In this paper, we address the problem faced by researchers attempting to decide the appropriate journal to submit their works for. Based on content analysis, we studied how semantically similar are journals blurb's sections with the articles published by the outlets. By considering such a methodology, we propose a new strategy for journal's selection for manuscript submission decision based on endogenous outcomes instead of traditional ones like journal's scores centered on dissemination achievements. Throughout, we illustrate our analysis with data from twenty current innovation-oriented journals. We use the articles published from 2010 to 2019 to develop a framework for understanding how historical contents shape publication opportunities for researchers. We emphasize the usefulness of contents already published to understand journals' selection practices. Current statistical approaches to content analysis can grasp the usefulness of already published abstract articles or journal blurbs section as a path to drive further submission decisions and offer reliable measures of influence that may have potential policy implications.

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