No abstract

The purpose of the paper is to illuminate the costs and benefits of crossing firm boundaries in inbound open innovation (OI) by determining the relationships among partner types, knowledge content and performance. The empirical part of the study is based on a survey of OI collaborations answered by R&D managers in 415 Italian, Finnish and Swedish firms. The results show that the depth of collaboration with different partners (academic/consultants, value chain partners, competitors and firms in other industries) is positively related to innovation performance, whereas the number of different partners and size have negative effects. The main result is that the knowledge content of the collaboration moderates the performance outcomes and the negative impact of having too many different kinds of partners. This illustrates how successful firms use selective collaboration strategies characterized by linking explorative and exploitative knowledge content to specific partners, to leverage the benefits and limit the costs of knowledge boundary crossing processes.

Measuring research and development (R&D) performance has become a fundamental concern for R&D managers and executives in the last decades. As a result, the issue has been extensively debated in innovation and R&D management literature. The paper contributes to this growing body of knowledge, adopting a systemic and contextual perspective to look into the problem of measuring R&D performance. In particular, it explores the interplay between measurement objectives, performance dimensions and contextual factors in the design of a performance measurement system (PMS) for R&D activities. The paper relies on a multiple case study analysis that involved 15 Italian technology‐intensive firms. The results indicate that firms measure R&D performance with different purposes, i.e. motivate researchers and engineers, monitor the progress of activities, evaluate the profitability of R&D projects, favour coordination and communication and stimulate organisational learning. These objectives are pursued in clusters, and the importance firms attach to each cluster is influenced by the context (type of R&D, industry belonging, size) in which measurement takes place. Furthermore, a firm's choice to measure R&D performance along a particular perspective (i.e. financial, customer, business processes or innovation and learning) is influenced by the classes of objectives (diagnostic, motivational or interactive) that are given higher priority. The implications of these results for R&D managers and scholars are discussed in the paper.

No abstract

PurposeMany companies claim they are adopting an open approach to innovation, but each of them with its own way. This paper aims to explore the different models for opening up the innovation process adopted in practice.Design/methodology/approachThe paper employs an extended survey among Italian manufacturing companies; cluster analysis; and ANOVA.FindingsThe study distinguishes four different open innovation models with respect to two variables, representing the degree of openness: the number and type of partners with whom the company collaborates (partner variety) and the number and type of phases of the innovation process actually open to external collaborations (innovation phase variety). They are: open innovators, closed innovators, integrated collaborators and specialised collaborators. The paper describes each cluster in terms of firm‐specific variables that characterize and support open innovation choices; finally, it tries to draw some tentative explanation of the influence of openness on the innovative performance of companies.Research limitations/implicationsThe number of respondents is still limited (i.e. about 100). Moreover, only the relationship between some firm‐specific factors and the degree of openness (defined specifically in terms of partner variety and phase variety) is studied: a wider investigation is recommended to include more contextual factors, i.e. external/environmental ones, or more variables that can help to define the openness degree.Practical implicationsThe paper provides managerial implications because it suggests that open innovation is not an “on/off” choice, but it can be interpreted and adopted with different degrees, consistently with the company's specific context.Originality/valueThe paper introduces a new perspective that integrates both the number/typology of partners and the number/typology of phases, in order to understand if such a perspective can confirm the existence of different open innovation models.