{"id":"W4405482814","doi":"10.1108/intr-02-2024-0195","title":"Artificial intelligence adoption and revenue growth in European SMEs: synergies with IoT and big data analytics","year":2024,"lang":"en","type":"article","venue":"Internet Research","topic":"Impact of AI and Big Data on Business and Society","field":"Decision Sciences","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mount Royal University","funders":"","keywords":"Revenue; Eurobarometer; Context (archaeology); Business; Analytics; Big data; Marketing; Small and medium-sized enterprises; Asset (computer security); Empirical evidence; Industrial organization; Computer science; Accounting; Data science; European union; Finance; Computer security","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00718329,0.00009749414,0.000152521,0.0003927173,0.00007461173,0.001567441,0.0007558929,0.00004653158,0.00004061724],"category_scores_gemma":[0.001433605,0.00006018705,0.00001580168,0.001136521,0.0004123504,0.0003747036,0.0009005952,0.0003836668,0.0000579388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002480522,"about_ca_system_score_gemma":0.00008751101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009131252,"about_ca_topic_score_gemma":0.000781797,"domain_scores_codex":[0.9976024,0.0003177279,0.0003156954,0.0005584815,0.000932824,0.0002728451],"domain_scores_gemma":[0.9984233,0.0007782916,0.00002913049,0.0004556381,0.0002232657,0.00009031662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00009732159,0.00005466344,0.00958762,0.00007415832,0.00002450543,0.0003048749,0.00263383,0.000008019947,0.0001765209,0.005521396,0.01810656,0.9634105],"study_design_scores_gemma":[0.0005152897,0.002231772,0.379421,0.004404223,0.00007304314,0.0004526281,0.03985455,0.2381862,0.002869894,0.1238884,0.2064887,0.001614389],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9867654,0.001354668,0.007105547,0.003083738,0.0002112119,0.0001350249,0.000211596,0.00002161611,0.001111156],"genre_scores_gemma":[0.9979252,0.0007119043,0.0002081798,0.00003608633,0.00019077,0.000001516945,0.0000253653,0.0000101424,0.0008908581],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9617962,"threshold_uncertainty_score":0.999469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5531287695313306,"score_gpt":0.47962614563248,"score_spread":0.0735026238988506,"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."}}