Developing a predictive model of construction industry-university research collaboration

M. Sutrisna, Dewi Tjia, Peng Wu
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引用次数: 2

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.
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构建建筑产学研合作预测模型
本文旨在识别和研究影响建筑产学研合作的因素,并开发建筑行业内潜在行业合作伙伴与大学合作的可能性模型。设计/方法/方法mix方法数据收集包括问卷调查和焦点小组的数据收集。收集的数据使用描述性和推断性统计方法进行分析,以确定和检查因素。这些发现随后被用于开发IU合作的可能性预测模型。利用一种著名的人工神经网络模型进行训练和交叉验证,建立预测模型。研究发现,公司规模(员工数量和大约的年营业额)、在建筑行业的经验长度、以前的IU合作、创新的重要性和短期创新的动机对合作的可能性有统计学上显著的影响。研究还显示,越来越多的公司对牛津大学的合作研究感兴趣。人工神经网络模型成功预测了潜在建设伙伴与大学合作的可能性,准确率为85.5%,被认为是一个相当好的模型。独创性/价值该研究调查了协作的本质以及可能影响潜在IU合作的因素,并在此基础上介绍了机器学习方法的实施,以检查IU合作的可能性。虽然开发的模型来源于对西澳大利亚建筑行业数据集的分析,但这里提出的方法可以作为其他地方建筑行业预测开发模型的基础,帮助大学评估与目标建筑公司合作和合作的可能性。
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