Topic modeling can help better understand the content of large collections of text. The objective of the research is to develop an approach to identify latent topics in the Technology Innovation Management Review journal with a collection of articles published between 2007 and 2017 and how topics have evolved using the Latent Dirichlet Allocation and Dynamic Topic Model algorithms. We identified 47 topics and categorized them into ten themes. While some topic trends became prominent over time, others disappeared. The distribution of the articles across topics in the LDA approach has been made more decisively so that of 597 articles, 503 most associated articles were identified, while this number is 299 articles in DTM. Furthermore, we discussed weaknesses and strengths of the algorithms based on defined criteria. We conclude that DTM provides more accurate word and topic trends over time. Finally, we document a repeatable process to replicate the results.