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Proper identification of the key motivating factors (or key drivers) is needed to ensure successful adaption and implementation of the lean concept for construction projects. However, there lacks a study investigating the complex interrelationships existing among the key drivers contributing to Sustainable and Successful Lean Construction (SSLC) implementation for such projects. To address this shortcoming, this study aims to uncover the main critical key drivers towards the implementation of SSLC for the very first time by capturing the complexity of this vexing problem.

In this study, a new hybrid framework is developed through the integration of Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Social Network Analysis (SNA). The novel developed framework is called the DSNA approach.

Considering the case of Malaysian construction projects, the developed DSNA gives the following major outcomes: (1) Most important critical key drivers are seen to be optimization, continuous improvement, and, improve company culture, and (2) For SSLC adoption, the critical drivers impacting other key drivers are seen to be “improve teamwork”, “reduce leadership conflict”, and “improve company culture”, thereby demanding more attention.

The outcomes of this study give insight for decisions and policymakers in the construction industry regarding critical key drivers and their complex interrelationships towards the further adoption of SSLC, promoting the sustainability paradigm within the respective sector.

This paper not only presents a list of critical drivers and the corresponding association among them towards SSLC adoption, but also proposes DSNA as a novel approach for uncovering the complex interrelationship existing in an intricate problem, improving the intricate process of decision-making.

This paper examines the role of institution in the combating crime in India. This study also assesses institutions for controlling property crime in India in the post-reform era.

Crime and socio-economic data are taken from National Crime Record Bureau and the Reserve Bank of India, respectively. Twenty major Indian states are selected for the study purpose for the period of 1994–2019. Fixed effect panel data technique is used for analysis purpose.

Property crime rate declines with economic growth, while it increases with financial development. Findings of fiscal policy instruments are different. Own tax is positively associated with property crime in India, while non-tax fiscal instruments such as fine, penalty, and so on, are inversely related to it. Property crime rate is inversely related to institutional factors like charge sheet and conviction rate.

Further research is needed for other crimes in India. State-level data are used here for analysis purpose; however, spatial or cluster analysis techniques might provide more insights for combating crimes in India.

This study suggests that economic growth and fiscal instrument along with institutional development are essential to control property crime in India.

Government should take steps to improve the law-and-order system to control property crime across states.

Impact of non-tax fiscal instrument reduces property crime while that of own tax is increases it in India. These findings are unique and added certain insight in the study. Institutional roles are captured its performances like charge sheet and convict rate, which are significantly reduce property crime in Indian states. Least square dummy variable model is applied to capture individual state effects.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-01-2023-0063

Blockchain technology (BT) is a relatively new technological innovation in all industries, including the construction industry, that is used to improve supply chain management. Therefore, this study assesses the drivers for the implementation of BT in the construction supply chain management in Nigeria.

This study used a quantitative research approach, with a questionnaire survey administered to professionals in the Nigerian construction industry using the snowball sampling method, yielding 155 respondents. The collected data were analysed using descriptive and exploratory factor analysis (EFA) while Cronbach’s alpha was used to evaluate the reliability.

The analysis revealed that all the identified drivers ranked higher than the average mean item score, with level of awareness of the new technology and data management ranking topmost. The identified drivers were clustered into five categories using EFA: technological driver, social-economic driver, management driver, transparency and security driver and information driver.

This research was carried out in the Southwestern region which is one of the six geo-political zones in Nigeria using a cross-sectional survey method.

The findings will be extremely useful to both professionals and practitioners in the Nigerian construction industry in gaining knowledge about the potential drivers to the implementation of BT in construction supply chain management.

The research categorized the drivers into technological, social-economic, management, transparency and security and information driver. It also identified that level of awareness of BT as the major driver in the implementation of BT in construction supply chain management.

Most global business organisations have begun to explore and embrace new work practices (NWPs) in reorganising their workplace strategies to enhance performance and face intense competition in the global market. This study aims to examine whether or not NWPs are being implemented in office occupying firms in Hong Kong and explore the drivers of the NWPs.

A total of 16 NWPs and 20 drivers were identified through the extant literature review and assessed through a questionnaire survey conducted in the finance, insurance, real estate and business (FIREB) service firms. Based on a comprehensive questionnaire survey, a quantitative approach was used to discover the occurrence of the NWPs in those firms and the driving factors. Based on the questionnaires retrieved, descriptive and inferential statistical analysis was conducted. The relationships between the NWPs occurrence and the driving factors were also investigated to draw necessary inferences.

