A User Community‐Based Approach to Leveraging Technological Competences: An Exploratory Case Study of a Technology Start‐Up from MIT

Abstract

Many studies highlight the impact of technology commercialization on innovation and wealth creation. However, this impact could be far greater, especially as many technologies developed with high costs and effort remain vastly underutilized. One important reason for this problem can be found at the front end of the technological competence leveraging process: searching for market opportunities for a technology is a formidable challenge. In many cases, alternative fields of application (or even a single viable market opportunity) for a given technology are simply unknown to the entity in charge of commercialization. Based on an extensive literature review, we identify two major shortcomings at the front end of the technological competence leveraging process which contribute to the underutilization problem: (1) the local search behaviour of the commercializing entity and (2) the use of solution‐based instead of problem‐based search specifications. On the basis of these insights, we discuss the potential role of user communities in the search for (additional) market opportunities for a given technology, a process usually referred to as technological competence leveraging. We then empirically explore a user community‐based approach in an illustrative case study with a start‐up from MIT. Our findings show that when users were included in the search process, the number of potential markets in which the technology could be applied was five times higher, and more far‐distant application areas as well as application areas previously unknown to the technology holder were reached. In addition, we discover design principles for a user community‐based search process, providing practitioners with a hands‐on guideline for employing a user community‐based approach to technological competence leveraging.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 41 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.