053002922","display_name":"Jinwha Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I148751991","display_name":"Sogang University","ror":"https://ror.org/056tn4839","country_code":"KR","type":"education","lineage":["https://openalex.org/I148751991"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jinwha Kim","raw_affiliation_strings":["Department of Business Administration, Sogang University, Seoul, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Business Administration, Sogang University, Seoul, Korea","institution_ids":["https://openalex.org/I148751991"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079130446","display_name":"Sang-Gun Lee","orcid":null},"institutions":[{"id":"https://openalex.org/I148751991","display_name":"Sogang University","ror":"https://ror.org/056tn4839","country_code":"KR","type":"education","lineage":["https://openalex.org/I148751991"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sang-Gun Lee","raw_affiliation_strings":["Department of Business Administration, Sogang University, Seoul, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Business Administration, Sogang University, Seoul, Korea","institution_ids":["https://openalex.org/I148751991"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I148751991"],"apc_list":null,"apc_paid":null,"fwci":8.3042,"has_fulltext":false,"cited_by_count":63,"citation_normalized_percentile":{"value":0.97042605,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"117","issue":"1","first_page":"90","last_page":"109"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.9995999932289124,"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"}},"topics":[{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.9995999932289124,"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/T10154","display_name":"Customer Service Quality and Loyalty","score":0.9944000244140625,"subfield":{"id":"https://openalex.org/subfields/1407","display_name":"Organizational Behavior and Human Resource Management"},"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/T11161","display_name":"Consumer Market Behavior and Pricing","score":0.9919000267982483,"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/computer-science","display_name":"Computer science","score":0.6448421478271484},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.5978635549545288},{"id":"https://openalex.org/keywords/originality","display_name":"Originality","score":0.5529263615608215},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.531575083732605},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.5287665128707886},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5173239707946777},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4650047719478607},{"id":"https://openalex.org/keywords/value","display_name":"Value (mathematics)","score":0.43907055258750916},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.43261411786079407},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38787055015563965},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3596426248550415},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.34550371766090393}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6448421478271484},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5978635549545288},{"id":"https://openalex.org/C2776950860","wikidata":"https://www.wikidata.org/wiki/Q2914681","display_name":"Originality","level":3,"score":0.5529263615608215},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.531575083732605},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.5287665128707886},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5173239707946777},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4650047719478607},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.43907055258750916},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.43261411786079407},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38787055015563965},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3596426248550415},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.34550371766090393},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C11012388","wikidata":"https://www.wikidata.org/wiki/Q170658","display_name":"Creativity","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1108/imds-12-2015-0509","is_oa":false,"landing_page_url":"https://doi.org/10.1108/imds-12-2015-0509","pdf_url":null,"source":{"id":"https://openalex.org/S37320504","display_name":"Industrial Management & Data Systems","issn_l":"0263-5577","issn":["0263-5577","1758-5783"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Industrial Management & Data Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.6399999856948853,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W97976214","https://openalex.org/W158727920","https://openalex.org/W1513345415","https://openalex.org/W1594031697","https://openalex.org/W1761371909","https://openalex.org/W1955600018","https://openalex.org/W1970768420","https://openalex.org/W1972145348","https://openalex.org/W1989456974","https://openalex.org/W1990844260","https://openalex.org/W1993708925","https://openalex.org/W1996869586","https://openalex.org/W2014231941","https://openalex.org/W2022443965","https://openalex.org/W2026219386","https://openalex.org/W2032599855","https://openalex.org/W2032740726","https://openalex.org/W2035527763","https://openalex.org/W2035782089","https://openalex.org/W2052716990","https://openalex.org/W2055655726","https://openalex.org/W2065052734","https://openalex.org/W2067594023","https://openalex.org/W2070083848","https://openalex.org/W2071496623","https://openalex.org/W2074326934","https://openalex.org/W2082849519","https://openalex.org/W2090552208","https://openalex.org/W2094165287","https://openalex.org/W2094207227","https://openalex.org/W2094215799","https://openalex.org/W2100406636","https://openalex.org/W2104364184","https://openalex.org/W2113984132","https://openalex.org/W2115383008","https://openalex.org/W2126634086","https://openalex.org/W2133939660","https://openalex.org/W2134519823","https://openalex.org/W2139305652","https://openalex.org/W2142752589","https://openalex.org/W2153476503","https://openalex.org/W2160460894","https://openalex.org/W2170207476","https://openalex.org/W2345457309","https://openalex.org/W2595552630","https://openalex.org/W3015634246","https://openalex.org/W4292157289"],"related_works":["https://openalex.org/W2593155302","https://openalex.org/W2041415459","https://openalex.org/W3126095231","https://openalex.org/W2072812638","https://openalex.org/W2352855287","https://openalex.org/W2378906650","https://openalex.org/W2384288445","https://openalex.org/W2382526412","https://openalex.org/W4200071106","https://openalex.org/W4367335967"],"abstract_inverted_index":{"Purpose":[0],"The":[1],"purpose":[2],"of":[3,11,14,108,135,154],"this":[4,28,111],"paper":[5],"is":[6,29],"to":[7,90,120,149],"identify":[8,121],"the":[9,12,22,45,51,106,109,151],"influence":[10],"frequency":[13],"word":[15],"exposure":[16],"on":[17,21,38],"online":[18],"news":[19],"based":[20],"availability":[23],"heuristic":[24],"concept.":[25],"So":[26],"that":[27,36],"different":[30],"from":[31],"most":[32],"churn":[33,46,59,136],"prediction":[34,47,87],"studies":[35,53,101],"focus":[37],"subscriber":[39,94],"data.":[40],"Design/methodology/approach":[41],"This":[42,84],"study":[43,85,112],"examined":[44],"through":[48],"words":[49,57,123],"presented":[50],"previous":[52,100],"and":[54,76,117,140],"additionally":[55],"identified":[56],"what":[58],"generate":[60],"using":[61],"data":[62],"mining":[63],"technology":[64],"in":[65],"combination":[66],"with":[67],"logistic":[68],"regression,":[69],"decision":[70,114],"tree":[71,115],"graphing,":[72],"neural":[73],"network":[74],"models,":[75],"a":[77],"partial":[78],"least":[79],"square":[80],"(PLS)":[81],"model.":[82],"Findings":[83],"found":[86],"rates":[88],"similar":[89],"those":[91],"delivered":[92],"by":[93],"data-based":[95],"analyses.":[96],"In":[97],"addition,":[98],"because":[99],"do":[102],"not":[103,157],"clearly":[104],"suggest":[105],"effects":[107],"factors,":[110],"uses":[113],"graphing":[116],"PLS":[118],"modeling":[119],"which":[122,156],"deliver":[124],"positive":[125],"or":[126],"negative":[127],"influences.":[128],"Originality/value":[129],"These":[130],"findings":[131],"imply":[132],"an":[133],"expansion":[134],"prediction,":[137],"advertising":[138],"effect,":[139],"various":[141],"psychological":[142],"studies.":[143],"It":[144],"also":[145,163],"proposes":[146],"concrete":[147],"ideas":[148],"advance":[150],"competitive":[152],"advantage":[153],"companies,":[155],"only":[158],"helps":[159],"corporate":[160],"development,":[161],"but":[162],"improves":[164],"industry-wide":[165],"efficiency.":[166]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":14},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":9},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":4}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}