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The purpose of this paper is to investigate the complex interdependence of the factors in driving or hindering construction productivity at the industry, project and activity levels in a systemic manner.

A mixed-methods design, which combines a critical literature review, an interview-based survey with 32 industry experts and five focus group meetings participated in by 109 representatives of a wide range of industry stakeholder groups, was employed to identify the drivers for and constraints on construction productivity enhancement in Hong Kong and explore the interrelated insights into the drivers and constraints.

The study conceptualised and validated a systemic framework for examining construction industry productivity, and developed three causal loop diagrams (CLDs) for illustrating the dynamic structures that underpin the complex systems of the drivers and constraints.

Although the scope of the study was limited to Hong Kong, the results could be interpreted for critical learning in other urban contexts.

The systemic perspective of construction productivity and the CLDs of the drivers and constraints support the systems thinking of industry stakeholders in the formulation of holistic strategies for long-term construction industry productivity enhancement.

The study conceptualises construction productivity from a systemic perspective and provides empirically supported CLDs to facilitate future investigations into the complex system of construction productivity.

While the investment in construction projects has increased over the past few decades, low construction project productivity (CPP) appeared to be persistent, thereby reflecting an “investment-in-failure” paradox between the investment and CPP. Hence, this paper aims to develop a systematic and holistic CPP evaluation framework to explain the apparent paradox in the construction industry.

The paper first reviews the literature about the theories of system, production, principal–agent and project success evaluation to re-conceptualise the CPP and proposes a two-stage CPP evaluation framework. The framework is subsequently explored through a sequential qualitative mixed-methods design within the context of the Hong Kong construction industry by combining 32 semi-structured interviews with senior industry experts and exploratory case studies, with three real-life construction projects.

The paper identifies three system boundaries for CPP evaluation, that is, parameter, timeframe and stakeholder, and develops a two-stage CPP evaluation framework to indicate site efficiency and utilisation effectiveness, thereby accessing the productivity of both the construction and post-construction stages. The “investment-in-failure” paradox associated with current CPP evaluation approaches is primarily attributed to the narrowly defined CPP boundaries.

The qualitative exploration of the evaluation framework only focusses on the Hong Kong construction industry. Further case studies within other urban contexts could be used to improve the generalisability of the findings. Quantitative research is also necessary to advance theoretical development of the two-stage CPP evaluation.

The systemic CPP conceptualisation and the two-stage CPP evaluation framework support the systems thinking of industry stakeholders and enable them to formulate holistic strategies for long-term CPP enhancement.

The research demonstrates the needs to expand the system boundaries of CPP to reflect its systemic value and to shift the paradigm of CPP evaluation from being output-orientated and quantity-focussed to being outcome-orientated and value-focussed.

This paper explores the vocational training of construction workers in Guangdong Province of China and identifies its position in the global political-economic spectrum of skill formation.

The paper reviews construction vocational education and training (VET) of major political economies to develop a theoretical framework that guides an in-depth case study of Guangdong. Document analysis, field trip observations, meetings and semi-structured interviews were combined to explore the political-economic environment, political stakeholders and quality assurance mechanisms of industrial training in Guangdong's construction sector. The findings were compared with construction VET of other economies reported in the literature.

Construction training in Guangdong is deeply rooted in the local history and culture, under strong dominance of the state, while continually evolves to respond to the fluid market and therefore can be conceptualised as “market-in-state”. The political stakeholders are embedded within the state to ensure that skills policies are implemented in-line with industry policies. The differences between the training of Guangdong and its foreign counterparts are attributed to their divergent political-economic models.

As the case study was undertaken only with Guangdong, the generalisability of its findings can be improved through future research within a broader context of multiple provinces of China through both qualitative and quantitative research approaches.

Plausible foreign VET approaches are likely adaptable to the Chinese context only when conducive political-economic environment could be enabled. The findings are useful for developing countries to learn from the VET experience of industrialised economies. Construction workers' training in Guangdong can be improved by strengthening labour regulation at lower subcontracting levels and ensuring the presence of industrial associations and unions for collective training supervision.

The paper contributes to the field of construction engineering and management with a theoretical framework that guides empirical studies on the influence of the political-economic environment upon the ways political stakeholders develop and participate in construction VET. The exploration based on this framework revealed the position of the vocational training of construction workers in Guangdong in the global political-economic spectrum of skill formation.

This study examines how chatbots could improve the client experience in the banking sector. Due to their quick and effective customer service, chatbots are becoming more and more popular, but it is still unknown whether they can improve the customer experience. In order to gather data from a simple random sample of Palestinian banking clients in the Gaza Strip, a survey was conducted utilizing the explanatory technique. To test hypotheses, data collected from 337 individuals was evaluated using simple regression analysis. According to the results, chatbots may enhance the customer experience by offering 24/7 availability, prompt support, and customized replies. However, issues with data privacy, lack of human interaction, and chatbot accuracy were also noted. The study comes to the conclusion that chatbots may be an effective tool for increasing customer experience in the banking sector, but their design, deployment, and interaction with current customer service channels must be carefully considered. This study significantly adds to the body of knowledge on chatbots and their potential influence on customer experience. The study offers useful insights into the particular difficulties and potential of employing chatbots in a highly regulated and customer-focused industry by concentrating exclusively on the banking sector. The results show that chatbot implementation in banking needs to be approached thoughtfully and strategically to maximize their potential for improving customer experience while limiting any possible downsides.

