Abstract Objectives: Non-alcoholic fatty liver disease (NAFLD) greatly affects cardiovascular disease, but evidence on the associations between NAFLD and markers of aortic calcification is limited. We aim to evaluate the association between NAFLD and aortic calcification in a cohort of Chinese adults using propensity score-matching (PSM) analysis. Methods: This prospective cohort study involved adults who underwent health-screening examinations from 2009 to 2016. NAFLD was diagnosed by abdominal ultrasonography at baseline, and aortic calcification was identified using a VCT LightSpeed 64 scanner. Analyses included Cox proportional-hazards regression analysis and PSM with predefined covariates (age, gender, marital and smoking status, and use of lipid-lowering drugs) to achieve a 1:1 balanced cohort. Results: Of 6,047 eligible participants, 2,729 (45.13%) were diagnosed with NAFLD at baseline, with a median age of 49.0 years [interquartile range: 44.0–55.0]. We selected 2,339 pairs o
This article introduces algorithmic bias in machine learning (ML) based marketing models. Although the dramatic growth of algorithmic decision making continues to gain momentum in marketing, research in this stream is still inadequate despite the devastating, asymmetric and oppressive impacts of algorithmic bias on various customer groups. To fill this void, this study presents a framework identifying the sources of algorithmic bias in marketing, drawing on the microfoundations of dynamic capability. Using a systematic literature review and in-depth interviews of ML professionals, the findings of the study show three primary dimensions (i.e., design bias, contextual bias and application bias) and ten corresponding subdimensions (model, data, method, cultural, social, personal, product, price, place and promotion). Synthesizing diverse perspectives using both theories and practices, we propose a framework to build a dynamic algorithm management capability to tackle algorithmic bias in M