{"id":"W4214553635","doi":"10.1080/08874417.2022.2037476","title":"A Semantic Model for Enterprise Digital Transformation Analysis","year":2022,"lang":"en","type":"article","venue":"Journal of Computer Information Systems","topic":"Information Technology Governance and Strategy","field":"Business, Management and Accounting","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Saint Mary's University","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Digital transformation; Computer science; Transformation (genetics); Enterprise modelling; Knowledge management; Enterprise information system; Model transformation; Process (computing); Enterprise integration; Semantic technology; Process management; Semantic computing; Enterprise software; Information retrieval; World Wide Web; Semantic Web; Artificial intelligence; Business; Programming language","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004355123,0.0008380162,0.0006681433,0.004415692,0.001814847,0.005651234,0.001764051,0.001854542,0.004859137],"category_scores_gemma":[0.004969363,0.0005263897,0.002720699,0.003779914,0.003696962,0.01290672,0.002707146,0.002390597,0.001275832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003930209,"about_ca_system_score_gemma":0.004969224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007729029,"about_ca_topic_score_gemma":0.005661177,"domain_scores_codex":[0.9969297,0.001270041,0.0004219695,0.0004019753,0.0007813692,0.0001948622],"domain_scores_gemma":[0.9976135,0.0009391918,0.0001756713,0.0005856906,0.0005456686,0.0001403028],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001496213,0.00002728536,0.000172131,0.00005147842,0.00001508765,0.0001063578,0.0004763696,0.005372107,0.0004261779,0.9793152,0.001469344,0.0125535],"study_design_scores_gemma":[0.00002973547,0.000038223,0.0002091139,0.0001581455,0.00004454123,0.0002691881,0.0007614375,0.08220813,0.00112609,0.8380804,0.07703674,0.00003824768],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003819247,0.0002680801,0.9735506,0.001463434,0.00009496079,0.0002365521,0.000458208,0.0006176032,0.01949147],"genre_scores_gemma":[0.1592873,0.0006954583,0.8311827,0.0004969387,0.00009519072,0.0006671469,0.001785112,0.000203352,0.005586821],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007729029,"threshold_uncertainty_score":0.02851582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01090577937140595,"score_gpt":0.2056634459713233,"score_spread":0.1947576665999173,"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."}}