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Business accelerators play a key role in the initial critical stages of assessment of commercial viability, offering mentorship provision of funding and protection of intellectual property (IP) for product development and refinement. However, little is known about the decision-making criteria and detailed analysis of the underlying criteria and interdependencies between the key factors used by accelerator organizations to fund start-ups. This article focuses on the decision-making criteria utilized by a leading £21M accelerator program, largely funded by the European Regional Development Fund for initial stage funding and IP protection for product and innovation commercialization. We incorporate a multimethodological interpretive-based approach based on Day's “Real-Win-Worth” framework to develop the interrelationships and ranking between the factors. The results highlight the significance and weighting attached to the factors associated with the technical competency of the proposer and evidence of demand existing for the product. We propose a new framework that models the key factor interrelationships offering additional insight to accelerator-based decision making. [ABSTRACT FROM AUTHOR]

Many firms increasingly collaborate with universities to create innovation through the commercialization of scientific knowledge. However, a variety of mismatches between a firm's interests and the value delivered by university partners (e.g., common knowledge base, strategic goals, agreements on intellectual property management, etc.) can cause rough interactions and relationships. Therefore, managers demand an effective practice to support the management of university-industry collaborations (UICs). This article builds on the previous work on a matching quality assessment model by extending its applicability to propose an analytical framework for developing a strategic roadmap to guide relationship development toward effective UIC. [ABSTRACT FROM AUTHOR]

Firms engaged in university–industry collaboration (UIC) desire to advance knowledge and new technologies with their academic partners, however the performance of knowledge creation and transfer is greatly affected by complementarity and compatibility between the firm and academic institute. In practice, many firms assess the appropriateness of academic partners using a broad range of criteria, with high subjectivity. This paper therefore seeks to address this challenge by introducing a systematic approach to help firms determine the appropriateness of academic partners, represented as “matching quality” in this paper. Three facets of matching quality are examined: complementarity of research ability, compatibility in business mindset, and compatibility in relationships. This paper proposes a framework and model for assessing matching quality, along with a case study to demonstrate how the proposed model works. [ABSTRACT FROM AUTHOR]