{"id":"W4401176378","doi":"10.1177/10949968241265855","title":"Unlocking Marketing Creativity Using Artificial Intelligence","year":2024,"lang":"en","type":"article","venue":"Journal of Interactive Marketing","topic":"Creativity in Education and Neuroscience","field":"Psychology","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Creativity; Agile software development; Computer science; Process (computing); Computational creativity; Ideation; Knowledge management; Creativity technique; Marketing and artificial intelligence; Comprehension; Generative grammar; Artificial intelligence; Management science; Data science; Psychology; Cognitive science; Engineering","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.006210626,0.0001810292,0.0002672782,0.0004833882,0.0001759307,0.0003209484,0.0002707723,0.00006301791,0.002329709],"category_scores_gemma":[0.00564524,0.0001604442,0.0002099287,0.0005954372,0.0001224803,0.000574565,0.00008001517,0.0007931911,0.0000408778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002568649,"about_ca_system_score_gemma":0.0001351223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002589333,"about_ca_topic_score_gemma":0.000004138616,"domain_scores_codex":[0.9969326,0.001377967,0.0007339793,0.0003091124,0.0003365273,0.0003098365],"domain_scores_gemma":[0.9918527,0.007084,0.0005271494,0.0001611525,0.0002653157,0.0001096749],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.003716824,0.000866251,0.01445945,0.0002055404,0.000378234,0.001056949,0.01563477,0.0003086552,0.1291143,0.005738209,0.002289759,0.8262311],"study_design_scores_gemma":[0.0008484491,0.001993194,0.2125433,0.02896697,0.001209983,0.02762797,0.2760369,0.2127546,0.1556716,0.01353732,0.06448627,0.004323398],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9355096,0.0004399077,0.02704402,0.0006372303,0.006936436,0.0001004611,0.000003978794,0.0000403819,0.02928802],"genre_scores_gemma":[0.9965828,0.00001922486,0.001973164,0.00009546478,0.0008469558,0.000002215903,2.866323e-7,0.00002522161,0.0004546655],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8219076,"threshold_uncertainty_score":0.9985823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0968395710764078,"score_gpt":0.4387368788219108,"score_spread":0.341897307745503,"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."}}