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A building's Indoor Air Quality (IAQ) has a direct impact on the health and productivity on its occupants. Understanding the effects of IAQ in educational buildings is essential in both the design and construction phases for decision-makers. The purpose of this paper is to outline the impact air quality has on occupants' performance, especially teachers and students in educational settings.

This study aims to evaluate the effects of IAQ on teachers' performances and to deliver air quality requirements to building information modelling-led school projects. The methodology of the research approach used a quasi-experiment through questionnaire surveys and physical measurements of indoor air parameters to associate correlation and deduction. A technical college building in Saudi Arabia was used for the case study. The study developed an artificial neural network (ANN) model to define and predict relationships between teachers' performance and IAQ.

This paper contains a detailed investigation into the impact of IAQ via direct parameters (relative humidity, ventilation rates and carbon dioxide) on teacher performance. Research findings indicated an optimal relative humidity with 65%, ranging between 650 to 750 ppm of CO₂, and 0.4 m/s ventilation rate. This ratio is considered optimum for both comfort and performance

This paper focuses on teacher performance in Saudi Arabia and used ANN to define and predict the relationship between performance and IAQ. There are few studies that focus on teacher performance in Saudi Arabia and very few that use ANN in data analysis.

This paper presents a bespoke model for understanding off-site construction (OSC) readiness among Indian construction organisations. This model presents 17 variables for discussion, the results from which help support OSC strategic decision-making.

Factor analysis was used to investigate the relationship between variables to group them into factors. After identifying 26 different variables, these were reduced to 17 using factor analysis and categorised into four groups. Descriptive statistical analysis and factor analysis using SPSS was used to develop a hierarchy of factors that affect OSC readiness in India. These findings were reinforced by five domain experts to support the results.

Minimising on-site duration, ensuring cost and time certainty and transportation issues were identified as the three most important factors, whereas lack of guidance and scepticism were among the lowest factors affecting the Indian OSC sector.

This research is specifically focused on OSC within the Indian construction sector. As such, data collection, propagation and analysis should be constrained to the population context regarding inference, generalisability and repeatability.

The proffered OSC readiness model offers OSC practitioners an ability to assess the OSC readiness of construction organisations in India. This includes the evaluation and benchmarking of processes in both strategic and operational phases, including highlighting areas of concern and scope for further development (to achieve optimal advantage of OSC methods).

Originality rests with the use of factor analysis and descriptive statistical analysis to study the influence of different construction-related factors and variables on the OSC sector in India. This impact readiness model is context-specific to the Indian OSC sector – providing a unique insight into the causal factors and dependencies that can affect the adoption and uptake of modern methods of construction in India.

This paper aims to present factors affecting the Indian construction organisations in adopting off-site construction (OSC) methods.

An existing readiness maturity model has been used to assess three large organisations in different parts of India. A case study methodology has been adopted in this paper to highlight critical issues in OSC adoption in India.

This paper presents three case studies and concludes the Indian construction sectors readiness to adopt the OSC methods. Through the case studies, different issues related to the adoption of OSC have been identified and highlighted for the Indian construction sector. Although the three companies are large, there are several small- and medium-sized enterprises (SME) operating in India's construction sector, and future research shall be needed to review these SMEs.

This research study is broadly focused on developing and assessing an OSC readiness framework for Indian construction organisations. The research scope and the population for data collection are limited to large construction organisations in India only.

The proposed OSC readiness maturity model guides construction practitioners in India through a structured process to assess their OSC readiness in the market. This assessment enables them to evaluate and benchmark their processes through the strategic and operational phases. This research will add to the existing knowledge of OSC in India by mapping issues relevant to India's construction industry. The research has provided background on the status of OSC, the drivers and barriers affecting the implementation of OSC techniques in the Indian construction industry.

Through the three case studies, several factors related to the implementation of OSC methods have been identified and highlighted within the Indian construction sector. Although the model has been applied to the Indian construction sector, it can easily be modified to fit into other areas and similar dynamics and business conditions.

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.