This study aims to understand a customer-purchase mechanism in the artificial intelligence (AI)-powered chatbot context based on the elaboration likelihood model (ELM) and technology acceptance model (TAM). The first objective is to examine how to boost chatbot adoption. The second objective is to investigate the role of information characteristics, technology-related characteristics and attitude toward AI in purchase intention.

Data was collected from a sample of 492 users in Vietnam, who are potential customers of chatbots for purchase. Structural equation modeling was applied for data analysis.

Results illustrate that chatbot adoption is significantly influenced by information credibility, technology-related factors (i.e. interactivity, relative advantage and perceived intelligence), attitude toward AI and perceived usefulness. Moreover, information quality and persuasiveness motivate information credibility. Information credibility and attitude toward AI are the essential motivations for perceived usefulness. Finally, chatbot adoption and information credibility determine purchase intention.

The results are insightful for practitioners to envisage the importance of chatbot use for customer purchase in the AI scenario. Additionally, this research offers a framework to practitioners for shaping customer engagement in chatbots.

The value of this work lies in the incorporation of technology-related characteristics into the two well-established theories, the ELM and TAM, to identify the importance of AI and its applications (i.e. chatbots) for purchase and to understand the formation of perceived usefulness and chatbot use through information credibility and attitude toward AI.

Recent knowledge management (KM) literature suggests that KM activities are not independent of each other, rather they interact with each other to form a process which receives input from both external and internal business environments, and then produces new knowledge for future utilisation. The purpose of this paper is to empirically investigate the relationships between KM activities within the construction business context in order to identify and map the pattern of their interactions.

A questionnaire survey was administered to a sample of contracting organisations operating in Hong Kong to elicit opinions of construction professionals on the intensity of KM activities currently being executed by their organisations in order to facilitate knowledge capture, sharing and utilisation. More than 150 respondents from 99 organisations responded to the survey. Additionally, a total of 15 semi‐structured interviews were undertaken to provide a unique perspective on many of the challenges facing local construction organisations when dealing with KM activities.

Knowledge acquisition and utilisation play paramount roles in the development of the organisational knowledge asset. The higher the intensity of these two activities, the larger the organisational knowledge pool which, in turn, demands greater knowledge dissemination capacity. This dissemination capacity enables more active and intense responses to market changes and clients' needs, thus facilitating and stimulating acquisition and utilisation of new tacit knowledge, thus improving organisational business performance.

Interactions between KM activities were empirically investigated, from a strategic perspective, in the construction business context.

Collaboration is thought to offer significant benefits over traditional contracts, however there is little existing research concerning how these benefits can be optimized. To address this gap, a survey investigated the impact of client characteristics on the time and cost efficiency of collaborative infrastructure projects. The paper aims to discuss these issues.

The survey of experienced senior practitioners of Australian collaborative infrastructure projects yielded 320 valid responses. Cluster analysis, one-way between group ANOVA tests and independent sample t-tests were used to confirm that three client characteristics are critical to time and cost performance: client sector (public/private); client experience with asset procurement; and client approach to price competition.

Projects procured by experienced private sector clients were found to meet targeted levels of performance, regardless of their approach to team selection. Among projects procured by experienced public sector clients, groups of relatively low and high performing projects could be distinguished, regardless of their approach to team selection. Projects run by teams selected competitively on non-price criteria prior to the pricing stage exhibited significantly better performance than those run by teams that competed on the price of the project to win the work.

This study focussed only on analysis of three client characteristics. Future research may consider a broader range of contextual variables. Results are based on perceptual data rather than objective data.

The findings show collaborative infrastructure clients in the construction sector should be less concerned about inexperience than they might have been, and more interested in single-team selection approaches than they might have been.

The results indicate significant performance differences between client types. In particular, experienced public sector clients had more polarized performance outcomes, compared to the private sector.

This paper aims to focus on the effect of the operating condition such as the impeller speed on the centrifugal fan performance and flow characteristics. The ability to predict the behavior of the airflow motion in a centrifugal blower is essential for obtaining the topology optimization design.

A physical model of the air blower consisting of these main parts in a blower system: collector, impeller, outlet flange and volute casing, and the appropriate boundary conditions are set up by ANSYS software. Computation fluid dynamics are performed for the numerical analysis. The calculation of blower performance parameters such as total pressure, efficiency and flow rate is based on the Reynolds averaged Navier–Stokes equations and k-εturbulence flow model.

The numerical results show that the change in operating conditions has a significant effect on the blower performance, and the pressure maintained inside the blower is higher for a larger impeller rotational speed.

This work is original and has not yet been submitted to elsewhere or published previously.

The purpose of this paper is to propose a new model to show how continuous joint learning of participant organisations improves project performance. Performance heterogeneity between collaborative infrastructure projects is typically examined by considering procurement systems and their governance mechanisms at static points in time. The literature neglects to consider the impact of dynamic learning capability, which is thought to reconfigure governance mechanisms over time in response to evolving market conditions.

There are two stages of conceptual development. In the first stage, the management literature is analysed to explain the standard model of dynamic learning capability that emphasises three learning phases for organisations. This standard model is extended to derive a novel circular model of dynamic learning capability that shows a new feedback loop between performance and learning. In the second stage, the construction management literature is consulted, adding project lifecycle, stakeholder diversity and three organisational levels to the analysis to arrive at the collaborative model of dynamic learning capability.

The collaborative model should enable construction organisations to successfully adapt and perform under changing market conditions. The complexity of learning cycles result in capabilities that are imperfectly imitable between organisations, explaining performance heterogeneity on projects.

The collaborative model provides a theoretically substantiated description of project performance, driven by the evolution of procurement systems and governance mechanisms. The model’s empirical value will be tested in future research.