{"id":"W4407731705","doi":"10.1016/j.indmarman.2025.01.016","title":"SMEs' use of AI for new product development: Adoption rates by application and readiness-to-adopt","year":2025,"lang":"en","type":"article","venue":"Industrial Marketing Management","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Business; New product development; Product (mathematics); Process management; Knowledge management; Marketing; Computer science","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":[0.0049377,0.0002013356,0.0002262816,0.002517555,0.0002465824,0.001311435,0.0004995962,0.0005096637,0.001701633],"category_scores_gemma":[0.0308716,0.0001979749,0.0004686717,0.002529855,0.0005876485,0.001764541,0.001814169,0.0007060936,0.0008354019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003876657,"about_ca_system_score_gemma":0.0003818388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003627577,"about_ca_topic_score_gemma":0.003207056,"domain_scores_codex":[0.9968821,0.0007988189,0.0005868938,0.0003345315,0.001010789,0.0003868307],"domain_scores_gemma":[0.9628041,0.01419625,0.01308371,0.001551389,0.006163417,0.002201111],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001057923,0.00004771796,0.9765727,0.00006594794,0.00003718065,0.0002200482,0.00515515,0.000117767,0.0006972826,0.0001763982,0.0003010764,0.01650295],"study_design_scores_gemma":[0.000003240593,0.0001253995,0.9917461,0.00004127113,0.00001299362,0.0002794685,0.006080318,0.0004139353,0.0003734335,0.00008355631,0.0008254746,0.000014772],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972217,0.0002634625,0.0002462053,0.0001800305,0.000003618691,0.00001275225,0.0002114993,0.00001282602,0.001847919],"genre_scores_gemma":[0.999229,0.0001617042,0.000111539,0.00002465867,0.000003377279,0.00001101036,0.0001593062,0.000003879429,0.0002954781],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0049377,"threshold_uncertainty_score":0.02611333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08015249584822812,"score_gpt":0.2982952213669864,"score_spread":0.2181427255187583,"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."}}