{"id":"W2115285426","doi":"10.1057/palgrave.ejis.3000528","title":"Understanding enterprise systems-enabled integration","year":2005,"lang":"en","type":"article","venue":"European Journal of Information Systems","topic":"ERP Systems Implementation and Impact","field":"Business, Management and Accounting","cited_by":149,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"National Science Foundation","keywords":"Salient; Enterprise information integration; System integration; Enterprise application integration; Data integration; Computer science; Business process; Information integration; Field (mathematics); Knowledge management; Process (computing); Process management; Reciprocal; Enterprise integration; Data science; Business; Data mining; Enterprise systems engineering; Marketing; Enterprise software; Enterprise architecture; Artificial intelligence; Database","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002970004,0.0001618863,0.0002509282,0.0007685543,0.0001686015,0.001674961,0.0002539267,0.00002373661,0.00007335471],"category_scores_gemma":[0.0001594719,0.0001266823,0.000103584,0.0003200181,0.00001591306,0.009819261,0.00003809538,0.0001586911,0.001172664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002742507,"about_ca_system_score_gemma":0.00003098292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003585029,"about_ca_topic_score_gemma":0.000002445062,"domain_scores_codex":[0.9974398,0.0001247848,0.001638917,0.00006170545,0.0005339459,0.0002008737],"domain_scores_gemma":[0.9970407,0.00003162582,0.002247197,0.0001441592,0.0004998309,0.00003645901],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004787537,0.0001660683,0.006812885,0.001728989,0.000459461,0.00006418164,0.009806407,0.05224059,0.00146381,0.2521843,0.6530451,0.02154948],"study_design_scores_gemma":[0.002735458,0.00007552934,0.001993362,0.0009371596,0.00006091952,0.0002606454,0.03211939,0.03622638,0.00002459747,0.00001707836,0.9252172,0.0003323356],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02726745,0.0002377113,0.6380392,0.0009057258,0.005445716,0.0007038425,0.000008931927,0.0001591938,0.3272322],"genre_scores_gemma":[0.996677,0.000008893729,0.0001052022,0.0006130342,0.002338443,0.000001856019,0.00002115839,0.00001900532,0.0002153747],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9694096,"threshold_uncertainty_score":0.9996051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07079019266842405,"score_gpt":0.2565073860338867,"score_spread":0.1857171933654626,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}