{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004070836,0.0004474063,0.0002825627,0.001839697,0.001278786,0.007381992,0.001060215,0.0010775,0.001961704],"category_scores_gemma":[0.009533061,0.000250748,0.0006616848,0.001140974,0.007936395,0.006722589,0.003903073,0.00190252,0.000214499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001564994,"about_ca_system_score_gemma":0.001449351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004768785,"about_ca_topic_score_gemma":0.0006153326,"domain_scores_codex":[0.9971783,0.001659459,0.0001034554,0.0002389744,0.000630498,0.0001892949],"domain_scores_gemma":[0.9866115,0.01110249,0.0007613577,0.0009035846,0.0003085962,0.0003124755],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002853694,0.0004351443,0.01806298,0.001165656,0.0001761478,0.0006792299,0.04756218,0.01177726,0.009766421,0.5042684,0.002993458,0.4028277],"study_design_scores_gemma":[0.0001621686,0.0003710523,0.01889878,0.0008335829,0.0001214414,0.001281409,0.02781869,0.04303284,0.009621708,0.818231,0.07948405,0.000143232],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.640843,0.006032881,0.160446,0.01274321,0.0001965778,0.0002249194,0.00004333708,0.0003452302,0.1791248],"genre_scores_gemma":[0.9578632,0.001228197,0.0383867,0.000493437,0.00006051406,0.00006354957,0.00001795294,0.00002633357,0.00186009],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007381992,"threshold_uncertainty_score":0.0215289,"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."}}