{"id":"W3020346920","doi":"10.1016/j.techfore.2020.120076","title":"Regional diversification and financial performance through an excess-capacity theory lens: A new explanation for mixed results","year":2020,"lang":"en","type":"article","venue":"Technological Forecasting and Social Change","topic":"Corporate Finance and Governance","field":"Business, Management and Accounting","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"University of Auckland","keywords":"Diversification (marketing strategy); Transaction cost; Portfolio; Modern portfolio theory; Economics; Through-the-lens metering; Business; Principal–agent problem; Industrial organization; Financial economics; Corporate governance; Finance; Marketing; Lens (geology)","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":[0.01272501,0.0007118735,0.000986347,0.001852895,0.0007746997,0.004098045,0.002736848,0.00153998,0.01654202],"category_scores_gemma":[0.0377965,0.0002572097,0.001243249,0.002754419,0.003569532,0.006636873,0.003101294,0.001794965,0.0009326516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001090084,"about_ca_system_score_gemma":0.0009187086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007583247,"about_ca_topic_score_gemma":0.00512205,"domain_scores_codex":[0.9968557,0.001419079,0.0002227989,0.0008316246,0.0003060188,0.0003646708],"domain_scores_gemma":[0.9195545,0.06025385,0.006624918,0.008908709,0.003199652,0.001458341],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00144902,0.0005075295,0.6864161,0.001269878,0.003820837,0.0009695571,0.006174305,0.01228605,0.001481769,0.1700485,0.006813131,0.1087633],"study_design_scores_gemma":[0.0002229377,0.000801007,0.6076612,0.0009636995,0.004157661,0.0007163451,0.0166678,0.02444582,0.00320644,0.3248359,0.01613643,0.000184646],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8996071,0.01680852,0.02005899,0.01709696,0.0001997518,0.00004999308,0.001043501,0.0001737489,0.04496155],"genre_scores_gemma":[0.9979193,0.0003579337,0.0005706196,0.0003411488,0.0001288861,0.000008136809,0.0001083876,0.00002222396,0.000543433],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01654202,"threshold_uncertainty_score":0.0672971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2960069268089333,"score_gpt":0.253279649901576,"score_spread":0.04272727690735734,"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."}}