Abstracts – Browse Results
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Abd Elsalam, M F (2021) Breaking through the classical determinants in the field of hyper urban planning. Construction Innovation, 21(4), 818-36.
Al Jassmi, H, Al Ahmad, M and Ahmed, S (2021) Automatic recognition of labor activity: a machine learning approach to capture activity physiological patterns using wearable sensors. Construction Innovation, 21(4), 555-75.
Alkhateeb, A M, Hyari, K H and Hiyassat, M A (2021) Analyzing bidding competitiveness and success rate of contractors competing for public construction projects. Construction Innovation, 21(4), 576-91.
Arai, K and Morimoto, E (2021) Productivity and innovation in the Japanese construction industry. Construction Innovation, 21(4), 917-33.
Bilge, E C and Yaman, H (2021) Information management roles in real estate development lifecycle: literature review on BIM and IPD framework. Construction Innovation, 21(4), 723-42.
Bosch-Sijtsema, P, Claeson-Jonsson, C, Johansson, M and Roupe, M (2021) The hype factor of digital technologies in AEC. Construction Innovation, 21(4), 899-916.
Charlson, J and Dimka, N (2021) Design, manufacture and construct procurement model for volumetric offsite manufacturing in the UK housing sector. Construction Innovation, 21(4), 800-17.
Dharmapalan, V, O'Brien, W J, Morrice, D and Jung, M (2021) Assessment of visibility in industrial construction projects: a viewpoint from supply chain stakeholders. Construction Innovation, 21(4), 782-99.
Ghansah, F A, Owusu-Manu, D G, Ayarkwa, J, Edwards, D J and Hosseini, M R (2021) Exploration of latent barriers inhibiting project management processes in adopting smart building technologies (SBTs) in the developing countries. Construction Innovation, 21(4), 685-707.
Gharouni Jafari, K, Noorzai, E and Hosseini, M R (2021) Assessing the capabilities of computing features in addressing the most common issues in the AEC industry. Construction Innovation, 21(4), 875-98.
Guven, G and Ergen, E (2021) Tracking major resources for automated progress monitoring of construction activities: masonry work case. Construction Innovation, 21(4), 648-67.
Kapogiannis, G, Fernando, T and Alkhard, A M (2021) Impact of proactive behaviour antecedents on construction project managers' performance. Construction Innovation, 21(4), 708-22.
Kasbar, M, Staub-French, S, Pilon, A, Poirier, E, Teshnizi, Z and Froese, T (2021) Construction productivity assessment on Brock Commons Tallwood House. Construction Innovation, 21(4), 951-68.
Lavikka, R, Chauhan, K, Peltokorpi, A and Seppänen, O (2021) Value creation and capture in systemic innovation implementation: case of mechanical, electrical and plumbing prefabrication in the Finnish construction sector. Construction Innovation, 21(4), 837-56.
M.E. Sepasgozar, S, Shirowzhan, S and Loosemore, M (2021) Information asymmetries between vendors and customers in the advanced construction technology diffusion process. Construction Innovation, 21(4), 857-74.
Obi, L I, Arif, M, Awuzie, B, Islam, R, Gupta, A D and Walton, R (2021) Critical success factors for cost management in public-housing projects. Construction Innovation, 21(4), 625-47.
Ofori-Kuragu, J K and Osei-Kyei, R (2021) Mainstreaming pre-manufactured offsite processes in construction – are we nearly there?. Construction Innovation, 21(4), 743-60.
Pablo, Z, London, K, Wong, P S P and Khalfan, M (2021) Actor-network theory and the evolution of complex adaptive supply networks. Construction Innovation, 21(4), 668-84.
Salama, T, Salah, A and Moselhi, O (2021) Integrating critical chain project management with last planner system for linear scheduling of modular construction. Construction Innovation, 21(4), 525-54.
Sarvari, H, Nassereddine, H, Chan, D W M, Amirkhani, M and Md Noor, N (2021) Determining and assessing the significant barriers of transferring unfinished construction projects from the public sector to the private sector in Iran. Construction Innovation, 21(4), 592-607.
Sergeeva, N and Duryan, M (2021) Reflecting on knowledge management as an enabler of innovation in project-based construction firms. Construction Innovation, 21(4), 934-50.
Sutrisna, M, Tjia, D and Wu, P (2021) Developing a predictive model of construction industry-university research collaboration. Construction Innovation, 21(4), 761-81.
- Type: Journal Article
- Keywords: collaboration model; collaboration; neural networks; research and development
- ISBN/ISSN:
- URL: https://doi.org/10.1108/CI-11-2019-0129
- Abstract:
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. 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. 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. 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.
Ying, F J, O'Sullivan, M and Adan, I (2021) Simulation of vehicle movements for planning construction logistics centres. Construction Innovation, 21(4), 608-24.