{"id":"W4406646598","doi":"10.1108/sl-07-2024-0062","title":"Managing difficult conversations on new technology integration: the case of generative AI","year":2025,"lang":"en","type":"article","venue":"Strategy and Leadership","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hillsborough Hospital","funders":"","keywords":"Generative grammar; Computer science; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02565987,0.00100207,0.0005936643,0.002464205,0.02381033,0.01985504,0.004088192,0.009224571,0.004118258],"category_scores_gemma":[0.04994182,0.0007257219,0.001446039,0.002683074,0.03707951,0.02067551,0.02530443,0.01054477,0.00108539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008094366,"about_ca_system_score_gemma":0.00534184,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004688204,"about_ca_topic_score_gemma":0.003987539,"domain_scores_codex":[0.944756,0.04528623,0.0008074142,0.001550506,0.004776967,0.002822926],"domain_scores_gemma":[0.9321808,0.05622951,0.00224896,0.004240789,0.001768913,0.003331113],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008485741,0.00009854978,0.002055282,0.0001726244,0.00002567228,0.007724245,0.7758624,0.001554884,0.001411345,0.1936685,0.002731718,0.01460992],"study_design_scores_gemma":[0.00004486215,0.00009852035,0.0009830493,0.0006132444,0.00004159395,0.00617702,0.6281728,0.009987047,0.002735,0.1633608,0.1876658,0.0001202205],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4985283,0.001775438,0.1652323,0.05039736,0.0006624039,0.0005349715,0.0001088986,0.0006541937,0.2821062],"genre_scores_gemma":[0.9744682,0.0004495618,0.0152111,0.002655016,0.00008618722,0.0002093408,0.00005555725,0.0001823517,0.006682772],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02565987,"threshold_uncertainty_score":0.135704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1498801400689001,"score_gpt":0.3156219024806017,"score_spread":0.1657417624117017,"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."}}