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The authors have attempted to understand how big data and predictive analytics (BDPA) can help retain employees in the organization.

This study is grounded in the positivism philosophy. The authors have used a resource-based view (RBV) to develop their research hypotheses. The authors tested their research hypotheses using primary data gathered using a single-informant questionnaire. The authors obtained 254 usable responses. The authors performed the assumptions test, performed confirmatory factor analysis (CFA) to test the validity of the proposed theoretical model, and further tested their research hypotheses using hierarchical regression analysis.

The statistical result suggests that the various human resource management strategies play a significant role in improving retention under the mediating effect of the BDPA.

The authors have grounded their study in the positivism philosophy. Moreover, the authors tested their hypotheses using single-informant cross-sectional data. Hence, the authors cannot ignore the effects of the common method bias on their research findings. Moreover, the research findings are based on a particular setting. Thus, the authors caution the readers that their findings must be examined in the light of their study limitations.

The study provided empirical findings based on survey data. Hence, the authors provide numerous guidelines to the practitioners that how the organization can invest in creating BDPA that helps analyze complex data to extract meaningful and relevant information. This information related to employee turnaround may guide top management to reduce the dissatisfaction level among the employees working in high-stress environments resulting from a high degree of uncertainty.

The study helps understand the complex factors that affect the morale of the employee. In the high-paced environment, the employees are often exposed to various negative forces that affect their morale which further affect their productivity. Due to lack of awareness and adequate information, most of the employees and their issues are not dealt with effectively and efficiently by their line managers. Thus, the BDPA can help tackle the most complex problem of society in a significant way.

This study offers some useful contributions to the literature which attempts to unfold the complex nexus between human resource management, information management and strategy. The study contributes to the BDPA literature and how it helps in the retention of employees is one of the areas which still remains elusive to the academic community. Moreover, the managers are still skeptical about the application of BDPA in understanding human-related issues due to a lack of understanding of how and to what extent the employee-related information can be stored and processed. This study’s findings further open the new avenues of research that need to be tackled.

This study explores the challenges of access to finance from local financial institutions (LFIs), i.e. local banks, for public–private partnership (PPP) infrastructure project delivery in Nigeria. The aim is to provide useful insights that could inform policy solutions to ease the local funding of PPP infrastructure projects in Nigeria and, by extension, other developing economies.

Adopting a qualitative research methodology, the study engaged PPP stakeholders involved in securing funds for PPP infrastructure projects in Nigeria. A total of 15 PPP stakeholders, drawn from the public and private sectors, were purposively selected and their views on the research problem obtained through recorded telephone interviews. The opinions of the research participants were subsequently analyzed and the results discussed with the outcome of the examination of relevant literature.

The study found that the significant factors affecting access to local finance for PPP infrastructure projects in Nigeria include low capital base by LFIs, weak project viability, lack of capacity to manage PPP-related activities, inconsistent government policy, poor legal framework and public perception of PPP.

Insights from this study are useful for PPP stakeholders in mitigating the barriers that influence access to local finance for PPP infrastructure projects in Nigeria and other developing economies. This study is also useful in enhancing the current policy structure in developing countries as a way of revamping the existing infrastructure framework through LFIs.

This study provides clarity on the peculiar challenges impeding access to finance from LFIs for PPP infrastructure projects in Nigeria and will be useful for debt providers and policymakers in evaluating the bankability of PPP infrastructure projects in Nigeria and other developing countries.

Circular economy (CE) and offsite construction (OSC) are two innovations for improving the construction industry's overall performance against a myriad of sustainability-driven agenda/initiatives. There is a real opportunity to conjoin OSC and CE to provide new insight and opportunities to deliver more evidence-based sustainable systems. This study analyses extant literature in CE and OSC (between 2000 and 2021) through a bibliometric review to tease out critical measures for their integration and transformation.

This study adopts a science mapping quantitative literature review approach employing bibliometric and visualisation techniques to systematically investigate data. The Web of Science (WoS) database was used to collect data, and the VOSviewer software to analyse the data collected to determine strengths, weights, clusters and research trends in OSC and CE.

