Developing a predictive model of construction industry-university research collaboration

Author:

Sutrisna Monty,Tjia Dewi,Wu Peng

Abstract

Purpose This paper aims to identify and examine the factors that influence construction industry-university (IU) collaboration and develop the likelihood model of a potential industry partner within the construction industry to collaborate with universities. Design/methodology/approach Mix method data collection including questionnaire survey and focus groups were used for data collection. The collected data were analysed using descriptive and inferential statistical methods to identify and examine factors. These findings were then used to develop the likelihood predictive model of IU collaboration. A well-known artificial neural network (ANN) model, was trained and cross-validated to develop the predictive model. Findings The study identified company size (number of employees and approximate annual turnover), the length of experience in the construction industry, previous IU collaboration, the importance of innovation and motivation of innovation for short term showed statistically significant influence on the likelihood of collaboration. The study also revealed there was an increase in interest amongst companies to engage the university in collaborative research. The ANN model successfully predicted the likelihood of a potential construction partner to collaborate with universities at the accuracy of 85.5%, which was considered as a reasonably good model. Originality/value The study investigated the nature of collaboration and the factors that can have an impact on the potential IU collaborations and based on that, introduced the implementation of machine learning approach to examine the likelihood of IU collaboration. While the developed model was derived from analysing data set from Western Australian construction industry, the methodology proposed here can be used as the basis of predictive developing models for construction industry elsewhere to help universities in assessing the likelihood for collaborating and partnering with the targeted construction companies.

Publisher

Emerald

Subject

Building and Construction,Architecture,Civil and Structural Engineering,General Computer Science,Control and Systems Engineering

Reference76 articles.

1. Universities–industry collaboration: a systematic review;Scandinavian Journal of Management,2015

2. Cooperation in R&D, firm size and type of partnership;European Journal of Management and Business Economics,2017

3. The relationship between R&D expenditures and economic growth: panel data analysis 1990-2013,2015

4. A dual-functional social innovation process model for low-cost houses through university-enabled initiative;Construction Innovation,2019

5. Universities‐industry collaboration strategies: a micro‐level perspective;European Journal of Innovation Management,2009

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