{"id":"https://openalex.org/W1980319624","doi":"https://doi.org/10.1145/1542130.1542161","title":"Leveraging latent growth models to better understand MIS theory","display_name":"Leveraging latent growth models to better understand MIS theory","publication_year":2009,"publication_date":"2009-05-28","ids":{"openalex":"https://openalex.org/W1980319624","doi":"https://doi.org/10.1145/1542130.1542161","mag":"1980319624"},"language":"en","primary_location":{"id":"doi:10.1145/1542130.1542161","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1542130.1542161","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the special interest group on management information system's 47th annual conference on Computer personnel research","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/A5108516680","display_name":"Hemant V. Kher","orcid":null},"institutions":[{"id":"https://openalex.org/I86501945","display_name":"University of Delaware","ror":"https://ror.org/01sbq1a82","country_code":"US","type":"education","lineage":["https://openalex.org/I86501945"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hemant Kher","raw_affiliation_strings":["University of Delaware, Newark, DE, USA","University of Delaware, Newark, DE. USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Delaware, Newark, DE, USA","institution_ids":["https://openalex.org/I86501945"]},{"raw_affiliation_string":"University of Delaware, Newark, DE. USA","institution_ids":["https://openalex.org/I86501945"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027528595","display_name":"Mark A. Serva","orcid":null},"institutions":[{"id":"https://openalex.org/I86501945","display_name":"University of Delaware","ror":"https://ror.org/01sbq1a82","country_code":"US","type":"education","lineage":["https://openalex.org/I86501945"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mark A. Serva","raw_affiliation_strings":["University of Delaware, Newark, DE, USA","University of Delaware, Newark, DE. USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Delaware, Newark, DE, USA","institution_ids":["https://openalex.org/I86501945"]},{"raw_affiliation_string":"University of Delaware, Newark, DE. USA","institution_ids":["https://openalex.org/I86501945"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081191972","display_name":"Spring Davidson","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Spring Davidson","raw_affiliation_strings":["Accounting and MIS, Newark, DE, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Accounting and MIS, Newark, DE, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033768129","display_name":"Ellen Monk","orcid":null},"institutions":[{"id":"https://openalex.org/I86501945","display_name":"University of Delaware","ror":"https://ror.org/01sbq1a82","country_code":"US","type":"education","lineage":["https://openalex.org/I86501945"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ellen Monk","raw_affiliation_strings":["University of Delaware, Newark, DE, USA","University of Delaware, Newark, DE. USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Delaware, Newark, DE, USA","institution_ids":["https://openalex.org/I86501945"]},{"raw_affiliation_string":"University of Delaware, Newark, DE. USA","institution_ids":["https://openalex.org/I86501945"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.4535,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.87212918,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"159","last_page":"166"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10068","display_name":"Technology Adoption and User Behaviour","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/1802","display_name":"Information Systems and Management"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10068","display_name":"Technology Adoption and User Behaviour","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/1802","display_name":"Information Systems and Management"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12515","display_name":"Gender and Technology in Education","score":0.977400004863739,"subfield":{"id":"https://openalex.org/subfields/3318","display_name":"Gender Studies"},"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/T10355","display_name":"Impact of Technology on Adolescents","score":0.9430000185966492,"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/structural-equation-modeling","display_name":"Structural equation modeling","score":0.6741342544555664},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5982264280319214},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.5587681531906128},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5040720701217651},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.488560289144516},{"id":"https://openalex.org/keywords/latent-class-model","display_name":"Latent class model","score":0.4672859311103821},{"id":"https://openalex.org/keywords/latent-variable-model","display_name":"Latent variable model","score":0.4577663242816925},{"id":"https://openalex.org/keywords/longitudinal-data","display_name":"Longitudinal data","score":0.41665154695510864},{"id":"https://openalex.org/keywords/latent-growth-modeling","display_name":"Latent growth