The results showed that all of the 16 NWPs identified from the literature are happening in Hong Kong FIREB firms. The results of the analysis also indicated that virtual collaboration positively drive the occurrence of NWPs in FIREB firms. Interestingly, innovation in business was shown as such that does not necessarily necessitate stringent hiring strategy.

This research may be of practical value for workplace strategists to make better, more careful forecasts of future workplace trends to ensure resource utilisation and improve space usage patterns and employee productivity.

This paper presents comprehensive overview of drivers that designers, developers, investors, facility managers and planners can use to provide sustainable NWPs. The study will enrich the growing body of international literature on implementing NWPs in global business organisations.

This paper describes how financial professionals' behavioral biases influence their financial forecast and decision-making process. Most of the earlier studies are focused on well-developed financial markets, and little is researched about financial professionals, such as institutional investors, portfolio managers, investment advisors, financial analysts, etc., in emerging markets.

An expert-validated questionnaire measure four prominent behavioral biases and Indian financial professionals' rational decision-making process. The final sample consists of 274 valid responses using the purposive sampling technique. IBM SPSS and AMOS structural equation modeling (SEM) software are used to build measurement and structural models, multivariate analysis including regression, factor analysis, etc.

The results provide empirical insights into the relationship between behavioral biases and the decision-making process. The results suggest that the structural path model closely fits the sample data. The presence of behavioral biases indicates that financial professionals' forecasting and decision-making is not always rational but bounded rational or irrational due to these factors. Furthermore, these biases (except overconfidence bias) have a markedly significant and positive relationship with irrational decision-making.

It is critical to eradicate these psychological errors, but awareness and attentiveness toward behavioral biases may help financial professionals to make informed decisions. Investors can improve their portfolio decisions and investments by recognizing their judgment errors and focusing on specific investment strategies to mitigate the impact of these biases. It is necessary to incorporate behavioral insights while developing training techniques for financial professionals. Rules of thumb, visual tools, financial coaching and implementing social-cultural elements in training programs enable financial professionals to develop simple, engaging, appealing and customized approaches for their clients.

This novel study is the first of this kind of research that examines the relationship between financial professionals' behavioral biases and rational decision-making process. This study significantly and remarkably provides insights into irrationality in financial professionals' decision-making.

The rise of artificial intelligence (AI) and differing attitudes towards its adoption in the building and environment (B&E) industry has an impact upon whether companies can meet changing demand and remain relevant and competitive. The emergence of Industry 4.0 technologies, coupled with the repercussions of COVID-19, increases the urgency and opportunities offered that companies must react to, as disruptive technologies impact how project management (PM) professionals work and necessitate acquisition of new skills. This paper attempts to identify the drivers of and barriers to, as well as the general perception and receptiveness of local PM professionals towards, AI adoption in PM and thereby propose potential strategies and recommendations to drive AI adoption in PM.

This study employs both quantitative and qualitative approaches to examine the findings gathered. A survey questionnaire was used as the primary method of gathering quantitative data from 60 local PM professionals. Statistical tests were performed to analyse the data. To substantiate and validate the findings, in-depth interviews with several experienced industry professionals were performed.

It is found that top drivers include support from top management and leadership, organisational readiness and the need for greater work productivity and efficiency. Top barriers were found to be the high cost of AI implementation and maintenance and the lack of top-down support and skilled employees trained in AI. These findings could be attributed to the present state of AI technologies being new and considerably underutilised in the industry. Hence, substantial top-down support with the right availability of resources and readiness, both in terms of cost and skilled employees, is paramount to kick-start AI implementation in PM.

Little research has been done on the use of AI in PM locally. AI's potential to improve the productivity and efficiency of PM processes in the B&E industry cannot be overlooked. An understanding of the drivers of, barriers to and attitudes towards AI adoption can facilitate more intentional and directed oversight of AI's strategic roll-out at both the governmental and corporate levels and thus mitigate potential challenges that may hinder the implementation process in the future.

Despite the constantly increasing number of publications in the field of business ecosystems, there are indications that a precise definition that appropriately captures the business ecosystem mindset is not yet available. Therefore, the purpose of this paper is to provide a consensus definition of business ecosystems.

