Anticipating industry convergence: semantic analyses vs IPC co-classification analyses of patents

Abstract

Purpose – The convergence of industries exposes the involved firms to various challenges. In such a setting, a firm's response time becomes key to its future success. Hence, different approaches to anticipating convergence have been developed in the recent past. So far, especially IPC co-classification patent analyses have been successfully applied in different industry settings to anticipate convergence on a broader industry/technology level. Here, the aim is to develop a concept to anticipate convergence even in small samples, simultaneously providing more detailed information on its origin and direction. Design/methodology/approach – The authors assigned 326 US-patents on phytosterols to four different technological fields and measured the semantic similarity of the patents from the different technological fields. Finally, they compared these results to those of an IPC co-classification analysis of the same patent sample. Findings – An increasing semantic similarity of food and pharmaceutical patents and personal care and pharmaceutical patents over time could be regarded as an indicator of convergence. The IPC co-classification analyses proved to be unsuitable for finding evidence for convergence here. Originality/value – Semantic analyses provide the opportunity to analyze convergence processes in greater detail, even if only limited data are available. However, IPC co-classification analyses are still relevant in analyzing large amounts of data. The appropriateness of the semantic similarity approach requires verification, e.g. by applying it to other convergence settings.