modeling","score":0.4160054326057434},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.40492701530456543},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2931823134422302},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.21732747554779053},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.14740338921546936},{"id":"https://openalex.org/keywords/ecology","display_name":"Ecology","score":0.08193162083625793}],"concepts":[{"id":"https://openalex.org/C71104824","wikidata":"https://www.wikidata.org/wiki/Q1476639","display_name":"Structural equation modeling","level":2,"score":0.6741342544555664},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5982264280319214},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.5587681531906128},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5040720701217651},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.488560289144516},{"id":"https://openalex.org/C70727504","wikidata":"https://www.wikidata.org/wiki/Q1806878","display_name":"Latent class model","level":2,"score":0.4672859311103821},{"id":"https://openalex.org/C65965080","wikidata":"https://www.wikidata.org/wiki/Q1806885","display_name":"Latent variable model","level":3,"score":0.4577663242816925},{"id":"https://openalex.org/C3020672099","wikidata":"https://www.wikidata.org/wiki/Q857354","display_name":"Longitudinal data","level":2,"score":0.41665154695510864},{"id":"https://openalex.org/C192806908","wikidata":"https://www.wikidata.org/wiki/Q6495487","display_name":"Latent growth modeling","level":2,"score":0.4160054326057434},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.40492701530456543},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2931823134422302},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.21732747554779053},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.14740338921546936},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.08193162083625793},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","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:10.1145/1542130.1542161","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1542130.1542161","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the special interest group on management information system's 47th annual conference on Computer personnel research","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.4099999964237213}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W79593656","https://openalex.org/W130596010","https://openalex.org/W1587682423","https://openalex.org/W1593544550","https://openalex.org/W2011551532","https://openalex.org/W2018093763","https://openalex.org/W2022631179","https://openalex.org/W2033943395","https://openalex.org/W2038984978","https://openalex.org/W2059334100","https://openalex.org/W2110078189","https://openalex.org/W2141536144","https://openalex.org/W2146434748","https://openalex.org/W2146733310","https://openalex.org/W2168569455","https://openalex.org/W2572099224","https://openalex.org/W2894762832","https://openalex.org/W3144139892","https://openalex.org/W4249831151","https://openalex.org/W4298091299"],"related_works":["https://openalex.org/W1501016332","https://openalex.org/W4237379778","https://openalex.org/W4230230730","https://openalex.org/W1535265092","https://openalex.org/W1603253275","https://openalex.org/W4381250654","https://openalex.org/W2103023456","https://openalex.org/W2162880285","https://openalex.org/W4236979260","https://openalex.org/W2150921722"],"abstract_inverted_index":{"The":[0],"purpose":[1],"of":[2,33,87,112,145,157],"this":[3,79],"didactic":[4],"study":[5,52],"is":[6,29,42],"to":[7,17,51,99,123],"demonstrate":[8],"how":[9],"latent":[10,58],"growth":[11],"models":[12,116],"(LGM)":[13],"can":[14,117,152],"be":[15],"utilized":[16],"measure":[18],"changes":[19,54],"in":[20,55,62,96,149],"student's":[21],"computer":[22],"self":[23],"efficacy":[24],"(CSE)":[25],"over":[26,60],"time.":[27],"LGM":[28,88],"a":[30,82],"special":[31],"application":[32,86],"structural":[34],"equation":[35],"modeling":[36],"(SEM),":[37],"an":[38],"analytic":[39],"tool":[40],"that":[41,151],"popular":[43],"among":[44],"MIS":[45,77,100,150],"researchers.":[46],"LGMs":[47],"have":[48],"been":[49],"used":[50],"longitudinal":[53,147],"observed":[56],"and/or":[57],"variables":[59],"time":[61],"several":[63],"other":[64],"fields":[65],"such":[66,115],"as":[67],"psychology,":[68],"sociology,":[69],"and":[70,105,108,133,140],"management.":[71],"To":[72],"promote":[73],"its":[74],"use":[75,156],"within":[76],"research,":[78],"paper":[80],"provides":[81],"primer":[83],"on":[84],"the":[85,97,110,155],"using":[89],"CSE":[90],"data":[91,124],"gathered":[92],"from":[93,154],"freshmen":[94],"enrolled":[95],"introduction":[98],"class.":[101],"We":[102,119],"illustrate":[103],"unconditional":[104],"conditional":[106],"LGMs,":[107,139],"highlight":[109],"types":[111],"research":[113,148],"questions":[114],"address.":[118],"discuss":[120],"issues":[121],"related":[122],"requirements,":[125,132],"model":[126,134],"identification,":[127],"estimation":[128],"methods,":[129],"sample":[130],"size":[131],"fit":[135],"assessment":[136],"statistics":[137],"for":[138],"conclude":[141],"by":[142],"providing":[143],"avenues":[144],"further":[146],"benefit":[153],"LGMs.":[158]},"counts_by_year":[{"year":2018,"cited_by_count":2},{"year":2014,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}