When conducting research at the organization level, scholars often do not have direct access to the data needed to measure key constructs in our theoretical models, necessitating some creativity in the data collection process. Although surveys can provide some valuable insights, it can be difficult to get responses from key informants within organizations, often making secondary data a preferred choice for large sample studies. Yet, it can be difficult to find secondary sources for all key variables. Here we provide a case study of our use of industry association newsletters as a source of data for research studies on franchise organizations and profile the advantages of using this novel data source, as well as the problems we encountered both in the process of conducting the study and in the review process. Industry and professional associations often collect data from their members and disseminate these data in a variety of ways, such as through reports and newsletters. When conducting a study on a particular industry sector or organizational type, the data collected by these professional or industry organizations may be a valuable data collection resource. We demonstrate in this case study how we used the electronic newsletter of the International Franchise Association, the IFA SmartBrief, as a data source for a series of research studies on the expansion of U.S. franchise companies into international markets. Like many researchers, we faced the problem that our key dependent variable of interest, the international expansion plans of U.S. franchise companies, was not available in most usual sources of company information, such as websites, annual reports, or financial statements. However, we found that companies often issued press releases about their plans to enter new markets, and that the International Franchise Association tracked and collated these announcements in its twice-weekly e-newsletter. In this case study, we describe how we used this industry newsletter, as well as other secondary sources, to measure key variables in our theoretical model such that we could empirically test our hypotheses. We discuss the pros and cons of collecting data from such sources and address the problems we encountered during the review process.