{"id":"W4281384084","doi":"10.1108/imds-09-2021-0543","title":"Understanding compliance intention of SNS users during the COVID-19 pandemic: a theory of appraisal and coping","year":2022,"lang":"en","type":"article","venue":"Industrial Management & Data Systems","topic":"Technology Adoption and User Behaviour","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Coping (psychology); Pandemic; Structural equation modeling; Psychology; Originality; Self-efficacy; Coronavirus disease 2019 (COVID-19); Transparency (behavior); Social psychology; Social media; Government (linguistics); Public relations; Business; Political science; Computer science; Medicine; Clinical psychology; Disease; Infectious disease (medical specialty)","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":[],"consensus_categories":[],"category_scores_codex":[0.007357965,0.0001123655,0.000292414,0.0003595966,0.0004280477,0.0001005404,0.001740819,0.00007499458,0.0000971365],"category_scores_gemma":[0.0009856039,0.00008467261,0.00004227091,0.0007550959,0.0002802925,0.0002662271,0.002179784,0.0003025532,0.000002672419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001751945,"about_ca_system_score_gemma":0.00003836353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001544263,"about_ca_topic_score_gemma":0.00001848241,"domain_scores_codex":[0.9969114,0.0007730999,0.0007896281,0.0004560434,0.0008932887,0.0001765326],"domain_scores_gemma":[0.9970846,0.0009335984,0.0007304794,0.001160263,0.00003635663,0.00005474777],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.0005390802,0.0001240428,0.7958468,0.0001768068,0.0002594671,0.00002651837,0.0008792588,0.002207239,0.0004779537,0.1748984,0.02199771,0.002566731],"study_design_scores_gemma":[0.01547419,0.0004746554,0.08963076,0.0006162017,0.0006158991,0.000373618,0.5452829,0.009859792,0.0000327848,0.01547675,0.3210766,0.001085809],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8693909,0.0003516423,0.1207926,0.001838228,0.003672661,0.002002342,0.001123271,0.0001807289,0.0006476634],"genre_scores_gemma":[0.9994596,0.00002170399,0.00002897982,0.0000510793,0.00003326489,0.00003373694,0.00003557376,0.000007695357,0.0003283921],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.706216,"threshold_uncertainty_score":0.3452849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7680631959978994,"score_gpt":0.4613286816223965,"score_spread":0.3067345143755029,"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."}}