Knowledge creation: A case study of international construction joint venture projects in Thailand

In recent years, companies around the world are trying to expand internationally through collaborative agreements. ‘International Construction Joint Ventures’ (ICJVs) have become of significant interest as the global construction market continues to be integrated into a more competitive business environment. Moreover, ICJVs can be a mechanism for creating, transferring and improving knowledge and skills between partners. Knowledge creation has also been recognized as the successful mechanism of creating knowledge between local and foreign partners. Therefore, local partners who wished to enter into the emerging market needed to quickly develop the required resources. Thus, it is especially important to understand how new knowledge in ICJV projects can be transferred and adopted. Therefore, the purpose of this study is to investigate and characterise the knowledge creation process in ICJV projects and explore to what extent projects facilitate the process. A case study approach is adopted using three ICJV projects. As a result, this research provides the establishment of specific knowledge creation processes through an empirical investigation of ICJV projects in Thailand

Social Networks and Knowledge Transfer in International Construction Joint Venture Projects: A Case Study in Thailand

International joint ventures (IJVs) are a specific type of strategic alliance between contractors from developed and developing countries and have been increasingly used. IJVs between multinational organisations are considered a successful strategy to benefit from international market opportunities in the globalised world. International construction joint ventures (ICJVs) have become of significant interest as the global construction market continues to be integrated into the more competitive business environment. The aim of this article is to uncover the knowledge transfer (KT) practices in an ICJV using social network analysis (SNA). The case presented here is the pilot study. A total of 19 questionnaire surveys were undertaken with selected team members. UCINET 6.0, an SNA package, was used to analyse the collected data and NetDraw was used to visualise the sociogram. This article first presents the actors' attributes; then, social network characteristics, which consist of network structure, network density and degree of centrality and cliques of actors, are presented. This analysis will be used to identify the key actors that influence the KT processes in this case study

KNOWLEDGE TRANSFER PROCESSES IN INTERNATIONAL CONSTRUCTION JOINT VENTURE PROJECTS IN THAILAND

In recent years, companies around the world are trying to expand internationally through collaborative agreements. ‘International Joint Ventures’ (IJVs) are a specific type of strategic alliance between contractors from developed and developing countries. The use of IJVs between multinational organisations is considered to be a successful strategy in order to benefit from international market opportunities in the globalised world. Moreover, ‘International Construction Joint Ventures’ (ICJVs) have become of significant interest as the global construction market continues to be integrated into the more competitive business environment. IJVs can be a mechanism for transferring knowledge between partners and a way of improving the knowledge and skills of the local partner(s). However, there is currently a gap in the existing literature on the key enabling and inhibiting factors that influence knowledge transfer processes in ICJV projects. Furthermore, despite the growing number of studies relating to ICJV projects, there is limited research conducted with regard to developing countries and especially in Thailand. Therefore, a better understanding of how knowledge in ICJV projects is transferred and adopted in Thailand is required. The purpose of this study is to identify and evaluate the processes of transferring knowledge in ICJV projects in Thailand. Previous research indicated that social network analysis (SNA) provides benefits to enable an understanding of how to enhance Knowledge Transfer (KT) processes in the projects. As a result, SNA was used to correlate KT practices in ICJV Projects in Thailand. A literature review led to the development of a conceptual framework that characterises the key concepts in the areas of knowledge transfer and social networks. A multiple case study approach was adopted to facilitate the investigation within the context of the ICJV projects. A combination of quantitative and qualitative data was utilised in this study. The data was analysed using a combination of descriptive statistics, Qualitative Content Analysis, SNA using UCINET and NetDraw Software Package, and the use of NVivo Software Package. The data analysis led to the identification of the key enabling and inhibiting factors in the areas of ICJV performance and success, knowledge facilitators, type of knowledge, and knowledge transfer methods and mechanisms all influence knowledge transfer processes. ICJVs performance and success factors determine the effectiveness and efficiency of successful knowledge transfer. The type of knowledge impact on knowledge transfers success. KT methods and mechanisms have been found to be critically essential in transferring the partners’ crucial knowledge. Knowledge facilitators were considered to be important in effective and efficient knowledge transfer. These findings were used to revise the conceptual framework. The knowledge transfer framework is the most important output of this study, which provides important contributions to examine and understand knowledge transfer in the context of ICJV projects. The framework can not only be applied in practice, but it can also be used by other researchers for further research in KT processes in different contexts. It is expected that these findings would serve as a valuable reference for ICJV projects for use by construction organisations in their KT processes to strengthen their competitive advantage

Prediction of FRCM–Concrete Bond Strength with Machine Learning Approach

Fibre-reinforced cement mortar (FRCM) has been widely utilised for the repair and restoration of building structures. The bond strength between FRCM and concrete typically takes precedence over the mechanical parameters. However, the bond behaviour of the FRCM–concrete interface is complex. Due to several failure modes, the prediction of bond strength is difficult to forecast. In this paper, effective machine learning models were employed in order to accurately predict the FRCM–concrete bond strength. This article employed a database of 382 test results available in the literature on single-lap and double-lap shear experiments on FRCM–concrete interfacial bonding. The compressive strength of concrete, width of concrete block, FRCM elastic modulus, thickness of textile layer, textile width, textile bond length, and bond strength of FRCM–concrete interface have been taken into consideration with popular machine learning models. The paper estimates the predictive accuracy of different machine learning models for estimating the FRCM–concrete bond strength and found that the GPR model has the highest accuracy with an R-value of 0.9336 for interfacial bond strength prediction. This study can be utilising in the estimation of bond strength to minimise the experimentation cost in minimum time

Prediction of FRCM–Concrete Bond Strength with Machine Learning Approach

Fibre-reinforced cement mortar (FRCM) has been widely utilised for the repair and restoration of building structures. The bond strength between FRCM and concrete typically takes precedence over the mechanical parameters. However, the bond behaviour of the FRCM–concrete interface is complex. Due to several failure modes, the prediction of bond strength is difficult to forecast. In this paper, effective machine learning models were employed in order to accurately predict the FRCM–concrete bond strength. This article employed a database of 382 test results available in the literature on single-lap and double-lap shear experiments on FRCM–concrete interfacial bonding. The compressive strength of concrete, width of concrete block, FRCM elastic modulus, thickness of textile layer, textile width, textile bond length, and bond strength of FRCM–concrete interface have been taken into consideration with popular machine learning models. The paper estimates the predictive accuracy of different machine learning models for estimating the FRCM–concrete bond strength and found that the GPR model has the highest accuracy with an R-value of 0.9336 for interfacial bond strength prediction. This study can be utilising in the estimation of bond strength to minimise the experimentation cost in minimum time