Important findings emerging from the study include extensive focus on sustainability, waste, life cycle assessment and building information modelling (BIM), which currently serve as strong interlinks to integrate OSC and CE. Circular business models, deconstruction and supply chain management are emerging areas, with strong links for integrating CE and OSC. These emerging areas influence organisational and operational decisions towards sustainable value creation, hence requiring more future empirical investigations.

This study is a novel research using bibliometric analysis to unpick underpinning conduits for integrating CE and OSC, providing a blueprint for circular OSC future research and practice. It provides the needed awareness to develop viable strategies for integrating CE in OSC, creating opportunities to transition to more sustainable systems in the construction sector.

Rapid urbanisation and recent shock events have reiterated the need for resilient infrastructure, as seen in the pandemic. Yet, knowledge gaps in construction robotics and human–robot teams (HRTs) research limit maximising these emerging technologies’ potentials. This paper aims to review the state of the art of research in this area to identify future research directions in HRTs able to aid the resilience and responsiveness of the architecture, engineering and construction (AEC) sector.

A total of 71 peer-reviewed journal articles centred on robotics and HRTs were reviewed through a quantitative approach using scientometric techniques using Gephi and VOSviewer. Research focus deductions were made through bibliometric analysis and co-occurrence analysis of reviewed publications.

This study revealed sparse and small research output in this area, indicating immense research potential. Existing clusters signifying the need for further studies are on automation in construction, human–robot teaming, safety in robotics and robotic designs. Key publication outlets and construction robotics contribution towards the built environment’s resilience are discussed.

The identified gaps in the thematic areas illustrate priorities for future research focus. It raises awareness on human factors in collaborative robots and potential design needs for construction resilience.

Rapid urbanisation and recent shock events have reiterated the need for resilient infrastructure, as seen in the pandemic. Yet, knowledge gaps in construction robotics and HRTs research limit maximising these emerging technologies’ potentials. This paper aims to review the state of the art of research in this area to identify future research directions in HRTs able to aid the resilience and responsiveness of the AEC sector.

The purpose of this paper is to expand the benefits of building information modeling (BIM) to include data analytics to analyze construction project performance. BIM is a great tool which improves communication and information flow between construction project parties. This research aims to integrate different types of data within the BIM environment, then, to perform descriptive data analytics. Data analytics helps in identifying hidden patterns and detecting relationships between different attributes in the database.

This research is considered to be an inductive research that starts with an observation of integrating BIM and descriptive data analytics. Thus, the project’s correspondence, daily progress reports and inspection requests are integrated within the project 5D BIM model. Subsequently, data mining comprising association analysis, clustering and trend analysis is performed. The research hypothesis is that descriptive data analytics and BIM have a great leverage to analyze construction project performance. Finally, a case study for a construction project is carried out to test the research hypothesis.

The research finds that integrating BIM and descriptive data analytics helps in improving project communication performance, in terms of integrating project data in a structured format, efficiently retrieving useful information from project raw data and visualizing analytics results within the BIM environment.

The research develops a dynamic model that helps in detecting hidden patterns and different progress attributes from construction project raw data.

This paper seeks to assess the performance levels of BA-GS-LSSVM compared to popular standalone algorithms used to build NO₂ prediction models. The purpose of this paper is to pre-process a relatively large data of NO₂ from Internet of Thing (IoT) sensors with time-corresponding weather and traffic data and to use the data to develop NO₂ prediction models using BA-GS-LSSVM and popular standalone algorithms to allow for a fair comparison.

This research installed and used data from 14 IoT emission sensors to develop machine learning predictive models for NO₂ pollution concentration. The authors used big data analytics infrastructure to retrieve the large volume of data collected in tens of seconds for over 5 months. Weather data from the UK meteorology department and traffic data from the department for transport were collected and merged for the corresponding time and location where the pollution sensors exist.

The results show that the hybrid BA-GS-LSSVM outperforms all other standalone machine learning predictive Model for NO₂ pollution.

This paper's hybrid model provides a basis for giving an informed decision on the NO₂ pollutant avoidance system.

This research installed and used data from 14 IoT emission sensors to develop machine learning predictive models for NO₂ pollution concentration.