{"id":"W2134240883","doi":"10.1287/isre.1080.0186","title":"<b>Research Note</b>—Investments in Information Technology: Indirect Effects and Information Technology Intensity","year":2009,"lang":"en","type":"article","venue":"Information Systems Research","topic":"Economic Growth and Productivity","field":"Economics, Econometrics and Finance","cited_by":96,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"University of Calgary; Arizona State University; Ohio State University","keywords":"Productivity; Production (economics); Capital intensity; Industrial organization; Value (mathematics); Production function; Function (biology); Manufacturing sector; Capital (architecture); Economics; Manufacturing; Information technology; Measure (data warehouse); Business; Microeconomics; Econometrics; Marketing; Computer science; Labour economics; Statistics; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.007789425,0.0001945086,0.0005133218,0.009603292,0.0003807046,0.0005604855,0.0004584056,0.0005601,0.000009953998],"category_scores_gemma":[0.002274527,0.0002195154,0.00003624759,0.003326286,0.0002773469,0.01251648,0.0002448262,0.001274808,0.003070565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006802915,"about_ca_system_score_gemma":0.0001206266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004588902,"about_ca_topic_score_gemma":0.00001020215,"domain_scores_codex":[0.997178,0.0001223469,0.001378017,0.0002416905,0.0002631148,0.0008168396],"domain_scores_gemma":[0.9980572,0.0001704295,0.0004206653,0.0005643292,0.0006492009,0.0001382154],"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":[0.0001660211,0.00009719763,0.1206362,0.0008341498,0.00004083944,0.000003360808,0.005978888,0.0001716492,0.00004805627,0.729086,0.002804411,0.1401333],"study_design_scores_gemma":[0.004728655,0.001200655,0.3448759,0.0004575638,0.000004555129,0.0001327661,0.006921128,0.0197657,0.002691,0.1784782,0.4396527,0.001091209],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9263355,0.000966059,0.002480307,0.005087323,0.0006958222,0.002850269,0.00009126822,0.0002511388,0.06124233],"genre_scores_gemma":[0.9989355,0.0002054101,0.0002834265,0.0001906095,0.00004568071,0.0002114027,0.00007763236,0.000006105979,0.00004422413],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5506078,"threshold_uncertainty_score":0.9977056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04587415711578399,"score_gpt":0.3007437176191038,"score_spread":0.2548695605033199,"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."}}