Management and organizational factors are increasingly cited as major contributors to operational safety problems in complex technological systems. A number of major events have revealed elements of organizational factors and safety culture to be among the primary or contributing causes. This paper describes a causal modeling framework to understand how organizational safety management practices can be measured, how those practices are linked to the human performance, and how this linkage impacts total risk outcome of the system. The proposed causal model is built on a combination of selected theories and methodologies in diverse domains of management, organizational psychology, human performance reliability, and risk analysis. It includes a macro-level framework of interactions of Business Management Practices (BMPs) and Safety Management Practices ( SMPs). The causal model focuses on SMPs and their detailed paths of influence, defined in such a way to facilitate developments of reliable predictors of safety performance. SMPs are the systematic and planned management-driven activities that aim at controlling hazards by preventing and/or intervening in the causation process of accidents and incidents. In the proposed, the SMPs are represented through "outward" and "perceived" factors. The former are those that can be assessed by objective auditing, interview and observation. The latter are the perceptions of people regarding safety management practices, as assessed by perception surveys. Although these two dimensions are interrelated, they are causal factors of human action through different paths of influence. In developing a representation of the SMPs for use in the model, the following points are considered: (1) different dimensions of the SMPs and their interdependency, (2) different measurement approaches, and (3) possible paths of influence. The full set of these aspects has not been considered in previous safety-oriented causal models. Some models have used safety management factors, mostly outward SMPs. Others have concentrated on safety climate factors, primarily perceived SMPs. Some of the methods have tried to combine the two, but failed to consider their interdependency, apply proper measurement approaches, and/or specify appropriate paths of influence. The proposed approach is the combination of a cross-level safety analysis and multi-layered safety modeling approach. It starts from outward SMPs (organization level), which impact the team (group level) and create team performance. The effects of human actions and other team factors (e.g. resources) on the team performance are represented through team effectiveness model. The Outward SMPs affect all team effectiveness factors (e.g. human actions and resources). Perceived SMPs are the reflection of Outward SMPs on a human model (individual level). The multi-layered safety modeling approach is adopted from Hybrid Causal Logic (HCL) developed by Mosleh et al . HCL is a combination of Event Sequence Diagrams (ESDs), Fault Trees (FTs) and Baysian Belief Net (BBNs). Team performance is integrated to the component performance model (failure events in FTs) using the Omega Factor approach . The fault tree outputs (hardware (HW) and human (HU) failure rates) are plugged into the ESDs. The model is based on the following three hypotheses: (1) "Perceived SMPs" mediate the effects of "Outward SMPs" on human action, (2) the effects of "Outward SMPs" and "Perceived SMPs" on human action are mediated by the individual's psychological, cognitive and physical states, and (3)"Perceived SMPs" is a function of "Outward SMPs", the nature of industry (e.g., aviation, nuclear), size of organization, the department or division (e.g., maintenance), the job responsibility (e.g., technicians, engineers, inspectors), knowledge, and some other personal factors (e.g., age, gender). As a final step, based on these hypotheses and utilizing the IDAC model , detailed paths of influence of "outward SMPs" to human action are proposed. As the research leading to the development of the methodology was done for the US Federal Aviation Administration, an actual example of the methodology from the aviation field is used to demonstrate its features and advantages.