{"id":"W4214942876","doi":"10.1007/s10796-022-10249-6","title":"Artificial Intelligence and Reduced SMEs’ Business Risks. A Dynamic Capabilities Analysis During the COVID-19 Pandemic","year":2022,"lang":"en","type":"article","venue":"Information Systems Frontiers","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":138,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"University of Toronto; University of Cambridge; Anglia Ruskin University","keywords":"Cash flow; Coronavirus disease 2019 (COVID-19); Business intelligence; Business; Scale (ratio); Pandemic; Business risks; Business operations; Marketing; Industrial organization; Computer science; Finance; Knowledge management; Risk analysis (engineering)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001186956,0.000267016,0.000344528,0.0008387004,0.0006032119,0.002532424,0.0003815548,0.001237584,0.005180656],"category_scores_gemma":[0.007079033,0.0001136062,0.0004223611,0.0008496111,0.001214879,0.002966292,0.001154269,0.001648025,0.0001392438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002110346,"about_ca_system_score_gemma":0.001244194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01122014,"about_ca_topic_score_gemma":0.006868307,"domain_scores_codex":[0.9996592,0.0001186783,0.00001135561,0.00003010916,0.00006055414,0.000120213],"domain_scores_gemma":[0.9976307,0.001436535,0.0003591077,0.00007514433,0.0002413758,0.0002569793],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0005414074,0.0002369054,0.03520047,0.000236424,0.0001334982,0.001542821,0.001305729,0.1378705,0.001496772,0.7530553,0.01128742,0.05709277],"study_design_scores_gemma":[0.0000772194,0.0003874984,0.06990797,0.0002356338,0.0001108522,0.0004440568,0.004845151,0.2636496,0.0009657448,0.6346725,0.02463245,0.00007138263],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8470395,0.002868403,0.01277534,0.02654487,0.00008529047,0.00005146105,0.0005819137,0.00005084463,0.1100023],"genre_scores_gemma":[0.9980246,0.0002742596,0.000307051,0.0001044672,0.00002105955,0.000005356147,0.00004016478,0.000003158376,0.001219966],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01122014,"threshold_uncertainty_score":0.02230966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06382053645731701,"score_gpt":0.2769719535621729,"score_spread":0.2131514171048559,"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."}}