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Firms in high-technology industries have faced great technological and market uncertainty and volatility in the past few decades. In order to be competitive and sustainable in this environment, firms have been pursuing technological innovation, product differentiation, vertical integration, and alliances, which eventually drive industry convergence, defined as the process of blurring boundaries between previously distinct industries. Although industry convergence has greatly affected industrial structure and the economy, little research has investigated this phenomenon, especially its diffusion patterns; thus, it is still unclear which industries are converging more rapidly or have a higher potential for convergence. This paper explores these issues by investigating industry convergence in U.S. high-technology industries, using a large set of newspaper articles from 1987 to 2012. We perform a co-occurrence-based analysis to obtain information on industry convergence and estimate its diffusion patterns using an internal-influence logistic model. We find heterogeneous diffusion patterns, depending on convergent-industry pairs and their wide dispersion. In addition, we find that the potential degree of industry convergence is significantly negatively associated with its growth rate, which indicates that a great deal of time will be required for industry convergence between high-technology industries with this high potential to achieve a high degree of convergence

Diffusion Patterns in Convergence among High-Technology Industries: A Co-Occurrence-Based Analysis of Newspaper Article Data