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The health of an economy is heavily dependent on the productivity of the economy's major industries including construction. While most macro-measures of productivity in the USA construction industry indicate a decline, corresponding studies at the individual task level indicate an increase in productivity. Therefore, this paper aims to identify areas where productivity challenges exist and thus provide recommendations for improvement in the construction industry.

A model that relates the way construction projects are executed with the sources of data that inform productivity analyses is developed and presented. This effort/value-flow model informs the data analysis that is performed to determine productivity trends for management and field labor. Further analysis for field labor productivity using field data and management productivity was separately conducted. Management productivity was particularly difficult to gauge, resulting in the use of surrogate measures.

It was observed that while both field labor and management productivities at the industry level have been decreasing, the decrease in management productivity was five times that of field labor productivity. A similar trend was observed for management productivity at the project level.

The primary contribution of this paper to the body of knowledge and industry is the introduction of a holistic analysis of USA construction productivity. Recommendations to improve management productivity include the use of technology, especially project management software.

Adapting to external and internal transformations is a difficult task that managers and scholars must face while attempting to keep their organisations alive and well-established. This chapter explores the various decision-making tools that can assist practitioners and scholars to improve their understanding of the external scenario to determine the contemporary appropriateness of these approaches for analysing the environment and their implications for various types of organisations. The chapter investigates the barriers and drivers of these methods and proposes existing alternative paradigms created by academics and practitioners to analyse and comprehend the context. It demonstrates how these decision-making tools can be implemented by providing examples and case studies.

Small islands are particularly vulnerable to environmental impacts, as multiple environmental as well as socio-economic changes are impacting their local communities and especially the most vulnerable segments of their population. Information and communication technologies (ICTs) are increasingly viewed as an opportunity for small islands to mitigate and adapt to climate change. ICT may help to monitor short-term and long-term climate trends, raise awareness, help protect the environment and reduce carbon emissions. Though the ICT sector has been recognised as crucial in ensuring sustainable development, it is also important to address its potential adverse impacts like energy consumption, electronic waste generation and digital inequality among others. The ICT-environment link is thus rather complex. While there is extensive literature on the ICT-climate change nexus, the evidence remains mixed. The evidence on small island economies is rather scant. The objective of this chapter is to investigate into the ICT and environment linkage for small islands taking on board the specificities of island economies. The Panel Vector Error Correction Model (PVECM) is used on 38 small islands over a period 2000–2020, and the long-run results show that higher use of ICT has resulted in lower carbon emissions.

Purpose: Entrepreneurship is one of the best ways towards self-sufficiency and poverty alleviation for women in a country without guaranteed employment. The research contributes and provides reasons for supporting women’s entrepreneurship, as although it is well-documented that women’s entrepreneurship increases economic growth, other approaches need attention, especially those related to sustainability.

Need for the study: Women have superior emotional intelligence, resilience, and capacity to maintain a healthy work–life balance; women make better business and entrepreneurial leaders. Many businesses see encouraging women to join the workforce as a key to achieving sustainability goals. This study will provide valuable insights into the role of female entrepreneurs in driving sustainable business practices in the USA and India – rural India and urban India.

Methodology: The chapter adopts a descriptive research design methodology in conducting a thorough literature review on the evolution of women entrepreneurship in India. The case-based approach has been adopted to depict women entrepreneurs and their growth stories. The analysis is based on qualitative analysis of secondary data and primary data. This research proposes exciting revelations regarding the trend of women entrepreneurship in India and, from a global perspective, challenges women entrepreneurs face in organised and unorganised sectors in India and policies in different countries that promote women entrepreneurship towards sustainability.

Findings: Women business owners in India help the economy thrive, reduce poverty, and have a greater chance of shaping India’s destiny. This chapter looks at how entrepreneurial actions affect cultural morals and values. Women’s business growth rates vary from country to country, and this variation is influenced by the fact that countries can be broadly categorised as either developed or developing.

The purpose of this paper is to propose a decision support framework that can be used by decision-makers to identify the most convenient disruption analysis (DA) methods for megaprojects and their stakeholders.

The framework was initially developed by conducting a comprehensive literature review to obtain extensive knowledge about disruption management and megaprojects. Focus group discussion (FGD) sessions with the participation of the construction practitioners were then organized to validate and strengthen the findings of the literature review. Consequently, 17 selection factors were identified and categorized as requirement, ability and outcome. Lastly, the most convenient DA methods for megaprojects were identified by performing integrated fuzzy analytical hierarchy process (AHP) and fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) analysis. Additionally, consistency analysis was also conducted to verify the reliability of the results.

