A New Method for Identifying Recombinations of Existing Knowledge Associated with High-Impact Innovation

How existing technologies and ideas are recombined into new innovations remains an important question, particularly as the store of prior technology, art, and work expands at an increasing rate. Yet, methodologies for identifying effective recombinations remain a nascent area of research. This paper extends our previous work, which developed a network methodology for assessing a scientific article's recombinations of prior work. The methodology uses information from the entire co-citation network of all papers recorded in the Web of Science to identify combinations of prior work that are conventional or atypical and then identifies the virtuous mix of conventional and atypical pairings associated with high impact work. Here, we summarize our prior method and findings, present new findings, and perform a case study application to the field of management science. First, the results show that despite an ever-increasing frontier of possible new combinations of prior work, atypical combinations of prior work are becoming increasingly rare with time, while the distribution of conventional pairings is increasing with time. Second, our analyses show that with time the atypical pairings found in hit papers have a relatively stable mean rate at which they become conventional pairing. Nevertheless, the variance around the mean is growing significantly, which indicates that there is a greater tendency over time for novel pairings either to be virtually never used again or to become conventional pairings.