Understanding knowledge translation in university–industry research projects: a case analysis in the automotive sector

Abstract

PurposeIn this paper, we depart from extant conceptualisations of knowledge translation mechanisms to examine projects as a way to achieve effective knowledge transfer. Our empirical analysis focused on a university–industry research project in the automotive industry.Design/methodology/approachThe empirical analysis was based on a qualitative investigation. We analysed material collected within a research project involving a partnership between two universities and Fiat-Chrysler Automotive (FCA), a multi-brand auto manufacturer with a product range covering several different market segments. We used three data collection techniques: internal document analysis, participant observation and semi-structured interviews.FindingsOur findings show that, in a U-I research project, goals represent a key dimension to support knowledge translation. Defining the goal implies an ongoing negotiation process, where researchers and company employees work together, in order to converge towards a shared meaning of the goal. In this sense, goal orientation and goal-based interaction have significant implications for knowledge translation processes.Originality/valueStudies to date have focussed on the concept of knowledge translation as a way to contextualise the transfer from the source of knowledge to the receiver and to interpret the knowledge to be exchanged. This study expands the understanding of knowledge translation mechanisms in university–industry research settings. It investigates the concept of projects as powerful knowledge translation mechanism in a dynamic and longitudinal perspective. Our contribution provides insight, reflecting on how the use of projects may represent a way to facilitate knowledge transfer and build up new ideas and solutions.

Visualizing knowledge for decision-making in Lean Production Development settings. Insights from the automotive industry

“…The process of translating and sharing knowledge is particularly challenging in the automotive industry because of the pressure towards process and product innovation. Carmakers' efforts to develop innovation outside conventional firm and supply chain boundaries have driven them to network with a variety of actors, such as academic researchers (Canonico et al, 2018(Canonico et al, , 2020. We investigated the adoption of a multiobjective optimization mathematical approach in an LPD setting in the automotive sector, clarifying how knowledge visualization could be useful to analyze and select alternatives in a lean production process.…”

“…Simeone et al (2018) stated that, given the different objectives and languages in academic and industrial contexts, translation mechanisms between these two groups of stakeholders are needed to establish a common platform of communication. Visualization tools fostered knowledge translation among researchers and the quality manager, creating the conditions for a dialogue between these two worlds (Canonico et al , 2020). These tools also enhanced managerial judgment by translating raw data and information into accessible forms to extract knowledge and selecting the “good” alternatives (step 5, Figure 1).…”

“…Step 4 Visualizing knowledge for decisionmaking the quality manager, creating the conditions for a dialogue between these two worlds (Canonico et al, 2020). These tools also enhanced managerial judgment by translating raw data and information into accessible forms to extract knowledge and selecting the "good" alternatives (step 5, Figure 1).…”

“…LPD settings typically require decision-makers to try to achieve multiple conflicting objectives for production processes at the plant level (Gurumurthy and Kodali, 2008; Jaiswal et al , 2021; Stoycheva et al , 2018). LPD relies on a set of engineering and work organization principles and techniques, many of which were first popularized by Toyota (Canonico et al , 2020). These are designed to achieve shorter lead times, reduced costs and higher quality than traditional product development.…”

Simulation research on knowledge flow in a collaborative innovation network

“…Knowledge flow refers to the process of spreading or transferring knowledge between a knowledge source and a knowledge receiver (Canonico et al, 2020; Lin et al, 2008). In recent years, scholars have constructed knowledge flow models from different perspectives, including knowledge diffusion in multinational corporations (Gaur et al, 2019), knowledge diffusion in product research and development (Kunpeng et al, 2017), and the national‐level knowledge flow brought by high‐tech talent (Miguelez & Temgoua, 2020).…”