The results revealed that the measured mile method is the most appropriate DA method for megaprojects. In case the measured mile method cannot be adopted due to various technical and contractual reasons, the decision-makers are proposed to consider program analysis, work or trade sampling, earned value analysis and control chart method, respectively. Second, the selection factors such as “Comprehensible analysis procedure,” “Existing knowledge and experience about a particular DA method,” “Ability to resolve greater number of disruption events,” “Ability to resolve complex disruption events,” “Ability to exclude factors that are not under the owner's responsibility” and “General acceptance by practitioners, courts, and arbitration, etc.” were given the top priority by the experts, highlighting the critical aspects of the DA methods.

Disruption claims in megaprojects are very critical for the contractors to compensate for the losses stemming from disruption events. Although the effective use of DA methods maximizes the accuracy and reliability of disruption claims, decision-makers can barely implement these methods adequately since past studies neglect to present extensive knowledge about the most convenient DA methods for megaprojects. Thus, developing a decision support framework for the selection of DA methods, this study is the earliest attempt that examines the mechanisms and inherent differences of DA methods. Additionally, owing to the robustness and versatility of this research approach, the research approach could be replicated also for future studies focusing on other project-based industries since disruption is also a challenging issue for many other industries.

This paper aims to explore, through a numerical study, buoyant convective phenomena in a porous cavity containing a hybrid nanofluid, taking into account the local thermal nonequilibrium (LTNE) approach. The cavity contains a solid block in the shape of a cross (+). It will be helpful to develop and optimize the thermal systems with intricate geometries under LTNE conditions for a variety of applications.

To attain the objective, the system governing partial differential equations (PDEs), expressed as functions of the current function and temperature, and are solved numerically by the finite difference approach. The authors carefully examine the heat transfer rates and dynamics of the micropolar hybrid nanofluid by presenting fluid flow contours, isotherms of the liquid and solid phases, as well as contours of streamlines, isotherms and concentration of the fluid. Key parameters analyzed include heated length (B = 0.1–0.5), porosity (ε = 0.1–0.9), heat absorption/generation (Q = 0–8), length wave (λ = 1–3) and the interphase heat transfer coefficient (H* = 0.05–10). The equations specific to the flow of a micropolar fluid are converted into classical Navier–Stokes equations by increasing the porosity and pore size.

The results showed that the shape, strength and position of the fluid circulation are dictated by the size of the inner obstacle (B) as well as the effective length of the heating wall. The lower value of obstruction size, as well as heating wall length, leads to a higher rate of heat transfer. Heat transfer is much higher for the higher amount of heat absorption instead of heat generation (Q). The higher porosity values lead to lesser fluid resistance, which leads to a superior heat transfer from the hot source to the cold walls. The surface waviness of 4 leads to superior heat transfer related to any other waviness.

This work can be further investigated by looking at thermal performance in the existence of various-shaped obstructions, curvature effects, orientations, boundary conditions and other variables. Numerical simulations or experimental studies in different multiphysical contexts can be used to achieve this.

Many technical fields, including heat exchanging unit, crystallization processes, microelectronic units, energy storage processes, mixing devices, food processing, air conditioning systems and many more, can benefit from the geometric configurations investigated in this study.

This work numerically explores the behavior of micropolar nanofluids (a mixture of copper, aluminum oxide and water) within a porous inclined enclosure with corrugated walls, containing a solid insert in the shape of a cross in the center, under the oriented magnetic field, by applying the nonlocal thermal equilibrium model. It analyzes in detail the heat transfer rates and dynamics of the micropolar nanoliquid by presenting the flow patterns, the temperature of liquid and solid phases, as well as the variations in the flow, thermal and concentration fields of the fluid.

Construction experts acknowledge the adverse effects of rework on project performance. However, the limited understanding of its underlying causes remains a significant challenge. Therefore, this study aimed to thoroughly investigate the sources of construction rework.

A mixed review using bibliometric analysis as a quantitative method and content analysis as a qualitative method was performed to understand the current knowledge in the field. The Web of Science (WoS) was selected for its comprehensive collection of major research articles and integrated analytical tools for generating representative data. The study involved an extensive bibliometric analysis of 107 journal articles on rework causes from 1991 to 2023. RStudio Bibliometrix, an R statistical programming package, was used to analyze rework origins. This method involved mapping the research landscape, identifying research gaps and analyzing emerging trends.