Using structured content analysis, this paper examines a total of 43 definitions in terms of their core components.

The results indicate that the existing definitions focus only on single components, e.g. “network of actors,” thereby omitting other essential components, such as “cocreated value proposition” or “shared fate.” Consequently, it seems appropriate to develop a consensus definition that combines the perspectives of the academic and practitioner communities.

The proposed definition is more comprehensive than the prevailing definitions and represents a synthesis of previous considerations on business ecosystems. Such a definition will support researchers in developing a sound business ecosystem theory that will guide practitioners in the efficient design and management of business ecosystems in the long term.

Clarifying the risk evolution mechanism of housing construction for work-safety management is essential. Existing studies have inadequately discussed the risk-accumulation process in housing construction. Therefore, this study aimed to use the complex network theory and risk allocation mechanisms to explore the evolution of risk factors.

The authors analysed a database of housing construction accidents in China from 2015 to 2020 to identify risk factors. Moreover, the causal relationship between risk factors was determined through a systematic analysis of the logical sequence of risk factors. A complex network was used to construct a risk network for housing construction accidents (RNHCA).

The risk matrix method was used to define the factor risk threshold, and a risk value was assigned based on the correlation between risk factors. This contributes to the examination of the evolution mechanism of risk networks in the process of risk factor transmission. The case verification results show that the RNHCA quantitative assessment model can better evaluate the system risk status of housing construction accidents. Furthermore, this model can identify the key risk factors and risk chains with high risk in the evolution of the risk network.

Accident investigation reports need to be classified and processed to analyse the evolution law of risk networks under different scales of construction project, such as high-rise buildings, middle-rise buildings, and low-rise buildings.

This study clarified the risk evolution process of complex systems in housing construction and provided a new method for analysing accidents.

This study clarifies the risk value allocation of risk factors in the transmission process and reveals the process of risk factor evolution in housing construction. This study explains the individual risk factors that form a systemic risk through the transmission chain. Moreover, this paper clarified the transformation relationship between system risk and accidents. The paper also provided a new perspective for risk analysis.

This paper aims to investigate the influence of scan speed on the corrosion and tribocorrosion features of the CoCrMoW samples fabricated via the selective laser melting (SLM) process.

CoCrMoW samples were produced by SLM at different scan speeds. Produced samples were made via structural surveys (X-ray diffraction examinations and scanning electron microscopic analyses), hardness measurements and electrochemical and tribocorrosion experiments.

Outcomes displayed that the corrosion and tribocorrosion properties of CoCrMoW alloy were significantly influenced by scanning speeds. Also, these properties of the alloy increased with increasing scanning speeds. CoCrMoW samples produced at a laser scan speed of 1,000 mm/s showed the best resistance to corrosion and tribocorrosion. This could be related to the high hardness and low grain structure of the fabricated samples.

This paper may be a practical reference and offers insight into the effect of scanning speeds on the increase of hardness, tribological and corrosion performance of CoCrMoW alloys. This study can help in the further advancement of cobalt-chromium alloy in situ produced by SLM for both electrochemical and tribocorrosion behavior for biomedical applications.

This paper aims to explore the technologization of peace work through “remote support monitors” that use social and digital media technologies like social media to alert local violence prevention actors to potentially violent situations during demonstrations.

Using a distributed cognition lens, the authors explore the information processing of monitors within peace organizations. The authors adopt a qualitative thematic analysis methodology composed of interviews with monitors and documents from their shared communication and discussion channels. The authors’ analysis seeks to highlight how information is transformed between social and technical actors through the process of monitoring.

The authors’ analysis identifies that the technologization of monitoring for violence prevention to assist nonviolent activists produces two principal and related forms of transformation: appropriation and hidden attributes. Monitors “appropriate” information from sources to fit new ends and modes of representation throughout the process of detection, verification and dissemination. The verification and dissemination processes likewise render latent supporting informational elements, hiding the aggregative nature of information flow in monitoring. The authors connect the ideas of appropriation and hidden attributes to broader discourses in surveillance and trust that challenge monitoring and its place in peace work going forward.

To the best of the authors’ knowledge, this study is the first to focus on the communicative and information processes of remote support monitors. The authors demonstrate that adoption of social and digital media information of incipient violence and response processes for its mitigation suggests both a social and technical precarity for the role of monitoring.