{"id":"W2887415006","doi":"10.1109/tem.2018.2858550","title":"Growth Through Franchises in Knowledge-Intensive Industries: Interplay of Routine Program and Expansion Mode","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Engineering Management","topic":"Franchising Strategies and Performance","field":"Business, Management and Accounting","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Social Sciences and Humanities Research Council of Canada; National Research Foundation of Korea; National University of Singapore","keywords":"Mode (computer interface); Business; Industrial organization; Knowledge management; Marketing; Economic geography; Computer science; Economics; Human–computer interaction","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002198108,0.0003560589,0.0002098483,0.001041045,0.001402961,0.004583198,0.0008137131,0.001157307,0.006245492],"category_scores_gemma":[0.005587021,0.0002300961,0.0003330555,0.0007524746,0.00314721,0.005343852,0.001888159,0.000910603,0.0004553969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003853965,"about_ca_system_score_gemma":0.001608083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01120852,"about_ca_topic_score_gemma":0.0112577,"domain_scores_codex":[0.9986479,0.0005454654,0.00005306272,0.0002132145,0.0001719321,0.0003685069],"domain_scores_gemma":[0.9944352,0.002240162,0.001031078,0.0007491068,0.0005438741,0.001000553],"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":[0.0003179154,0.000445751,0.0539651,0.00009620011,0.00003975812,0.0007025668,0.006733985,0.0381557,0.005058208,0.8262277,0.001208786,0.06704834],"study_design_scores_gemma":[0.0001524718,0.0008744006,0.1170091,0.0003363148,0.0001476003,0.001148056,0.02805675,0.3048229,0.004974114,0.4985187,0.04377452,0.0001850578],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8178036,0.0001420656,0.05993216,0.0008100696,0.00001048789,0.0001087162,0.0001029417,0.0001135813,0.1209763],"genre_scores_gemma":[0.9935625,0.0000453672,0.003395678,0.0000153438,0.000003141189,0.00001651691,0.00002163427,0.000007621769,0.002932292],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01120852,"threshold_uncertainty_score":0.02796257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01172828998129353,"score_gpt":0.2489153679161011,"score_spread":0.2371870779348076,"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."}}