The understanding of the present productive processes is essential at this moment when knowledge became an important value creator. A holistic vision of the pieces of knowledge that are spread out and dispersed among practitioners, consultants and academics is necessary for the synthesis of new theories of production. Operations management researchers often use causal mapping as a key tool for building and communicating theory, particularly in support of empirical research. The widely accepted approaches for capturing cognitive data for a causal map are informal brainstorming and interviews, which require a time- consuming and significant cost of implementation. This dissertation aims at creating a methodology (Evocative Causal Mapping Methodology - ECMM) intended for use in operations management research for collecting and structuring dispersed data spread out as practical and research knowledge, and experience contained in the opinions of a large number of specialists demographically and geographically scattered. This is accomplished by evoking opinions, encoding them into variables and reducing the resulting set to concepts and relationships. A special concern is to achieve this goal in a feasible time and cost- efficient way. ECMM consists of two or three round, Delphi- like, Internet-based asynchronous data collection, and a data analysis that uses a coding panel of experts, hierarchical cluster analysis and multidimensional scaling for identifying concepts on cognitive map formats. Applications illustrate ECMM and demonstrate its feasibility. They were developed on supply chain management (SCM) and service management (SM) involving about 1,300 respondents of companies and universities of about 100 countries. Among possible unfolding future studies, this dissertation proposes to apply ECMM in SCM and SM aiming at unifying them into a single topic: service supply chain management.