Evolution of journal preference based on topic focus: A case study in the technology innovation management field

Topic evolution is essential for exploring a field; however, the journal’s contribution has not been explored in topic evolution research. In this work, we interpret a journal’s contribution as a journal preference and investigate the concept based on topic focus, as shown in the journal-topic distribution. To analyse the topic focus, we first processed the data into documents consisting of only fine-grained topic words. Document vectors were generated using Sci-BERT and clustered using the k-means algorithm after dimensionality reduction. By matching journals with topic clusters, we calculated the journal preference score based on topic focus and then added a time factor to represent the evolution of journal preference. Simultaneously, we used the Zipfian distribution to classify fine-grained topic words into core and rare topic words, which were then used to establish topic relations in the evolutionary analysis and calculate the novelty scores of journal topic words. We use the technology innovation management (TIM) field to conduct a case study. There were 8 typical and 16 derivative topics, totalling 24 different topics. We focused on four important topics: R&D activity, technology management, innovation activity, and climate change, and found that they all have a relatively innovative evolution in a given year. The study indicates that within a given topic, while the composition and ranking of top journal preferences fluctuate over time, a subset of journals consistently exhibits dominance, appearing in the top ranks across most years. Although no clear relationship exists between journal preferences and ratings, A- and B-rated journals often dominate preferences for specific topics. Additionally, A- and B-rated journals with high or long preferences showed limited novelty. Most journals that preferred to interact with novel issues were C-rated.