The causes of rework can be classified into three main clusters: human- and contractual-based rework causes, design-, quality- and project management-based rework causes and organizational-based rework causes.

Although several studies have addressed rework causes from various perspectives and methods, the topic has not been investigated holistically. This study is the first to leverage the quantitative and qualitative analytical capabilities of the RStudio Bibliometrix package. Innovative approaches, including the use of metrics, such as the h-index, thematic mapping and trend topic analysis, were employed for a comprehensive understanding of rework causes.

This study aims to investigate the circumstances where the positive influences of leader’s self-confidence are weakened in situations of conflict management (CM) arising due to innovation implementation. Specifically, this study tests the moderating influence of financial slack, internal opposition posturing and performance feedback on the relationship between self-confidence and conflict management styles (CMS). The findings are not complete.

This experimental study was conducted in two stages: In Stage 1, the authors studied moderating effects of financial slack (sound/unsound) and internal opposition posture (encouraging/ discouraging) on the relationship between leader’s self-confidence and CMS, namely, dominating, integrating and avoiding. In Stage 2, the authors studied the moderating effect of performance feedback (success/failure) on the relationship between leader’s self-confidence and three CMS. Data were collected from 268 senior-level Indian managers in Stage 1, out of which 235 managers also responded in Stage 2. These participants assumed the positions of chief executive officers (CEOs) within major Indian manufacturer specializing in electrical components, enabling us to empirically test the proposed model. Furthermore, a qualitative analysis of 10 semistructured interviews with Indian CEOs were conducted to enrich the discussion of the results.

Leader’s self-confidence determined the three CMS with highly self-confident leaders displaying irrational behavior in persisting with innovation through dominating style. Internal opposition posture and performance feedback exerted main effects on dominating style.

This study supports behavioral decision theory of firm and escalation of commitment theory.

This study underscores the need for personality and self-awareness training amongst senior managers to mitigate irrational behavior due to excessive self-confidence and enhanced effective CM.

This study identifies a crucial boundary condition where high self-confidence in innovation implementation may lead to irrational behavior and ineffective CM.

This study investigates the coupling effects between temperature, permeability and stress fields during the development of geothermal reservoirs, comparing the impacts of inter-well pressure differentials, reservoir temperature and heat extraction fluid on geothermal extraction.

This study employs theoretical analysis and numerical simulation to explore the coupling mechanisms of temperature, permeability and stress fields in a geothermal reservoir using a thermal-hydrological-mechanical (THM) three-field coupling model.

The results reveal that the pressure differential between wells significantly impacts geothermal extraction capacity, with SC-CO2 achieving 1.83 times the capacity of water. Increasing the aperture of hydraulic and natural fractures effectively enhances geothermal production, with a notable enhancement for natural fractures.

The research provides a critical theoretical foundation for understanding THM coupling mechanisms in geothermal extraction, supporting the optimization of geothermal resource development and utilization.

This study examines how top global brands changed their corporate social responsibility (CSR) communication on social media during a victim crisis, and how their CSR communication on social media influenced consumer sentiment.

Using 18,502 firms’ Facebook posts and their most relevant consumer comments from pre-pandemic and during-pandemic timeframes, this study integrates machine learning techniques (BERTopic) with human-based qualitative analysis to analyze CSR posts. It also measures the polarity and magnitude of consumer sentiment with Google Natural Language AI. We tested seven hypotheses using Hierarchical Linear Modeling (HLM).

The machine learning-based topic modeling analysis showed that firms increased CSR communications intensity on social media and they more intentionally chose different CSR communication strategies for different topics on social media during the victim crisis. The hypothesis testing results show proactive, accommodative and interactive strategies have a significant impact on consumer sentiment polarity and magnitude, and these effects are moderated by the level of interactivity and industry type.

(1) This study takes a dynamic view to examine the firms’ CSR communication on social media during a victim crisis. It used machine learning-based text analytics and found many interesting results on how firms changed their CSR communication topics and strategies on social media during the crisis. (2) It measures both consumer sentiment polarity and sentiment magnitude to conduct sentiment analysis. The results indicate that the CSR communication strategies have different impacts on the two sentiment components. (3) It integrates machine learning techniques with human-based qualitative analysis. It shows how researchers can gain the benefits of both approaches.