{"id":"W4409787778","doi":"10.61091/jcmcc127a-484","title":"A Study on Improving Organizational Structure and Cultural Alignment Based on Iterative Computing in Enterprise Digital Transformation","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Technology Adoption and User Behaviour","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Transformation (genetics); Digital transformation; Organizational structure; Computer science; Knowledge management; Process management; Business; Management; World Wide Web","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001546036,0.0002502179,0.000574029,0.0006644891,0.0002762387,0.0007260158,0.0003637256,0.0001468092,0.000004657927],"category_scores_gemma":[0.001314952,0.000186935,0.00007772635,0.0006715692,0.00006888653,0.0003304497,0.0001364263,0.0005272879,9.023141e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001282135,"about_ca_system_score_gemma":0.0001016657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001660601,"about_ca_topic_score_gemma":4.460621e-7,"domain_scores_codex":[0.9970214,0.0001674761,0.00133604,0.0002835578,0.0009908157,0.0002007465],"domain_scores_gemma":[0.9972864,0.001141113,0.0007765869,0.0001908236,0.0005212,0.00008387821],"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":[0.0008108989,0.004955477,0.1484255,0.0001007594,0.0001409663,0.00009521184,0.02714417,0.001540125,0.0005802729,0.7951998,0.0001645637,0.0208423],"study_design_scores_gemma":[0.03241753,0.005767425,0.09367508,0.001490567,0.0001580004,0.00008968911,0.02859833,0.1180998,0.001505338,0.71716,0.00014766,0.0008905381],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9904925,0.00001929018,0.005272485,0.0003107245,0.003290556,0.0003831541,0.000005687813,0.00002290055,0.0002026784],"genre_scores_gemma":[0.9993039,9.999292e-7,0.0004893277,0.00006432477,0.0001247518,7.9851e-7,0.000001739773,0.00001054448,0.000003607669],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1165597,"threshold_uncertainty_score":0.7622987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02123243405511657,"score_gpt":0.3171397729423739,"score_spread":0.2959073388872574,"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."}}